TY - JOUR TI - From VRIN to Velocity: Integrating Resource-Based View and Dynamic Capabilities for Competitive Advantage in AI-Accelerated Markets AU - Chen, Kecun PY - 2026 JO - International Theory and Practice in Humanities and Social Sciences VL - 3 IS - 1 SP - 72 EP - 89 DO - 10.70693/itphss.v3i1.282 AB - In dynamic and turbulent markets, sustaining competitive advantage increasingly depends not only on possessing strategically valuable resources but also on renewing and redeploying those resources as competitive conditions evolve. This paper develops an integrated framework that links the resource-based view (RBV) and dynamic capabilities (DCs) through a stock–flow logic. Specifically, VRIN-type resource stocks provide the foundation for differentiation, while renewal flows—organized around sensing, seizing, and transforming—enable firms to continuously refresh, recombine, and reconfigure reso ER - TY - JOUR TI - Studying Complex Adaptive Systems AU - Holland, John H. PY - 2006 JO - Journal of Systems Science and Complexity VL - 19 IS - 1 SP - 1 EP - 8 DO - 10.1007/s11424-006-0001-z ER - TY - JOUR TI - Balancing regulation and innovation: the need for agile AI governance in higher education–a cross-country study AU - Şen E. AU - Vaněček D. AU - Adnan M. PY - 2026 JO - Studies in Higher Education DO - 10.1080/03075079.2026.2614986 AB - The rapid integration of artificial intelligence (AI) necessitates governance strategies that effectively balance regulation and innovation. This mixed-methods comparative study explores academic staff perceptions of AI governance and policy readiness across two distinct universities: MSKU in Türkiye, characterized by non-binding regulations, and CTU in Czechia, which follows the European Union’s rights-based regulatory model, a structured and binding framework. The study revealed significant discrepancies between current AI adoption and institutional policy. A thematic analysis identified key areas, including ‘Perceived Value and Ethical Concerns', ‘Institutional Readiness and Training Needs', and ‘Governance and Policy Gaps'. Quantitative findings, assessing agile governance dimensions, indicated that while CTU's structured environment supports practical AI integration, it may limit perceived adaptability. Conversely, MSKU's flexible context, despite perceived autonomy, showed lower scores in user-centeredness and transparency, suggesting a risk of fragmented practices without clearer institutional guidance. This study advances the concept of agile AI governance as a practical framework for universities navigating regulatory and innovation imperatives. Such a governance framework can enable institutions to proactively navigate AI's evolving landscape, ensuring both ethical responsibility and institutional legitimacy. This study contends that agility is crucial for public policy makers to integrate AI innovatively, ethically, and sustainably. © 2026 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. KW - agile governance KW - AI regulations KW - Artificial intelligence (AI) KW - higher education institutions KW - higher education policies CY - Czech Republic, Turkey ER - TY - JOUR TI - Integrating artificial intelligence into risk management frameworks: a mixed-methods analysis of the Palestinian banking sector AU - Tanbour K.M. AU - Ben Saada M. AU - Nour A.I. AU - Elnaas N.K. PY - 2025 JO - Journal of Financial Reporting and Accounting SP - 1 EP - 42 DO - 10.1108/JFRA-06-2025-0458 AB - Purpose – This study aims to investigate the impact of artificial intelligence (AI) on risk management practices within Palestinian banks, specifically examining its application in credit, market and operational risk domains. The research assesses the extent to which AI enhances risk mitigation effectiveness within the unique economic and regulatory context of Palestine. Design/methodology/approach – The study used an explanatory sequential mixed-methods design. The initial quantitative phase involved surveying 80 internal auditors, selected via simple random sampling from a population of 95. This was followed by a qualitative phase comprising in-depth interviews with 23 purposively selected participants to contextualize and elaborate on the quantitative findings. Data were analyzed using statistical methods and deductive thematic analysis, guided theoretically by the DeLone and McLean (D&M) IS Success Model (2003). Findings – Findings demonstrate AI’s effectiveness in enhancing credit and operational risk management through improved decision-making accuracy, process automation and real-time anomaly detection. However, its potential contribution to market risk management is significantly constrained by infrastructural limitations, shortages in specialized expertise and competing strategic priorities, thereby underscoring the critical influence of contextual factors on successful AI adoption. Research limitations/implications – The study acknowledges certain limitations. Primary reliance on internal auditors, while offering crucial oversight, may not capture the full experiential range; future work could benefit from including risk managers, IT specialists and senior management. The unique Palestinian politico-economic context necessarily limits direct generalizability, though identified themes regarding infrastructure, skills and strategy likely resonate with other emerging economies. Building on this study, future research should explore the longitudinal evolution of AI’s impact as infrastructure and skills develop. Comparative cross-country studies within diverse emerging markets would further elucidate national context influences. Integrating deeper analysis of organizational culture, change management and specific ethical considerations related to AI decision-making in risk management represents another fruitful avenue. Exploring the specific impact of different AI techniques (e.g. machine learning vs deep learning) across risk domains would also yield valuable insights. Such research will deepen the understanding of how AI can be effectively and responsibly leveraged to foster resilient global financial systems. Practical implications – The findings yield significant practical implications for stakeholders within the Palestinian banking sector and, by extension, for other emerging economies confronting similar challenges. First, AI’s differential impact underscores the imperative for banks to adopt a nuanced, risk-specific integration strategy. For credit and operational risks, where AI is effective, institutions should optimize existing systems and ensure robust governance frameworks upholding transparency, accountability and regulatory compliance. Second, identified infrastructural and human capital deficiencies, pivotal impediments in market risk management, necessitate strategic investment in data infrastructure (especially real-time capabilities) and specialized expertise through training, recruitment and partnerships. Third, regulatory bodies should consider developing adaptive governance frameworks, balancing innovation with financial stability and ethics. Incorporating standards like ISO/IEC 42001:2023, with flexibility for local contexts, can guide responsible AI adoption. Finally, a phased, context-sensitive implementation, aligned with continuous evaluation of system performance and organizational readiness, is advocated over wholesale adoption to enhance long-term success and resilience, empowering leaders to maximize AI’s potential within resource-constrained and volatile environments. Originality/value – This study advances understanding of AI in finance by providing empirical evidence on its differentiated impact across credit, market and operational risks within the Palestinian banking sector, a context marked by institutional and regulatory challenges. Theoretically, it extends the DeLone and McLean IS Success Model to AI-driven risk management. Practically, it offers actionable guidance on human capital, technological infrastructure and governance, fostering sustainable, context-sensitive AI-enabled risk management in emerging economies. © 2025 Emerald Publishing Limited KW - Artificial intelligence (AI) KW - Banking KW - Credit risk KW - DeLone and McLean IS success model KW - Market risk KW - Mixed-methods research KW - Operational risk KW - Palestine KW - Risk management CY - Tunisia, Palestine ER - TY - JOUR TI - From Efficiency to Deliberation: Rethinking AI’s Role in Institutionalizing Democratic Innovations AU - Ohren A. AU - Calderón Lüning E. AU - Markov Č. PY - 2026 JO - Politics and Governance VL - 14 SP - 10632 DO - 10.17645/pag.10632 AB - As AI becomes increasingly embedded in democratic innovation (DI), critical questions arise about how these technologies shape deliberative quality, civic agency, and institutional design. While AI promises to expand and scale deliberative mini-publics, it also risks undermining the democratic goods that make such processes meaningful, particularly inclusiveness, popular control, considered judgment, and transparency. This article introduces the democracy-in-the-loop (DITL) framework as both a normative and practical approach to integrating AI into democratic settings. Building upon and expanding models like human-in-the-loop and society-in-the-loop, DITL embeds contestation, reflexivity, and participant agency into the operation and governance of AI systems. A key feature of the DITL approach is the intentional use of “meaningful frictions” (disruptions designed to slow down interaction, surface assumptions, and invite critical engagement). We explore the DITL model through the Digital Democracy Lab, a series of four experimental workshops held in 2024 in Brussels, Madrid, Kraków, and Dublin as part of the EU-funded Knowledge Technologies for Democracy project. Each workshop combined a purpose-built AI Demonstrator platform with facilitated deliberation to explore how AI can support, rather than supplant, democratic reasoning. Findings suggest that AI-enabled DIs should focus on flexibility, contestability, and democratic oversight, not merely technical efficiency. Institutionalizing DIs in the age of AI requires more than simply scaling tools; it calls for embedding democratic values into the design, deployment, and evolution of socio-technical systems. © 2026 by the author(s). KW - AI KW - algorithmic accountability KW - deliberative democracy KW - deliberative mini-publics KW - democracy-in-the-loop KW - democratic innovations KW - digital deliberation KW - human–AI interaction CY - Norway, Belgium, Serbia ER - TY - JOUR TI - AI Orientation, Capabilities, and Business Value: Case Study AU - Lee M.C.M. AU - Scheepers H. AU - Lui A.K.H. AU - Ngai E.W.T. PY - 2024 JO - Journal of Computer Information Systems DO - 10.1080/08874417.2024.2423190 AB - In this study, artificial intelligence (AI) orientation, AI capabilities, as well as process-oriented dynamic capabilities (PDCs) within the realm of AI business value creation, are unpacked through multiple case studies in Hong Kong. We propose a conceptual framework suggesting that AI resources enable organizations to develop PDCs, manifesting in several abilities, thereby contributing to business value. In addition, the case study’s findings indicate that AI capabilities developed by organizations correlate with their AI orientation, which is their overall strategic direction and aspiration of employing AI technology. Apart from basic AI capabilities, AI-oriented organizations would develop advanced AI capabilities. The proposed conceptual framework and findings can guide and assist practitioners in utilizing AI resources and building AI capabilities. This study also enriches the growing body of research on AI and contributes to the limited understanding of AI capabilities in the extant literature. © 2024 International Association for Computer Information Systems. KW - artificial intelligence capabilities KW - artificial intelligence orientation KW - Dynamic capabilities KW - process-oriented dynamic capabilities KW - Artificial intelligence capability KW - Artificial intelligence orientation KW - Business value KW - Case-studies KW - Conceptual frameworks KW - Dynamics capability KW - Orientation-capability KW - Process-oriented KW - Process-oriented dynamic capability CY - Australia ER - TY - JOUR TI - Research on the impact of generative artificial intelligence (GenAI) on enterprise innovation performance: a knowledge management perspective AU - Zhang Q. AU - Zuo J. AU - Yang S. PY - 2025 JO - Journal of Knowledge Management VL - 29 IS - 7 SP - 2238 EP - 2257 DO - 10.1108/JKM-10-2024-1198 AB - Purpose This study aims to investigate the impact of generative artificial intelligence (GenAI) on enterprise innovation performance, particularly from the perspective of knowledge management. It addresses key challenges in GenAI adoption – such as data biases, information overload and technological dependence – and proposes strategies to overcome these obstacles to enhance innovation. Design/methodology/approach Adopting a theoretical approach, this research analyzes the role of knowledge management in bridging the gap between GenAI and enterprise innovation. A structured framework based on four essential knowledge management processes – knowledge creation, retrieval and storage, transfer and sharing and application – is developed to tackle these challenges effectively. Findings The study reveals that while GenAI presents both opportunities and challenges for enterprise innovation, leveraging a structured knowledge management framework is key to unlocking its potential. It underscores the critical role of human–AI collaboration in mitigating issues such as data biases and integration challenges, ultimately improving innovation performance. The findings highlight the importance of complementing AI capabilities with human judgment to ensure successful outcomes in GenAI-driven innovation. Research limitations/implications This conceptual study calls for further empirical research to validate the findings and expand their generalizability. Future studies should explore contextual factors such as organizational characteristics, business environments and policy frameworks to refine the proposed framework. Originality/value This research offers novel insights into the intersection of GenAI, knowledge management and enterprise innovation. It stresses the importance of human involvement alongside GenAI, providing actionable recommendations for organizations navigating the complexities of AI adoption. In addition, it contributes to the evolving discourse on AI and innovation management, offering pathways for businesses to harness GenAI’s full potential and drive performance. © 2025 Emerald Publishing Limited KW - Enterprise innovation performance KW - Generative artificial intelligence KW - Human–AI collaboration KW - Knowledge management CY - China ER - TY - JOUR TI - Reframing Digital Literacy in ELT: Integrating SAMR, AI-TPACK, and Connectivism in the Global South AU - Nualprasert B. AU - Punkhoom W. AU - Jehma H. PY - 2025 JO - International Journal of Interactive Mobile Technologies VL - 19 IS - 20 SP - 55 EP - 68 DO - 10.3991/ijim.v19i20.56333 AB - This study investigates digital literacy integration within English Language Teaching (ELT) curricula across Thai local government universities through documentary analysis of 108 courses. Employing frameworks including Technological Pedagogical Content Knowledge (TPACK), Substitution, Augmentation, Modification, Redefinition (SAMR), European Digital Competence Framework (DIGCOMP), and Connectivism, we identify a hierarchical readiness gap: near-universal adoption of foundational skills (DIGCOMP: 93% strong alignment) and technological-pedagogical integration (TPACK: 84%) contrasts sharply with lagging transformative (SAMR: 64%) and networked practices (Connectivism: 50%). Crucially, socio-cultural barriers, teacher-centered traditions, rigid assessment systems, and Western-centric assumptions of learner autonomy explain persistent Connectivism underperformance, particularly in humanities disciplines. Regional disparities (e.g., 20% vs. 68% connectivism alignment across provinces) further reflect infrastructural inequities and pedagogical conservatism. Mirroring Global South trajectories, Thailand’s foundations-first approach prioritizes technical literacy over pedagogical reimagination, leaving graduates ill-equipped for AI-disrupted classrooms. This study proposes three imperatives, including an expanded AI-TPACK model integrating ethical AI governance, hybrid frameworks (e.g., SAMR + HeDiCom) for low-resource contexts, and decolonized digital integration centering cultural responsiveness. These innovations offer replicable pathways for teacher education in resource-constrained ecosystems globally. © 2025 by the authors. KW - Augmentation KW - digital literacy KW - English Language Teaching (ELT) KW - Modification KW - Redefinition (SAMR) model KW - Substitution KW - Technological Pedagogical Content Knowledge (TPACK) KW - Thailand higher education KW - Artificial intelligence KW - Curricula KW - Digital integrated circuits KW - E-learning KW - Engineering education KW - Ethical aspects KW - Learning systems KW - Teaching KW - Augmentation KW - Digital literacies KW - English language teaching KW - High educations KW - Modification KW - Redefinition (substitution, augmentation, modification, redefinition) model KW - Technological pedagogical content knowledge KW - Thailand KW - Thailand high education KW - Integration CY - Thailand ER - TY - JOUR TI - Balancing the efficiency of and ethical concerns surrounding artificial intelligence for responsible management education: a scoping review AU - Jimoh I. AU - ElAlfy A. AU - Al-Kwifi O.S. AU - Sakka G. PY - 2026 JO - Journal of Knowledge Management SP - 1 EP - 20 DO - 10.1108/JKM-11-2025-1684 AB - Purpose – This study aims to examine how artificial intelligence (AI) transforms knowledge processes within responsible management education (RME). By mapping existing research, the paper explores the adoption and application of AI within management education and its consequences for teaching, ethical concerns and knowledge management. Design/methodology/approach – A scoping review was conducted following Arksey and O’Malley’s framework and PRISMA guidelines to synthesize evidence from 40 peer-reviewed studies published between 2015 and 2025. The review systematically analyzed literature across Scopus and Web of Science using thematic mapping aligned with knowledge management clusters. Findings – AI is transforming how knowledge is created and shared in higher education. It improves efficiency by automating knowledge retrieval and connecting human insight with machine learning to support innovation in teaching and research. Yet, these advantages come with serious ethical concerns, including plagiarism, bias, data privacy and a lack of transparency that can undermine academic integrity. The review also reveals a strong concentration of research in developed countries. Practical implications – The findings highlight the need for HEIs to adopt comprehensive frameworks that integrate knowledge management systems with ethical governance mechanisms. Universities can leverage AI to strengthen absorptive capacity and organizational learning while instituting clear accountability, transparency and data ethics protocols to ensure responsible AI adoption in education and research. Originality/value – This study advances knowledge management research by linking AI-driven knowledge processes with the ethical and sustainability principles of RME. It broadens existing theory by showing how the transformation of knowledge, from individual insight to collective learning, and the view of knowledge as a strategic organizational resource can be aligned with responsible and transparent innovation. © 2026 Emerald Publishing Limited KW - Artificial intelligence KW - Ethics KW - Higher education KW - Knowledge management KW - Knowledge-based view KW - Responsible management education CY - Nigeria, Canada, Qatar, Cyprus ER - TY - JOUR TI - Navigating ethical challenges in digital transformation: insights on climate adaptation, microbiology, healthcare, robotics, and AI under the EU AI act: an experts panel discussion AU - Alghamdi S.M. AU - Chikwendu O.C. AU - Chukwuma O.U. AU - Okech D.O. AU - Okwu M.O. AU - Khalid S. AU - Vlachostergiou A. PY - 2025 JO - Global Bioethics VL - 36 IS - 1 SP - 2550823 DO - 10.1080/11287462.2025.2550823 AB - The ethical complexities of technological advancement are growing as fields such as climate adaptation, microbiology, healthcare, robotics, and artificial intelligence (AI) evolve rapidly. While these technologies offer innovative solutions to global challenges, they raise significant ethical concerns. In climate adaptation, AI-driven models and remote sensing technologies prompt questions about data privacy, environmental justice, and equitable access, especially for vulnerable populations. Similarly, advancements in microbiology and healthcare, such as genetic research and digital health tools, present ethical dilemmas related to informed consent, data security, and the exploitation of marginalized communities. In robotics and AI, ethical concerns are heightened due to their potential to automate decision-making, affect employment, and infringe on personal freedoms. The influence of AI in healthcare, law enforcement, and public services highlights the urgent need for ethical oversight to prevent bias and protect human rights. The EU AI Act addresses these challenges by categorizing AI systems by risk and setting stringent guidelines for high-risk applications, especially in sensitive sectors like healthcare. This article emphasizes the importance of balancing innovation with ethical responsibility, advocating for comprehensive regulatory frameworks, interdisciplinary collaboration, and global cooperation to ensure that technological advancements align with ethical standards and societal values. © 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. KW - AI regulation KW - climate KW - EU AI act KW - healthcare KW - robotics KW - Technological ethics CY - Greece ER - TY - JOUR TI - Challenges of Automating Fact-Checking: A Technographic Case Study AU - Kavtaradze L. PY - 2024 JO - Emerging Media VL - 2 IS - 2 Theme: Governing, Misinformation, and Discrimination SP - 236 EP - 258 DO - 10.1177/27523543241280195 AB - The prevalence of disinformation in media ecosystems has spurred efforts by researchers from various disciplines and media professionals to find effective methods for verifying information at scale. Automated fact-checking has emerged as a promising solution to combat disinformation. However, fully automated tools have not yet materialized. This technographic case study of a start-up company, “X,” investigated the challenges associated with this process. By conceptualizing automated fact-checking as a technological innovation within journalistic knowledge production, the article uncovered the reasons behind the gap between “X's” initial enthusiasm about AI's capabilities in verifying information and the actual performance of such tools. These reasons cross the disciplinary boundaries relating to the technological aspects of automated fact-checking and a requirement for such tools to be epistemically authoritative. The study revealed significant hurdles faced by the start-up, including issues with the accuracy of the AI editor and its adoption by the industry. Key obstacles included the elusive nature of truth claims, the rigidity of so-called binary epistemology (ascribing true/false values to information claims), data scarcity, algorithmic deficiencies, issues with the transparency of results, and industry-tool compatibility. While focused on a single company's experience, the study offers valuable insights for researchers and practitioners navigating the evolving landscape of automated fact-checking. © The Author(s) 2024. This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage). KW - AI KW - Automated fact-checking KW - disinformation KW - emerging technologies KW - epistemic authority KW - technography CY - Norway ER - TY - JOUR TI - National strategic artificial intelligence plans: A multi-dimensional analysis AU - Fatima S. AU - Desouza K.C. AU - Dawson G.S. PY - 2020 JO - Economic Analysis and Policy VL - 67 SP - 178 EP - 194 DO - 10.1016/j.eap.2020.07.008 AB - Nations have recognized the transformational potential of artificial intelligence (AI). Advances in AI will impact all facets of society. A spate of recently released national strategic AI plans provides valuable insights into how nations are considering their future trajectories. These strategic plans offer a rich source of evidence to understand national-level strategic actions, both proactive and reactive, in the face of rapid technological innovation. Based on a comprehensive content analysis of thirty-four national strategic plans, this article reports on (1) opportunities for AI to modernize the public sector and enhance industry competitiveness, (2) the role of the public sector in ensuring that the two most critical elements of AI systems, data and algorithms, are managed responsibly, (3) the role of the public sector in the governance of AI systems, and (4) how nations plan to invest in capacity development initiatives to strengthen their AI capabilities. © 2020 Economic Society of Australia, Queensland KW - Artificial intelligence KW - Autonomous systems KW - Intelligent systems KW - Public agencies KW - Science and technology policy KW - Strategic plans KW - Technological innovation CY - Australia, United States ER - TY - JOUR TI - The co-evolution of AI technology and information environment: Diagnosing social impacts and exploring governance strategies AU - Cha S. AU - Seo B.-G. AU - Kim T. AU - Kim J. PY - 2024 JO - Journal of Infrastructure, Policy and Development VL - 8 IS - 8 SP - 6605 DO - 10.24294/jipd.v8i8.6605 AB - The rapid advancement of artificial intelligence (AI) technology is profoundly transforming the information ecosystem, reshaping the ways in which information is produced, distributed, and consumed. This study explores the impact of AI on the information environment, examining the challenges and opportunities for sustainable development in the age of AI. The research is motivated by the need to address the growing concerns about the reliability and sustainability of the information ecosystem in the face of AI-driven changes. Through a comprehensive analysis of the current AI landscape, including a review of existing literature and case studies, the study diagnoses the social implications of AI-driven changes in information ecosystems. The findings reveal a complex interplay between technological innovation and social responsibility, highlighting the need for collaborative governance strategies to navigate the tensions between the benefits and risks of AI. The study contributes to the growing discourse on AI governance by proposing a multi-stakeholder framework that emphasizes the importance of inclusive participation, transparency, and accountability in shaping the future of information. The research offers actionable insights for policymakers, industry leaders, and civil society organizations seeking to foster a trustworthy and inclusive information environment in the era of AI, while harnessing the potential of AI-driven innovations for sustainable development. © 2024 by author(s). KW - artificial intelligence KW - governance strategies KW - information ecosystem KW - social impact KW - sustainability CY - South Korea ER - TY - JOUR TI - A Strategic Roadmap for Corporate Excellence in AI AU - Mazzeo J. PY - 2024 JO - Research Technology Management VL - 67 IS - 6 SP - 27 EP - 32 DO - 10.1080/08956308.2024.2400001 AB - Companies can adopt this four-stage AI Proficiency Framework to develop organization-wide expertise in using AI tools. There are four stages of AI proficiency—Beginner, Intermediate, Proficient, and Expert—and each stage represents a significant leap in capability, organizational integration, and strategic impact. Transforming a company through AI requires sustained commitment, significant investment, and a willingness to reimagine the business through the lens of AI capabilities. © Copyright © 2024, The National Association of Manufacturers. KW - AI proficiency KW - AI proficiency framework KW - Artificial intelligence KW - Innovation KW - Innovation culture KW - AI proficiency KW - AI proficiency framework KW - Corporates KW - Innovation KW - Innovation Culture KW - Organisational KW - Roadmap KW - Strategic impacts KW - Through the lens ER - TY - JOUR TI - An Analysis of Artificial Intelligence (AI) Capability in Libraries and Archives AU - Pinar A. AU - Cox A. PY - 2025 JO - Cataloging and Classification Quarterly VL - 63 IS - 6-7 SP - 566 EP - 599 DO - 10.1080/01639374.2025.2539790 AB - This paper seeks to evaluate the AI capability of libraries and archives using a qualitative content analysis of 54 case studies of AI uses published between 2018 and 2024. It is framed by the model of AI capability proposed by Mikalef and Gupta (Patrick Mikalef and Manjul Gupta, ‘Artificial Intelligence Capability: Conceptualization, Measurement Calibration, and Empirical Study on Its Impact on Organizational Creativity and Firm Performance’, Information & Management 58, no. 3 (2021): 103434.). The findings of the analysis largely confirm the model, but suggest that there are many gaps in library and archive AI capability, especially in areas such as infrastructure and technical resources, data issues arising from metadata inconsistencies, and financial resources. © 2025 The Author(s). Published with license by Taylor & Francis Group, LLC. KW - AI Capability KW - Archives KW - Artificial Intelligence KW - Libraries KW - Organizational Change CY - Turkey, United Kingdom ER - TY - JOUR TI - Educational leadership in the digital era: Bridging global disparities with inclusive management strategies AU - Mariyono D. AU - Yunus M. AU - Junaidi J. AU - Syam N. AU - Mazhabi Z. PY - 2026 JO - Educational Management Administration and Leadership DO - 10.1177/17411432261419475 AB - This study examines educational leadership in the digital era with a particular focus on how strategic management and artificial intelligence (AI) can be mobilized to reduce global disparities and foster inclusive learning. Drawing on a systematic literature review of 75 peer-reviewed studies identified through Scopus, Web of Science, JSTOR, and IEEE Xplore, the research employs a hybrid thematic content—Strengths, Weaknesses, Opportunities, Threats (SWOT) approach that combines inductive thematic coding with strategic analysis. The findings reveal that digital inequalities remain persistent, disproportionately affecting marginalized learners and institutions with limited resources. Transformative and AI-supported leadership models demonstrate potential to bridge technological and social gaps, yet they also raise ethical, cultural, and contextual challenges that require human oversight. Effective leaders in this environment must integrate technical expertise, cognitive agility, and socioemotional intelligence to ensure that digital transformation supports educational justice rather than deepening divides. The paper contributes theoretically by advancing a hybrid methodological framework for analyzing digital leadership and practically by offering policy recommendations, including the development of global funding mechanisms, public–private partnerships, and ethical governance of AI. While the study is limited by potential regional bias and the static nature of SWOT analysis, it provides a replicable framework for examining how inclusive leadership can navigate the tensions between technological innovation and equity in education. © The Author(s) 2026 KW - artificial intelligence KW - digital divide KW - digital transformation KW - Educational leadership KW - inclusive education ER - TY - JOUR TI - AI Platforms as Cooperation Enablers Favoring the Development of Strategic Situating Capabilities Within Solution Delivery Ecosystems AU - Vaillant Y. AU - Lafuente E. AU - Vendrell-Herrero F. PY - 2025 JO - Journal of Product Innovation Management DO - 10.1111/jpim.12807 AB - Academic Summary: By integrating artificial intelligence (AI) platforms into their processes, firms aim to enhance their strategic capabilities and gain a competitive advantage. This study investigates the impact of such platforms on value generation within solution-based strategies, proposing two connected mechanisms. First, AI platforms foster collaborative value systems between firms and value-chain agents across the stages of the solution delivery process (i.e., problem identification, solution development, and solution implementation). Second, such cooperation could foster the development of situating capabilities (i.e., grounding, bounding, and recasting), which are conceptually linked to the mitigation of situated agency constraints that stifle value creation within productive systems. These relationships underscore the value generation potential of AI platforms for solution providers, extending the premise of situated AI capabilities to the organizational and inter-organizational level. Data collected from 570 Spanish manufacturing firms in 2023 reveals that firms utilizing AI platforms exhibit greater cooperative and situating capability-building behavior during the problem identification and solution implementation stages. However, no significant association is found between AI platforms and the more creative stage of solution development. The study provides novel insights into the interplay between AI platforms, user cooperation, situated agency, and strategic capabilities as drivers of value generation and advancement of the AI-dominated paradigm. Theoretical and practical implications are discussed. Managerial Summary: This study highlights the strategic role of AI platforms in enhancing collaboration between manufacturers and solution seekers throughout the solution delivery process. AI technologies facilitate collective learning, adaptation, and knowledge sharing, particularly during the diagnostic and implementation stages, where real-time data processing and predictive analytics help tailor solutions to user-specific challenges. This more effective coordination is essential for mitigating agency problems that arise due to asymmetric information or misaligned objectives within complex solution systems. However, the findings reveal that AI's influence is limited in the co-creation of solution design and development, which relies heavily on human insight, creativity, and contextual judgment. Managers should therefore not view AI as a substitute for human input, but rather as a complementary tool that enhances efficacy and integration. For firms seeking to strengthen their solution-oriented strategies, the key takeaway is that maintaining a balanced approach—combining AI-enabled collaboration with human ingenuity—will improve solution outcomes and sustain competitive advantage in markets increasingly shaped by personalization and customer-specific problem solving. © 2025 The Author(s). Journal of Product Innovation Management published by Wiley Periodicals LLC on behalf of Product Development & Management Association. KW - AI platforms KW - inter-organizational cooperation KW - situated agency KW - situated AI theory KW - solution business model KW - solution delivery process KW - strategic capabilities KW - value ecosystems KW - Behavioral research KW - Competition KW - Coordination reactions KW - Knowledge management KW - Solution mining KW - Artificial intelligence platform KW - Business models KW - Delivery process KW - Interorganizational cooperation KW - Situated agency KW - Situated artificial intelligence theory KW - Solution business model KW - Solution delivery process KW - Solutions deliveries KW - Strategic capability KW - Value ecosystem KW - Predictive analytics CY - France, United Kingdom, Finland ER - TY - JOUR TI - Incumbent strategic renewal drivers to AI disruption AU - Bughin J. PY - 2025 JO - Technology Analysis and Strategic Management DO - 10.1080/09537325.2025.2509233 AB - In light of destructive technology theories, AI technologies disrupt markets while simultaneously offering opportunities for strategic renewal (SR). We assess how major corporations worldwide are modifying their strategies as well as organisational and AI capabilities to counter AI-induced market pressures. AI-driven stress fosters innovation, yet the primary force behind change remains internal organisational dynamics. Companies must strike a balance of exploration/exploitation when using AI for both automation and radical innovation and prioritise AI capabilities/resources, such as data and AI dynamic capability. © 2025 Informa UK Limited, trading as Taylor & Francis Group. KW - AI technologies KW - AI transformation KW - endogenous task production models KW - Strategic renewal CY - Belgium ER - TY - JOUR TI - Generative Artificial Intelligence (GenAI) in entrepreneurial education and practice: emerging insights, the GAIN Framework, and research agenda AU - Dwivedi Y.K. PY - 2025 JO - International Entrepreneurship and Management Journal VL - 21 IS - 1 SP - 82 DO - 10.1007/s11365-025-01089-2 AB - Generative AI (GenAI) is reshaping entrepreneurship by transforming the landscape of innovation, education, and ethical practices within the entrepreneurial domain. This paper provides a comprehensive review of the nascent yet impactful scholarly discourse on the role of GenAI (e.g., ChatGPT, DeepSeek, and Google Gemini) in entrepreneurship. Drawing from 39 studies, it examines four emerging themes: technology adoption, education and skill development, innovation, and performance enhancement. The analysis highlights GenAI's transformative potential to democratize entrepreneurial resources, foster creativity, and improve decision-making. However, it also identifies pressing challenges, including ethical data practices and equitable access. The paper proposes and presents the GAIN Framework along with five associated research propositions to guide future research in this domain. It concludes by emphasizing the need for a balanced integration of human ingenuity and AI capabilities, while raising critical questions about fostering ethical inclusivity and driving innovation. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025. KW - ChatGPT KW - DeepSeek KW - Entrepreneurs KW - Entrepreneurship KW - Gemini KW - Innovation KW - LLMs KW - The GAIN Framework CY - Saudi Arabia ER - TY - JOUR TI - Shopper AI: Integrating Capabilities and Parasocial Skills AU - Kennedy K.J. AU - He H. AU - Sarantopoulos P. PY - 2026 JO - Journal of Service Research DO - 10.1177/10946705261433842 AB - Shopper artificial intelligence (AI) presents a striking paradox: while massive investments drive rapid expansion and increasingly sophisticated AI solutions, two-thirds of consumers express dissatisfaction with AI shopping assistants, citing frustrations with pushy upselling, poor understanding, and inaccurate recommendations. This disconnect motivates our development of the Shopper AI taxonomy. To develop our taxonomy, we synthesized insights from multiple disciplines through a design science research process with empirical validation. Grounded in customer experience management (CEM) theory, our taxonomy identifies 14 dimensions within two meta-characteristics: AI capabilities (knowledge, intelligence, autonomy, breadth of use, quality of work, data privacy) and AI parasocial skills (personalization, anthropomorphism, communications mode, emotion recognition, emotion expression, empathy, influence, engagement). The taxonomy advances service research theory in three ways. First, we extend CEM theory by revealing how AI creates value through interrelated but discrete capabilities and parasocial dimensions. Second, we identify how AI capabilities enable autonomous value creation without active customer participation, representing a new form of value pre-creation. Third, we reveal complex dimensional interactions, where improvements in one dimension can enhance or diminish others. This multidimensional taxonomy provides managers with actionable guidance for navigating dimensional trade-offs, designing efficient, balanced AI systems, identifying context-specific investment priorities, and avoiding common pitfalls. © The Author(s) 2026 KW - artificial intelligence KW - customer experience management KW - parasocial relationships KW - shopper AI KW - taxonomy CY - United States, United Kingdom, Greece ER - TY - JOUR TI - AI capability and environmental sustainability performance: Moderating role of green knowledge management AU - Kumar S. AU - Kumar V. AU - Chaudhuri R. AU - Chatterjee S. AU - Vrontis D. PY - 2025 JO - Technology in Society VL - 81 SP - 102870 DO - 10.1016/j.techsoc.2025.102870 AB - The capabilities of AI may not only foster green technology innovations but also enhance the environmental sustainability performance of organizations. Despite this, the interplay between AI capabilities, green technology innovations, and environmental sustainability performance largely remain unexplored in view of green knowledge management. The present study aims to fill this gap by examining the impact of AI capabilities on green technology innovations and ultimately on environmental sustainability performance. This study also examines how green technology innovations mediates between AI capabilities and environmental sustainability performance, and how green knowledge management moderates the relationships between AI capabilities and green technology innovations and between AI capabilities and environmental sustainability performance. To validate the proposed conceptual relationships, data were collected from IT companies of Pune, India from 237 respondents, and they were validated with PLS-SEM 3.0 software. The results of the present study contribute theoretically to the resources-based view, knowledge-based view, dynamic capability, and absorptive capacity theories. Moreover, this study has also contributed practically by revealing the potential of AI capabilities and green technology innovations to enhance environmental sustainability performance through green knowledge management. The insights of the study could also help managers to leverage the AI capabilities to enhance green technology innovations and to improve environmental sustainability performance of organizations. © 2025 Elsevier Ltd KW - Artificial intelligence KW - Environmental sustainability performance KW - Green innovation KW - Green knowledge management KW - India KW - Maharashtra KW - Pune KW - Green economy KW - Sustainable development goals KW - Environmental sustainability KW - Environmental sustainability performance KW - Green innovations KW - Green knowledge management KW - Green technology KW - IT companies KW - Relationship data KW - Resource-based view KW - Sustainability performance KW - Technology innovation KW - artificial intelligence KW - conceptual framework KW - high technology industry KW - information technology KW - innovation KW - knowledge KW - sustainability KW - sustainable development KW - Green development CY - India, France, Cyprus, Singapore ER - TY - JOUR TI - From Man vs. Machine to Man + Machine: The art and AI of stock analyses AU - Cao S. AU - Jiang W. AU - Wang J. AU - Yang B. PY - 2024 JO - Journal of Financial Economics VL - 160 SP - 103910 DO - 10.1016/j.jfineco.2024.103910 AB - An AI analyst trained to digest corporate disclosures, industry trends, and macroeconomic indicators surpasses most analysts in stock return predictions. Nevertheless, humans win “Man vs. Machine” when institutional knowledge is crucial, e.g., involving intangible assets and financial distress. AI wins when information is transparent but voluminous. Humans provide significant incremental value in “Man + Machine”, which also substantially reduces extreme errors. Analysts catch up with machines after “alternative data” become available if their employers build AI capabilities. Documented synergies between humans and machines inform how humans can leverage their advantage for better adaptation to the growing AI prowess. © 2024 Elsevier B.V. KW - Alternative data KW - Artificial intelligence KW - Disruptive innovation KW - FinTech KW - Machine learning KW - Stock analyst CY - United States ER - TY - JOUR TI - Artificial intelligence, innovation and the new architecture of exploitation: Towards reconfiguring humanness in the age of algorithmic labour AU - Pepple D. AU - Muthuthantrige N. PY - 2026 JO - Journal of Innovation and Knowledge VL - 11 SP - 100878 DO - 10.1016/j.jik.2025.100878 AB - Purpose This conceptual study explores how artificial intelligence (AI) is transforming the nature of work and reconfiguring the experience of humanness, particularly among low-skilled and informal workers. Method Using an integrative literature review methodology, the study synthesises interdisciplinary research from organisational studies, sociology, and AI ethics to examine the mechanisms through which AI-driven labour displacement, algorithmic management, and structural precarity contribute to new forms of exploitation. Findings The study develops a novel conceptual framework that links technological transformation to the erosion of the relational, moral, and emotional dimensions of work conditions, resulting in conditions increasingly resembling modern slavery. Originality the study’s novelty lies in its reframing of AI as a socio-technical actor with ontological consequences for worker identity, autonomy, and dignity. The findings underscore the need for ethical AI design, inclusive policy frameworks, and human-centred organisational practices. Practical implications This paper offers practical implications for policymakers, technologists, and business leaders seeking to align innovation with social justice and sustainable labour futures. Plain summary Artificial intelligence (AI) is reshaping the nature of work and disrupting the human experience, especially for low-skilled and informal workers, highlighting the urgency and complexity of this research. AI-driven labour displacement and algorithmic management contribute to new forms of exploitation that echo modern slavery. The erosion of humanness at work is linked to reduced autonomy, empathy, and moral agency under opaque algorithmic systems. A socio-technical framework is needed to address AI’s impact on dignity and agency, with ethical design and inclusive governance at its core. JEL Code O330, O31, O32 © 2025 The Author(s). KW - Artificial intelligence KW - Digital labour KW - Ethical innovation KW - Humanness KW - Labour displacement KW - lgorithmic management KW - Modern slavery CY - United Kingdom ER - TY - JOUR TI - Construction of an Artificial Intelligence Literacy Ability Framework and Training System for College Students; [高校学生AI 素养能力框架及培训体系建设] AU - Hu A. PY - 2026 JO - Journal of Library and Information Science in Agriculture VL - 38 IS - 2 SP - 42 EP - 55 DO - 10.13998/j.cnki.issn1002-1248.25-0448 AB - [Purpose/Significance] The rapid proliferation of generative artificial intelligence (AI), exemplified by models like DeepSeek-R1, has precipitated a paradigm shift across various sectors, positioning AI literacy as an indispensable competency for the future workforce. University students, as digital natives and pivotal agents of technological adoption and innovation, stand at the forefront of this transformation. Their proficiency in understanding, utilizing, and critically evaluating AI technologies directly influences their academic performance, research capabilities, and long-term career adaptability. Although existing literature has begun to explore the conceptual landscape of AI literacy, a significant gap remains. There is an absence of a robust, empirically validated competency framework specifically tailored to the unique learning contexts, developmental needs, and future roles of university students within China’s higher education system. This study aims to address this critical gap by constructing and validating a comprehensive AI literacy competency framework for college students. Its primary significance lies in its ability to move beyond theoretical discourse and provide an evidence-based model that can guide the systematical development of targeted training programs. This enriches the theoretical underpinnings of AI literacy education and offers practical guidance for cultivating high-quality talent equipped for the intelligent era. [Method/Process] This research employed a mixed-methods approach, integrating qualitative and quantitative methods to provide both theoretical grounding and empirical robustness. The study commenced with a qualitative phase utilizing the grounded theory methodology. A systematic analysis of 112 core academic publications (2019-2024) from databases such as CNKI and Web of Science was conducted. Through a rigorous process of open coding, axial coding, and selective coding, facilitated by NVivo11 software, we extracted 300 initial concepts, which were subsequently synthesized into 26 sub-categories and ultimately 4 main categories. This process resulted in the preliminary construction of a four-dimensional AI literacy competency framework. Following this, a quantitative phase was implemented to test and refine the framework. A detailed questionnaire was developed based on the identified dimensions and indicators. Utilizing a five-point Likert scale, the questionnaire measured 26 variables corresponding to the framework’s sub-components. A total of 586 valid responses were collected from undergraduate students across universities in Jiangsu Province, China. The dataset was randomly split into two halves. The first subset (N=293) underwent exploratory factor analysis (EFA) using SPSS to uncover the underlying factor structure and assess the internal consistency reliability via Cronbach’s alpha. The second subset (N=293) was subjected to confirmatory factor analysis (CFA) using AMOS to verify the hypothesized factor structure, evaluate model fit indices (e.g., CMIN/DF, CFI, TLI, RMSEA), and establish convergent and discriminant validity by examining average variance extracted (AVE) and composite reliability (CR). [Results/Conclusions] The empirical analyses strongly support the validity and reliability of the proposed competency framework. The EFA clearly identified four distinct factors that aligned perfectly with the predefined dimensions, with a total variance explained of 69.916% and all factor loadings exceeding 0.6. The CFA results demonstrated excellent model fit (CMIN/DF=1.921, CFI=0.950, TLI=0.943, RMSEA= 0.056), confirming the structural integrity of the framework. Furthermore, all constructs exhibited high internal consistency (Cronbach’s α>0.90) and satisfactory convergent (AVE>0.5, CR>0.7) and discriminant validity. The finalized framework, therefore, comprises four interconnected core dimensions: AI Cognition (encompassing knowledge of basic concepts, applications, value, and risks), AI Skills (covering practical abilities from tool usage and programming to critical evaluation and innovation), AI Ethics (emphasizing social responsibility, privacy, intellectual property, and legal compliance), and AI Thinking (fostering higher-order cognitive abilities like computational, critical, and systemic thinking). Based on this validated framework, the study proposes a systematic and multi-faceted training system. This system outlines clear training objectives, identifies key stakeholders (e. g., university libraries, teaching centers, schools, and external enterprises), designs layered training content and pathways corresponding to each dimension, and suggests implementation strategies focusing on faculty development, a comprehensive assessment and feedback mechanism, and the strategic integration of AI-related resources. The main limitation of this study is that the respondents of the questionnaire were primarily college students during the empirical test stage. Future research can include teachers, business employers, and AI experts to modify and improve the index weight and content of the competency framework from multiple perspectives. This can be done through the Delphi method, expert interviews, and other methods, so as to enhance the framework’s authority and universality. © 2026, Agricultural Information Institute, Chinese Academy of Agricultural Sciences. All rights reserved. KW - ability framework KW - artificial intelligence literacy KW - factor analysis KW - grounded theory KW - training system CY - China ER - TY - JOUR TI - AI Adoption in the Public Sector: Organizational Readiness and the Pursuit of Public Value AU - Jonathan G.M. AU - Yalew S.D. AU - Gebremeskel B.K. AU - Watat J.K. PY - 2025 JO - Complex Systems Informatics and Modeling Quarterly IS - 45 DO - 10.7250/csimq.2025-45.03 AB - Artificial Intelligence (AI) has attracted significant attention among researchers and practitioners as it emerges as a strategic asset for organizations across sectors and industries. Within the public sector, the deployment of AI is anticipated to enhance the responsiveness of public organizations in delivering appropriate services and addressing complex societal challenges. This study examines the readiness of public organizations for AI adoption within Kenya’s public sector and explores its implications for public value creation. Anchored in the Technology-Organization-Environment framework and informed by Dynamic Capabilities theory, the article analyzes how structural conditions within organizations interact with adaptive capabilities to shape trajectories of AI readiness. Drawing on qualitative interviews with seventeen public sector experts, the study identifies a set and dynamic interdependence of critical readiness factors, including technological infrastructure, data quality, leadership commitment, staff competencies, organizational culture, regulatory frameworks, public trust, and external partnerships. By offering an empirically grounded and comparative perspective, the study aims to enhance our understanding of the relationship between AI readiness and public value creation, drawing on Kenya’s example. The results may also provide valuable inputs for policymakers in formulating actionable plans concerning differentiated implementation pathways, capacity development, and the ethical governance of AI in the public sector. © (2025), (Riga Technical University). All rights reserved. KW - AI Readiness KW - Artificial Intelligence (AI) KW - Dynamic Capability Theory KW - Public Value Creation KW - Technology–Organization–Environment (TOE) Framework. CY - Sweden, Ethiopia, Norway ER - TY - JOUR TI - ALGORITHMIC AUTHORITY AND THE POSTHUMAN TURN IN MANAGEMENT CULTURAL, ETHICAL, AND TECHNOLOGICAL RECONFIGURATIONS IN THE AGE OF SCIENTIFIC INNOVATION AU - Save V. AU - S N.P.K. AU - Gujar S.V. AU - Sangeetha P. AU - Chandratreya A. AU - G P.K.K. AU - Jauhari R. PY - 2025 JO - Scientific Culture VL - 11 IS - 3-2 SP - 1564 EP - 1577 DO - 10.5281/zenodo.113225119 AB - Automation, machinic agency, algorithmic decision-making, predictive infrastructures, and computational governance—these overlapping formations now constitute the dominant lexicon of power in contemporary organizational life. This article interrogates the dissolution of human-centric managerial authority in favor of ambient algorithmic control across six globally significant platforms: Uber, Amazon, HireVue, Deliveroo, TikTok Hiring, and Zoom Workforce. Drawing on a mixed-methods design that combines secondary data synthesis, comparative platform analysis, and posthumanist critique, the study reveals that managerial decision-making is increasingly embedded in non-human systems of control that operate through surveillance, scoring, nudging, and behavioral prediction. These systems enact governance without deliberation, rendering workers and users legible as datafied subjects within infrastructures that lack contestability, transparency, or ethical accountability. Findings show not only a high degree of task automation and operational opacity but also the emergence of psychological and affective consequences—evidenced by elevated stress levels, low perceived fairness, and algorithm-induced burnout. The discussion engages with theoretical frameworks from Zuboff’s surveillance capitalism, Braidotti’s posthumanism, Barad’s entanglement, and Rouvroy’s algorithmic governmentality to argue that these platforms are not merely optimizing productivity, but actively transforming the ontological foundations of labor, agency, and ethical governance. The article concludes with a call for a re-politicization of algorithmic systems through epistemic justice, participatory oversight, and post-anthropocentric ethical frameworks that can re-inscribe accountability and equity into digital labor ecologies. This research contributes a theoretically grounded, empirically rich examination of how algorithmic governance displaces human authority, challenging dominant models of platform regulation and AI ethics. © 2025, University of AEGEAN. All rights reserved. KW - AI Ethics KW - Algorithmic Governance KW - Digital Labor KW - Platform Capitalism KW - Posthumanism CY - India ER - TY - JOUR TI - A novel legal analysis of Jordanian corporate governance legislation in the age of artificial intelligence AU - Albalawee N. AU - Fahoum A.A. PY - 2024 JO - Cogent Business and Management VL - 11 IS - 1 SP - 2297465 DO - 10.1080/23311975.2023.2297465 AB - This scholarly investigation explores the complex correlation between corporate governance and artificial intelligence (AI), recognizing the dynamic nature of the digital revolution. The research consists of two primary interactions: the initial interaction explicates the importance of corporate governance, and the subsequent one examines the incorporation of artificial intelligence applications into governance frameworks. By utilizing a descriptive and analytical approach, this study examines the extent to which current legal frameworks are congruent with the opportunities and challenges presented by digital transformation. The function of AI in reducing the risks associated with unethical financial and managerial practices is a primary concern, as it contributes to the ethical fortification of nations and businesses. The results emphasize the criticality for organizations to strictly comply with governance regulations, highlighting compliance as a fundamental element in demonstrating financial well-being, promoting expansion, and fortifying competitiveness in the corporate sphere. The findings have significant ramifications for both policymakers and organizations. Policymakers and organizations should adopt a proactive strategy to utilize AI’s capabilities, improving corporate governance practices and effectively navigating the intricacies of the digital age. © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. KW - artificial intelligence KW - Collins Ntim, University of Southampton, United Kingdom of Great Britain and Northern Ireland KW - companies leverage KW - competitive advantage KW - digital transformation KW - Governance KW - Law and Economics KW - Regulation and Business Law CY - Jordan ER - TY - JOUR TI - The ethical dimensions of big data in refugee contexts: A scoping review of empirical studies in the social sciences AU - Neiva L. AU - Borges G.M. PY - 2026 JO - Social Sciences and Humanities Open VL - 13 SP - 102522 DO - 10.1016/j.ssaho.2026.102522 AB - This scoping review examines the ethical dimensions of Big Data use as framed in empirical social science research on refugee protection and humanitarian response. It addresses the question: How are ethical issues conceptualized and addressed in empirical research on Big Data in refugee contexts? Following established scoping review frameworks, we systematically searched Web of Science, Scopus, Annual Reviews, and Google Scholar for peer-reviewed studies published between 2017 and 2025. Twenty-four studies met the inclusion criteria. Descriptive analysis shows that most research originates from Europe and North America, with limited contributions from refugee-hosting regions in the Global South. The reviewed studies used diverse data technologies—including social media analytics, remote sensing, machine learning, and mobile network data—to predict displacement, monitor mobility, and inform humanitarian decision-making. Thematic synthesis identified three recurring ethical tensions: (1) data-driven surveillance and refugee visibility, (2) predictive systems and algorithmic governance, and (3) humanitarian innovation and techno-solutionism. These findings reveal that while Big Data can enhance humanitarian action, it also reproduces structural inequalities and raises concerns around privacy, accountability, and refugee agency. The review concludes that ethical data practices in refugee governance must be participatory, transparent, and justice-oriented, balancing technological innovation with human rights and dignity. Copyright © 2026. Published by Elsevier Ltd. KW - Big data KW - Datafication KW - Ethics KW - Humanitarian response KW - Refugee protection KW - Surveillance CY - Portugal ER - TY - JOUR TI - Exponential Threats, Linear Responses U.S. Homeland Security Governance in the Age of Generative AI AU - Sofi J.I. PY - 2026 JO - Democracy and Security DO - 10.1080/17419166.2026.2617651 AB - While adversaries operationalize generative AI for financial fraud, infrastructure attacks, and influence operations, the U.S. homeland security apparatus responds with voluntary frameworks and process-oriented measures. This study examines this critical governance gap through qualitative content analysis of 23 documents including federal strategies, audit reports, congressional testimony, and industry frameworks (January 2023–June 2025). Drawing on Beck’s risk society theory, the analysis reveals a fundamental mismatch between exponentially evolving AI threats and linear bureaucratic adaptation. Federal agencies default to managing calculable social harms–bias, fairness, transparency–while ignoring incalculable security threats. Government audits confirm systematic failures: DHS lacks implementation plans for its AI strategy three years post-drafting; financial regulators cannot legally examine AI vendors processing trillions in daily transactions. This constitutes what Beck termed “organized irresponsibility”—conscientious proceduralism that masks collective security failure. Misaligned institutional incentives and incompatible stakeholder risk definitions reinforce this pattern.The temporal dimension proves particularly damaging: AI capabilities double every six months while policy cycles require 18–24 months. The evidence shows systematic inability to match adversarial innovation with defensive adaptation, not isolated failures. Without fundamental reconceptualization from voluntary to mandatory security controls, including proposed red-teaming requirements and procurement mandates, current trajectories point toward systematic defensive failure by 2027, when AI-enabled attacks on critical infrastructure will be limited only by adversary imagination rather than technical constraints. © 2026 Taylor & Francis Group, LLC. KW - Artificial intelligence governance KW - critical infrastructure protection KW - homeland security policy KW - policy subsystems CY - United States ER - TY - JOUR TI - The Role of Innovation Intermediaries in Bridging the AI Talent Gap AU - Hann C. AU - Chung-Wei K. AU - Chung-Han Y. AU - Yun-Chieh C. AU - Yu-Cheng C. PY - 2024 JO - Nanotechnology Perceptions VL - 20 IS - S3 SP - 82 EP - 94 DO - 10.62441/nano-ntp.v20iS3.6 AB - Artificial intelligence (AI) adoption is growing rapidly, yet talent shortages threaten implementation. This study explores how innovation intermediaries facilitated an AI talent development program in Taiwan. AIGO Team for Mid-Senior Level Talent, the three-stage talent development program leveraged course training, AI program prototype, and performance validation to build practical AI capabilities and ready organizations for adoption through partnerships with industry experts and practitioners. The study employed a qualitative research approach and adopted a semi-structured interview with a focus group format as an instrument to better understand how innovation intermediaries are operating and evolving in the context of AI talent. Coding identified the workflow of program process and five valuable innovation intermediary roles in AI talent program and AI project success: trustworthy databases, consultation and observation, stakeholder management, flexibility toward innovation, and logistic arrangement. Intermediaries empowered exploratory, customized learning aligned with the program’s knowledge-sharing mission. By leveraging connections and adaptability, they catalyzed talent growth and organizational change. The paper provides an overlook of innovation intermediary best practices for bridging talent gaps critical to emerging technology deployment. As rapid AI evolution necessitates lifelong learning, intermediaries can play a vital role in revitalizing a sustainable talent ecosystem. © 2024, Collegium Basilea. All rights reserved. KW - AI Talent KW - Innovation Intermediaries KW - Talent Ecosystem CY - Taiwan ER - TY - JOUR TI - Artificial intelligence regulation in the United Kingdom: a path to good governance and global leadership? AU - Roberts H. AU - Babuta A. AU - Morley J. AU - Thomas C. AU - Taddeo M. AU - Floridi L. PY - 2023 JO - Internet Policy Review VL - 12 IS - 2 DO - 10.14763/2023.2.1709 AB - On 29 March 2023 the United Kingdom (UK) government published its AI Regulation White Paper, a “proportionate and pro-innovation regulatory framework” for AI designed to support innovation, identify and address risks, and establish the UK as an “AI superpower”. In this article, we assess whether the approach outlined in this policy document is appropriate for meeting the country’s stated ambitions. We argue that the proposed continuation of a sector-led approach, which relies on existing regulators addressing risks that fall within their remits, could support contextually appropriate and novel AI governance initiatives. However, a growing emphasis from the central government on promoting innovation through weakening checks, combined with domestic tensions between Westminster and the UK’s devolved nations, will undermine the effectiveness and ethical permissibility of UK AI governance initiatives. At the same time, the likelihood of the UK’s initiatives proving successful is contingent on relationships with, and decisions from, other jurisdictions, particularly the European Union. If left unaddressed in subsequent policy, these factors risk transforming the UK into a reluctant follower, rather than a global leader, in AI governance. We conclude this paper by outlining a set of recommendations for UK policymakers to mitigate the domestic and international risks associated with the country’s current trajectory. © 2023, Alexander von Humboldt Institute for Internet and Society. All rights reserved. KW - AI governance KW - Artificial intelligence KW - Brussels Effect KW - Ethics KW - United Kingdom CY - United Kingdom, Italy ER - TY - JOUR TI - Understanding GenAI Teammates in the Workplace: A Sensemaking and Sensegiving Analysis of User Reviews AU - Agarwal A. AU - Sebastian M.P. AU - Krishnan S. PY - 2026 JO - Information Systems Frontiers DO - 10.1007/s10796-025-10672-5 AB - Generative AI (GenAI) applications, such as ChatGPT, are increasingly shaping work practices and employee engagement in organizations. Understanding how employees interact with these tools is critical for designing effective and responsible AI-enabled workplaces. This study analyzes 443,338 user reviews from the Google Play Store to examine how GenAI tools influence user satisfaction, continued use and their behaviors, which in turn impact productivity and well-being. Drawing on Sensemaking and Sensegiving theories, we develop a four-stage framework integrated into a 3E model (Envision-Evolve-Engage) comprising seven propositions. Findings highlight GenAI’s potential to enhance workplace effectiveness, decision-making and employee well-being, and to advance Sustainable Development Goal 8 (SDG 8) by promoting productive, inclusive, and meaningful work. The study also identifies challenges related to trust, privacy, adaptability, and ethical use. These insights offer practical guidance for designing user-centric GenAI systems and provide a theory-driven perspective for supporting responsible adoption and engagement in workplace contexts. © The Author(s) 2026. KW - AI teammates KW - Big data analytics KW - Conversational AI KW - Employee productivity KW - Employee well-being KW - Future of work KW - Generative AI KW - Text mining KW - User review KW - Workplace innovation KW - Advanced Analytics KW - Artificial intelligence KW - Behavioral research KW - Data mining KW - Decision making KW - Personnel KW - Sustainable development KW - AI teammate KW - Big data analytic KW - Conversational AI KW - Data analytics KW - Employee productivity KW - Employee well-being KW - Future of works KW - Generative AI KW - Text-mining KW - User reviews KW - Well being KW - Workplace innovation KW - Big data CY - India, Finland ER - TY - JOUR TI - The interplay of intelligent manufacturing, innovation equilibrium and cost stickiness in the artificial intelligence era AU - Wang F. AU - Li Q. AU - Chen H. PY - 2025 JO - Systems Research and Behavioral Science VL - 42 IS - 4 SP - 1232 EP - 1244 DO - 10.1002/sres.3046 AB - This study investigates the impact of intelligent manufacturing methods driven by artificial intelligence (AI) on cost stickiness in Chinese manufacturing enterprises. Leveraging the ABJ model， a regression analysis explores how different AI-enabled intelligent manufacturing approaches influence cost stickiness through the lens of innovation equilibrium. The sample comprises manufacturing companies listed on China's A-share market from 2013 to 2021. The findings reveal a negative correlation between intelligent manufacturing adoption and cost stickiness among these firms. Specifically, production-based intelligent manufacturing exhibits a more significant effect on reducing cost stickiness compared with collaborative intelligent manufacturing methods. Moreover, intelligent manufacturing positively impacts both joint equilibrium innovation and matching equilibrium innovation. While joint equilibrium innovation is negatively associated with cost stickiness, matching equilibrium innovation shows no significant relationship with cost stickiness. The results indicate that innovation equilibrium plays a mediating role in the relationship between AI-driven intelligent manufacturing and cost stickiness. Overall, this research sheds light on how AI capabilities enabling intelligent manufacturing processes and innovation equilibrium dynamics can help alleviate cost stickiness issues faced by manufacturing enterprises. It highlights the strategic value of adopting AI systems to enhance operational efficiency and cost management flexibility within manufacturing contexts. © 2024 John Wiley & Sons Ltd. KW - artificial intelligence KW - cost stickiness KW - innovation equilibrium KW - intelligent manufacturing KW - Cost benefit analysis KW - Industrial research KW - Regression analysis KW - Chinese manufacturing enterprise KW - Cost stickiness KW - Innovation equilibrium KW - Intelligent Manufacturing KW - Manufacturing companies KW - Manufacturing innovation KW - Manufacturing methods KW - Matchings KW - Share market KW - Through the lens KW - Artificial intelligence CY - China, United States ER - TY - JOUR TI - Unlocking the value of artificial intelligence in human resource management through AI capability framework AU - Chowdhury S. AU - Dey P. AU - Joel-Edgar S. AU - Bhattacharya S. AU - Rodriguez-Espindola O. AU - Abadie A. AU - Truong L. PY - 2023 JO - Human Resource Management Review VL - 33 IS - 1 SP - 100899 DO - 10.1016/j.hrmr.2022.100899 AB - Artificial Intelligence (AI) is increasingly adopted within Human Resource management (HRM) due to its potential to create value for consumers, employees, and organisations. However, recent studies have found that organisations are yet to experience the anticipated benefits from AI adoption, despite investing time, effort, and resources. The existing studies in HRM have examined the applications of AI, anticipated benefits, and its impact on human workforce and organisations. The aim of this paper is to systematically review the multi-disciplinary literature stemming from International Business, Information Management, Operations Management, General Management and HRM to provide a comprehensive and objective understanding of the organisational resources required to develop AI capability in HRM. Our findings show that organisations need to look beyond technical resources, and put their emphasis on developing non-technical ones such as human skills and competencies, leadership, team co-ordination, organisational culture and innovation mindset, governance strategy, and AI-employee integration strategies, to benefit from AI adoption. Based on these findings, we contribute five research propositions to advance AI scholarship in HRM. Theoretically, we identify the organisational resources necessary to achieve business benefits by proposing the AI capability framework, integrating resource-based view and knowledge-based view theories. From a practitioner's standpoint, our framework offers a systematic way for the managers to objectively self-assess organisational readiness and develop strategies to adopt and implement AI-enabled practices and processes in HRM. © 2022 Elsevier Inc. KW - AI capability KW - AI-employee collaboration KW - Artificial intelligence KW - Human resource management KW - Organisational resources KW - Systematic review CY - France, United Kingdom, Morocco ER - TY - JOUR TI - Generative AI for cyber threat intelligence: applications, challenges, and analysis of real-world case studies AU - Balasubramanian P. AU - Liyana S. AU - Sankaran H. AU - Sivaramakrishnan S. AU - Pusuluri S. AU - Pirttikangas S. AU - Peltonen E. PY - 2025 JO - Artificial Intelligence Review VL - 58 IS - 11 SP - 336 DO - 10.1007/s10462-025-11338-z AB - This paper presents a comprehensive survey of the applications, challenges, and limitations of Generative AI (GenAI) in enhancing threat intelligence within cybersecurity, supported by real-world case studies. We examine a wide range of data sources in Cyber Threat Intelligence (CTI), including security reports, blogs, social media, network traffic, malware samples, dark web data, and threat intelligence platforms (TIPs). This survey provides a full reference for integrating GenAI into CTI. We discuss various GenAI models such as Large Language Models (LLMs) and Deep Generative Models (DGMs) like Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Diffusion Models, explaining their roles in detecting and addressing complex cyber threats. The survey highlights key applications in areas such as malware detection, network traffic analysis, phishing detection, threat actor attribution, and social engineering defense. We also explore critical challenges in deploying GenAI, including data privacy, security concerns, and the need for interpretable and transparent models. As regulations like the European Commission’s AI Act emerge, ensuring trustworthy AI solutions is becoming more crucial. Real-world case studies, such as the impact of the WannaCry ransomware, the rise of deepfakes, and AI-driven social engineering, demonstrate both the potential and current limitations of GenAI in CTI. Our goal is to provide foundational insights and strategic direction for advancing GenAI’s role in future cybersecurity frameworks, emphasizing the importance of innovation, adaptability, and ongoing learning to enhance resilience against evolving cyber threats. Ultimately, this survey offers critical insights into how GenAI can shape the future of cybersecurity by addressing key challenges and providing actionable guidance for effective implementation. © The Author(s) 2025. KW - artificial intelligence KW - Cyber threat intelligence KW - Cybersecurity KW - GAN KW - Generative artificial intelligence KW - Large language models KW - Cybersecurity KW - Generative adversarial networks KW - Malware KW - Network security KW - Social networking (online) KW - Adversarial networks KW - Case-studies KW - Cybe threat intelligence KW - Cyber security KW - Cyber threats KW - Generative artificial intelligence KW - Language model KW - Large language model KW - Real-world KW - Social engineering KW - Data privacy CY - Finland, United States ER - TY - JOUR TI - Orchestrating artificial intelligence for urban sustainability AU - Zhang D. AU - Pee L.G. AU - Pan S.L. AU - Liu W. PY - 2022 JO - Government Information Quarterly VL - 39 IS - 4 SP - 101720 DO - 10.1016/j.giq.2022.101720 AB - Artificial intelligence (AI) is regarded as the next digital frontier in government, with many potential applications for economic development as well as sustainable urbanization. Governments have started experimenting with AI, but empirical research on how to leverage and implement AI remains limited. This study analyzed two cases of AI implementation in a large city and identified various AI capabilities useful for government. More importantly, purposeful orchestration of AI-related resources such as data, knowledge, algorithms, and information systems is necessary for developing strong AI capabilities. The findings indicate two different types of orchestration: policy-driven orchestration focuses on the integration of resources, while innovation-driven orchestration focuses on triangulation. This study contributes to the growing body of knowledge on AI in government by revealing and conceptualizing different paths and approaches to AI implementation. They also serve to inform practitioners' planning of AI implementation. © 2022 KW - Artificial intelligence KW - Big data KW - Resilient urbanization KW - Resource orchestration KW - SDGs KW - Sustainability CY - China, Singapore, Australia ER - TY - JOUR TI - Nurturing human intelligence in the age of AI: rethinking education for the future AU - Luckin R. PY - 2025 JO - Development and Learning in Organizations VL - 39 IS - 1 SP - 1 EP - 4 DO - 10.1108/DLO-04-2024-0108 AB - Purpose: The purpose of the article “Nurturing Human Intelligence in the Age of AI: Rethinking Education for the Future” is to explore the profound impact of Artificial Intelligence (AI) on education and to emphasize the need for a fundamental shift in current education systems. The article aims to provide practitioners with actionable insights on how to navigate the rapidly evolving landscape of AI in education while preparing young people for their crucial role as the workforce of tomorrow. It seeks to highlight the potential of AI to revolutionize education while also acknowledging the importance of preserving the unique human touch in the learning process. Design/methodology/approach: This article explores the disruptive impact of Artificial Intelligence (AI) on education and emphasizes the need for a fundamental shift in current education systems to prepare young people for an AI-driven future. It highlights the potential of AI to revolutionize education through personalized learning experiences, enhanced teacher professional development and automation of administrative tasks while acknowledging the importance of approaching AI implementation with caution and preserving the unique human touch in education. The article argues for a shift in focus from rote learning to fostering critical thinking, creativity and problem-solving skills, emphasizing the development of Learning Mastery and Knowledge Mastery. It underscores the vital role of educators in leveraging AI technologies and preparing young people for the future, along with the need for responsive educational policies and curriculum frameworks that integrate AI literacy and ethical considerations. The article concludes by calling for reimagining the schooling system, prioritizing high-level thinking and nurturing the unique capabilities of human intelligence. The future of education lies in harnessing the power of AI while celebrating and cultivating distinctively human qualities. Educational practitioners play a crucial role in shaping this future by bridging the gap between research and practice, ensuring a positive and prosperous future for society in an AI-driven world. Findings: (1) AI can revolutionize education through personalized learning, enhanced teacher development and task automation. (2) Balance is needed between AI and human touch in education. Current education systems fail to cultivate critical thinking and creativity. (3) Learning Mastery and Knowledge Mastery should be emphasized to foster independent thinking and problem-solving. (4) Educators play a vital role in integrating AI into the learning process. (5). AI can redefine success in education and cultivate future-proof skills. (6). Responsive and adaptable educational policies are necessary. (7) The future of education lies in harnessing AI while nurturing human intelligence. Research limitations/implications: Not appropriate for style of text. Practical implications: (1) Educators should actively engage with AI technologies and explore ways to integrate them into the learning process to enhance personalized learning experiences. (2) Professional development programs should be designed to equip teachers with the necessary skills to effectively utilize AI tools and leverage them to improve instructional practices. (3) Curriculum frameworks need to be revised to integrate AI literacy, digital citizenship and ethical considerations into the educational journey of young learners. (4) Educational institutions should invest in AI-powered assessment tools that provide a holistic understanding of a student’s abilities, capturing their strengths and areas for improvement beyond test scores. (5) Educators should focus on teaching metacognitive strategies, encouraging self-reflection and self-assessment and providing opportunities for students to develop problem-solving and critical-thinking skills. (6) Active learning strategies, such as project-based learning, problem-based learning and inquiry-based learning, should be employed to foster deep learning and knowledge mastery. (7) Educational policies should encourage innovation and collaboration between educational institutions, government bodies and industry stakeholders to ensure responsiveness to the rapidly evolving landscape of AI in education. (8) Educators should strive to create a learning environment that nurtures and celebrates the unique capabilities of human intelligence while harnessing the power of AI to enhance the learning experience. Social implications: (1) Workforce preparedness for an AI-driven future. (2) Potential exacerbation of societal inequalities. (3) Fostering human–AI collaboration skills. (4) Addressing ethical concerns regarding data privacy and security. (5) Emphasizing lifelong learning to adapt to changing demands. (6) Redefining success through a holistic view of student abilities. (7) Shaping societal values that balance human intelligence and AI capabilities. The education system must address these implications to ensure equitable access to AI-enhanced learning, maintain public trust and prepare individuals for a society where human–AI collaboration is essential, while promoting a balanced and harmonious coexistence between human intelligence and AI. Originality/value: The article “Nurturing Human Intelligence in the Age of AI: Rethinking Education for the Future” offers a fresh perspective on the impact of AI on education. While the topic of AI in education is not novel, the article’s emphasis on nurturing human intelligence alongside AI integration sets it apart. The author’s call for a fundamental shift in education systems to prioritise critical thinking, creativity and problem-solving skills is a unique approach. The article’s exploration of Learning Mastery and Knowledge Mastery as key concepts in preparing students for an AI-driven future adds originality to the discussion. Overall, the article presents a thought-provoking and original viewpoint on the future of education in the age of AI. © 2024, Emerald Publishing Limited. KW - Artificial Intelligence KW - Education KW - Ethics KW - Future-proof skills KW - Personalized learning KW - Teacher professional development CY - United Kingdom ER - TY - JOUR TI - Responsible AI and career sustainability: the intersectional role of knowledge, emotion, and capability in Vietnam AU - Tran Le Tuyet T. AU - Nguyen K.M. PY - 2026 JO - Cognition, Technology and Work DO - 10.1007/s10111-025-00851-4 AB - This study investigates how responsible AI signals (RAS) such as autonomy, justice, beneficence, explainability, and nonmaleficence enhance employees’ career sustainability, with particular focus on dynamic capability, AI emotional response, and AI knowledge management (knowledge sharing, acquisition, and application). Grounded in the Cognition–Affect–Conation (CAC) framework, the study extends its scope from psychology to human resource management by explaining how cognitive, emotional, and behavioral mechanisms jointly shape employees’ responses to responsible AI practices. A quantitative research design was employed using an online questionnaire, gathering responses from a sample of 717 employees in Vietnam and analyzed using PLS-SEM. The findings reveal that RAS strongly promotes AI emotional response, dynamic capability, and knowledge management processes, including knowledge sharing, acquisition, and application. In turn, AI emotional response, dynamic capability, and the application and sharing of knowledge exert significant positive effects on employee well-being and innovation performance, whereas knowledge acquisition shows no meaningful impact. The study advances theory by integrating the CAC framework with Responsible AI principles to explain how employees adapt and collaborate with AI in organizations. Practically, the findings indicate that adopting responsible AI principles can enhance employee creativity, emotional engagement, and adaptability by promoting knowledge sharing, supportive policies, and transparent AI practices. Managers are encouraged to design learning-oriented environments, continuous AI ethics training, and participatory mechanisms that allow employees to engage with AI fairly and autonomously, thereby fostering well-being, innovation, and long-term career sustainability. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2026. KW - AI knowledge management KW - Career sustainability KW - Dynamic capability KW - Emotional response KW - Responsible AI KW - Behavioral research KW - Employment KW - Human resource management KW - Knowledge acquisition KW - Knowledge management KW - Knowledge transfer KW - Mergers and acquisitions KW - Professional aspects KW - Sustainable development KW - AI knowledge management KW - Career sustainability KW - Dynamics capability KW - Emotional response KW - Knowledge application KW - Knowledge-sharing KW - ON dynamics KW - Responsible AI KW - Viet Nam KW - Well being KW - Personnel training ER - TY - JOUR TI - The Role of Lecturers’ AI Leadership in Enhancing Postgraduate Student Teachers’ Integration of Mobile AI Tools: A Mixed-Methods Study in Malaysian Education Faculties AU - Tang S.S. AU - Beh W.F. AU - Cheah K.S.L. PY - 2025 JO - International Journal of Interactive Mobile Technologies VL - 19 IS - 7 SP - 136 EP - 158 DO - 10.3991/ijim.v19i07.51971 AB - This study examined the influence of lecturers’ artificial intelligence (AI) leadership on postgraduate student teachers’ motivation to integrate AI into their curricula in Malaysian higher education. Using a sample of 62 participants, the study employed a mixed-methods approach to explore ethical implications and the alignment of AI with traditional teaching practices. By means of open-ended questions and online surveys, the study generated both quantitative and qualitative understanding of how leadership influences acceptance of AI in educational settings. Key findings showed that transformative and visionary AI leadership approaches not only improve feedback systems and tailored learning opportunities but also inspire teachers by means of interactive, game-like learning activities. AI leadership enables early identification of learning gaps by means of real-time analytics, enabling targeted interventions and a more inclusive learning environment. However, over-reliance on AI highlights the need for strategic planning to ensure that AI complements rather than replaces traditional teaching methods. The research emphasized the need for strategic leadership and professional development in embedding AI ethically and successfully inside curricula, offering a framework for both curriculum design and educator training programs. These results support current debates on educational innovation and place leadership as key in promoting a balanced, ethical AI integration matched with present educational aims. © 2025 by the authors. KW - AI integration in education KW - artificial intelligence (AI) transforming leadership KW - challenges of AI integration KW - educational technology KW - postgraduate student teachers KW - transformational leadership in AI KW - Adversarial machine learning KW - Curricula KW - Federated learning KW - Strategic planning KW - Students KW - Teaching KW - Artificial intelligence transforming leadership KW - Artificial intelligence integration in education KW - Challenge of artificial intelligence integration KW - Intelligence integration KW - Postgraduate student teacher KW - Postgraduate students KW - Student teachers KW - Transformational leadership KW - Transformational leadership in artificial intelligence KW - Contrastive Learning CY - Malaysia ER - TY - JOUR TI - How green knowledge-oriented leadership drives green innovation in SMEs: the mediating role of environmental strategy and the moderating role of green AI capability AU - Al Koliby I.S. AU - Al-Swidi A.K. AU - Al-Hakimi M.A. AU - Farhan S.A.G. PY - 2025 JO - Cogent Business and Management VL - 12 IS - 1 SP - 2520914 DO - 10.1080/23311975.2025.2520914 AB - This study examines how green knowledge-oriented leadership (GKOL) drives green innovation (GI) in manufacturing SMEs, with a focus on the mediating role of environmental strategy (ES) and the moderating effect of green artificial intelligence capability (GAIC). Drawing on the Natural Resource-Based View (NRBV) and Dynamic Capability Theory (DCT), the study developed and empirically tested an integrated framework that captures the interplay between leadership, strategy, and digital capability in promoting sustainable innovation. Data were collected from 219 Malaysian manufacturing SMEs using a structured questionnaire, and structural equation modeling was employed via SmartPLS to evaluate the proposed relationships, with the reliability and validity of the constructs verified through composite reliability, average variance extracted, and discriminant validity. The findings reveal that GKOL significantly enhances ES, which in turn enhances GI, with ES partially mediating this relationship. Additionally, GAIC strengthens the effect of GKOL on GI, underscoring the role of AI-enabled capabilities in amplifying green leadership outcomes. This study contributes to the literature by offering a unified leadership–strategy–technology framework for understanding sustainability transformation in resource-constrained SME settings and provides actionable insights for managers and policymakers on leveraging GKOL and digital transformation for sustainable development. © 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. KW - Business, Management and Accounting KW - Environment & Business KW - Environmental Management KW - environmental strategy KW - green artificial intelligence capability KW - green innovation KW - Green knowledge-oriented leadership KW - Manufacturing SMEs CY - China ER - TY - JOUR TI - Effectiveness in the furniture industry: artificial intelligence, big data and sustainable design AU - Adiguzel Z. AU - Sonmez Cakir F. AU - Altay Morgul U. PY - 2026 JO - Management Decision VL - 64 IS - 3 SP - 1063 EP - 1086 DO - 10.1108/MD-05-2024-1022 AB - Purpose – This research aims to investigate the interaction between artificial intelligence (AI) capability, big data capabilities, sustainability design and organizational effectiveness in the context of the furniture industry. It aims to explore how investments in AI and big data technologies can spur sustainability-focused innovation and ultimately increase corporate performance. Design/methodology/approach – Based on data collected from businesses operating in the furniture industry, this research uses a quantitative approach to analyze the relationships between independent variables (AI capability and big data features), mediating variable (sustainability design) and dependent variable (organizational effectiveness). The structural equation modeling (SEM) technique was used to test the proposed theoretical model and hypotheses. The SmartPLS program was used for analysis. Findings – Analysis results show a significant positive relationship between AI capability, big data capabilities, sustainability design and organizational effectiveness in the furniture industry. Moreover, sustainability design demonstrates its important role in translating technological advances into tangible performance results by mediating the relationship between AI capability, big data capabilities and organizational effectiveness. Research limitations/implications – Although this research contributes valuable insights, it also has limitations. It would not be appropriate to make a general assessment of the generalizability of the findings due to the focus on the furniture industry and the fact that the data of the research were collected from furniture-producing companies in Istanbul. Future research could explore additional industries and incorporate qualitative methods to provide a deeper understanding of the underlying mechanisms driving the observed relationships. Practical implications – The findings offer valuable insights to industry practitioners seeking to leverage the potential of AI and big data technologies to increase sustainable organizational effectiveness. Practical implications include strategic recommendations for integrating sustainability principles into organizational strategies, leveraging data-driven decision-making processes and encouraging innovation through technological investments. Originality/value – The originality of this research lies in its comprehensive examination of the intertwined dynamics between AI capability, big data capabilities, sustainability design and organizational effectiveness, especially in the context of the furniture industry. By combining knowledge from multiple disciplines, this research offers a new perspective on the strategic implications of technological innovation for sustainable business practices. © 2025 Emerald Publishing Limited KW - AI capability KW - Big data characteristics KW - Furniture industry KW - Organizational effective performance KW - Sustainability design CY - Turkey ER - TY - JOUR TI - Navigating artificial general intelligence development: societal, technological, ethical, and brain-inspired pathways AU - Raman R. AU - Kowalski R. AU - Achuthan K. AU - Iyer A. AU - Nedungadi P. PY - 2025 JO - Scientific Reports VL - 15 IS - 1 SP - 8443 DO - 10.1038/s41598-025-92190-7 AB - This study examines the imperative to align artificial general intelligence (AGI) development with societal, technological, ethical, and brain-inspired pathways to ensure its responsible integration into human systems. Using the PRISMA framework and BERTopic modeling, it identifies five key pathways shaping AGI’s trajectory: (1) societal integration, addressing AGI’s broader societal impacts, public adoption, and policy considerations; (2) technological advancement, exploring real-world applications, implementation challenges, and scalability; (3) explainability, enhancing transparency, trust, and interpretability in AGI decision-making; (4) cognitive and ethical considerations, linking AGI’s evolving architectures to ethical frameworks, accountability, and societal consequences; and (5) brain-inspired systems, leveraging human neural models to improve AGI’s learning efficiency, adaptability, and reasoning capabilities. This study makes a unique contribution by systematically uncovering underexplored AGI themes, proposing a conceptual framework that connects AI advancements to practical applications, and addressing the multifaceted technical, ethical, and societal challenges of AGI development. The findings call for interdisciplinary collaboration to bridge critical gaps in transparency, governance, and societal alignment while proposing strategies for equitable access, workforce adaptation, and sustainable integration. Additionally, the study highlights emerging research frontiers, such as AGI-consciousness interfaces and collective intelligence systems, offering new pathways to integrate AGI into human-centered applications. By synthesizing insights across disciplines, this study provides a comprehensive roadmap for guiding AGI development in ways that balance technological innovation with ethical and societal responsibilities, advancing societal progress and well-being. © The Author(s) 2025. KW - Artificial general intelligence KW - Brain inspired KW - Ethical AI KW - Ethics KW - Human-like AI KW - Responsible AI KW - Strong AI KW - Superintelligence KW - Topic modeling KW - Weak AI KW - Artificial Intelligence KW - Brain KW - Decision Making KW - Humans KW - article KW - artificial general intelligence KW - biological model KW - conceptual framework KW - consciousness KW - human KW - intelligence KW - Preferred Reporting Items for Systematic Reviews and Meta-Analyses KW - reasoning KW - responsible artificial intelligence KW - workforce KW - artificial intelligence KW - brain KW - decision making KW - ethics KW - physiology CY - India, United States ER - TY - JOUR TI - Artificial Intelligence (AI) Capabilities and the R&D Performance of Organizations: The Moderating Role of Environmental Dynamism AU - Kumar V. AU - Kumar S. AU - Chatterjee S. AU - Mariani M. PY - 2024 JO - IEEE Transactions on Engineering Management VL - 71 SP - 11522 EP - 11532 DO - 10.1109/TEM.2024.3423669 AB - The potential of artificial intelligence capabilities (AICs) extends beyond fostering both explorative and exploitative innovations (EXO and EXI); it may also enhance the overall performance of organizations. Despite this, the interplay between AIC and research and development performance (RDP) remains unexplored. In this article, we aim to fill this gap by investigating the influence of AIC on RDP, considering both EXO and EXI. Additionally, the study examines the potential moderating role of environmental dynamism in shaping the relationship between AIC and the two types of innovations, ultimately impacting the enhancement of RDP in organizations. To achieve this, a conceptual model was developed based on the existing literature and subsequently validated using the partial least square structural equation modeling. The research gathered 289 responses from a diverse group of industry professionals. The findings of this study contribute both theoretically and practically by shedding light on the pivotal role played by artificial intelligence (AI) capabilities, exploration, and EXI in improving the research and development (R&D) performance of organizations. Understanding these dynamics will provide valuable insights for organizations seeking to leverage AI for strategic advancement in their R&D endeavors. © 1988-2012 IEEE. KW - Artificial intelligence (AI) capability (AIC) KW - environmental dynamism (ED) KW - exploitative innovation (EXI) KW - exploration innovation (EXO) KW - research and development (R&D) performance KW - Artificial intelligence capability KW - Dynamic scheduling KW - Environmental dynamisms KW - Exploitative innovation KW - Exploration innovation KW - Performance KW - R&D performance KW - Research and development KW - Research performance KW - Technological innovation KW - Artificial intelligence CY - India, United Kingdom, Italy ER - TY - JOUR TI - Artificial intelligence capability, CEO-TMT interface and corporate innovation failure AU - Shang J. AU - Zhang K. PY - 2026 JO - Humanities and Social Sciences Communications VL - 13 IS - 1 SP - 515 DO - 10.1057/s41599-026-06856-2 AB - Artificial intelligence (AI) capability has demonstrated significant potential in driving knowledge recombination, fostering knowledge discovery and enhancing organisational innovation performance. However, the interplay between AI capability and corporate innovation failure remains underexplored. To address this research gap, this study investigated the impact of AI capability on corporate innovation failure while further examining the moderating role of top management team (TMT) digital knowledge and its variation under different levels of integrative leadership shown by chief executive officers (CEOs). Using panel data from 3,829 firm-year observations in China from 2017 to 2022, the empirical analysis reveals that AI capability significantly reduces the likelihood of corporate innovation failure. Moreover, TMT digital knowledge exerts a significant positive moderating effect on this relationship. Further analysis reveals that this positive moderating effect is more pronounced when the CEO exhibits a high level of integrative leadership. © The Author(s) 2026. CY - China ER - TY - JOUR TI - DATA GOVERNANCE FOR ARTIFICIAL INTELLIGENCE IMPLEMENTATION IN THE FINANCIAL SECTOR: AN INDONESIAN PERSPECTIVE AU - Damaris R. AU - Rosadi S.D. AU - Bratadana I.M.D. PY - 2025 JO - Journal of Central Banking Law and Institutions VL - 4 IS - 3 SP - 445 EP - 472 DO - 10.21098/jcli.v4i3.430 AB - The fast-evolving landscape of Artificial Intelligence (AI) is transforming industries worldwide, including Indonesia’s financial sector. While AI presents immense opportunities for innovation and efficiency, it also poses complex challenges in data governance. This paper explores the need for Indonesia to establish a comprehensive and forward-thinking data governance framework tailored to AI implementation in the financial sector. Using a literature review method and drawing on global and local regulatory developments, the paper outlines key principles for AI-related data governance, including transparency, accountability, specificity, enforceability, and adaptability. By reimagining its approach to data governance, Indonesia can mitigate the risks of data misuse, enhance personal data protection, and foster an environment conducive to responsible AI innovation. The research addresses the foregoing issues by offering a conceptual foundation for policymakers, regulators, and financial institutions in Indonesia to develop better rules and practices for managing AI-related data to strengthen Indonesia’s technological sovereignty, particularly in the financial sector. The study finds that Indonesia’s current data governance framework in the financial sector is not yet optimal for supporting AI implementation. Indonesia’s data governance framework requires adjustments in key areas, namely specificity, enforceability, and adaptability, while also promoting stronger cooperation among stakeholders. © 2025, Bank Indonesia Institute. All rights reserved. KW - AI governance KW - artificial intelligence KW - data governance KW - financial sector KW - technology regulation CY - Indonesia ER - TY - JOUR TI - The nexus of managerial and technical AI knowledge, disruptive innovation and the circular economy: The role of organizational change capability and financial resilience AU - Al Halbusi H. AU - Popa S. AU - Soto-Acosta P. AU - Alshallaqi M. PY - 2025 JO - Technology in Society VL - 82 SP - 102937 DO - 10.1016/j.techsoc.2025.102937 AB - Drawing on the dynamic capabilities theory, this study investigates how managerial and technical artificial intelligence (AI) knowledge influence disruptive innovation and its impact on the circular economy. It also examines the moderating effects of organizational change capability and financial resilience on the relationship between disruptive innovation and the circular economy. The proposed model and its associated hypotheses were tested using Partial Least Squares (PLS) structural equation modeling (SEM). This study is based on two-wave data collected from 242 general, IT, and operations managers in firms located within a central hub for the technology and manufacturing industries in Baghdad. The findings indicate that managerial and technical AI knowledge significantly boost disruptive innovation, which, in turn, enhances the circular economy. Moreover, organizational change capability and financial resilience strengthen the relationship between disruptive innovation and the circular economy. This research contributes to the field of AI capabilities by highlighting their role in fostering disruptive innovation and promoting sustainability through the circular economy. Additionally, it provides insights into how organizational and financial resilience, alongside AI skills and knowledge, can support sustainable business models. Practically, the study underscores the necessity for technology and manufacturing companies to invest in both managerial and technical AI skills while prioritizing robust organizational change capabilities and financial resilience to maximize the benefits of disruptive innovation and support a circular economy. © 2025 Elsevier Ltd KW - Artificial intelligence KW - Disruptive innovation KW - Managerial and technical skills KW - Organizational change KW - Resilience KW - Sustainability KW - Baghdad [Iraq] KW - Iraq KW - Circular economy KW - Disruptive innovations KW - Dynamics capability KW - Managerial skills KW - Moderating effect KW - Organizational change KW - Partial least-squares KW - Resilience KW - Structural equation models KW - Technical skills KW - artificial intelligence KW - circular economy KW - environmental economics KW - finance KW - industrial technology KW - innovation KW - knowledge KW - least squares method KW - manufacturing KW - organizational change KW - sustainability KW - Investments CY - Qatar, Spain, Saudi Arabia ER - TY - JOUR TI - Artificial Intelligence Applications in High-Frequency Magnetic Components Design for Power Electronics Systems: An Overview AU - Shen X. AU - Zuo Y. AU - Kong J. AU - Martinez W. PY - 2024 JO - IEEE Transactions on Power Electronics VL - 39 IS - 7 SP - 8478 EP - 8496 DO - 10.1109/TPEL.2024.3381431 AB - This article provides an overview of how artificial intelligence (AI) is applied in designing high-frequency magnetic components, primarily high-frequency inductors and transformers, for power electronics systems. Four categories of AI, including expert systems, fuzzy logic, metaheuristic methods, and machine learning techniques, are addressed. First, AI models for estimating losses in high-frequency magnetic components are discussed. Subsequently, AI-based design methods in high-frequency inductors and transformers are observed. Then, AI tools applied to the automatic design of high-frequency magnetic components are introduced and compared. Drawing insights from an analysis of over 200 publications, this article highlights significant advancements: the development of AI-driven models for precise loss estimation in high-frequency magnetic components, the application of AI in optimizing design configurations for the components, and the automation of design processes. These achievements demonstrate AI's capability to enhance the efficiency, performance, and innovation in high-frequency magnetic component design, offering a roadmap for future research in power electronics systems. © 1986-2012 IEEE. KW - Artificial intelligence (AI) KW - HF magnetic components KW - HF transformer design KW - high-frequency (HF) inductor design KW - loss models KW - Expert systems KW - Fuzzy logic KW - Learning systems KW - Magnetic resonance KW - Power electronics KW - Soft magnetic materials KW - Amorphous magnetic materials KW - High-frequency inductor design KW - High-frequency inductors KW - High-frequency magnetic component KW - High-frequency magnetics KW - High-frequency transformer desig KW - High-frequency transformers KW - Inductor design KW - Loss model KW - Magnetic components KW - Saturation magnetization CY - Belgium ER - TY - JOUR TI - Domain Knowledge-Based Human Capital Strategy in Manufacturing AI AU - Chung E. PY - 2023 JO - IEEE Engineering Management Review VL - 51 IS - 1 SP - 108 EP - 122 DO - 10.1109/EMR.2022.3215074 AB - As the Industry 4.0 paradigm accelerates, the importance of artificial intelligence (AI) in manufacturing industry is increasing. Manufacturing AI requires balanced capabilities between industry-specific domain knowledge and AI capability. Nevertheless, many manufacturing AI startups are mainly focusing on human capital based on AI or data science capability. This article focused on the importance of human capital based on domain knowledge for the success of manufacturing AI. In this article, the relationship between domain knowledge and corporate performance was analyzed for 127 global manufacturing AI startups. Furthermore, the moderating effects of educational level and cofounder size for domain knowledge were analyzed. In addition, an expanded analysis was conducted on effect between domain knowledge, educational level, cofounder size, and corporate performance by business model. This article has new implications for and provides practical contributions to the human capital strategy for manufacturing AI corporates. © 1973-2011 IEEE. KW - Artificial intelligence (AI) KW - domain knowledge KW - human capital KW - industry 4.0 KW - manufacturing KW - smart factory KW - Cost engineering KW - Domain Knowledge KW - Foundries KW - Knowledge engineering KW - Artificial intelligence KW - Corporate performance KW - Domain knowledge KW - Fourth industrial revolution KW - Human capitals KW - Industrial revolutions KW - Manufacturing KW - Smart factory KW - Technological innovation KW - Industry 4.0 CY - South Korea ER - TY - JOUR TI - Can Artificial Intelligence Technologies Advance Environmental Sustainability? The Role of Institutional Adaptability and Skill-Biased Technological Transformation AU - Bergougui B. PY - 2026 JO - Sustainable Development VL - 34 IS - S2 SP - 222 EP - 244 DO - 10.1002/sd.70296 AB - The ubiquitous proliferation of artificial intelligence (AI) technologies across contemporary global economic systems necessitates a comprehensive empirical examination of their environmental ramifications, particularly with respect to environmental sustainability paradigms. This study leverages a longitudinal panel of 29 advanced and developing economies over the period 2005–2024, employing AI patent filing frequencies as a quantitative proxy for national AI capability. Our econometric analysis reveals a statistically robust and economically meaningful relationship: higher AI activity is consistently associated with increases in the load capacity factor (LCF), a composite indicator of environmental sustainability. This association endures across multiple model specifications, remains significant under instrumental-variable estimation to address endogeneity, and passes a battery of robustness and sensitivity checks. Mechanism analysis uncovers two principal transmission channels. First, AI drives technological transformation in labor markets—favoring non-routine and high-skilled occupations—which in turn enhances resource efficiency and elevates LCF. Second, institutional flexibility—proxied by regulatory quality and innovation-friendly governance—magnifies AI's positive environmental effects by lowering transaction costs and facilitating diffusion. Heterogeneity tests further demonstrate that countries geographically proximate to global AI leaders experience stronger LCF gains, underscoring the importance of knowledge spillovers. Moreover, lower-income and fossil-fuel–dependent economies exhibit more pronounced benefits, indicating AI's potential as a transitional “leapfrog” technology. Among AI subfields, patents in energy-management applications deliver the largest LCF improvements. Overall, our evidence underscores the pivotal role of AI-driven patented technologies in strengthening environmental sustainability. Policies that incentivize AI innovation, support institutional adaptability, and foster international technology transfer are therefore essential to accelerate global progress toward sustainable development targets. © 2025 The Author(s). Sustainable Development published by ERP Environment and John Wiley & Sons Ltd. KW - AI KW - environmental sustainability KW - institutional framework KW - load capacity factor KW - technological transformation KW - artificial intelligence KW - econometrics KW - institutional framework KW - panel data KW - sustainability KW - sustainable development KW - technology transfer CY - Algeria, Netherlands, Qatar ER - TY - JOUR TI - Artificial intelligence and the five laws: a new vision for library science AU - Kalbande D. AU - Hemke D. AU - Motewar N. PY - 2025 JO - Library Hi Tech News VL - 42 IS - 4 SP - 1 EP - 3 DO - 10.1108/LHTN-01-2025-0005 AB - Purpose: This conceptual paper reinterprets S.R. Ranganathan’s five laws of library science in the context of artificial intelligence (AI), examining their continued relevance and adaptability in the digital age. By aligning AI capabilities with these foundational principles, this paper aims to explore how AI can enhance information access, optimize resource management and personalize library services while maintaining the ethical and philosophical core of Library and Information Science (LIS). Design/methodology/approach: This study uses a conceptual analysis approach to critically examine AI applications in LIS, including automated cataloging, AI-driven search systems, personalized recommendations and intelligent chatbots. It also addresses ethical considerations such as algorithmic bias, data privacy and equitable access. This paper proposes an AI-enhanced reinterpretation of Ranganathan’s laws, offering a guiding framework for responsible AI adoption in libraries. Findings: This study highlights the transformative potential of AI in libraries, demonstrating its ability to improve operational efficiency, user engagement and accessibility. However, it also emphasizes the necessity of aligning AI implementation with ethical principles to prevent biases and ensure inclusivity. By conceptualizing an AI-driven adaptation of Ranganathan’s laws, this paper provides a roadmap for integrating AI into library services without compromising their core values. Originality/value: This research offers a novel perspective by reconceptualizing Ranganathan’s five laws in the era of AI, providing LIS professionals with a theoretical framework to guide AI integration. It contributes to the discourse on ethical and sustainable AI adoption in libraries, ensuring that technological advancements support rather than undermine traditional LIS principles. © 2025, Emerald Publishing Limited. KW - Artificial intelligence in libraries KW - Digital transformation KW - Ethical AI KW - Library science KW - LIS innovation KW - Ranganathan’s five laws CY - India ER - TY - JOUR TI - Will the technological singularity come soon? Modeling the dynamics of artificial intelligence development via multi-logistic growth process AU - Jin G. AU - Ni X. AU - Wei K. AU - Zhao J. AU - Zhang H. AU - Jia L. PY - 2025 JO - Physica A: Statistical Mechanics and its Applications VL - 664 SP - 130450 DO - 10.1016/j.physa.2025.130450 AB - We are currently in an era of escalating technological complexity and profound societal transformations, where artificial intelligence (AI) technologies exemplified by large language models (LLMs) have reignited discussions on the ‘Technological Singularity’. ‘Technological Singularity’ is a philosophical concept referring to an irreversible and profound transformation that occurs when AI capabilities surpass those of humans comprehensively. However, quantitative modeling and analysis of the historical evolution and future trends of AI technologies remain scarce, failing to substantiate the singularity hypothesis adequately. This paper hypothesizes that the development of AI technologies could be characterized by the superposition of multiple logistic growth processes. To explore this hypothesis, we propose a multi-logistic growth process model and validate it using two real-world datasets: AI Historical Statistics and Arxiv AI Papers. Our analysis of the AI Historical Statistics dataset assesses the effectiveness of the multi-logistic model and evaluates the current and future trends in AI technology development. Additionally, cross-validation experiments on the Arxiv AI Paper, GPU Transistor and Internet User dataset enhance the robustness of our conclusions derived from the AI Historical Statistics dataset. The experimental results reveal that around 2024 marks the fastest point of the current AI wave, and the deep learning-based AI technologies are projected to decline around 2035–2040 if no fundamental technological innovation emerges. Consequently, the technological singularity appears unlikely to arrive in the foreseeable future. © 2025 Elsevier B.V. KW - Artificial intelligence KW - Development dynamics KW - Multi-logistic growth KW - Technological singularity KW - 'current KW - Artificial intelligence technologies KW - Development dynamics KW - Future trends KW - Growth process KW - Language model KW - Logistic growth KW - Multi-logistic growth KW - Technological complexity KW - Technological singularity CY - China ER - TY - JOUR TI - Enhancing Problem-Solving Skills with AI: A Case Study on Innovation and Creativity in a Business Setting AU - Hajj C. AU - Schmitt C. AU - Azoury N. PY - 2025 JO - Administrative Sciences VL - 15 IS - 10 SP - 388 DO - 10.3390/admsci15100388 AB - The adoption of artificial intelligence has risen, yet research on its impact on innovation processes between actual businesses remains sparse. This research fills the present gap by investigating ten workers from a tech startup who utilize artificial intelligence tools in operational and creative activities. The paper analyzes business-related AI functionality through a qualitative analysis of ten tech start-up employees. The examination reveals that AI produces significant enhancements in problem resolution by executing mundane actions while analyzing large datasets to deliver data-driven suggestions to users. The interview respondents mentioned that AI’s role in diminishing supply chains is 15%, while allowing AI to manage customer service without employee engagement in 80% of interactions. The implementation costs, along with data dependency and occasional contextual blindness in AI systems, represented some of the problems in this system. Analysis demonstrated that AI tools enable the development of innovative concepts and challenge established viewpoints, prompting participants to create a gamified loyalty system and dynamic content planning. Participants in the study emphasized the need for human involvement to refine AI-based insights, recognizing how human imagination complements AI capabilities effectively. The work enhances academic discussions about AI-related problem-solving and creativity while offering specific business-related recommendations for implementation. The recommendations begin with establishing initial experimental programs, while providing support for employee’s skills development, and fostering strong alliances between technical AI personnel and professional subject matter experts. Research topics focused on AI application fields and the anticipated impacts on company decision-making, as well as the ethical ramifications, need further exploration. This research confirms the revolutionary potential of artificial intelligence systems for problem-solving methods, but requires proper execution, along with human supervision, to fully realize their advantages. © 2025 by the authors. KW - AI tools KW - Artificial Intelligence (AI) KW - business innovation KW - case study KW - data-driven insights KW - human–AI collaboration KW - problem-solving skill KW - qualitative research CY - Lebanon, France ER - TY - JOUR TI - AI policies in school education: a comparative study on China, Singapore, Finland, and the US AU - Kundu A. AU - Bej T. PY - 2025 JO - Journal of Science and Technology Policy Management DO - 10.1108/JSTPM-06-2024-0218 AB - Purpose – The purpose of this study is to compare artificial intelligence (AI)-integration strategies in school education across China, Singapore, Finland and the USA, aiming to uncover shared patterns and localized innovations that could inform a globally responsive AI education framework. Design/methodology/approach – The qualitative desktop study draws on secondary data from five recent government policy documents: China’s New Generation Artificial Intelligence Development Plan, Singapore’s EdTech Masterplan 2030, Finland’s Age of Artificial Intelligence, California’s Computer Science Strategic Implementation Plan and Massachusetts’ Digital Literacy and Computer Science Standards. These were analyzed using the SMART criteria and a researcher-constructed “Nine-point framework of operational components in AI policy for schools.” Findings – Despite varying governance models and socio-cultural contexts, all four countries share a common intent to integrate AI into school education. Nine thematic propositions emerged: “SMART” policy design, balanced vision, curriculum and ethics integration, dynamic teacher training, equitable funding, multi-stakeholder partnerships, adaptive monitoring, localized implementation and contextual alignment. Finland and Singapore demonstrate strong ethical and human-centered policies, while China and the USA lean toward innovation and workforce development. Implementation remains challenged by equity gaps, teacher readiness and contextual mismatches. Research limitations/implications – These diverse models offer critical lessons: future global frameworks must prioritize ethical safeguards, localized adaptability, inclusive training and dynamic monitoring systems to ensure AI supports equity and relevance across school contexts. Originality/value – This study offers original insights derived from systematic, comparative analysis of the national AIEd policies using a robust evaluative framework. © 2025 Emerald Publishing Limited KW - AI Policies KW - AIEd KW - China KW - Finland KW - School Education KW - Singapore KW - SMART KW - The US CY - India ER - TY - JOUR TI - Artificial intelligence in school mathematics education: awareness, readiness, and usage among mathematics teachers; [Искусственный интеллект в школьном математическом образовании: осведомленность, готовность и использование учителями математики] AU - Kuzmenko M.V. PY - 2025 JO - Psychological Science and Education VL - 30 IS - 3 SP - 125 EP - 139 DO - 10.17759/pse.2025300310 AB - Context and relevance. This article presents the results of the study conducted among mathematics teachers - the category of teachers particularly inclined toward critical thinking and evidence-based application of innovations in education. Objective. The objective of this study is to identify the awareness of math teachers about the AI capabilities and potential in teaching as well as the practice of their application in the educational process. Methods and materials. To achieve this objective, a questionnaire was developed, comprising three main sections: awareness, readiness, and practical application. The survey was conducted online using Yandex Forms. A total of 122 mathematics teachers from 44 regions of the Russian Federation, varying in age and teaching experience, participated in the study. Results. The results showed that approximately 70% of the respondents express a willingness to use AI in their teaching process. The directions in which math teachers are most and least inclined to trust AI have been identified. The proportion of teachers currently using AI technologies and specific software products based on AI ranges from 13% to 40%. Conclusions. A significant part of teachers is generally aware of AI’s potential. However, their knowledge is fragmentary, covering only certain aspects and lacking systematic understanding. Promising directions for further research include examining the issues surrounding the use of AI technologies in the educational process while taking into account their specific characteristics. Special attention is recommended to improving teaching methodologies based on AI technologies and identifying effective ways to apply them for the development of students’ cognitive abilities. © 2025 Irmansyah J, Mujriah, Syarifoeddin EW, Syah H KW - AI in education KW - artificial intelligence KW - digitalization of education KW - mathematics teachers KW - neural networks KW - neural networks in education KW - teacher readiness ER - TY - JOUR TI - Exploring the role of open innovation and artificial intelligence in green innovation: A dynamic capabilities approach AU - Cassânego V.M. AU - Moralles H.F. AU - Nascimento D.L.D.M. AU - Tortorella G.L. PY - 2025 JO - Journal of Innovation and Knowledge VL - 10 IS - 5 SP - 100774 DO - 10.1016/j.jik.2025.100774 AB - Addressing current environmental challenges is not solely a matter of governmental policies. Organizations are key stakeholders who play a vital role through strategic partnerships and adoption of innovative technologies. Based on the dynamic capabilities framework, this research investigates the influence of open innovation partnerships in incorporating corporate green innovation (CGI), specifically green product and process innovation. It also elucidates the potential role of artificial intelligence (AI) capabilities in developing green innovation. Our study, based on a sample of approximately 1780 firms from 93 countries distributed across five continents, show that firms actively searching for and consolidating partnerships in open innovation can enhance green innovation in products and processes. Similarly, firms that develop or incorporate AI capabilities can catalyze the output of green product and process innovation because they incentivize open innovation partnerships, indicating that adopting both simultaneously is preferable. The results also show that the impact on green process innovation is greater than the impact on green product innovation. We recommend that policymakers and firms invest in AI capabilities and open green partnerships, leveraging these synergies to enhance innovation efficiency, and adapt green strategies to varying technological, institutional, and regional contexts. © 2025 The Author(s) KW - Artificial intelligence KW - Corporate green innovation KW - Dynamic capabilities KW - Open innovation CY - Brazil, Spain, Australia, Argentina ER - TY - JOUR TI - Ethical AI in medical text generation: balancing innovation with privacy in public health AU - Liang M. PY - 2025 JO - Frontiers in Public Health VL - 13 SP - 1583507 DO - 10.3389/fpubh.2025.1583507 AB - Introduction: The integration of artificial intelligence (AI) into medical text generation is transforming public health by enhancing clinical documentation, patient education, and decision support. However, the widespread deployment of AI in this domain introduces significant ethical challenges, including fairness, privacy protection, and accountability. Traditional AI-driven medical text generation models often inherit biases from training data, resulting in disparities in healthcare communication across different demographic groups. Moreover, ensuring patient data confidentiality while maintaining transparency in AI-generated content remains a critical concern. Existing approaches either lack robust bias mitigation mechanisms or fail to provide interpretable and privacy-preserving outputs, compromising ethical compliance and regulatory adherence. Methods: To address these challenges, this paper proposes an innovative framework that combines privacy-preserving AI techniques with interpretable model architectures to achieve ethical compliance in medical text generation. The method employs a hybrid approach that integrates knowledge-based reasoning with deep learning, ensuring both accuracy and transparency. Privacy-enhancing technologies, such as homomorphic encryption and secure multi-party computation, are incorporated to safeguard sensitive medical data throughout the text generation process. Fairness-aware training protocols are introduced to mitigate biases in generated content and enhance trustworthiness. Results and discussion: The proposed approach effectively addresses critical challenges of bias, privacy, and interpretability in medical text generation. By combining symbolic reasoning with data-driven learning and embedding ethical principles at the system design level, the framework ensures regulatory alignment and improves public trust. This methodology lays the groundwork for broader deployment of ethically sound AI systems in healthcare communication. Copyright © 2025 Liang. KW - AI ethics KW - bias mitigation KW - ethical challenges KW - healthcare regulation KW - legal compliance KW - medical AI KW - privacy protection KW - text generation KW - Artificial Intelligence KW - Confidentiality KW - Electronic Health Records KW - Humans KW - Privacy KW - Public Health KW - artificial intelligence KW - confidentiality KW - electronic health record KW - ethics KW - human KW - privacy KW - public health CY - China ER - TY - JOUR TI - Banana republic: Copyright law and the extractive logic of generative AI AU - Lim D. PY - 2025 JO - Journal of Intellectual Property Law and Practice VL - 20 IS - 9 SP - 573 EP - 583 DO - 10.1093/jiplp/jpaf047 AB - This article uses Maurizio Cattelan's Comedian, a banana duct-taped to a gallery wall, as a metaphor to examine the extractive dynamics of generative artificial intelligence (AI). It argues that the AI-driven creative economy replicates colonial patterns of appropriation, transforming human expression into commodified outputs while marginalizing the creators whose work makes these systems possible. Through the figures of the fruit seller, the buyer and the artist, the article interrogates who is valued, who is erased and who reaps the rewards in this evolving landscape. The analysis turns next to the banana itself as an object of constructed value, exploring how copyright's doctrines of authorship, originality and fair use struggle to accommodate the layered and distributed nature of AI-mediated creation. These doctrinal limitations, the article contends, leave creators vulnerable while enabling dominant platforms to entrench extractive practices under the guise of innovation. Finally, the article examines the 'wall', the metaphorical and institutional surfaces against which generative AI is made legible and legitimate. It begins by situating current AI governance within broader global trends of legal fragmentation and jurisdictional arbitrage, highlighting how regulatory divergence reflects deeper normative commitments - some prioritizing innovation, others dignity and distributive justice. It then critiques reactive proposals that rely on private licensing regimes or piecemeal litigation, arguing that such approaches risk entrenching opacity and extractive control. In their place, the article advocates for structural reforms grounded in transparency, attribution and participatory design, legal scaffolding that can recognize distributed authorship and protect against enclosure. Without these interventions, the generative AI economy may replicate the very conditions that Comedian satirizes: spectacle without substance, progress without equity. © 2025 The Author(s). Published by Oxford University Press. All rights reserved. CY - United States ER - TY - JOUR TI - AI Ecosystem and Value Chain: A Multi-Layered Framework for Analyzing Supply, Value Creation, and Delivery Mechanisms AU - Billones R.K.C. AU - Lauresta D.A.S. AU - Dellosa J.T. AU - Bong Y. AU - Stergioulas L.K. AU - Yunus S. PY - 2025 JO - Technologies VL - 13 IS - 9 SP - 421 DO - 10.3390/technologies13090421 AB - Despite the rapid adoption of artificial intelligence (AI) on a global scale, a comprehensive framework that maps its end-to-end value chain is missing. The presented study employed a multi-layered framework to analyze the value creation and delivery mechanism of the five core layers of an AI value chain, including (1) hardware, (2) data management, (3) foundational AI, (4) advanced AI capabilities, and (5) AI delivery. Using a qualitative–descriptive approach with a multi-faceted thematic analysis and a SWOT-based bottleneck analysis of each core layer, the study maps a sequential value flow from a globally dependent hardware foundation to the deployment of AI services. The analysis reveals that international knowledge flows shape the ecosystem, while the “last-mile” integration challenge is not merely a technical issue; instead, it highlights a significant socio-technical disconnect between technological advancements and the preparedness of the workforce. This study provides a holistic framework that frames the AI value chain as a socio-technical system, offering critical insights for stakeholders. The findings emphasize that unlocking AI’s full potential requires strategic investment in the managerial competencies and digital skills that constitute human–capital readiness. © 2025 by the authors. KW - AI as a service (AIaaS) KW - AI governance KW - bottleneck analysis KW - data management KW - digital transformation KW - global value chain KW - socio-technical system KW - statist triple helix KW - supply chain management KW - SWOT KW - Artificial intelligence KW - Chains KW - Information management KW - Investments KW - Metadata KW - Supply chains KW - Artificial intelligence as a service KW - Artificial intelligence governance KW - Bottleneck analysis KW - Chain management KW - Digital transformation KW - Global value chain KW - Sociotechnical systems KW - Statist triple helix KW - SWOT KW - Triple helixes KW - Supply chain management CY - Netherlands ER - TY - JOUR TI - Artificial intelligence capability: Conceptualization, measurement calibration, and empirical study on its impact on organizational creativity and firm performance AU - Mikalef P. AU - Gupta M. PY - 2021 JO - Information and Management VL - 58 IS - 3 SP - 103434 DO - 10.1016/j.im.2021.103434 AB - Artificial intelligence (AI) has been heralded by many as the next source of business value. Grounded on the resource-based theory of the firm and on recent work on AI at the organizational context, this study (1) identifies the AI-specific resources that jointly create an AI capability and provides a definition, (2) develops an instrument to capture the AI capability of the firms, and (3) examines the relationship between an AI capability and organizational creativity and performance. Findings empirically support the suggested theoretical framework and corresponding instrument and provide evidence that an AI capability results in increased organizational creativity and performance. © 2021 The Author(s) KW - Artificial intelligence KW - Capability KW - Firm performance KW - Instrument development KW - Organizational creativity KW - Resource-based theory KW - Information science KW - Information systems KW - Business value KW - Empirical studies KW - Firm Performance KW - Measurement calibration KW - Organizational context KW - Resource-based theory KW - Theoretical framework KW - Artificial intelligence CY - Norway, United States ER - TY - JOUR TI - Fostering Athletes’ Mental Resilience: Artistic Innovation and AI in Sports AU - Xia Q. PY - 2023 JO - Revista de Psicologia del Deporte VL - 32 IS - 4 SP - 213 EP - 224 AB - As artificial intelligence (AI) technology rapidly advances, the world of artistic innovation in sports paintings encounters both unprecedented challenges and exciting opportunities. AI’s capacity to learn pushes the boundaries of traditional creative thinking within the realm of sports art, introducing a more diverse and intelligent approach to the creative process. However, the actual ecosystem for creativity and its application within this context lacks a robust management model, necessitating fundamental theoretical innovation and standardized discipline.This exploration embarks on a journey to elucidate the practical applications of AI technology in the creation of sports art. It delves into the pivotal roles played by AI professionals, creative artists, and viewers in shaping the future of sports art innovation. It paves the way for an innovative approach to sports painting and decorative art, grounded in AI intelligence.Emphasizing the symbiotic relationship between human and AI capabilities, intelligent product development, artistic creation, and the constraints on creative behavior, this inquiry dives into emerging application areas. Through this lens, it seeks to gain a deeper understanding of the essential cycles of innovation in the field of sports plastic art, driven by the transformative power of artificial intelligence © 2023 Sociedad Revista de Psicologia del Deporte. All rights reserved. KW - Artificial intelligence era KW - Athletes’ KW - painting applications KW - sports KW - sports painting creation CY - South Korea ER - TY - JOUR TI - THE AI PARADOX IN CENTRAL BANKING: NEW POWERS, NEW VULNERABILITIES AU - Koroye T. AU - Alaekwe S. PY - 2025 JO - Journal of Central Banking Law and Institutions VL - 4 IS - 3 SP - 533 EP - 566 DO - 10.21098/jcli.v4i3.441 AB - The integration of artificial intelligence into central banking disrupts the traditional bank-regulator relationship, creating asymmetries that private institutions exploit. This paper examines how AI-driven market surveillance and predictive risk modelling erode private banks’ informational advantages, compelling them into a Schumpeterian race for survival in which innovation becomes imperative. Using a qualitative analysis of regulatory developments and financial market adaptations, this study argues that enhanced central bank AI capabilities paradoxically accelerate the emergence of opaque financial segments designed to evade oversight. The findings indicate that this shift transforms regulatory dynamics, positioning central banks as real-time market participants while private institutions develop increasingly sophisticated methods of regulatory evasion. This evolution generates systemic risks that existing regulatory frameworks struggle to address, necessitating adaptive oversight mechanisms. The study concludes that the imperative progressively drives financial innovation to maintain opacity in response to algorithmic supervision, underscoring the need for regulatory models that balance AI’s benefits with emerging vulnerabilities. © 2025, Bank Indonesia Institute. All rights reserved. KW - ai-resistant markets KW - algorithmic supervision KW - financial innovation and opacity KW - information asymmetry KW - regulatory evasion CY - United Kingdom ER - TY - JOUR TI - Revisiting artificial intelligence in start-ups: A theoretical perspective on integration, opportunities, challenges, and strategic advancement AU - Abbas A.F. AU - Al-Lawati E.H. PY - 2025 JO - Journal of the International Council for Small Business DO - 10.1080/26437015.2025.2549059 AB - This study adopts a theory elaboration approach to systematically review 148 peer-reviewed articles published between 2016 and 2025 on the integration of artificial intelligence (AI) in start-ups. Drawing on foundational theories such as the technology acceptance model, resource-based view (RBV), dynamic capabilities, and institutional theory, it develops a conceptual framework that highlights both opportunities and challenges associated with AI adoption. Opportunities include enhanced operational efficiency, innovation enablement, and improved decision making. Challenges involve regulatory complexity, ethical concerns, scalability issues, and limited resources. The study contributes theoretically by identifying emerging constructs and refining interconstruct relationships within AI-driven start-up ecosystems. It proposes a future research agenda calling for empirical validation of the framework across sectors and geographies. Practical implications are discussed for start-up founders, investors, and policy makers, emphasizing the need for strategic alignment, AI governance, and talent development to ensure responsible and sustainable integration of AI within entrepreneurial environments in start-ups. © 2025 International Council for Small Business. KW - Artificial intelligence KW - innovation KW - start-ups KW - strategic integration KW - theory elaboration CY - Malaysia, Oman ER - TY - JOUR TI - AI washing: A conceptual exploration AU - Elhajjar S. AU - Itani O.S. PY - 2025 JO - AMS Review VL - 15 IS - 3-4 SP - 519 EP - 538 DO - 10.1007/s13162-025-00323-y AB - This paper introduces and explores the concept of AI washing, a phenomenon where companies misrepresent or exaggerate their artificial intelligence (AI) capabilities to enhance marketing appeal and gain a competitive advantage. Despite its increasing prevalence, AI washing has received limited theoretical attention. Drawing on literature from similar practices, this paper develops a conceptual framework and typology to categorize various forms of AI washing. Theoretical implications include extending marketing ethics frameworks and existing theories to the domain of AI. The paper also highlights avenues for future empirical research, particularly in validating the proposed typology and exploring the consequences of AI washing on trust and brand reputation. From a practical standpoint, the paper offers recommendations for businesses to adopt more transparent AI marketing strategies and calls for regulatory interventions to mitigate the risks of AI washing. Finally, it discusses limitations and directions for further study. © Academy of Marketing Science 2025. KW - AI KW - AI washing KW - Artificial intelligence KW - Bluewashing KW - Business ethics KW - Greenwashing KW - Technological ethics CY - Singapore, Lebanon ER - TY - JOUR TI - University Student Attitudes Towards Artificial Intelligence Integration into their Academic Performance AU - Le T.T.Q. AU - Doan C.T. AU - Vu T.V. PY - 2025 JO - Indian Journal of Information Sources and Services VL - 15 IS - 4 SP - 21 EP - 30 DO - 10.51983/ijiss-2025.IJISS.15.4.03 AB - The integration of artificial intelligence (AI) becomes more common in education, so it is crucial to be aware of students' perspectives towards the effective use of implementing AI in education. This study employed a mixed-methods approach to incorporate a researcher-made questionnaire with 385 participants and semi-structured interviews with 69 students from three institutions in Vietnam. Descriptive statistics and correlation analysis were used to examine the quantitative data, and thematic analysis was employed to address the qualitative data. The results reveal that although the participants have neutral stances on the acceptability of AI in education, they also express positive and negative opinions on the acceptance and uncertainty about AI's capabilities to enhance their academic performance. Besides, digital skills, previous experience with AI, institutional support, and ethical concerns are the main factors of acceptance and use. The participants feel concerned about whether the AI application may improve their academic integrity, privacy, and critical thinking. It is, therefore, necessary for students to receive institutional support for AI training by providing more explicit principles and resources for AI adoption. Additionally, the study reveals that AI has great potential, but it should be integrated into higher education with considerable care to solve ethical issues and allow the students to be trained and supported. Universities must give importance to AI literacy programs and set principles of ethics to foster a more positive and productive relationship between students and AI technologies. © The Research Publication,. KW - AI Integration KW - Educational Policy KW - Ethical Considerations KW - Higher Education KW - Pedagogical Innovation ER - TY - JOUR TI - Adoption of artificial intelligence in property management transactions: a systematic review and trend analysis AU - Adediran A.O. AU - Mohd Aini A. AU - Ajibade S.M. PY - 2026 JO - Property Management VL - 44 IS - 2 SP - 199 EP - 231 DO - 10.1108/PM-02-2025-0007 AB - Purpose – The integration of Artificial Intelligence (AI) in property management transactions is transforming the real estate sector via improved automation, predictive analytics, intelligent property management and enhanced decision-making. This study investigates how AI enhances property management transactions as well as the significant barriers to its implementation. Design/methodology/approach – This research employs a systematic literature review (SLR) and NVivo-based qualitative analysis to discern significant trends, innovations and obstacles in the adoption of AI. The study analyzes existing literature and industry reports to identify patterns, challenges and emerging solutions in AI-driven property management. Findings – The results indicate that AI markedly enhances efficiency (automation and predictive analytics), tenant engagement (behavior analysis and intelligent communication), property value (AI-driven assessments) and sustainability (energy optimization and waste minimization). Nevertheless, obstacles to widespread adoption persist, including data privacy issues, legal and ethical challenges, budgetary limitations and opposition from stakeholders. Smaller real estate enterprises have heightened hurdles stemming from the digital divide, security vulnerabilities and algorithmic prejudice. Research limitations/implications – The study is mostly based on secondary data from literature and industry sources, which may limit the findings' applicability to real-world scenarios. Future research could use empirical data, such as case studies or surveys, to confirm AI’s practical influence in a variety of property markets. Practical implications – The findings offer valuable insights for real estate professionals, investors and AI developers on how to effectively integrate AI into property management. Key areas for practical implication include predictive maintenance relating to IoT usage; property valuation automation; AI-powered tenant screening; Site selection and market forecasting; Chabot and NLP for leasing; and blockchain integration and fraud detection. To achieve effective integration, industry stakeholders must emphasize ethical AI governance, stringent data security and cooperation between AI and humans. Additionally, AI’s synergy with cloud computing, blockchain and the Internet of Things (IoTs) may enhance transparency, security and efficiency in real estate transactions. Social implications – The adoption of AI in property management has broader societal consequences, including the possibility of job displacement and the necessity for reskilling initiatives to assist real estate workers. An equitable strategy that encourages innovation, reduces risks and increases worker flexibility is required to realize AI’s full potential in property management. This study emphasizes the importance of collaboration among researchers, real estate companies, legislators and AI technologies developers. Originality/value – This study contributes to the expanding body of knowledge on AI in real estate by providing a structured qualitative synthesis of AI uses, barriers and future potential. Unlike prior studies that have focused only on AI benefits, this study offers a balanced evaluation of both the promise and constraints of AI-driven property management transactions. © Emerald Publishing Limited KW - AI adoption KW - Artificial intelligence KW - Property management KW - PropTech KW - Real estate transactions CY - Malaysia ER - TY - JOUR TI - Harnessing inclusive innovation to build socially impactful AI: Embracing social impact, cultural diversity, and equity AU - Vega R.P. AU - Rivero C.S. AU - Castro A.O. AU - Bosques C.V. PY - 2024 JO - Issues in Information Systems VL - 25 IS - 4 SP - 442 EP - 454 DO - 10.48009/4_iis_2024_134 AB - Society’s interest in AI/Big Data: that governments, companies, and NGOs can invest in the development of the world and obtain a successful realization of the policy of CSR & SDG. discussed in this paper is to show how governments, businesses, and NGOs can prove that they are ready to make a difference and join the global commitment to building an integrated and sustainable community that will leverage this unique opportunity to promote the common social interest by utilizing such a promising and complex technology as artificial intelligence. Consequently, the current paper integrates a range of cases. It utilizes a mixed-methods design, establishing that restricted access to resources, primarily relating to education, health, and the economic status that can be traced to marginalization dimensions, determines increased suffering because of the pandemic. These recommendations amalgamate to perform the roles of the diverse sectors to assure that AI benefits all the stages of society by promoting the equity steps of welfare. The study applies a contingency framework, and from the framework, the study develops a plan for how best to approach inclusive innovation to mitigate inequalities, which in turn suggests that societal and environmental objectives should be integrated into the observed innovation to take sustainable development principles. The research contributes to how society can employ the prospects in AI to solve societal problems, notating that such employment of the advantage in AI must incite professionalism and incorporation of society in the design of the measures. On this basis, this study contributes to the theoretical development and the practical recommendations related to using potential AI capabilities to build long-term opportunities for firms and governments to support the sustainable development of the world economy based on achieving the goals set in the UN 2030 agenda. This piece seeks to analyze the events, achievements, and barriers that culminated in the formation of this new week and lays down directions on how the strategy that focuses on inclusive innovation can be implemented. © 2024 International Association for Computer Information Systems. All rights reserved. KW - Artificial Intelligence (AI) KW - Corporate Social Responsibility (CSR KW - Inclusive Innovation KW - Strategic Implementation KW - Sustainable Development ER - TY - JOUR TI - An Engineering Framework for Artificial Intelligence-Based Marketing Systems and Digital Consumer Engagement Models AU - Kumar P. AU - Aruna V. AU - Pathamuthu P. AU - Rajamani K. PY - 2025 JO - International Academic Journal of Science and Engineering VL - 12 IS - 3 SP - 318 EP - 327 DO - 10.71086/IAJSE/V12I3/IAJSE1268 AB - This study presents the method of incorporating AI technology in digital marketing systems to establish a high degree of customer-business relationship based on the AI capabilities in digital media platforms like Twitter, Facebook, Google, etc., and AI technology like ML, NLP, and Predictive analytics. The model designed within the framework of the current work enables creation of individual marketing plans that will contribute to better customer interaction, company profitability, and business performance in general. Based on the data collected for this study, the researchers found that the use of AI technology to develop marketing systems has greatly improved consumer behavior due to the delivery of custom content that matches their individual tastes. The case study of Amazon's AI-driven recommendation system illustrates the effectiveness of using AI to increase sales and enhance the way Amazon interacts with its customers. Since 2018, Amazon has utilized AI technologies to create a more personalized shopping experience, increasing the level of engagement and sales through the use of AI as a key component of its e-commerce strategy. Amazon's recommendation system is responsible for generating a significant portion of its sales through the analysis of individual consumer purchase history, browsing activity, and preferences, with nearly 35% of total sales being accounted for by Amazon's AI-driven recommendation system (2020). To fully take advantage of AI's capabilities in marketing, more refinement needs to be done on the AI algorithm to allow for real time content personalization and also to adapt to changing consumer buying behaviour This study concluded that although AI has great potential for helping organizations grow their revenue, in order for an organization to realize the true benefits of AI they will also have to consider the ethical implications of using customer data and how much data should be available through AI technologies. © 2025, International Academic Institute for Science and Technology. All rights reserved. KW - AI-Driven Marketing KW - Consumer Engagement KW - Conversion Rate KW - Customer Satisfaction KW - Machine Learning KW - Personalization KW - Predictive Analytics CY - India ER - TY - JOUR TI - Smart insights, stronger performance: Leveraging business intelligence and dynamic capabilities in tourism and hospitality AU - Tajeddini O. AU - Tajeddini K. AU - Gamage T.C. AU - Hameed W.U. PY - 2026 JO - International Journal of Hospitality Management VL - 133 SP - 104410 DO - 10.1016/j.ijhm.2025.104410 AB - The rapid advancement of artificial intelligence (AI) and business intelligence (BI) compels tourism and hospitality firms to redefine their capabilities. This need stems from the growing imperative to fully leverage these technologies for performance enhancement—an area still underexplored in the tourism and hospitality literature. Drawing on the dynamic capabilities view, this paper investigates the interrelationships among resource orchestration capabilities (ROCs), digital marketing capabilities (DMCs), AI capabilities, and firm performance, with a specific focus on the mediating role of BI adoption and the moderating effect of technology orientation (TO). Using data from 297 tourism and hospitality firms across four major Japanese cities, the findings reveal that BI adoption mediates the relationships among ROCs, DMCs, AI capabilities, and firm performance. As anticipated, TO does not moderate the DMC–BI adoption link, potentially due to firm-specific factors warranting further exploration in different contexts. The study contributes to theory by proposing an integrative framework that conceptualizes ROCs, DMCs, and AI capabilities as distinct yet interrelated dynamic capabilities driving performance in tourism and hospitality firms. Practically, the findings encourage tourism and hospitality managers to refine their strategies to better leverage these capabilities, particularly in pursuing digital transformation. © 2025 KW - Artificial intelligence capabilities KW - Business intelligence adoption KW - Digital marketing capabilities KW - Resource orchestration capabilities KW - Technology orientation KW - Tourism and hospitality firms CY - Switzerland ER - TY - JOUR TI - Exploring how artificial intelligence capabilities impact corporate sustainability performance: Insights from Chinese manufacturing firms AU - Renfei C. AU - Zhongwen L. AU - Guangfei Y. AU - Wenli L. AU - Zitong G. PY - 2026 JO - Technovation VL - 149 SP - 103429 DO - 10.1016/j.technovation.2025.103429 AB - In the context of digital transformation, the driving mechanism of Artificial Intelligence capabilities (AIC) on corporate sustainability performance (CSP) and its organizational boundary requirements have emerged as a key topic in advancing digital sustainability, both in theory and practice. This study is based on dynamic capabilities theory (DCT) and sustainable development theory, and reveals the micro-mechanisms and boundary conditions of AIC driving CSP. By extending the traditional application of AI and dynamic capabilities from competitive advantage to sustainability, this study provides a novel theoretical lens for digital sustainability. This study empirically analyzes data from Chinese manufacturing enterprises through the PLS-SEM method. The findings reveal that AIC significantly enhances CSP through a tiered enabling mechanism, but prescriptive capabilities fail to achieve their intended effects. The type of manufacturing firm constitutes a core boundary condition, and there is significant variability in the sustainable performance of AIC exerted under traditional manufacturing and Industry 4.0 manufacturing firms. The impact of AIC on CSP is significantly weaker in traditional manufacturing firms than in Industry 4.0 manufacturing firms. By adopting a dynamic capabilities perspective, this study examines the role of DCT-based AIC in enhancing CSP, expanding the prevailing technical viewpoint on their interrelationship and providing a compelling justification for integrating AI into digital sustainability frameworks. These findings offer manufacturers actionable insights for harnessing AI technologies to achieve sustainable performance during digital transformation. © 2025 Elsevier Ltd KW - Artificial intelligence capabilities KW - Corporate sustainability performance KW - Dynamic capabilities KW - Types of manufacturing firms KW - Artificial intelligence KW - Industry 4.0 KW - Sustainable development KW - Artificial intelligence capability KW - Condition KW - Corporate sustainability performance KW - Corporate-sustainability KW - Digital transformation KW - Dynamics capability KW - Manufacturing firms KW - Sustainability performance KW - Sustainable performance KW - Type of manufacturing firm KW - Competition CY - China ER - TY - JOUR TI - Digital marketing innovation and industrial marketing: evidence from restaurants' service robots AU - Ku E.C.S. PY - 2024 JO - Asia Pacific Journal of Marketing and Logistics VL - 36 IS - 11 SP - 3099 EP - 3117 DO - 10.1108/APJML-02-2024-0185 AB - Purpose: This study aims to explore how perceived anthropomorphism, perceived warmth, and customer–artificial intelligence (AI) assisted exchange (CAIX) of service robots affect customers’ satisfaction via digital marketing innovation. Design/methodology/approach: A customer satisfaction model was formulated based on the perspective of parasocial relationships and hybrid intelligence; 236 completed questionnaires were returned by partial least squares structural equation modeling analysis. Findings: This study demonstrates that perceived anthropomorphism, perceived warmth and CAIX's impact on digital marketing innovation were supported, and customer satisfaction impacted the continued intention to use service robots. Originality/value: Restaurants that leverage service robots differentiate themselves from competitors by offering innovative and technologically advanced dining experiences. Integrating AI capabilities sets these restaurants apart and attracts tech-savvy customers who value convenience and efficiency. © 2024, Emerald Publishing Limited. KW - Customer satisfaction KW - Customer–AI assisted exchange KW - Digital marketing innovation KW - Restaurant KW - Service robots CY - Taiwan ER - TY - JOUR TI - Designing Artificial Intelligence: Exploring Inclusion, Diversity, Equity, Accessibility, and Safety in Human-Centric Emerging Technologies AU - Zallio M. AU - Ike C.B. AU - Chivăran C. PY - 2025 JO - AI (Switzerland) VL - 6 IS - 7 SP - 143 DO - 10.3390/ai6070143 AB - Background: The implementation of artificial intelligence (AI) has become a pivotal interdisciplinary challenge, creating new opportunities for sharing information, driving innovation, and transforming societal interactions with technology. While AI offers numerous benefits, its rapid evolution raises critical concerns about its impact on inclusion, diversity, equity, accessibility, and safety (IDEAS). Method: This pilot study aimed to explore these issues and identify ways to embed the IDEAS principles into AI design. A qualitative study was conducted with industrial and academic experts in the field. Semi-structured interviews gathered insights into the opportunities, challenges, and future implications of AI from diverse professional and cultural perspectives. Result: Findings highlight uncertainties in AI’s trajectory and its profound cross-sector influence. Key issues emerged, including bias, data privacy, transparency, and accessibility. Participants stressed the need for greater awareness and structured dialogue to integrate the IDEAS principles throughout the AI lifecycle. Conclusion: This study underscores the urgency of addressing AI’s ethical and societal impacts. Embedding the IDEAS principles into its development can help mitigate risks and foster more inclusive, equitable, and accessible technologies. © 2025 by the authors. KW - accessibility KW - artificial intelligence KW - diversity and equity KW - emerging technology KW - generative artificial intelligence KW - inclusion KW - inclusive design KW - safety by design CY - United Kingdom, Italy ER - TY - JOUR TI - Adopting artificial intelligence and big data tools across industry sectors in Morocco: an integrative literature review AU - Ejjami R. PY - 2024 JO - International Journal of Environment, Workplace and Employment VL - 8 IS - 2 SP - 171 EP - 198 DO - 10.1504/IJEWE.2024.141574 AB - Morocco’s strategy to integrate its industries into the Europe-Mediterranean-Africa network faces challenges in digital transformation, primarily due to a knowledge gap among leaders regarding AI and big data benefits. This literature review addresses this gap, focusing on adopting these technologies as agile innovations across Moroccan sectors. It aims to enlighten organisational leaders and IT policymakers on leveraging AI and big data to enhance operational efficiency, decision-making, and competitiveness. The study emphasises data protection, training, government support, and global partnerships. Key recommendations include fostering agile innovation, ensuring data privacy, upskilling the workforce, building collaborative ecosystems, supporting SMEs, incorporating ethical AI, and promoting interdisciplinary research. Addressing this knowledge gap is crucial for Morocco’s economic growth and digital transformation, highlighting the need for strategic technology adoption to enhance national progress and sustain competitive advantage. Copyright © 2024 Inderscience Enterprises Ltd. KW - agile innovations KW - AI KW - big data KW - digitalisation KW - industry sectors KW - Morocco KW - organisational sustainability KW - regional competitiveness CY - Morocco ER - TY - JOUR TI - Steering back to the real: does artificial intelligence promote corporate de-financialization? AU - Wang D. AU - Wang Y. PY - 2026 JO - Applied Economics DO - 10.1080/00036846.2026.2653208 AB - While the productivity effects of Artificial Intelligence (AI) have been widely studied, limited attention has been paid to its role in ‘steering back to the real economy’. Based on the investment motivation framework and data from Chinese A-share listed companies, this study finds that enhancing AI capabilities significantly diminishes corporate financialization. This effect operates via resource reallocation channels: AI influences financial distress, alleviates financing constraints, and reduces reliance on government subsidies. Specifically, a one-unit increase in AI intensity reduces financialization by 3.4%. The inhibitory effect is more pronounced among large-scale enterprises, high-innovation regions, and high-tech industries. Moreover, quantile regression reveals the effect is significant only for highly financialized firms, suggesting that operational efficiency motives outweigh speculative motives. The study provides empirical evidence on the technological drivers of de-financialization in emerging economies and offers implications for revitalizing the real economy. © 2026 Informa UK Limited, trading as Taylor & Francis Group. KW - AI KW - financial distress KW - financialization KW - financing constraints KW - government subsidies CY - China ER - TY - JOUR TI - Artificial Intelligence-based Psychotherapy: A Qualitative Exploration of Usability, Personalization, and the Perception of Therapeutic Progress AU - Beg M.J. AU - Verma M.K. PY - 2025 JO - Indian Journal of Psychological Medicine SP - 02537176251357477 DO - 10.1177/02537176251357477 AB - Background: AI-based psychotherapy apps offer accessibility and structured interventions but face challenges regarding emotional depth, personalization, engagement, and ethical concerns. This study critically examines user experiences, identifying key advantages, limitations, and areas for refinement. Methods: A qualitative approach was employed, using thematic analysis of semi-structured interviews with 17 participants (aged 18–45) who had used AI-based psychotherapy apps for at least four weeks. Ten participants had prior clinical diagnoses (e.g., anxiety, depression, adjustment disorder), while others reported subclinical psychological distress. Engagement duration ranged from 2 to 11 months, with most using the apps two to five times per week. Results: Ten core themes emerged, revealing a paradox of accessibility versus therapeutic depth. While users valued immediacy and anonymity, they struggled with fragmented therapeutic narratives, scripted empathy, and algorithmic stagnation in personalization. The over-reliance on CBT frameworks limited adaptability to diverse emotional needs, while linguistic and cultural microaggressions led to disengagement. Privacy concerns stemmed from a mismatch between perceived and actual risks, and AI-induced dependence raised ethical questions about user autonomy. Conclusions: The AI psychotherapy must evolve beyond static, standardized interventions by integrating emotionally responsive, culturally adaptive, and ethically responsible AI models. Enhancing therapeutic continuity, adaptive learning, and human-AI hybrid models can bridge the gap between accessibility and authentic engagement. These findings inform future AI-driven mental health innovations, ensuring they align with psychological, ethical, and cultural expectations. © 2025 The Author(s). This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage). KW - AI psychotherapy KW - cultural adaptation KW - emotional depth KW - ethical AI therapy KW - personalization KW - user engagement CY - India ER - TY - JOUR TI - Differentiating artificial intelligence activity clusters in Australia AU - Bratanova A. AU - Pham H. AU - Mason C. AU - Hajkowicz S. AU - Naughtin C. AU - Schleiger E. AU - Sanderson C. AU - Chen C. AU - Karimi S. PY - 2022 JO - Technology in Society VL - 71 SP - 102104 DO - 10.1016/j.techsoc.2022.102104 AB - We demonstrate how cluster analysis underpinned by analysis of revealed technology advantage can be used to differentiate geographic regions by activity in artificial intelligence (AI). Our analysis uses novel datasets on Australian AI businesses, intellectual property patents and labour markets to explore location, concentration and intensity of AI activities across 333 geographical regions. We find that Australia's AI business and innovation activity is clustered in geographic locations with higher investment in research and development. Through cluster analysis we identify three tiers of AI capability regions that are developing across the economy: ‘AI hotspots’ (10 regions), ‘Emerging AI regions’ (85 regions) and ‘Nascent AI regions’ (238 regions). While the AI hotspots are mainly concentrated in central business district (CBD) locations, there are examples when they also appear outside CBD in areas where there has been significant investment in innovation and technology hubs. Policy makers and investors can use these results to learn about the current landscape of AI business and innovation activities in Australia, identify potential growth opportunities in AI capabilities and to guide future policy and business decisions. © 2022 The Authors KW - Artificial intelligence KW - Australia KW - Cluster KW - Regional innovation KW - Revealed technology advantage KW - Australia KW - Cluster analysis KW - Employment KW - Geographical regions KW - Investments KW - Patents and inventions KW - Regional planning KW - Australia KW - Business activities KW - Central business districts KW - Cluster KW - Geographics KW - Hotspots KW - Innovation activity KW - Intelligence activities KW - Regional innovation KW - Revealed technology advantage KW - artificial intelligence KW - central business district KW - cluster analysis KW - geographical variation KW - industrial investment KW - innovation KW - Location CY - Australia ER - TY - JOUR TI - Orchestrating scalability: how patents render cloud imaginaries in CAV innovation AU - Gekker A. AU - Hind S. AU - Pereira G. AU - van der Vlist F.N. PY - 2026 JO - Information Communication and Society DO - 10.1080/1369118X.2026.2631709 AB - This article explores how cloud computing enables and shapes the scaling of connected and autonomous vehicles (CAVs), positioning cloud infrastructure as a technology, strategy, and imaginary central to the scaling of AI. Using a dataset of 69,421 global patent families, we analyse how diverse actors–including automotive manufacturers, chipmakers, electronics companies, autonomous vehicle firms, and telecom/mapping providers–mobilise cloud technologies to expand AI capabilities, manage resources, and coordinate complex socio-technical systems. Approaching patents through ‘sociotechnical imaginaries’, we show how they simultaneously codify technical innovations while projecting visions of scalable, cloud-enabled CAV futures. Our analysis identifies four thematic clusters–vehicle communication, machine vision, network architectures, and edge computing–through which cloud technologies are operationalised and imagined. We argue that the cloud functions as a technology of orchestration, with cloudification exemplifying AI’s industrialisation as it moves from laboratory research to globally scalable systems. The article contributes to debates on scale by highlighting the interplay between technical, organisational, and imaginative dimensions in shaping AI-enabled mobility. © 2026 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. KW - AI KW - Cloud computing KW - connected and autonomous vehicles KW - imaginaries KW - patents KW - scale CY - Netherlands, United Kingdom ER - TY - JOUR TI - AI capabilities for sustainable entrepreneurial innovation: the role of orientation, resilience, and turbulence effects in SMEs AU - Shahzad M.F. AU - Xu S. AU - Zahid H. AU - Khan S.A. AU - Sandhu M.A. PY - 2026 JO - Journal of Innovation and Knowledge VL - 17 SP - 101051 DO - 10.1016/j.jik.2026.101051 AB - In the evolving landscape of sustainable development, artificial intelligence (AI) has become a critical enabler for small and medium-sized enterprises (SME) striving to achieve entrepreneurial sustainable development goals (ESDG). This study investigates how AI capabilities, encompassing human skills, technology, data-driven culture (DDC), and organizational learning intensity, contribute to ESDGs through both internal organizational mechanisms and external market dynamics. Data are collected from 460 employees across various SMEs in China and analyzed via partial least squares-structural equation modeling. The results demonstrate that green ambidexterity innovation (GAI) significantly mediates the relationship between AI capabilities and ESDGs. Market resilience capacity (MRC) also serves as a significant mediator across most AI dimensions, except for DDC. Meanwhile, green entrepreneurial orientation (GEO) shows significant but negative mediating effects across AI dimensions. Market turbulence does not significantly moderate the effects of GAI and MRC on ESDGs; meanwhile, it positively moderates the relationship between GEO and ESDGs. These findings contribute to the sustainability and entrepreneurship literature by revealing how AI readiness and innovation orientation enable SMEs to circumnavigate complex market environments. They also underscore the need for SMEs and policymakers to prioritize AI-driven strategies and innovation, and build resilience for sustainable entrepreneurial performance. © 2026 The Authors. KW - Artificial intelligence KW - Green ambidexterity innovation KW - Green entrepreneurial orientation KW - Market resilience KW - Market turbulence KW - Sustainable development goals CY - China, Pakistan, United Arab Emirates ER - TY - JOUR TI - The frame problem: The ai “arms race" isn’t one AU - Roff H.M. PY - 2019 JO - Bulletin of the Atomic Scientists VL - 75 IS - 3 SP - 95 EP - 98 DO - 10.1080/00963402.2019.1604836 AB - There needs to be a change in thinking about AI. Those dealing with AI must insist on greater clarity about its definition. If policy makers and other leaders are not clear about what the term means and entails, they cannot possibly formulate best practices and governance mechanisms. It would help matters if artificial intelligence discussions were framed in an “AI +" framework, because in many cases, AI is merely a tool included in a system involving other functions or capabilities. The news media should stop framing the global artificial intelligence competition as an “arms race." This misrepresents the competition going on among countries. The policy community needs a clear-eyed appraisal of AI’s capabilities and limitations. Without that orientation, those who hope to steer research and development in positive directions will create more problems than they solve. © 2019 Bulletin of the Atomic Scientists. KW - Arms race KW - Artificial intelligence KW - Definitions KW - Responsible innovation KW - Risk CY - United Kingdom ER - TY - JOUR TI - Integrating artificial intelligence with market research: A dual approach to boosting brand value AU - Skare M. AU - Sinkovic D. AU - Kowalska M. AU - Szwajlik A. PY - 2026 JO - Journal of Innovation and Knowledge VL - 11 SP - 100853 DO - 10.1016/j.jik.2025.100853 AB - This study investigates the impact of artificial intelligence (AI) capabilities on brand management and value creation, proposing a comprehensive competitiveness framework for firms. Utilizing a panel dataset spanning 26 years across 30 countries, Arellano-Bond and Blundell-Bond System GMM estimations examine the relationship between AI stock, AI impact, and brand value. The findings reveal that AI stock and impact significantly contribute to brand value, with their interaction yielding synergistic effects. The study highlights the mediating role of gross value-added (GVA) in the relationship between AI stock and brand value, suggesting that AI-driven productivity gains enhance brand equity. Investments in computing and communication equipment and efforts to strengthen brand relationship strength (BRS) also influence brand value. Integrating AI capabilities with market research amplifies brand value through data-driven insights and consumer engagement strategies. Furthermore, novel AI-enhanced measurement methodologies capture brand value creation more accurately than traditional metrics. These findings offer valuable insights for firms seeking to leverage AI capabilities to enhance brand equity and gain a competitive advantage in a rapidly evolving marketplace. Copyright © 2025. Published by Elsevier España, S.L.U. KW - Artificial intelligence KW - Brand management KW - Customer engagement KW - Firms’ competitiveness KW - Marketing strategies KW - Value creation CY - Croatia, Poland ER - TY - JOUR TI - Navigating the dark side of AI in service ecosystems: an ethical leadership framework for risk mitigation; [穿越服务生态系统中人工智能的阴暗面：基于伦理领导力的风险缓解框架] AU - Sposato M. AU - Dittmar E.C. AU - Portillo J.P.V. PY - 2026 JO - Service Industries Journal DO - 10.1080/02642069.2026.2643384 AB - The rapid integration of artificial intelligence (AI) into service ecosystems is transforming value cocreation while generating significant ethical risks that threaten customer trust, organisational legitimacy, and social sustainability. This paper develops the Ethical AI Risk Mitigation (EAIRM) model to examine how different configurations of human-AI collaboration create distinct ethical challenges across fairness, autonomy, transparency, and accountability dimensions. Drawing on a structured literature synthesis, we identify four leadership approaches (compliance-oriented, values-based, stakeholder-engaged, and anticipatory) that systematically mitigate ethical risks while enabling service innovation. Through integrative theory building, the model contributes to service research and practice by: (1) revealing how identical ethical risks operate through different causal mechanisms depending on human-AI resource configuration; (2) specifying multi-actor governance structures for service ecosystems where no single actor controls ethical outcomes; (3) theorizing leadership mechanisms and organisational mediators that convert ethical principles into operational practices; and (4) generating testable propositions with boundary conditions, moderators, and feedback dynamics. This framework advances service ecosystem theory by demonstrating that resource relations carry ethical risk implications requiring polycentric governance, not merely value creation potential. © 2026 Informa UK Limited, trading as Taylor & Francis Group. KW - AI governance KW - Artificial intelligence KW - ethical leadership KW - human-AI relations KW - responsible AI KW - risk mitigation KW - service ecosystems CY - United Arab Emirates, Spain ER - TY - JOUR TI - AI-Enabled Circular Business Model Transition for Mitigating Climate Change: A Natural Resource-Based View Perspective on Business Strategies AU - Wang J. AU - Chaudhary S. AU - Kamal M. AU - Almasabi S. AU - Remsei S. PY - 2026 JO - Business Strategy and the Environment DO - 10.1002/bse.70649 AB - The role of artificial intelligence (AI) in achieving sustainability goals has garnered attention in academic literature. While AI has been argued to be crucial in addressing circularity challenges, organizations face challenges in configuring a business model. Designing new business calls for insights on how AI can be integrated into value creation and capture mechanisms. There is a lack of clarity on how organizations deploy AI as they transition to circular business model innovation. The purpose of the study is to explore how AI is integrated into organizational processes while adopting circular business models. We conducted online open-ended interviews with 55 participants to explore the potential role of AI in enabling the adoption of circular business models. Our findings have implications for theory building relating to AI business model innovation and provide a novel avenue for further research on business model innovation literature. Building on a natural resource-based view, the findings indicate that while implementing a circular business model is challenging, AI enables organizations to create, transfer, and capture value through resource efficiency and the reuse of resources. As AI technologies continue to evolve, organizations must develop adaptive capabilities to continually explore opportunities. AI enables organizations to reduce costs, develop novel value-creation strategies, and capture opportunities, resulting in improved efficiency. Transitioning to a circular business model requires developing routines, and organizations must adapt existing systems to ensure these systems result in pollution prevention, product stewardship, and sustainable development. It is important for managers to develop organizational resources and capabilities that enable the development of AI capabilities. © 2026 The Author(s). Business Strategy and the Environment published by ERP Environment and John Wiley & Sons Ltd. KW - artificial intelligence KW - circular business models KW - climate change KW - pollution control KW - stewardship KW - sustainable development CY - Hungary ER - TY - JOUR TI - A2C: A modular multi-stage collaborative decision framework for human–AI teams AU - Tariq S. AU - Baruwal Chhetri M. AU - Nepal S. AU - Paris C. PY - 2025 JO - Expert Systems with Applications VL - 282 SP - 127318 DO - 10.1016/j.eswa.2025.127318 AB - The increasing complexity of decision-making in dynamic environments, particularly in high-stakes domains like cybersecurity, demands more than automated solutions—it requires effective integration of human expertise with advanced AI capabilities. While approaches like ensemble learning and Mixture of Experts (MoE) enhance automated decision-making, they struggle with handling uncertainty and novel scenarios. Techniques such as learning to defer and learning to complement mitigate this by incorporating human input, but assume that a definitive expert is always available—an assumption that often fails in real-world settings. To bridge this gap, we introduce A2C, a modular, multi-stage collaborative decision-making framework that enhances adaptability and decision robustness under uncertainty by seamlessly transitioning between three decision-making modes: Automated, Augmented Deferral, and Collaborative Exploration (CoEx). A key innovation of CoEX is its ability to handle cases where both AI and human experts face uncertainty, overcoming a critical limitation of traditional deferral systems. We validate A2C through experiments on benchmark datasets, Large Language Model (LLM) simulations of human–AI collaboration, and real-world human–AI interaction studies with cybersecurity researchers. Results show that A2C consistently outperforms conventional approaches that rely solely on full automation or selective human intervention, demonstrating its potential as a practical and scalable solution for expert decision-making in complex domains. For image detection on CIFAR-10, detection rates improved from 37.8% with automation alone to 64.75% with augmented deferral, and further to 92.95% with collaborative exploration. Similarly, for intrusion detection on KDDCup, rates rose from 33.43% with automation to 35.18% with augmented deferral, and finally reached 87.04% with CoEx, highlighting its effectiveness in handling uncertainty. © 2025 The Authors KW - AI-augmented decision support KW - Collaborative decision-making KW - Decision-making under uncertainty KW - Human-centric AI KW - Human–AI systems KW - Intelligent systems KW - Adversarial machine learning KW - Chatbots KW - Contrastive Learning KW - Federated learning KW - Intelligent systems KW - Intrusion detection KW - AI systems KW - AI-augmented decision support KW - Collaborative decision making KW - Decision making under uncertainty KW - Decision supports KW - Decisions makings KW - Human-centric KW - Human-centric AI KW - Human–AI system KW - Uncertainty KW - Cybersecurity CY - Australia ER - TY - JOUR TI - DEVELOPMENT OF EXPORT POTENTIAL OF BUSINESS IN THE AI ECONOMY THROUGH PRODUCT QUALITY MANAGEMENT WITH THE HELP OF CORPORATE INFORMATION SYSTEMS AU - Dzhailova A.D. AU - Mannapova R.A. AU - Ivanova I.G. AU - Akopov S.E. AU - Tikhomirov K.Y. PY - 2025 JO - Proceedings on Engineering Sciences VL - 7 IS - 1 SP - 137 EP - 146 DO - 10.24874/PES07.01A.005 AB - This paper dwells on the influence of corporate information systems and artificial intelligence (AI) on quality management and the development of the export potential of business in the conditions of the AI economy. The role of information and information systems in product quality is considered, specifics of the AI economy and its features in international trade are disclosed, different types of corporate information systems from the position of formation of competitive advantages and ensuring better results in product quality management are characterised, the key directions of the influence of the integration of AI and corporate information systems on product quality and export potential of companies, which are achieved due to predictive servicing, visual inspection, personalisation of products to consumer demands, and optimal management of supply chains, are determined. The methodological basis of this paper is comprised of the system and process approaches which allow combining theoretical and practical aspects of studying the problem from the position of different scientific spheres and views, including international trade, quality management, the digital economy, the concepts of development of the digital economy, and Industry 4.0. The research is based on the concepts of Total Quality Management (TQM), the theory of international trade, and modern views of the development of the digital economy and AI. To substantiate the conclusions, the methods of analysis, synthesis, comparison, and generalisation, as well as table and graphical methods are used. The main value of this paper lies in progressive consideration of the role and impact of information, corporate information systems, and quality management systems on the development of the export potential of business in the conditions of the AI economy. Attention is paid to identifying interrelations between the integration of AI and corporate information systems, an increase in product quality due to better management of information flows and the use of AI capabilities, as well as features of the development of the export potential of business in the conditions of the AI economy. By the examples of the leading companies, the practical results of this integration and its influence on competitiveness in international markets are demonstrated. © 2025 Published by Faculty of Engineering. KW - AI Economy KW - Artificial Intelligence KW - Competitiveness KW - Corporate Information Systems KW - Export Potential KW - International Trade KW - Product Quality Management CY - Kyrgyzstan, Uzbekistan ER - TY - JOUR TI - Deepfake-Style AI Tutors in Higher Education: A Mixed-Methods Review and Governance Framework for Sustainable Digital Education AU - Sharif H. AU - Atif A. AU - Nagra A.A. PY - 2025 JO - Sustainability (Switzerland) VL - 17 IS - 21 SP - 9793 DO - 10.3390/su17219793 AB - Deepfake-style AI tutors are emerging in online education, offering personalized and multilingual instruction while introducing risks to integrity, privacy, and trust. This study aims to understand their pedagogical potential and governance needs for responsible integration. A PRISMA-guided, systematic review of 42 peer-reviewed studies (2015–early 2025) was conducted from 362 screened records, complemented by semi-structured questionnaires with 12 assistant professors (mean experience = 7 years). Thematic analysis using deductive codes achieved strong inter-coder reliability (κ = 0.81). Four major themes were identified: personalization and engagement, detection challenges and integrity risks, governance and policy gaps, and ethical and societal implications. The results indicate that while deepfake AI tutors enhance engagement, adaptability, and scalability, they also pose risks of impersonation, assessment fraud, and algorithmic bias. Current detection approaches based on pixel-level artifacts, frequency features, and physiological signals remain imperfect. To mitigate these challenges, a four-pillar governance framework is proposed, encompassing Transparency and Disclosure, Data Governance and Privacy, Integrity and Detection, and Ethical Oversight and Accountability, supported by a policy checklist, responsibility matrix, and risk-tier model. Deepfake AI tutors hold promise for expanding access to education, but fairness-aware detection, robust safeguards, and AI literacy initiatives are essential to sustain trust and ensure equitable adoption. These findings not only strengthen the ethical and governance foundations for generative AI in higher education but also contribute to the broader agenda of sustainable digital education. By promoting transparency, fairness, and equitable access, the proposed framework advances the long-term sustainability of learning ecosystems and aligns with the United Nations Sustainable Development Goal 4 (Quality Education) through responsible innovation and institutional resilience. © 2025 by the authors. KW - academic integrity KW - AI ethics in education KW - AI literacy KW - deepfake AI tutors KW - detection of deepfakes KW - digital sustainability KW - online education governance KW - privacy and fairness in AI KW - SDG 4 quality education KW - sustainable education KW - synthetic media in education KW - artificial intelligence KW - educational development KW - governance approach KW - higher education KW - sustainability KW - Sustainable Development Goal KW - technology adoption KW - transparency CY - Pakistan, Australia ER - TY - JOUR TI - Artificial-Intelligence-Enabled Innovation Ecosystems: A Novel Triple-Layer Framework for Micro, Small, and Medium-Sized Enterprises in the Chinese Apparel-Manufacturing Industry AU - Qu C. AU - Kim E. PY - 2025 JO - Sustainability (Switzerland) VL - 17 IS - 11 SP - 5019 DO - 10.3390/su17115019 AB - The rapid advancement of artificial intelligence (AI) in the traditional-apparel-manufacturing sector is accelerating innovation and transformation, as cutting-edge AI applications have been increasingly integrated into the industry in recent years. While China has made outstanding achievements in applying AI in the apparel-manufacturing sector, the adoption of AI by traditional apparel manufacturers has progressed slowly. This study aims to develop a sustainable triple-layer framework of an AI-enabled innovation ecosystem from grounded required AI capabilities and barriers to AI adoption, thereby generating the conceptual propositions for micro, small, and medium-sized Chinese apparel manufacturing. Through semi-structured interviews conducted with 20 organizations, this study qualitatively analyzes interviews with representatives from enterprises, universities, and apparel associations to determine the required AI capabilities and barriers to adopting AI. It proposes 13 propositions within a theoretical framework that addresses barriers and aligns multi-actor collaborations, ultimately forming a sustainable AI-enabled Triple-Layer Innovation Ecosystem Framework. This novel framework reflects the dynamic interplay between external knowledge absorption capacity and a firm’s internal innovation capacity, providing a theoretical foundation for understanding and advancing AI-driven innovation in the apparel-manufacturing sector. © 2025 by the authors. KW - artificial intelligence (AI) capabilities KW - Chinese apparel manufacturing KW - innovation ecosystems KW - micro, small, and medium-sized enterprises KW - sustainable triple-layer framework KW - China KW - artificial intelligence KW - ecosystem approach KW - innovation KW - manufacturing KW - small and medium-sized enterprise KW - sustainability KW - technology adoption CY - China, Japan ER - TY - JOUR TI - The impact of AI capability on responsible innovation in high-tech SMEs from the perspective of the knowledge-based view AU - Teng X. AU - Zhang X.-E. AU - Li Y. AU - Dong Y. PY - 2026 JO - Journal of Innovation and Knowledge VL - 11 SP - 100875 DO - 10.1016/j.jik.2025.100875 AB - The emergence of artificial intelligence (AI) technologies brings numerous opportunities andchallenges to business innovation. Understanding how firms can leverage AI technologies to create business value, propose responsible solutions to social problems, and achieve sustainable development has become important. Based on the knowledge-based view (KBV), a theoretical model is proposed to examine the impact of AI capability on responsible innovation. The study focuses on the mediating role of boundary-spanning search and the moderating roles of knowledge field activity and tech-for-good culture. Hierarchical regression analysis and bootstrapping are applied to data from 520 Chinese high-tech small and medium-sized enterprises (SMEs). The results indicate that AI capability positively influences responsible innovation. This relationship is fully mediated by boundary-spanning search. Knowledge field activity and tech-for-good culture moderate the relationships between AI capability and boundary-spanning search and between boundary-spanning search and responsibleinnovation. They also moderate the indirect effect of boundary-spanning search on the relationship between AI capability and responsible innovation. This study contributes to the literature on AI capabilities and innovation outcomes. It also provides practical insights for managers of high-tech SMEs and policymakers to foster responsible innovation. © 2025 The Authors. KW - AI capability KW - Boundary-spanning search KW - High-tech SMEs KW - Knowledge field activity KW - Responsible innovation KW - Tech-for-good culture CY - China ER - TY - JOUR TI - Unpacking the Black Box: How AI Capability Enhances Human Resource Functions in China’s Healthcare Sector AU - Chen X. AU - Martínez-Ruiz M.P. AU - Bulmer E. AU - Yáñez-Araque B. PY - 2025 JO - Information (Switzerland) VL - 16 IS - 8 SP - 705 DO - 10.3390/info16080705 AB - Artificial intelligence (AI) is transforming organizational functions across sectors; however, its application to human resource management (HRM) within healthcare remains underexplored. This study aims to unpack the black-box nature of AI capability’s impact on HR functions within China’s healthcare sector, a domain undergoing rapid digital transformation, driven by national innovation policies. Grounded in resource-based theory, the study conceptualizes AI capability as a multidimensional construct encompassing tangible resources, human resources, and organizational intangibles. Using a structural equation modeling approach (PLS-SEM), the analysis draws on survey data from 331 professionals across five hospitals in three Chinese cities. The results demonstrate a strong, positive, and statistically significant relationship between AI capability and HR functions, accounting for 75.2% of the explained variance. These findings indicate that AI capability enhances HR performance through smarter recruitment, personalized training, and data-driven talent management. By empirically illuminating the mechanisms linking AI capability to HR outcomes, the study contributes to theoretical development and offers actionable insights for healthcare administrators and policymakers. It positions AI not merely as a technological tool but as a strategic resource to address talent shortages and improve equity in workforce distribution. This work helps to clarify a previously opaque area of AI application in healthcare HRM. © 2025 by the authors. KW - artificial intelligence KW - capability KW - human resource functions KW - PLS-SEM KW - resource-based theory KW - Health care KW - Human resource management KW - Information management KW - Metadata KW - Black boxes KW - Capability KW - Digital transformation KW - Healthcare sectors KW - Human resource functions KW - Human resources management KW - ITS applications KW - Organizational functions KW - PLS-SEM KW - Resource-based theory KW - Artificial intelligence CY - Spain ER - TY - JOUR TI - Smarter, not harder: the AI capability paradox in emerging-market SMEs AU - Lambert J.M. AU - Laskovaia A. AU - Garanina O. AU - Bogatyreva K. PY - 2026 JO - Journal of Entrepreneurship in Emerging Economies VL - 18 IS - 3 SP - 813 EP - 836 DO - 10.1108/JEEE-10-2025-0632 AB - Purpose – This study aims to identify configurations of artificial intelligence (AI)-related organisational capabilities that lead to superior performance in small and medium-sized enterprises (SMEs) operating in an emerging market, moving beyond the assumption that “more AI usage is better”. Drawing on resource orchestration theory, the authors conceptualise how governance, skills, ethics, leadership and data infrastructure jointly enable value creation from AI. Design/methodology/approach – This study relies on a cross-sectional survey of Russian SMEs (October 2024 to January 2025). Of 384 firms, 47 that reported AI use were analysed. Using fuzzy-set qualitative comparative analysis (fsQCA), the authors examined how AI usage intensity combines with internal enablers, AI governance, data infrastructure, employee AI/digital skills, top management team (TMT) involvement and AI ethics preparedness, to explain four outcomes: operational efficiency, strategic decision quality, product/service innovation and customer responsiveness. The authors calibrated the conditions using the direct method and explored the robustness of configurations across alternative consistency and frequency thresholds. Findings – Across all outcomes, high AI usage intensity was not a core condition. Instead, multiple high-performance pathways featured AI governance as a central ingredient, frequently complemented by ethics preparedness and either employee training or active TMT involvement. Where governance was weaker, strong employee capabilities could serve as a substitute. These results show that SMEs can achieve strong performance with moderate AI intensity when organisational capabilities are well-aligned. In emerging-market SMEs, this points to an “AI capability paradox”: under the dual constraints of limited resources and weaker institutional environments, more intensive AI use does not necessarily yield better outcomes unless complemented by appropriate capability bundles. Originality/value – The authors shift the debate from “how much AI” to “how AI is governed and supported”. By applying a configurational lens in an emerging-market SME context, the authors reveal equifinal capability bundles, highlighting governance and ethics, paired with skills and leadership, as more decisive than sheer adoption intensity. The authors extend AI-related capabilities research to the focus on SMEs in emerging markets. Methodologically, the authors use fsQCA to identify multiple, empirically grounded resource and capability configurations associated with superior performance. © 2026 Emerald Publishing Limited KW - Artificial intelligence KW - Digital technology KW - fsQCA KW - Management of innovation KW - SMEs ER - TY - JOUR TI - Do artificial intelligence capabilities impact sustainability-oriented innovation performance: exploring the role of green intellectual capital and learning orientation AU - Zhang Y. AU - Shi J. AU - Huang Y. PY - 2025 JO - Journal of Intellectual Capital DO - 10.1108/JIC-10-2024-0315 AB - Purpose: This study examines the impact of artificial intelligence (AI) capabilities on sustainability-oriented innovation performance. Furthermore, it explores the mediating role of green intellectual capital and the moderating role of learning orientation. Design/methodology/approach: To verify the hypothesised relationships, we conducted a hierarchical regression analysis and bootstrapping method with survey data collected from 355 Chinese firms. Findings: Grounded in organisational learning theory, the study found that AI capabilities have a positive influence on green intellectual capital (i.e. green human capital, green structural capital and green relational capital), and this connection is further reinforced by learning orientation. The analysis also reveals that green intellectual capital serves as a mediator in the relationship between AI capabilities and sustainability-oriented innovation performance. Originality/value: This research explores the relationships among AI capabilities, green intellectual capital, learning orientation and sustainability-oriented innovation performance in a comprehensive model. This is the first known study to highlight that AI capabilities can improve sustainability-oriented innovation performance and gives managers implications on how to align AI capabilities while pursuing sustainability-oriented innovation performance. © 2025, Emerald Publishing Limited. KW - Artificial intelligence capabilities KW - Green intellectual capital KW - Learning orientation KW - Sustainability-oriented innovation performance CY - China ER - TY - JOUR TI - Governance by satellite: Remote sensing, bureaucrats and agency in the Common Agricultural Policy of the European Union AU - van der Velden D. AU - Klerkx L. AU - Dessein J. AU - Debruyne L. PY - 2025 JO - Journal of Rural Studies VL - 114 SP - 103558 DO - 10.1016/j.jrurstud.2024.103558 AB - Increasingly, European member states are using remote sensing technologies to determine if farmers comply with measures of the Common Agricultural Policy (CAP). Member states use satellite images, aerial photographs and geotagged pictures, and combine this with advanced algorithms and machine learning to determine if farmers comply with requirements that they have set out in their CAP strategic plans. Our research analyses the use of satellite images and the software used to process this data at paying agencies to understand how these technologies are enabling new forms of governance and what these technologies mean for how farmers are seen (literally and figuratively) by government agencies. This research is based on 12 semi-structured interviews with the developers of the technologies used to monitor compliance, which includes people working for paying agencies as well as people working at research institutes and companies where they provide technical support to the development and use of remote sensing at paying agencies. This research reveals that the governance of the Common Agricultural Policy (CAP), facilitated by remote sensing, fosters an audit culture characterized by strict control and compliance. The emphasis on mapping, quantifying, and representing agriculture underpins this governance model and drives further technological advancements. However, participants highlight the shortcomings of remote sensing technologies in effectively controlling farming practices. By integrating theories of bureaucracy and governance with a critical perspective on techno-utopianism, we examine these dynamics. Our findings indicate that the current application of remote sensing within the CAP is constrained not only by technical limitations but also by the existing governance framework. The push for quantification leads respondents to advocate for the further adoption of technical innovations to enhance control over agriculture. In conclusion, we suggest that a policy shift is necessary to break free from this technology trap. © 2025 The Authors KW - Algorithmic governance KW - Digitalization KW - Environmental monitoring KW - Farming subsidies KW - Street level bureaucrats KW - Common Agricultural Policy KW - digitization KW - environmental monitoring KW - European Union KW - remote sensing KW - subsidy system CY - Belgium, Chile, Netherlands ER - TY - JOUR TI - A Dynamic AI Maturity Model for Agile Audit: A Roadmap for Enhanced Effectiveness and Innovation AU - Amraoui S. AU - Elmaallam M. AU - Nassar M. PY - 2026 JO - Journal of Computer Science VL - 22 IS - 1 SP - 87 EP - 99 DO - 10.3844/jcssp.2026.87.99 AB - The intersection of Artificial Intelligence (AI) and agile methodologies is transforming information systems audit by enabling real-time risk assessment, anomaly detection, and automated control testing. These capabilities enhance the security, efficiency, and reliability of IT environments. This article introduces a dynamic AI maturity model for agile audit, structured into five levels of AI integration. Each level reflects increasing AI capabilities and outlines key transition points. The model supports strategic AI adoption across various audit domains, including data analysis, cybersecurity, compliance monitoring and fraud detection. We validate this model using interviews and a case study in a public-sector audit institution. Ethical concerns such as transparency, fairness, and accountability are integrated, recognizing the potential impact of AI on privacy, compliance, and governance. By applying this maturity model, organizations can systematically strengthen their agile audit practices while maintaining control over their information systems. © 2026 Soumaya Amraoui, Mina Elmaallam and Mahmoud Nassar. KW - Agile Audit KW - Artificial Intelligence (AI) KW - Information Systems Audit KW - Maturity Model CY - Morocco ER - TY - JOUR TI - Transparency in large language model (LLM)-powered digital human twins: the AI ethics perspective AU - Pigac T. PY - 2026 JO - AI and Society VL - 41 IS - 3 SP - 2109 EP - 2118 DO - 10.1007/s00146-025-02617-y AB - Digital human twins (DHTs), powered by large language models (LLMs), are transforming industries such as healthcare and finance by mimicking human behaviors, preferences, and decision-making processes. While their adoption offers unprecedented personalization and engagement, it also raises significant ethical concerns, particularly regarding transparency. Ensuring users understand how these systems function is critical to fostering trust and accountability. This study explores transparency in LLM-powered DHTs through qualitative analysis of 30 semi-structured interviews with users across diverse sectors. The findings reveal critical challenges, including algorithmic opacity, data privacy vulnerabilities, and threats to user autonomy. Participants consistently expressed a need for clear disclosures about data practices and emphasized the importance of robust ethical safeguards to prevent misuse. The research highlights the tension between achieving transparency and maintaining the seamless functionality of DHT systems. It underscores the risks of oversimplifying algorithmic processes while pointing out the erosion of trust caused by opaque operations. To address these challenges, the study proposes actionable strategies, including tiered transparency models, enhanced regulatory oversight, and user-centric design principles. By bridging ethical principles with practical applications, this research provides a roadmap for fostering responsible AI innovation. It advances the discourse on ethical AI by addressing transparency challenges in LLM-powered DHTs, emphasizing the need for systems that uphold trust, accountability, and user autonomy. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025. KW - AI ethics KW - Data privacy KW - Digital human twins KW - LLM KW - Personalization KW - Transparency KW - Behavioral research KW - Decision making KW - Ethical technology KW - AI ethic KW - Decision-making process KW - Digital human twin KW - Digital humans KW - Human behaviors KW - Human decisions KW - Language model KW - Large language model KW - Personalizations KW - User autonomy KW - Data privacy KW - Transparency ER - TY - JOUR TI - How can firms leverage responsible artificial intelligence to build competitive advantages? The role of supply chain justice and complexity AU - Li L. AU - Shi R. AU - Zhang R. AU - Chen L. PY - 2026 JO - International Journal of Production Economics VL - 297 SP - 110010 DO - 10.1016/j.ijpe.2026.110010 AB - In highly interconnected supply chains, firms are increasingly adopting artificial intelligence (AI) to improve their forecasting, resource allocation, and responsiveness. However, opaque and bias-prone algorithms can obscure decision-making processes and undermine collaboration between supply chain partners. Responsible AI, in which AI is designed, developed, and deployed in such a way that ensures its ethical conduct, transparency, and alignment with human values, offers a potential remedy, but its relational effects in supply chains remain unclear. Drawing on organizational justice theory, we test whether supply chain justice, encompassing both distributive and procedural justice, mediates the responsible AI–competitive advantage link, with supply chain complexity as a boundary condition. Survey data from 218 Chinese firms show that responsible AI fosters both distributive and procedural justice, which in turn facilitates firms’ competitive advantages. Further, we identify an asymmetric moderating effect: high supply chain complexity weakens the mediating effect of distributive but not procedural justice. Our study advances the research on AI-enabled supply chains by identifying how ethical AI practices can foster firm performance via supply chain justice and when supply chain complexity weakens this effect. Practically, our findings suggest that in complex supply networks, ensuring transparent and consistent decision-making processes provides a more robust mechanism for sustaining competitive advantages compared with focusing only on outcome allocation. Copyright © 2026. Published by Elsevier B.V. KW - Competitive advantages KW - Organizational justice theory KW - Responsible AI KW - Supply chain complexity KW - Supply chain justice KW - Artificial intelligence KW - Behavioral research KW - Competitive intelligence KW - Decision making KW - Decision theory KW - Ethical aspects KW - Supply chains KW - Competitive advantage KW - Decision-making process KW - Organisational KW - Organizational justice theory KW - Procedural justice KW - Resources allocation KW - Responsible artificial intelligence KW - Supply chain complexity KW - Supply chain justice KW - Supply chain partners KW - Competition CY - China ER - TY - JOUR TI - Artificial intelligence innovation to sustainable knowledge: The dual role of enterprise resilience AU - Wang S. AU - Ma L. AU - Hao F. AU - Zhang H. PY - 2025 JO - Journal of Innovation and Knowledge VL - 10 IS - 6 SP - 100832 DO - 10.1016/j.jik.2025.100832 AB - The rapid evolution of artificial intelligence (AI) presents unprecedented opportunities for knowledge creation and sustainable innovation in global digital commerce. This study investigates how AI orchestration capability generates new forms of knowledge that drive sustainable development in cross-border e-commerce multinational enterprises, with enterprise resilience serving as a critical knowledge transformation mechanism. Drawing on resource orchestration theory and employing a mixed-methods approach, we analyze data from 444 enterprises across China and Europe using partial least squares-structural equation modeling (PLS-SEM), importance–performance map analysis, and fuzzy-set qualitative comparative analysis (fsQCA), and executive interviews. Our findings reveal that AI orchestration capability—encompassing planning, integration, and reconfiguration dimensions—creates actionable knowledge that significantly enhances sustainable development both directly and indirectly through enterprise resilience. Enterprise resilience emerges as a dual-function capability that not only mediates knowledge flows between AI systems and sustainability outcomes but also amplifies the innovation potential of AI-generated insights. Regional analysis uncovers distinct knowledge creation pathways: Chinese enterprises excel at transforming AI capabilities into resilience-based knowledge, while European firms demonstrate superior translation of resilience-derived knowledge into sustainability innovations. Configurational analysis identifies multiple equifinal combinations of AI capabilities and resilience dimensions that generate high-impact sustainable innovations. This research advances our understanding of how digital technologies create enduring knowledge for sustainability, offering novel theoretical insights into innovation–knowledge dynamics and practical guidance for leveraging AI as a catalyst for sustainable business transformation in the digital economy. © 2025 The Author(s) KW - Artificial intelligence KW - Digital transformation KW - Enterprise resilience KW - Knowledge creation KW - Resource orchestration KW - Sustainable innovation CY - China ER - TY - JOUR TI - State positioning in European military AI networks: a social network analysis of European partnerships in military AI AU - Javadi M. AU - Onderco M. PY - 2025 JO - Journal of Contemporary European Studies VL - 33 IS - 4 SP - 1312 EP - 1331 DO - 10.1080/14782804.2025.2514846 AB - This study examines the positioning of nations with established AI capabilities within European military AI collaboration networks, exploring their influence and strategic centrality. Applying Social Network Analysis (SNA) to data from a 2023 expert survey (N = 479), supplemented by qualitative insights from targeted interviews, the research uncovers the structural dynamics shaping military AI cooperation. The findings indicate that countries with dedicated national AI strategies in defence leverage institutional readiness to become key nodes in the network, driving both innovation and governance. However, the overall network remains fragmented, with low density and limited cohesion, suggesting unrealized potential for deeper cooperation. The study highlights the pivotal roles of NATO and the European Defence Agency in strengthening integration and coordination. By employing SNA, this research offers new perspectives on European strategic affairs, shedding light on the evolving landscape of military AI governance and technological advancement. © 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. KW - Artificial intelligence KW - Europe KW - expert survey KW - military AI KW - social network analysis CY - Netherlands, Belgium, Czech Republic ER - TY - JOUR TI - Special report: The AgAID AI institute for transforming workforce and decision support in agriculture AU - Kalyanaraman A. AU - Burnett M. AU - Fern A. AU - Khot L. AU - Viers J. PY - 2022 JO - Computers and Electronics in Agriculture VL - 197 SP - 106944 DO - 10.1016/j.compag.2022.106944 AB - Tackling the grand challenges of 21st century agriculture (Ag) will require a fundamental shift in the way we envision the role of artificial intelligence (AI) technologies, and in the way we build agricultural AI systems. This shift is needed especially for complex, high-value agricultural ecosystems such as those in the Western U.S., where 300+ crops are grown. Farmers and policy makers in this region face variable profitability, major crop loss and poor crop quality owing to several challenges, including increased labor costs and shortages of skilled workers, weather and management uncertainties, and water scarcity. While AI is expected to be a significant tool for addressing these challenges, AI capabilities must be expanded and will need to account for human input and human behavior – calling for a strong AI-Ag coalition that also creates new opportunities to achieve sustained innovation. Accomplishing this goal goes well beyond the scope of any specific research project or disciplinary silo and requires a more holistic transdisciplinary effort in research, development, and training. To respond to this need, we initiated the AgAID Institute, a multi-institution, transdisciplinary National AI Research Institute that will build new public-private partnerships involving a diverse range of stakeholders in both agriculture and AI. The institute focuses its efforts on providing AI solutions to specialty crop agriculture where the challenges pertaining to water availability, climate variability and extreme weather, and labor shortages, are all significantly pronounced. Our approach to all AgAID Institute activities is being guided by three cross-cutting principles: (i) adoption as a first principle in AI design; (ii) adaptability to changing environments and scales, and (iii) amplification of human skills and machine efficiency. The AgAID Institute is conducting a range of activities including: using agricultural AI applications as testbeds for developing innovative AI technologies and workflows; laying the technological foundations for climate-smart agriculture; serving as a nexus for culturally inclusive collaborative and transdisciplinary learning and knowledge co-production; preparing the next generation workforce for careers at the intersection of Ag and AI technology; and facilitating technology adoption and transfer. © 2022 KW - Agriculture KW - AI KW - Decision support KW - Artificial intelligence KW - Behavioral research KW - Crops KW - Ecosystems KW - Employment KW - Human resource management KW - Wages KW - Agricultural ecosystems KW - Artificial intelligence systems KW - Artificial intelligence technologies KW - Crop loss KW - Crop quality KW - Decision supports KW - Grand Challenge KW - Labor costs KW - Labor shortages KW - Policy makers KW - agriculture KW - amplification KW - artificial intelligence KW - human behavior KW - learning KW - technology adoption KW - Decision support systems CY - United States ER - TY - JOUR TI - Do FinTech and financial incumbents have different experiences and perspectives on the adoption of artificial intelligence? AU - Zhang B.Z. AU - Ashta A. AU - Barton M.E. PY - 2021 JO - Strategic Change VL - 30 IS - 3 SP - 223 EP - 234 DO - 10.1002/jsc.2405 AB - Although FinTechs and incumbents are applying artificial intelligence (AI) differently, they both expect that the status-quo will likely be maintained through collaboration rather than competition. Both perceive BigTechs as a strategic threat given their AI capabilities and their entrance into financial services. Incumbents are experimenting with more different kinds of AI than FinTechs: FinTechs use the technologies for new products and services while incumbents are using them for incremental innovations to existing products and services. The incumbents expect that adopting AI will lead to a loss in jobs of 9% over the next 10 years and, because these companies represent a large percentage of the workforce (median company size surveyed has more than 10,000 employees), this loss in jobs cannot be compensated by the 19% increase in jobs provided by existing FinTechs (median company size surveyed has less than 50 employees). AI can reduce and increase risk, and most incumbents and FinTechs agree that there will be no effect on risk at the organizational level but that there will be an increase in risk at the societal level. While both FinTechs and incumbents agree on the relative importance of legal and human hurdles and consider the biggest hurdle is related to data and regulations concerning data, FinTechs perceive these hurdles to be greater than do incumbents. © 2021 John Wiley & Sons Ltd. KW - artificial intelligence KW - competitive advantage KW - financial services KW - Fintech KW - machine learning CY - United Kingdom, France, United States ER - TY - JOUR TI - Ethical and Regulatory Frameworks for Artificial Intelligence in Clinical Research: A European Perspective on the Artificial Intelligence Act for Ethics Committees and Researchers AU - Barucci A. AU - Colcelli V. AU - De Masi S. AU - Falconi M. AU - Leo M.C. AU - Marzola A. AU - Romagnuolo I. AU - Sforzi C. AU - Pini R. PY - 2026 JO - European Cardiology Review VL - 21 SP - 1 EP - 8 DO - 10.15420/ecr.2025.59 AB - The rapid integration of artificial intelligence (AI) into clinical research is transforming the landscape of biomedical innovation, influencing numerous phases of research, with critical ethical and legal implications. Regulation (EU) 2024/1689, commonly referred to as the AI Act and issued in 2024, introduced a new regulatory framework that classifies AI systems used in clinical settings as ‘high risk’, requiring increased scrutiny by ethics committees and national authorities. This review addresses ethical and regulatory challenges and discusses the application of the AI Act within real-world clinical research. We propose a three-phase lifecycle (training, real-world testing and post-marketing monitoring) to align regulatory burdens with AI maturity. Our recommendations include transparent protocol design with explicit data-use declarations, complementary application of the Medical Device Regulation and the AI Act, with particular attention to the early research phases. This approach provides practical indications for researchers and operational evaluation criteria for ethics committees to ensure patient safety while fostering trustworthy AI deployment in clinical trials. © The Author(s) 2026. KW - Artificial intelligence KW - clinical research KW - ethical principles KW - ethics committees KW - European Union regulations KW - article KW - artificial intelligence KW - clinical research KW - European KW - European Union KW - human KW - medical device regulation KW - patient safety KW - postmarketing surveillance KW - professional standard KW - scientist CY - Italy ER - TY - JOUR TI - Public administration with, of, and through AI: toward a new paradigm in the era of intelligence AU - Zhu X. PY - 2026 JO - Journal of Chinese Governance VL - 11 IS - 1 SP - 30 EP - 57 DO - 10.1080/23812346.2025.2578589 AB - This paper examines the future trajectory of public administration in the era of intelligence, focusing on the transformative implications of artificial intelligence (AI). It proposes a new triadic paradigm, ‘Public Administration with, of, and through AI,’ to conceptualize how AI is reshaping the theory, practice, and research of governance. The framework outlines three interrelated dimensions: the strengthening of administrative capacity with AI, the ethical and regulatory governance of AI, and the methodological advancement of the discipline through AI. Grounded in the historical evolution of public administration paradigms, the paper identifies key challenges in talent cultivation, research methodology, and evidence-based policymaking during digital transformation. As governments around the world integrate AI into administrative systems, the paper highlights the need for interdisciplinary collaboration, methodological innovation, and ethical vigilance. It concludes by advocating three strategic integrations that can guide the discipline’s renewal: scientific rigor with practical relevance, agility with long-termism, and globalization with indigenization. These integrations aim to ensure that governance in the intelligent age remains both effective and ethically grounded. © 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. KW - artificial intelligence KW - digital governance KW - intelligence era KW - paradigm shifts KW - Public administration CY - China ER - TY - JOUR TI - A Game-Theoretic Approach to Containing Artificial General Intelligence: Insights From Highly Autonomous Aggressive Malware AU - Mcintosh T.R. AU - Susnjak T. AU - Liu T. AU - Watters P. AU - Ng A. AU - Halgamuge M.N. PY - 2024 JO - IEEE Transactions on Artificial Intelligence VL - 5 IS - 12 SP - 6290 EP - 6303 DO - 10.1109/TAI.2024.3394392 AB - Artificial general intelligence (AGI) promises transformative societal changes but poses safety and containment challenges. Large language models such as ChatGPT have intensified public expectations and apprehensions regarding AGI capabilities and risks. Existing research underestimated replicating human intelligence and lacks effective containment strategies scaled for AGI's complexity. We developed a cybersecurity-inspired framework to reconceptualize AGI containment as securing critical infrastructure indispensable for its operation. We applied game theory to model the strategic interplay between AGI and humans, drawing parallels with highly autonomous malware, emphasizing infrastructural dependencies and human countermeasures. We introduced offensive/defensive containment strategies and an AGI Kill Chain model profiling escalating AGI threats. Our game-theoretic approach examined complex AGI-human interactions revealing insights for adaptive oversight mechanisms. Game simulations demonstrated AGI carefully manages resources and autonomy balancing benefits against risks, necessitating strategic human responses. Our findings provided detailed containment tactics, emphasizing flexibility to address AGI's dynamic evolution. We proposed comprehensive, multidisciplinary containment strategies, effective governance evaluating long-term efficacy, and emphasize ongoing innovation for aligning AGI progression with utility and security. © 2024 IEEE. KW - AGI containment KW - AI Governance KW - AI safety KW - Artificial general intelligence (AGI) KW - game theory KW - risk assessment KW - Artificial intelligence KW - Computer games KW - Cybersecurity KW - Malware KW - Risk assessment KW - Safety engineering KW - AI governance KW - AI safety KW - Artificial general intelligence KW - Artificial general intelligence containment KW - Artificial general intelligences KW - Australia KW - Chatbots KW - Complexity theory KW - Risks assessments KW - Game theory CY - Australia, New Zealand ER - TY - JOUR TI - Bridging Innovation and Practice: A Literature Review on Artificial Intelligence’s (AI’s) Expanding Role in Therapy AU - Shepperson C. AU - Chen H.-M. AU - Quek K.M.-T. PY - 2026 JO - International Journal of Systemic Therapy DO - 10.1080/2692398X.2026.2629637 AB - Artificial Intelligence (AI) encompassing machine learning (ML), deep learning, and generative AI (GenAI), is increasingly shaping clinical and therapeutic landscapes. This paper conducts a review of current literature on the use of AI in various therapeutic contexts. It examines how AI technologies such as chatbots, predictive models, and AI-enabled robotics are being applied across diverse populations to support prescreening, diagnosis processes, symptom tracking, behavioral monitoring, and clinical systemic interventions. These tools have demonstrated potential in improving therapeutic outcomes, including reducing symptoms of depression and anxiety and enhancing cognitive and behavioral functioning. The review highlights specific benefits and explores the broader implications of these applications within clinical practice. However, concerns such as algorithmic bias, data privacy risks, and the lack of emotional nuance in AI responses pose significant risks to therapeutic integrity and relational attunement. Moreover, the absence of formal training and clearly defined ethical guidelines for clinicians raises questions about responsible implementation. Systemic therapists, in particular, must remain vigilant about how AI may reinforce dominant cultural narratives or marginalize underrepresented voices. Therefore, as AI continues to evolve, ongoing professional engagement and ethical scrutiny are essential to ensure its responsible and equitable integration into clinical care. Given the rapidly changing nature of these technologies, therapists are recommended to remain informed and approach their use with flexibility and caution. © 2026 Taylor & Francis Group, LLC. KW - Artificial intelligence (AI) KW - Ethical Considerations KW - Systemic Therapy KW - Therapy CY - United States ER - TY - JOUR TI - Artificial Intelligence in Reliability of Engineering Design-an Overview AU - Afolalu S.A. AU - Olawale O.C. AU - Oso F. PY - 2025 JO - NIPES - Journal of Science and Technology Research VL - 7 IS - 2 SP - 2919 EP - 2927 DO - 10.37933/nipes/7.4.2025.SI348 AB - This study explores the transformative role of artificial intelligence (AI) in enhancing reliability within engineering systems. It highlights various applications of AI, including predictive maintenance, real-time monitoring, and advanced data analytics, which significantly contribute to reducing system failures and improving overall performance. By leveraging historical data and current sensor inputs, AI enables engineers to predict equipment failures before they occur, optimizing maintenance schedules and extending the life of critical systems. The research also discusses AI's capabilities in data acquisition and pattern recognition, which facilitate better understanding of failure modes and inform design decisions that enhance reliability. Furthermore, the study examines the impact of AI-driven simulations and modeling on engineering design processes, allowing for virtual testing of designs under various conditions and minimizing costly iterations. The implications of AI in automated quality control and decision support systems are also addressed, underscoring its potential to improve product quality and operational efficiency. Ultimately, the findings suggest that the integration of AI technologies across engineering disciplines can lead to more robust, efficient, and sustainable systems, paving the way for future innovations in reliability engineering. © 2025 NIPES Pub. KW - Artificial Intelligence KW - Data Analytics KW - Engineering Design KW - Predictive Maintenance KW - Reliability Engineering CY - Nigeria, South Africa ER - TY - JOUR TI - A Functionally-Grounded Benchmark Framework for XAI Methods: Insights and Foundations from a Systematic Literature Review AU - Canha D. AU - Kubler S. AU - Främling K. AU - Fagherazzi G. PY - 2025 JO - ACM Computing Surveys VL - 57 IS - 12 SP - ART320 DO - 10.1145/3737445 AB - Artificial Intelligence (AI) is transforming industries, offering new opportunities to manage and enhance innovation. However, these advancements bring significant challenges for scientists and businesses, with one of the most critical being the ‘trustworthiness” of AI systems. A key requirement of trustworthiness is transparency, closely linked to explicability. Consequently, the exponential growth of eXplainable AI (XAI) has led to the development of numerous methods and metrics for explainability. Nevertheless, this has resulted in a lack of standardized and formal definitions for fundamental XAI properties (e.g., what do soundness, completeness, and faithfulness of an explanation entail? How is the stability of an XAI method defined?). This lack of consensus makes it difficult for XAI practitioners to establish a shared foundation, thereby impeding the effective benchmarking of XAI methods. This survey article addresses these challenges with two primary objectives. First, it systematically reviews and categorizes XAI properties, distinguishing them between human-centered (relying on empirical studies involving explainees) or functionally-grounded (quantitative metrics independent of explainees). Second, it expands this analysis by introducing a hierarchically structured, functionally grounded benchmark framework for XAI methods, providing formal definitions of XAI properties. The framework’s practicality is demonstrated by applying it to two widely used methods: LIME and SHAP. © 2025 Copyright held by the owner/author(s). KW - Artificial intelligence KW - eXplainable AI (XAI) KW - interpretability KW - machine learning KW - responsible AI KW - transparency KW - trustworthiness KW - Artificial intelligence KW - Benchmarking KW - Information systems KW - Learning systems KW - Reviews KW - Artificial intelligence systems KW - Explainable artificial intelligence (XAI) KW - Exponential growth KW - Formal definition KW - Interpretability KW - Machine-learning KW - Property KW - Responsible artificial intelligence KW - Systematic literature review KW - Trustworthiness KW - Transparency CY - Luxembourg, Sweden, Finland ER - TY - JOUR TI - A Cloud-Based Computing Framework for Artificial Intelligence Innovation in Support of Multidomain Operations AU - Robertson J. AU - Fossaceca J. AU - Bennett K. PY - 2022 JO - IEEE Transactions on Engineering Management VL - 69 IS - 6 SP - 3913 EP - 3922 DO - 10.1109/TEM.2021.3088382 AB - The DoD's artificial intelligence (AI) strategy requires the delivery of transformative and disruptive capabilities that impact the 'character of the future battlefield and the pace of threats' that US forces must be prepared to handle. Candidate frameworks must also address key mission areas while enabling partnerships with the private sector, academia, and global allies. To meet these challenges, a flexible, cost-effective, and scalable computing infrastructure that incorporates cutting edge technologies and complies with stringent information assurance requirements is necessary. The DoD AI strategy mandates the agile employment of innovative AI capabilities that 'rapidly and iteratively' execute experimentation with new operating concepts, and leverage lessons learned in subsequent experiments. Using cloud computing, we present a flexible approach to solve complex systems problems. Promoting 'rapid experimentation' and collaboration on problems such as recursive algorithm implementation, deep learning, and inference in neural networks has enabled inherent advantages over existing computing frameworks. Leveraging the cloud to implement shared responsibility security models, serverless architectures, and high-performance virtual machines, aspects of the AI lifecycle including build, deploy, and monitor have resulted in an adaptable and scalable computing framework that is not only disruptive to the current computing paradigm but also promotes enhanced and productive collaboration. © 1988-2012 IEEE. KW - Artificial intelligence (AI) KW - artificial intelligence for technology management KW - cloud computing KW - collaboration KW - design of experiments KW - IS design KW - new product development process KW - productivity in software development KW - RandD management KW - systems engineering KW - technology adoption KW - technology evaluation KW - Big data KW - Cost effectiveness KW - Deep learning KW - Design of experiments KW - Engineering education KW - Inference engines KW - Iterative methods KW - Product design KW - Software design KW - Artificial intelligence KW - Artificial intelligence for technology management KW - Cloud-computing KW - Collaboration KW - D management KW - IS design KW - New product development process KW - Productivity in software development KW - R& KW - Security KW - Technology adoption KW - Technology evaluation KW - Technology managements KW - US Department of Defence KW - Cloud computing CY - United States ER - TY - JOUR TI - A feast of knowledge: How AI-powered food councils can transform policymaking in the digital era AU - Vliet L.G.V. AU - Turk J.D. PY - 2026 JO - International Journal of Innovation Studies VL - 10 IS - 2 SP - 100169 DO - 10.1016/j.ijis.2025.11.004 AB - As global food systems face intensifying challenges—from climate change and biodiversity loss to nutritional insecurity and fractured supply chains—policymakers are under increasing pressure to formulate agile, informed, and inclusive responses. While digital tools and artificial intelligence (AI) offer transformative potential, few initiatives have explored their application within food system governance. This article presents a discussion-based case study of the Council of Foods, an experimental AI-powered policymaking platform developed within the EU Horizon-funded Hungry EcoCities project. Through playful, food-character-driven dialogue, the platform enables stakeholders to explore complex policy dilemmas, surface diverse perspectives, and receive dynamically generated recommendations. Drawing on experiences from early testing environments, including public forums and conference sessions, we reflect on the conceptual framework, technical design, and pedagogical value of the Council of Foods. While the tool is still in its developmental stages, we argue that such AI-mediated, participatory platforms can help bridge knowledge gaps, stimulate policy learning, and support just transitions in food governance. The article concludes with key implications for future research, platform development, and the ethical integration of AI in public policy. Copyright © 2026. Publishing services by Elsevier B.V. KW - AI in policymaking KW - AI-Powered agents KW - Digital transformation KW - Food policy innovation KW - Food system governance KW - Green transition KW - Just KW - Sustainable food systems KW - Transition Ethical AI CY - Netherlands, Belgium ER - TY - JOUR TI - AI Capabilities and Its Impact on Organisational Innovation in Malaysian SMEs: The Role of Transformational Leadership and Digital Organisational Culture AU - Ismail R.T.Y. AU - Karamanlıoğlu A.U. PY - 2026 JO - Sustainability (Switzerland) VL - 18 IS - 3 SP - 1473 DO - 10.3390/su18031473 AB - Artificial Intelligence capabilities make the organisational innovation process more critical and sustainable, especially in SMEs. This research explored the influence of AI capabilities on organisational innovation within Malaysian SMEs and the role of transformational leadership as a mediator for the above effects, while considering the moderating role of digital organisational culture. The questionnaire was distributed electronically via Google Forms to a study sample of (900) SMEs in Kuala Lumpur, Selangor, and Johor Bahru, targeting owners and managers. Two weeks after distribution, (565) questionnaires were received; however, (215) questionnaires were excluded because the respondents were neither managers nor owners. A total of (350) questionnaires were valid for analysis. Using SMART-PLS software v.4.1.1.6 (PLS-SEM analysis) in analysing data, the study found that AI capability has a positive impact on organisational innovation and a positive impact on transformational leadership. Moreover, transformational leadership has a positive impact on organisational innovation, and transformational leadership mediates the relationship between AI capability and organisational innovation. Furthermore, the study found that digital organisational culture does not moderate the relationship between AI capability and transformational leadership. Digital organisational culture moderates the relationship between AI capability and organisational innovation; also, digital organisational culture moderates the relationship between transformational leadership and organisational innovation. © 2026 by the authors. KW - artificial intelligence KW - digital organisational culture KW - Malaysia KW - organisational Innovation KW - SMEs KW - transformational leadership KW - Johor KW - Johor Bahru KW - Kuala Lumpur KW - Malaysia KW - Selangor KW - West Malaysia KW - artificial intelligence KW - innovation KW - leadership KW - questionnaire survey KW - small and medium-sized enterprise KW - software KW - spatiotemporal analysis CY - Turkey ER - TY - JOUR TI - Digital Transformation Leadership and AI Capabilities as Drivers of Sustainable Competitive Advantage: The Mediating Role of Organizational Agility in Spain’s New S-Curve Industries AU - Alioune A. PY - 2026 JO - Managing Global Transitions VL - 24 IS - 1 SP - 33 EP - 62 DO - 10.26493/1854-6935.24.33-62 AB - The modern trend among various industrial companies to adopt digital technologies in their operations through digital transformation and reliance on artificial intelligence has become an imperative, especially with the increasing intensity of competition in global markets. To gain a realistic understanding of the role of digital transformation leadership (DTL) and AI capabilities (AIC) in achieving sustainable competitive advantage (SCA) for organizations, this study employs organizational agility (OA) as a mediating variable, based on data collected from 441 employees in Spanish ‘New S-Curve’ industries, which are innovative sectors achieving high growth returns within the framework of the ‘Spain 5.0’ national strategy. The statistical and analytical framework of Structural Equation Modelling (SEM) revealed that DTL has a significant impact on SCA, while the effect of AIC was not significant. Furthermore, OA was found to be an important mediator, reinforcing the indirect effects of both DTL and AIC on achieving SCA for Spanish organizations. © Author. KW - artificial intelligence capabilities KW - digital leadership KW - organizational agility KW - Spain’s S-curve industries KW - sustainable competitive advantage CY - Algeria ER - TY - JOUR TI - Bridging human and machine intelligence: How design thinking and generative AI capabilities drive exploratory and exploitative innovation AU - Cai Y. AU - Xin X. AU - Li L. AU - Shang Y. AU - Chen L. PY - 2026 JO - Technological Forecasting and Social Change VL - 228 SP - 124662 DO - 10.1016/j.techfore.2026.124662 AB - Although the literature has highlighted the roles of generative artificial intelligence (GenAI) and design thinking (DT) in innovation, the interplay between them remains unclear. To address this gap, we leverage the technological appropriation perspective to examine how three GenAI capabilities (i.e., relational, analytical, and creative) interact with five DT aspects (i.e., user focus, problem framing, visualization, experimentation and iteration, and embracing diversity) to influence exploratory and exploitative innovation. We use fuzzy-set qualitative comparative analysis to analyze survey data from 303 Chinese firms. The empirical results indicate that to achieve high exploratory and exploitative innovation, firms need to rely on focusing simultaneously on GenAI capabilities and DT, rather than on either factor individually. More interestingly, to achieve high exploratory innovation, firms require to focus on the combination of analytical capability, creative capability, problem framing, and embracing diversity. In contrast, to achieve high exploitative innovation, they must focus on the combination of relational capability, user focus, and visualization. Our findings contribute to the existing innovation literature by revealing the significant interplay between GenAI capabilities and DT. Our study also provides managerial implications for firms to coordinate human intelligence and machine intelligence more effectively. © 2026 Elsevier Inc. KW - Design thinking KW - Exploitative innovation KW - Exploratory innovation KW - fsQCA KW - GenAI capabilities KW - Artificial intelligence KW - Iterative methods KW - Creatives KW - Design thinking KW - Exploitative innovation KW - Exploratory innovation KW - FsQCA KW - Generative artificial intelligence capability KW - Human intelligence KW - Machine intelligence KW - Problem framings KW - User focus KW - artificial intelligence KW - innovation KW - qualitative analysis KW - visualization KW - Visualization CY - China ER - TY - JOUR TI - Digital servitization value co-creation framework for AI services: a research agenda for digital transformation in financial service ecosystems AU - Manser Payne E.H. AU - Dahl A.J. AU - Peltier J. PY - 2021 JO - Journal of Research in Interactive Marketing VL - 15 IS - 2 SP - 200 EP - 222 DO - 10.1108/JRIM-12-2020-0252 AB - Purpose: Innovative firms have rapidly developed artificial intelligence (AI) capabilities into their service ecosystems, essentially changing perceptions of what is service quality and service delivery in their respective industries. Nonetheless, the issues surrounding AI services remain relatively unknown. The purpose for this paper is to offer a digital servitization framework for understanding how AI services impact value perceptions, consumer engagement and firm performance measures. The authors use the financial service ecosystem to explore this topic. Design/methodology/approach: The authors explore relevant literature on digital servitization, service-dominant logic and AI/disruptive innovation. Next, a conceptual framework, organized by AI-Service Exchange Antecedents, Context of AI Usage and Digital Servitization Consequences, is developed. The authors conceptualize consequences for consumers and firms. Findings: The main findings suggest that the linkages between consumers, financial institutions and fintech companies with AI usage in a service ecosystem should be identified; how value is created among multiple SD Logic-AI network actors should be analyzed; and the effects of AI-consumer interactions (lower-level and higher levels of engagement) on firm performance measures should be explored. Research limitations/implications: The conceptual framework identifies gaps in the literature and suggests research questions for future studies. Practical implications: This paper may assist practitioners with the development of AI-enabled banking activities that involve direct consumer engagement. Originality/value: To the authors’ best knowledge, this research agenda is the first comprehensive framework for understanding value co-creation in the context of AI in financial services, linking antecedents, usage and consequences. © 2021, Emerald Publishing Limited. KW - Blogs KW - Consumer behaviour internet KW - Customer value KW - Digitalizations KW - e-commerce KW - Eservice quality KW - Financial services KW - Information technology KW - Online consumer behavior KW - Service quality KW - Services marketing CY - United States ER - TY - JOUR TI - Shaping the Future of Healthcare: Ethical Clinical Challenges and Pathways to Trustworthy AI AU - Goktas P. AU - Grzybowski A. PY - 2025 JO - Journal of Clinical Medicine VL - 14 IS - 5 SP - 1605 DO - 10.3390/jcm14051605 AB - Background/Objectives: Artificial intelligence (AI) is transforming healthcare, enabling advances in diagnostics, treatment optimization, and patient care. Yet, its integration raises ethical, regulatory, and societal challenges. Key concerns include data privacy risks, algorithmic bias, and regulatory gaps that struggle to keep pace with AI advancements. This study aims to synthesize a multidisciplinary framework for trustworthy AI in healthcare, focusing on transparency, accountability, fairness, sustainability, and global collaboration. It moves beyond high-level ethical discussions to provide actionable strategies for implementing trustworthy AI in clinical contexts. Methods: A structured literature review was conducted using PubMed, Scopus, and Web of Science. Studies were selected based on relevance to AI ethics, governance, and policy in healthcare, prioritizing peer-reviewed articles, policy analyses, case studies, and ethical guidelines from authoritative sources published within the last decade. The conceptual approach integrates perspectives from clinicians, ethicists, policymakers, and technologists, offering a holistic “ecosystem” view of AI. No clinical trials or patient-level interventions were conducted. Results: The analysis identifies key gaps in current AI governance and introduces the Regulatory Genome—an adaptive AI oversight framework aligned with global policy trends and Sustainable Development Goals. It introduces quantifiable trustworthiness metrics, a comparative analysis of AI categories for clinical applications, and bias mitigation strategies. Additionally, it presents interdisciplinary policy recommendations for aligning AI deployment with ethical, regulatory, and environmental sustainability goals. This study emphasizes measurable standards, multi-stakeholder engagement strategies, and global partnerships to ensure that future AI innovations meet ethical and practical healthcare needs. Conclusions: Trustworthy AI in healthcare requires more than technical advancements—it demands robust ethical safeguards, proactive regulation, and continuous collaboration. By adopting the recommended roadmap, stakeholders can foster responsible innovation, improve patient outcomes, and maintain public trust in AI-driven healthcare. © 2025 by the authors. KW - artificial intelligence KW - bias KW - ethics KW - health policy KW - large language model KW - machine learning KW - natural language processing KW - privacy KW - regulation KW - algorithm bias KW - Article KW - artificial intelligence KW - benchmarking KW - bias mitigation KW - clinical practice guideline KW - clinician KW - data privacy KW - environmental sustainability KW - ethicist KW - fairness KW - health care KW - health care need KW - health care policy KW - human KW - large language model KW - machine learning KW - natural language processing KW - patient care KW - practice guideline KW - stakeholder engagement KW - sustainable development goal KW - treatment outcome KW - trustworthiness CY - Ireland, Poland ER - TY - JOUR TI - The Adoption of Open Source Software Among Universities in Iraq: The Moderating Role of AI Capability AU - Qasim M.M. AU - Abdulkareem A.R. AU - Sneesl R. PY - 2025 JO - Human Behavior and Emerging Technologies VL - 2025 IS - 1 SP - 9937783 DO - 10.1155/hbe2/9937783 AB - Open source software (OSS) is a trendy innovation that is being used by all organizations. However, the usage of OSS is still limited in higher education. This research examines the adoption of OSS among universities in Iraq, focusing on the moderating role of artificial intelligence (AI) capabilities. The research is aimed at exploring how factors such as perceived ease of use (PEOU), compatibility, perceived risk, security, and cost-effectiveness influence OSS adoption. Using a quantitative research methodology, data was collected from 272 university decision-makers and analysed using Smart PLS 4. The results of the study indicate that factors such as PEOU, compatibility, perceived risk, security, and cost-effectiveness have a significant positive influence on the adoption of OSS. The research findings provide valuable insights for decision-makers in university settings who are grappling with the intricate process of adopting OSS. These findings offer valuable insights for higher education institutions in Iraq and other developing regions seeking to adopt OSS. Copyright © 2025 Mustafa Moosa Qasim et al. Human Behavior and Emerging Technologies published by John Wiley & Sons Ltd. KW - adoption KW - artificial intelligence KW - higher education KW - open source software KW - software engineering KW - TAM CY - Iraq ER - TY - JOUR TI - The role of AI capabilities in environmental management: Evidence from USA firms AU - Jiao A. AU - Lu J. AU - Ren H. AU - Wei J. PY - 2024 JO - Energy Economics VL - 134 SP - 107653 DO - 10.1016/j.eneco.2024.107653 AB - This study investigates the role of Artificial Intelligence (AI) capabilities in influencing firms' greenwashing behaviors. We find a robust negative association between firms' AI capabilities and unrepresentative environmental disclosure. An instrumental variable approach is employed to establish causality. The effects are more pronounced for firms (1) with a greater exposure to regulatory climate risk, (2) managed by Republican-leaning managers, (3) with stronger governance structures, (4) possessing greater product market pricing power, (5) operating in multiple regions, and (6) with CEOs with higher pay-for-performance sensitivity. We further demonstrate that AI capabilities aid firms in transitioning to green operations through engaging in green and clean innovation. Finally, we find that AI capabilities correlate with lower greenhouse gas emissions. Overall, our findings shed light on the real impact of AI-related technologies in the energy industry. © 2024 Elsevier B.V. KW - Artificial intelligence KW - Environmental management KW - Greenwashing KW - Innovation KW - IT investment KW - United States KW - Economics KW - Environmental management KW - Gas emissions KW - Greenhouse gases KW - Investments KW - Governance structures KW - Greenwashing KW - Innovation KW - Instrumental variables KW - IT investments KW - Market pricing KW - Multiple regions KW - Performance sensitivity KW - Power KW - Product markets KW - artificial intelligence KW - environmental economics KW - environmental management KW - information technology KW - innovation KW - investment KW - Artificial intelligence CY - China, United States ER - TY - JOUR TI - The nexus of artificial intelligence literacy collaborative knowledge practices and inclusive leadership development among higher education students in Bangladesh China Finland and Turkey AU - Asghar M.Z. AU - Iqbal J. AU - Özbilen F.M. AU - Abedin J. AU - Järvenoja H. AU - Widanapathirana U. PY - 2025 JO - Discover Computing VL - 28 IS - 1 SP - 172 DO - 10.1007/s10791-025-09695-y AB - This study investigates how Artificial Intelligence Literacy (AIL) fosters Inclusive Leadership (IL) development among university students through Collaborative Knowledge Practices (CKP), with cross-cultural insights from Bangladesh, China, Finland, and Turkey. Using a mixed-methods design—combining quantitative surveys of 458 students and qualitative interviews with 40 participants, this research integrates three frameworks: (1) the ABC-E model of AIL, encompassing affective, behavioral, cognitive, and ethical dimensions; (2) CKP, which involves competences such as collaboration, integration, creativity, sustainability, adaptability, engagement, and technological aspects; and (3) IL principles. Cultural interpretations are informed by Hofstede’s six-dimensional model of national culture, with a focus on the Power Distance Index (PDI) and the Individualism–Collectivism (IDV) dimensions. Quantitative analysis employed PLS-SEM and Fuzzy Set Qualitative Comparative Analysis (fsQCA) to uncover linear and non-linear relationships, while qualitative findings supported the multi-group analysis. Cross-cultural comparisons revealed that Finland emphasizes ethical AI use, China highlights innovation, Bangladesh focuses on problem-solving applications, and Turkey reflects multicultural collaboration—each of which influences students’ engagement with AI tools in distinct ways. The findings underscore CKP as a critical bridge between AIL and IL, highlighting the need for context-sensitive, collaborative pedagogies that equip students to address AI-driven challenges and lead inclusively in diverse global settings. © The Author(s) 2025. KW - Artificial intelligence literacy KW - Collaborative knowledge practices KW - Higher education KW - Inclusive leadership CY - Finland, China, Turkey ER - TY - JOUR TI - Generative artificial intelligence in supply chain and operations management: a capability-based framework for analysis and implementation AU - Jackson I. AU - Ivanov D. AU - Dolgui A. AU - Namdar J. PY - 2024 JO - International Journal of Production Research VL - 62 IS - 17 SP - 6120 EP - 6145 DO - 10.1080/00207543.2024.2309309 AB - This research examines the transformative potential of artificial intelligence (AI) in general and Generative AI (GAI) in particular in supply chain and operations management (SCOM). Through the lens of the resource-based view and based on key AI capabilities such as learning, perception, prediction, interaction, adaptation, and reasoning, we explore how AI and GAI can impact 13 distinct SCOM decision-making areas. These areas include but are not limited to demand forecasting, inventory management, supply chain design, and risk management. With its outcomes, this study provides a comprehensive understanding of AI and GAI's functionality and applications in the SCOM context, offering a practical framework for both practitioners and researchers. The proposed framework systematically identifies where and how AI and GAI can be applied in SCOM, focussing on decision-making enhancement, process optimisation, investment prioritisation, and skills development. Managers can use it as a guidance to evaluate their operational processes and identify areas where AI and GAI can deliver improved efficiency, accuracy, resilience, and overall effectiveness. The research underscores that AI and GAI, with their multifaceted capabilities and applications, open a revolutionary potential and substantial implications for future SCOM practices, innovations, and research. © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. KW - artificial intelligence KW - GAI KW - Generative artificial intelligence KW - operations management KW - supply chain KW - Decision making KW - Inventory control KW - Optimization KW - Risk management KW - Supply chain management KW - Supply chains KW - Demand forecasting KW - Generative AI KW - Generative artificial intelligence KW - Inventory management KW - Management decision-making KW - Operation management KW - Resource-based view KW - Supply-chain designs KW - Supply-chain risks KW - Through the lens KW - Artificial intelligence CY - United States, Germany, France ER - TY - JOUR TI - Policy Narratives’ Salience: A Comparative Analysis of Artificial Intelligence Policy Responsiveness to Public Attention in China and the United States AU - Xiao H. AU - Ge W. AU - Shi X. PY - 2025 JO - Journal of Comparative Policy Analysis: Research and Practice VL - 27 IS - 5-6 SP - 555 EP - 577 DO - 10.1080/13876988.2025.2551038 AB - In ever-increasing AI policies, the responsive narratives of government policies to public attention have become a crucial aspect of comparative policy analysis. This study obtained the AI policy texts and public web search indexes of China and the United States from 2017 to 2023, summarizes six major policy topics using the LDA method, and constructs a correspondence matrix. The article empirically tests the differences between China and the US in policy responsiveness to public attention on the six topics, and comparatively analyzes the reasons for the different levels of responsiveness in terms of technology development and technological philosophy. The findings indicate that, in general, both Chinese and US AI policies have responded to public attention to some extent. However, there are differences in responsive narratives on topics of application service, technology innovation, risk management, individual development, market competition, and social development. The findings contribute to understanding the correlation between policy responsiveness and public attention in Chinese and US contexts, and enrich the cutting-edge comparative AI policy research. © 2025 The Editor, Journal of Comparative Policy Analysis: Research and Practice. KW - artificial intelligence KW - comparison of Chinese and American policies KW - policy attention KW - policy responsiveness KW - public attention CY - China ER - TY - JOUR TI - Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of Artificial Intelligence on Knowledge Worker Productivity and Quality AU - Dell’acqua F. AU - McFowland E. AU - Mollick E. AU - Lifshitz H. AU - Kellogg K.C. AU - Rajendran S. AU - Krayer L. AU - Candelon F. AU - Lakhani K.R. PY - 2026 JO - Organization Science VL - 37 IS - 2 SP - 403 EP - 423 DO - 10.1287/orsc.2025.21838 AB - We introduce and study the concept of a “jagged technology frontier” to describe the uneven impact of artificial intelligence (AI) capabilities, where AI assistance improves performance for some tasks but worsens it for others, even within the same knowledge workflow and with a seemingly similar level of difficulty. In collaboration with the global management consulting firm Boston Consulting Group, we have developed realistic management consulting tasks and examined the human performance implications of using AI to perform complex and knowledge-intensive work. The preregistered experiment involved 758 knowledge workers. After establishing a performance baseline on similar tasks, subjects were randomly assigned to one of three conditions: no AI access, GPT-4 AI access, or GPT-4 AI access with a prompt engineering overview. For each one of a set of 18 realistic knowledge tasks within the frontier of AI capabilities ranging from creative to analytical tasks, subjects using AI outperformed those not using AI, completing 12.2% more tasks and completing them 25.1% more quickly on average while also delivering solutions of significantly improved quality. However, for a complex managerial task selected to be outside the frontier, subjects using AI were 19% less likely to produce correct solutions compared with those without AI, pointing to potential limitations of AI supporting knowledge workers. We discuss the positive and negative implications of AI-aided human performance in knowledge-intensive tasks. © 2026 The Author(s). KW - economics and organization KW - field experiments KW - implementation of new technology KW - organization and management theory KW - organizational economics KW - organizational processes KW - research design and methods KW - technology and innovation management CY - United States, United Kingdom ER - TY - JOUR TI - A conceptual model of employees’ behavioral intention to use generative artificial intelligence technology in mid-sized organizations AU - Wisedpanich N. AU - Wittayakom S. PY - 2026 JO - Multidisciplinary Science Journal VL - 8 IS - 8 SP - e2026531 DO - 10.31893/multiscience.2026531 AB - Rapid technological advancements have positioned Generative Artificial Intelligence (GenAI) as a strategic asset for businesses; however, its adoption in resource-constrained environments remains complex. Specifically, in the context of emerging economies where mid-sized firms drive significant employment yet face distinct digital hurdles, understanding these dynamics is crucial. This conceptual research develops a theoretical model explaining employees’ behavioral intention to use GenAI in mid-sized organizations, a sector often overlooked in favor of large corporations or small startups. The study integrates the Technology Acceptance Model (TAM), the Unified Theory of Acceptance and Use of Technology (UTAUT), Adaptive Governance Theory, and the Dynamic Capabilities Framework to capture the multidimensional interplay among organizational, governance, and psychological factors. Unlike traditional models that focus solely on utility, this research posits that organizational conditions and governance conditions act as primary antecedents influencing employees’ perceived risk and trust, which in turn determine behavioral intention toward GenAI use. Eleven research propositions (P1–P11) were formulated to describe both direct and indirect causal relationships, highlighting the mediating roles of psychological safety mechanisms. The study contributes theoretically by extending technology acceptance models beyond cognitive dimensions to include governance and ethical oversight as structural determinants of employee behavior. It also introduces perceived governance as an integrative construct linking organizational readiness to trust and risk perception. Practically, the framework provides actionable guidance for mid-sized organizations to design adaptive governance systems, strengthen transparency, and foster trust-based cultures that encourage responsible GenAI adoption. By highlighting the balance between innovation and accountability, this conceptual model establishes a robust foundation for future empirical validation and policy development aimed at promoting sustainable and ethical AI integration in organizational contexts. Copyright (c) 2026 The Authors. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. https://creativecommons.org/licenses/by-nc-nd/4.0/ KW - adaptive governance KW - organizational readiness KW - perceived risk KW - SMEs KW - technology acceptance KW - trust CY - Thailand ER - TY - JOUR TI - Artificial Intelligence Capabilities, Sustainable Innovation and SMEs’ Resilience: A Serial-Parallel Mediation Model of Dynamic and Digital Platform Capabilities AU - Ragmoun W. AU - Aloulou W.J. PY - 2026 JO - Sustainability (Switzerland) VL - 18 IS - 9 SP - 4320 DO - 10.3390/su18094320 AB - The development of digital capability for resilience remains a major challenge for small and medium-sized enterprises (SMEs). Drawing on dynamic capability theory (DCT), this research develops and tests a model linking artificial intelligence capabilities (AIC), digital platform capabilities (DPC), sustainable innovation (SI) and SME resilience (R). The data were collected from 321 Saudi SMEs and analysed using SmartPLS for structural equation modelling. Findings confirm AIC’s mediating effect on both SI and R. Additionally, the results support the conclusion that the DPC developed through AI mediates only the relationship between AIC and SI. Moreover, the sequential parallel mediating model confirms the complementary roles of DPC and dynamic capabilities in linking AIC to SI and R. The validated model offers a practical framework for operations managers seeking to enhance resilience and sustainability and to extend this effect to the corresponding mechanisms linking AIC, SI, and R. In fact, this study clarifies a digital capability pathway in SMEs. In terms of managerial implications, it highlights the importance of AI-driven capabilities as a strategic priority. However, the use of cross-sectional data and a sectoral scope can limit the research contributions and suggest new directions for future longitudinal and cross-country research. © 2026 by the authors. KW - artificial intelligence capabilities KW - digital platform capabilities KW - dynamic capabilities KW - resilience KW - small and medium enterprises KW - sustainable innovation CY - Saudi Arabia ER - TY - JOUR TI - Quantitative evaluation of China’s artificial intelligence policies: A PMC index-based modeling approach AU - Liu X. AU - Zhuang X. AU - Zhang H. AU - Zhang H. AU - Wang Y. AU - Chen J. PY - 2026 JO - PLOS ONE VL - 21 IS - 2 February SP - e0335423 DO - 10.1371/journal.pone.0335423 AB - With the rapid development of artificial intelligence (AI), various countries have introduced policies to address the social, economic, and ethical challenges brought by technological advancements. This study systematically evaluates the effectiveness of China’s AI policies based on the Policy Model Consistency (PMC) method and conducts a comparative analysis with policies from developed countries in Europe and the United States. By constructing a multi-dimensional quantitative assessment system that encompasses indicators such as policy types, timeliness, content, fields, evaluation, tools, and effectiveness levels, this study fills a gap in the existing research on quantitative evaluation. Text mining and high-frequency word analysis revealed the core themes and focus areas of the policies, laying the groundwork for subsequent quantitative analysis. The study finds that China’s AI policies have achieved significant results in promoting technological innovation, industrial development, and social transformation; however, shortcomings remain in legal protection, ethical regulation, cross-domain collaboration, and sustainable development issues. Further cross-national comparisons indicate that there are differences between China and developed countries in Europe and the United States in terms of AI policy design and implementation, particularly regarding the application of policy tools and the driving forces behind international collaboration. Based on the empirical analysis results using the PMC index model, this study proposes targeted policy optimization suggestions aimed at enhancing policy execution and adaptability. This study not only provides an innovative framework for the quantitative evaluation of AI policies but also offers theoretical support for the collaborative development of global AI policies. © 2026 Liu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. KW - Artificial Intelligence KW - China KW - Europe KW - Humans KW - Models, Theoretical KW - Public Policy KW - United States KW - article KW - artificial intelligence KW - developed country KW - Europe KW - human KW - industrialization KW - open access publishing KW - quantitative analysis KW - sustainable development KW - timeliness KW - United States KW - China KW - public policy KW - theoretical model CY - China, South Korea ER - TY - JOUR TI - Artificial Intelligence and Strategic Decision-Making: Evidence from Entrepreneurs and Investors AU - Csaszar F.A. AU - Ketkar H. AU - Kim H. PY - 2024 JO - Strategy Science VL - 9 IS - 4 SP - 322 EP - 345 DO - 10.1287/stsc.2024.0190 AB - This paper explores how artificial intelligence (AI) may impact the strategic decision-making (SDM) process in firms. We illustrate how AI could augment existing SDM tools and provide empirical evidence from a leading accelerator program and a startup competition that current large language models can generate and evaluate strategies at a level comparable to entrepreneurs and investors. We then examine implications for the key cognitive processes underlying SDM—search, representation, and aggregation. Our analysis suggests that AI has the potential to enhance the speed, quality, and scale of strategic analysis, while also enabling new approaches, like virtual strategy simulations. However, the ultimate impact on firm performance will depend on competitive dynamics as AI capabilities progress. We propose a framework connecting AI use in SDM to firm outcomes and discuss how AI may reshape sources of competitive advantage. We conclude by considering how AI could both support and challenge core tenets of the theory-based view of strategy. Overall, our work maps out an emerging research frontier at the intersection of AI and strategy. Copyright: © 2024 The Author(s) KW - aggregation KW - artificial intelligence KW - experiments KW - representation KW - search KW - strategic decision-making KW - theory-based view CY - United States, Singapore ER - TY - JOUR TI - Impact of AI capability, digital strategy, and digital maturity on organisational performance AU - Kwiotkowska A. PY - 2025 JO - Engineering Management in Production and Services VL - 17 IS - 4 SP - 15 EP - 28 DO - 10.2478/emj-2025-0024 AB - In the face of the inevitable digitalisation of enterprises, limited research has investigated the impact of digital strategy, digital maturity, and AI capability on organisational performance. Drawing on the resource-based theory and recent work on AI in the organisational context, this research aims to uncover the configurations under which a firm’s digital strategy, digital maturity, and AI capability would jointly lead to higher performance. This study uses a unique fuzzy-set qualitative comparative analysis methodology to analyse data collected from 56 SMEs to investigate three domains of AI capability, along with digital strategy and digital maturity. The results suggest that high organisational performance does not depend on a single condition but rather on complex synergistic interactions among the studied conditions. The results indicate that three equifinal configurations lead to high performance of SMEs. The study suggests that AI technical resources are mandatory for any viable solution. This study provides pioneering insights into the empirical contributions of AI capability, digital strategy and digital maturity and their relationships to organisational performance in SMEs, by using a configurational approach. The adopted theoretical perspective addresses the need for a holistic approach to uncover the mechanisms underlying digital strategy and digital maturity in relation to AI capabilities in SMEs, and their mutual impact on organisational performance. These results have practical implications for decision-makers and owners of SMEs, providing new insights into the combination of factors that drive high performance. © 2025 Anna Kwiotkowska, published by Bialystok University of Technology. KW - artificial intelligence capability KW - digital transformation KW - firm performance KW - fsQCA KW - Artificial intelligence KW - Artificial intelligence capability KW - Condition KW - Digital strategies KW - Digital transformation KW - Firm Performance KW - FsQCA KW - Organizational context KW - Organizational performance KW - Performance KW - Resource-based theory KW - Decision making CY - Poland ER - TY - JOUR TI - Are projects ready for AI — or for the value it generates? AU - Mariani C. PY - 2026 JO - International Journal of Project Management SP - 102846 DO - 10.1016/j.ijproman.2026.102846 AB - Artificial intelligence is increasingly entering project environments. Recent research has emphasized the importance of developing organizational and project readiness for AI adoption. However, from a project management perspective, readiness to deploy AI technologies does not necessarily guarantee that these technologies will generate meaningful project value. Building on the recent debate on AI readiness, this essay argues that an important conceptual gap remains between the adoption of AI tools and the realization of value in projects. Drawing on the project value literature, this work introduces the notion of AI value generation, defined as the capability of project organizations to translate AI-enabled insights into value definition, value delivery, and value capture across multiple stakeholders. By distinguishing between AI readiness and AI value generation, this work highlights a new research frontier for understanding how artificial intelligence can effectively contribute to project success and stakeholder value creation. © 2026 Elsevier Ltd, APM and IPMA. KW - AI capabilities KW - AI value generation KW - Artificial intelligence KW - Engineering research KW - Project management KW - AI capability KW - AI Technologies KW - AI value generation KW - Organisational KW - Project environment KW - Project organization KW - Project values KW - Recent researches KW - Value captures KW - Value delivery KW - Artificial intelligence CY - Italy ER - TY - JOUR TI - Framing artificial intelligence in Chilean digital press before and after the launch of ChatGPT: From concern to optimism AU - Bucchi A. AU - Neira-Mellado C. AU - Sanchez-Sabate R. AU - Mora-Chepo M. PY - 2026 JO - PLOS ONE VL - 21 IS - 5 May SP - e0348680 DO - 10.1371/journal.pone.0348680 AB - This article examines the evolution of AI framing in Chilean national digital media before and after the launch of ChatGPT in 2022. Through topic modeling and emotion analysis of 1,466 articles published by six media outlets over periods that begin between 2000 and 2014, depending on the outlet, and extend in all cases until 2024, this study identifies significant thematic and affective changes. In the pre-ChatGPT period, coverage combined recognition of AI’s capabilities with concerns regarding labor displacement, governance, and human identity, frequently referencing global corporations and state institutions. In the post-ChatGPT period, topics became narrower in scope, with fewer actors and greater emphasis on universities and cultural organizations implementing AI. The focus shifted from the development of AI systems to their applications, predominantly framed in positive terms. At the same time, negative sentiment was largely confined to epistemological uncertainty. This transformation aligns with national surveys indicating growing public optimism, diverges from Global North debates centered on regulation and privacy, and converges with East Asian framings that are oriented toward innovation and creativity. These findings suggest that the launch of ChatGPT coincided with a reinforcement of a sociotechnical imaginary of AI as inevitable and beneficial, yet potentially limiting the diversity of public debate. © 2026 Bucchi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. CY - Chile ER - TY - JOUR TI - Safeguarding Intellectual Property in the Digital Age through Artificial Intelligence AU - Nagpal C.S. AU - Raviwada A.R. AU - Kumar D.G. PY - 2025 JO - Journal of Intellectual Property Rights VL - 30 IS - 6 SP - 708 EP - 717 DO - 10.56042/jipr.v30i6.12793 AB - The landscape of Intellectual Property is facing various challenges with the onset of rapid digitalization. The current Intellectual Property (henceforth ‘IP’ in short) protection is dependent on the traditional law for enforcement and implementation of the Intellectual Property Rights (henceforth ‘IPR’ in short) Holder’s rights, but the significant technological advancements and threats from cyberspace are highlighting threats and challenges towards IP protection, leading IP experts to delve into the ramifications of Artificial Intelligence (henceforth ‘AI’ in short) assisted problemsolving for IP related issues in cyberspace. Whereas the world is looking at a cohesive effort to ideally eradicate the issues that lead to cybersecurity breaches, a comprehensive and sustained effort towards addressing IP-related cyber threats is also being explored. At present, there are no tangent solutions for this. The authors attempt to address the potential of AI-assisted solutions to target IP infringements in cyberspace. By responsibly leveraging AI capabilities, we can fortify IP protection in the digital era and foster innovation and creation in a cyber-secure environment. © 2025, National Institute of Science Communication and Policy Research. All rights reserved. KW - AI KW - Cybersecurity KW - Cyberthreat KW - Digital KW - Internet KW - IPR CY - India ER - TY - JOUR TI - Artificial Intelligence-Ready Doctor of Nursing Practice Education: A Competency-Based Approach AU - Quattrini V. AU - Taylor L. AU - Lynch-Smith D. AU - Hemphill T. AU - Gibson T. AU - Ford A. AU - Fox E. AU - Roesch A. AU - Turner T. AU - Hilliard W. AU - Anthamatten A. PY - 2026 JO - Journal of Doctoral Nursing Practice VL - 19 IS - 1 SP - 11 EP - 23 DO - 10.1891/JDNP-2025-0051 AB - Background: Artificial intelligence (AI) is rapidly transforming nursing education and clinical practice. As AI becomes increasingly embedded in health care delivery, integrating AI competencies into Doctor of Nursing Practice (DNP) education is essential to prepare advanced practice registered nurses (APRNs) to utilize these tools effectively and ethically. Objective: This manuscript examines the integration of AI into DNP education, addressing policy implications, best practices, and strategies to prepare APRNs for leadership in AI-enhanced environments. Methods: A review of institutional innovations and faculty strategies demonstrates the application of AI in nursing education through adaptive learning platforms, virtual simulations, predictive analytics, and AI-driven clinical decision support systems. Case exemplars highlight implementation approaches and educational outcomes. Results: AI-enhanced tools have demonstrated several benefits, such as improved student engagement, individualized learning, and enhanced clinical reasoning. Case-based reflections revealed enhanced decision-making, mentorship, and student competency tracking. Limitations and potential risks of AI are also identified. Key guiding principles include evaluating existing competencies within the context of AI capabilities, defining emerging AI needs, supporting faculty development through AI training, and advancing policies for responsible and ethical AI use. Conclusions: The nursing profession is well recognized for its innovative approach to adopting new technologies. Embedding AI into DNP education requires intentional curricular reform, strong leadership support, and ethical oversight to ensure sustainable adoption. Nursing faculty must champion the strategic and responsible use of AI to prepare APRNs for evidence-based, technology-driven practice. DNP-prepared nurses, with their expertise in quality improvement and ethical practice, are uniquely positioned to shape the development and implementation of AI tools. © 2026 Springer Publishing Company. KW - advanced practice registered nurses (APRNs) KW - artificial intelligence (AI) KW - Doctor of Nursing Practice (DNP) KW - nursing education KW - Advanced Practice Nursing KW - Artificial Intelligence KW - Clinical Competence KW - Competency-Based Education KW - Curriculum KW - Education, Nursing, Graduate KW - Humans KW - advanced practice nursing KW - artificial intelligence KW - clinical competence KW - competency-based education KW - curriculum KW - education KW - human KW - nursing education KW - organization and management CY - United States ER - TY - JOUR TI - How will AI change intelligence and decision-making? AU - Barneaa A. PY - 2020 JO - Journal of Intelligence Studies in Business VL - 10 IS - 1 SP - 75 EP - 80 DO - 10.37380/JISIB.V1I1.564 AB - The world is facing a rapid pace of changes with a heightened sense of uncertainty, ambiguity, and complexity in both government and business landscapes. New threats and major changes in the world order are creating an external environment that demands closer monitoring and greater anticipatory and predictive skills. Deeper analysis and speed of action are becoming more important for agile organizations and governments. The needs to upgrade the capabilities of intelligence analysts, mostly in strategic intelligence, have been known for quite a long time. Scholars who are looking into intelligence failures1 and other major national security2 and business3 events when decision-makers were not warned in time, seek expert tools and methodologies to avoid these failures4. Management is constantly concerned, aspiring to receive better decisions by relying on solid analysis in order to better understand the challenges ahead5. The current direction is in the same direction, while new emerging technologies enable theory and practice to move forward. Artificial intelligence (AI) capabilities definitely are jumping two stairs up. It looks that through new AI tools, the value of humans will not become redundant but rather improve its outcomes by relying on better intelligence for their decisions. © 2020 Halmstad University. KW - Artificial intelligence (AI) KW - Competition KW - Competitive advantage KW - Decision-making KW - Intelligence failures KW - Prediction KW - Strategic surprises CY - Israel ER - TY - JOUR TI - Artificial intelligence and corporate ESG performance AU - Li J. AU - Wu T. AU - Hu B. AU - Pan D. AU - Zhou Y. PY - 2025 JO - International Review of Financial Analysis VL - 102 SP - 104036 DO - 10.1016/j.irfa.2025.104036 AB - This study examined how artificial intelligence (AI) capabilities strengthen corporate environmental, social, and governance (ESG) performance while focusing on the mediating role of green resilience and the moderating effect of organizational resilience. AI has transformative potential for ESG performance; however, its role in emerging markets remains underexplored. While AI can optimize resource use, improve workplace safety, and enhance governance through transparency, challenges such as data limitations, infrastructure gaps, and ethical issues may hinder its impact. Bridging this gap requires focused research on how AI capabilities drive sustainable outcomes in these markets, identifying practical tools, and fostering supportive policies. We employed robust statistical techniques to establish reliable findings from a comprehensive dataset of Chinese-listed companies from 2011 to 2022. The findings indicate that AI capabilities significantly strengthen ESG performance. The relationship was facilitated through green innovation initiatives. Organizational resilience enhances AI's positive impact on ESG performance, especially in technology-intensive industries. However, the influence varies significantly by context, with stronger effects observed in nonhigh-polluting sectors and state-owned enterprises, highlighting the need for tailored approaches to maximize sustainable outcomes. Our findings augment the theoretical understanding of technology-driven sustainability by elucidating how AI capabilities strengthen ESG performance through innovation pathways. We identified key organizational factors, such as resilience and innovation capacity, as well as contextual factors, including industry type, regulatory frameworks, and ownership structures, that influence the relationship between AI and ESG performance. These findings provide valuable insights for organizations in emerging markets aiming to leverage AI for enhanced sustainability. © 2024 KW - Artificial intelligence KW - Corporate sustainability KW - ESG performance KW - Green innovation KW - Organizational resilience CY - China, United States, United Kingdom ER - TY - JOUR TI - Artificial intelligence in digital marketing automation: Enhancing personalization, predictive analytics, and ethical integration AU - Islam M.A. AU - Fakir S.I. AU - Masud S.B. AU - Hossen M.D. AU - Islam M.T. AU - Siddiky M.R. PY - 2024 JO - Edelweiss Applied Science and Technology VL - 8 IS - 6 SP - 6498 EP - 6516 DO - 10.55214/25768484.v8i6.3404 AB - Artificial Intelligence (AI) is revolutionizing digital marketing automation by enhancing efficiency, personalization, and predictive capabilities. This study examines the role of AI in transforming marketing practices, focusing on its applications, benefits, ethical considerations, and future directions. By leveraging AI tools such as predictive analytics, NLP, and chatbots, businesses can achieve improved customer segmentation, content personalization, and campaign optimization in marketing strategies. Secondary data from journals, articles, and conference papers were synthesized to provide insights into AI's impact on digital marketing automation. A systematic literature review utilizing the PRISMA methodology initially identified 2,850 records from database searches. Following the removal of duplicates and non-relevant studies, 1,035 records were screened for eligibility based on defined criteria, resulting in the inclusion of 150 relevant studies and 25 high-quality reports for detailed analysis. This robust approach ensured the inclusion of high-quality research, minimizing biases. The findings reveal that AI enhances digital marketing by streamlining processes, automating repetitive tasks, and delivering hyper-personalized customer experiences. Predictive analytics helps anticipate consumer behavior, while chatbots improve real-time customer engagement. However, challenges such as data privacy, algorithmic bias, and the high costs of AI adoption persist. AI adoption allows businesses to make data-driven decisions, improve customer retention, and maximize return on investment. Ethical AI practices, such as transparency and algorithm fairness, are essential for maintaining consumer trust. The study primarily focuses on existing literature, with limited empirical validation. Future research should explore long-term effects of AI-driven marketing on consumer behavior and investigate its integration with emerging technologies like the Internet of Things (IoT) and blockchain. Additionally, tailored AI solutions for SMEs and under-researched areas, such as B2B marketing, are critical for inclusive growth. © 2024 by the authors. KW - Artificial intelligence (AI) KW - Chatbots and NLP KW - Customer personalization KW - Digital marketing automation KW - Ethical AI practices KW - Marketing innovation KW - Predictive analytics KW - PRISMA CY - United States, Bangladesh ER - TY - JOUR TI - Unveiling Market Dynamics through Machine Learning: Strategic Insights and Analysis AU - Gaikwad V.S. AU - Deore S.S. AU - Poddar G.M. AU - Patil R.V. AU - Hirolikar D.S. AU - Borawake M.P. AU - Swarnkar S.K. PY - 2024 JO - International Journal of Intelligent Systems and Applications in Engineering VL - 12 IS - 14s SP - 388 EP - 397 AB - Due to their extensive knowledge and potential to change the game, artificial intelligence (ML) and strategic analysis have become significant players in more competitive and global markets. The article "Unveiling Market Dynamics through Machine Learning: Strategic Insights and Analysis" provides the first in-depth analysis of the strong connection between machine learning and market analysis, illustrating how these two fields can collaborate to understand the complex market dynamics. Thanks to this research, businesses may now analyse complex patterns, hidden trends, and untapped opportunities in complicated market economies. He accomplishes this with the help of AI's capabilities. Another essential element of this relationship is emotion analysis, which makes use of the deep learning and natural language processing to examine public sentiment and provide vital information for improving marketing and product development strategies. The ability of ML to recognise fresh opportunities and niche markets gives it a competitive advantage. Furthermore, it excels at proactively identifying anomalies, cracks, and risks. This study highlights the integration of various data sources and the growing significance of ethical considerations in addition to providing a broad overview of ML's applications in market analysis. Thi s research expands our understanding of the potential for data-driven decision-making as we navigate the intersection of ML and strategic market analysis. It also provides a road map for organisations looking to harness ML's transformative power to make knowledgeable, quick, and strategic decisions in today's dynamic business environment. © 2024, Ismail Saritas. All rights reserved. KW - Data-driven Decision-making KW - Machine Learning KW - Market Dynamics KW - Predictive Modeling KW - Strategic Insights CY - India ER - TY - JOUR TI - Sustainable development with Artificial Intelligence: Examining the absorptive capacity pathways to green innovation AU - Zhang W. AU - Xu H. AU - Grebinevych O. AU - Chen M. PY - 2025 JO - Journal of Environmental Management VL - 381 SP - 125219 DO - 10.1016/j.jenvman.2025.125219 AB - Artificial intelligence holds a lot of promise in tackling global societal challenges. However, there is still no consensus on how companies can effectively harness AI to promote green innovation (GI). We develop a moderated mediation model grounded in absorptive capacity theory to fill this research gap. In this paper, our empirical study based on data drawn from 361 Chinese firms reveals the significant roles of two critical capacities, potential absorptive capacity (PAC) and realized absorptive capacity (RAC), in fostering a positive relationship between AI capabilities and GI. Notably, this study's results show that environmental heterogeneity (EH) amplifies the mediating effects of PAC and RAC. This implies that companies with access to Artificial intelligence (AI) capabilities will likely learn and absorb available information and knowledge outside the organizations better. This improves GI, mainly when EH levels are high. The present work advances the research by addressing how AI impacts GI through different mediating and moderating factors. It can help inform companies wanting to achieve GI amid sustainability imperatives. © 2025 Elsevier Ltd KW - Absorptive capacity KW - AI capabilities KW - Environmental heterogeneity KW - Green innovation KW - Artificial Intelligence KW - China KW - Conservation of Natural Resources KW - Sustainable Development KW - Green economy KW - Absorptive capacity KW - Artificial intelligence capability KW - Chinese firms KW - Empirical studies KW - Environmental heterogeneity KW - Green innovations KW - Learn+ KW - Mediating effect KW - Moderating factors KW - Research gaps KW - artificial intelligence KW - heterogeneity KW - innovation KW - knowledge KW - sustainable development KW - absorption KW - article KW - artificial intelligence KW - empiricism KW - human KW - sustainable development KW - China KW - environmental protection KW - Green development CY - China, France ER - TY - JOUR TI - Artificial intelligence and the impact of the EU AI Act in business organizations AU - Cors M.S. AU - Thiébaut R. PY - 2025 JO - AI Magazine VL - 46 IS - 4 SP - e70039 DO - 10.1002/aaai.70039 AB - Artificial intelligence (AI) is transforming industries worldwide, and the e-commerce sector is at the forefront of leveraging its capabilities to drive innovation and efficiency. The paper explores the integration of artificial intelligence in e-commerce, focusing on the ethical and regulatory implications introduced by the EU AI Act. This legislative framework aims to ensure the responsible deployment of AI by classifying AI systems into risk categories and imposing compliance requirements. It also underscores both the opportunities and challenges that AI presents to businesses, particularly in enhancing consumer experiences through automation and data-driven decision-making processes. The paper provides a comprehensive review of the AI landscape in Europe, analyzing the impact of the EU AI Act, particularly on small and medium-sized enterprises and startups. Through a mixed-methods approach, the study investigates how regulatory compliance may influence business innovation, market competitiveness, and consumer trust. The recommendations proposed aim to develop a trustworthy AI ecosystem that could stimulate long-term growth and enhance the global positioning of small European businesses. © 2025 The Author(s). AI Magazine published by John Wiley & Sons Ltd on behalf of Association for the Advancement of Artificial Intelligence. KW - Artificial intelligence KW - Competition KW - Decision making KW - Electronic commerce KW - Marketplaces KW - Artificial intelligence systems KW - Business innovation KW - Business organizations KW - Data driven decision KW - Decision-making process KW - E- commerces KW - Legislative frameworks KW - Mixed method KW - Risk categories KW - Small and medium-sized enterprise KW - Regulatory compliance CY - Spain, United Arab Emirates ER - TY - JOUR TI - The influence of individuals’ capability to use generative AI on their idea generation: the mediating role of cognitive information-processing styles AU - Held P. AU - Heubeck T. AU - Meckl R. PY - 2025 JO - European Journal of Innovation Management VL - 28 IS - 10 SP - 5376 EP - 5399 DO - 10.1108/EJIM-06-2025-0711 AB - Purpose – This study investigates how individuals’ capability to use generative artificial intelligence (GenAI) influences their idea generation and explores the cognitive mechanisms underlying this relationship. Drawing on cognitive experiential theory, which posits that individuals rely on two distinct and stable information processing styles (rational and experiential), this study examines how these styles mediate the link between GenAI usage capability and idea generation and all underlying relationships between these constructs. Design/methodology/approach – This study employs a quantitative research design based on survey data from 399 business consultants located in Germany, Austria, and Switzerland at a leading global consultancy. Partial least squares structural equation modeling (PLS-SEM) is applied to test the hypothesized structural relationships. Findings – The findings demonstrate that (1) individuals’ capability to use GenAI enhances their idea generation, (2) individuals’ capability to use GenAI influences both information processing styles, (3) rational information processing style enhances idea generation and not experiential information processing and (4) significant mediation effect of individuals’ tendency to rely on the rational system that translates GenAI usage capability into idea generation. Originality/value – This study enriches GenAI research in innovation management by identifying individuals’ capability to use GenAI as a critical antecedent of idea generation. This capability perspective complements recent studies focusing on the extent, frequency or purpose of GenAI usage and its influence on creative outputs. © 2025 Emerald Publishing Limited KW - AI capability KW - Cognitive experiential theory KW - Generative AI KW - Generative AI usage KW - Idea generation KW - Innovation management CY - Germany ER - TY - JOUR TI - Managing artificial intelligence across functions for enhanced retail firm performance AU - Cao L. AU - Yang J. AU - Chen Y.-T. PY - 2025 JO - European Management Review DO - 10.1111/emre.70042 AB - Firms are increasingly deploying AI across business functions, yet the core challenge lies not in adoption itself, but in integrating these applications to achieve coherent, firm-level outcomes. Despite growing interest, prior research has largely overlooked the dynamic interdependencies, unpredictable interactions, and organization-wide effects that shape how AI-enabled functions collectively drive performance. Addressing this gap, we draw on a business process perspective and dynamic capability theory to develop a multi-level framework of AI integration at the task, function, and firm levels. Using a sequential mixed-methods design, we first construct a validated measurement instrument based on grounded analysis of 6,519 AI-related documents from 37 retailers. We then apply fuzzy-set qualitative comparative analysis (fsQCA) to survey data from 140 executives to identify configurations of AI-enabled functions associated with efficiency, innovation, or both. The results show that superior performance stems not from isolated AI uses but from synergistic combinations—particularly those involving customer service and cybersecurity—interacting with other functions. These configurations appear to give rise to emergent capabilities such as adaptive learning, predictive analytics, and uncertainty mitigation, enabling firms to reconcile exploitation and exploration. This study offers a dynamic, process-based view of AI capability and provides strategic guidance for designing ambidextrous AI portfolios in retail. © 2025 European Academy of Management (EURAM). KW - AI implementation KW - artificial intelligence KW - business process management KW - innovation configuration KW - organizational efficiency KW - retail performance CY - France ER - TY - JOUR TI - Generative AI on innovation performance of construction enterprises: the role of knowledge-based dynamic capabilities and enterprise AI capabilities AU - Qiao S. AU - Zhiwei L. AU - Jie W. AU - Yuxi M. AU - Guo Z. AU - Han W. PY - 2025 JO - Engineering, Construction and Architectural Management DO - 10.1108/ECAM-01-2025-0051 AB - Purpose: The aim of this study was to investigate the associations among generative artificial intelligence (AI), knowledge-based dynamic capabilities, enterprise AI capabilities (EAIC) and innovation performance of the construction enterprises. Design/methodology/approach: The structural equation model was used in this study. First, the hypothesis of the relationship between generative AI, knowledge-based dynamic capabilities, EAIC and innovation performance was proposed based on the previous relevant literature; then, the research data were collected by 310 questionnaires; finally, these hypotheses were tested through data analysis. Findings: Generative AI positively influenced knowledge-based dynamic capabilities and innovation performance of the construction enterprises; knowledge-based dynamic capabilities had a mediating effect on the influence of generative AI on innovation performance of the construction enterprises; EAIC had a positive moderating effect on the influence of generative AI on innovation performance of the construction enterprises. Originality/value: In this study, knowledge-based dynamic capability and EAIC are introduced into the relationship model between generative AI and innovation performance of the construction enterprises, and an integrated model is proposed about the relationship between these factors. This study enriches the research content of AI application, dynamic capability and innovation management. The research results are conducive to generative AI in the innovation process and the formulation of innovation strategies. © 2025, Emerald Publishing Limited. KW - Construction enterprises KW - Enterprise AI capabilities KW - Generative AI KW - Innovation performance KW - Knowledge-based dynamic capabilities KW - Construction enterprise KW - Design/methodology/approach KW - Dynamics capability KW - Enterprise artificial intelligence capability KW - Generative artificial intelligence KW - Innovation performance KW - Knowledge based KW - Knowledge-based dynamic capability KW - Research data KW - Structural equation models KW - Knowledge based systems CY - China ER - TY - JOUR TI - Transforming weed management in sustainable agriculture with artificial intelligence: A systematic literature review towards weed identification and deep learning AU - Vasileiou M. AU - Kyrgiakos L.S. AU - Kleisiari C. AU - Kleftodimos G. AU - Vlontzos G. AU - Belhouchette H. AU - Pardalos P.M. PY - 2024 JO - Crop Protection VL - 176 SP - 106522 DO - 10.1016/j.cropro.2023.106522 AB - In the face of increasing agricultural demands and environmental concerns, the effective management of weeds presents a pressing challenge in modern agriculture. Weeds not only compete with crops for resources but also pose threats to food safety and agricultural sustainability through the indiscriminate use of herbicides, which can lead to environmental contamination and herbicide-resistant weed populations. Artificial Intelligence (AI) has ushered in a paradigm shift in agriculture, particularly in the domain of weed management. AI's utilization in this domain extends beyond mere innovation, offering precise and eco-friendly solutions for the identification and control of weeds, thereby addressing critical agricultural challenges. This article aims to examine the application of AI in weed management in the context of weed detection and the increasing impact of deep learning techniques in the agricultural sector. Through an assessment of research articles, this study identifies critical factors influencing the adoption and implementation of AI in weed management. These criteria encompass factors of AI adoption (food safety, increased effectiveness, and eco-friendliness through herbicides reduction), AI implementation factors (capture technology, training datasets, AI models, and outcomes and accuracy), ancillary technologies (IoT, UAV, field robots, and herbicides), and the related impact of AI methods adoption (economic, social, technological, and environmental). Of the 5821 documents found, 99 full-text articles were assessed, and 68 were included in this study. The review highlights AI's role in enhancing food safety by reducing herbicide residues, increasing effectiveness in weed control strategies, and promoting eco-friendliness through judicious herbicide use. It underscores the importance of capture technology, training datasets, AI models, and accuracy metrics in AI implementation, emphasizing their synergy in revolutionizing weed management practices. Ancillary technologies, such as IoT, UAVs, field robots, and AI-enhanced herbicides, complement AI's capabilities, offering holistic and data-driven approaches to weed control. Additionally, the adoption of AI methods influences economic, social, technological, and environmental dimensions of agriculture. Last but not least, digital literacy emerges as a crucial enabler, empowering stakeholders to navigate AI technologies effectively and contribute to the sustainable transformation of weed management practices in agriculture. © 2023 KW - Agroecology KW - Artificial intelligence KW - Deep learning KW - Precision agriculture KW - Sustainability KW - Weed management KW - agroecology KW - alternative agriculture KW - artificial intelligence KW - food safety KW - herbicide KW - paradigm shift KW - weed control CY - Greece, France, United States ER - TY - JOUR TI - From Satisfaction to Strategy: A Structural Model for Implementing Generative AI Chatbots in Campus Bureaucracy Toward Sustainable Service Innovation AU - Sofiyah F.R. AU - Dilham A. AU - Lubis M.A. AU - Lubis A.S. AU - Marpaung J.L. AU - Hayatunnufus PY - 2025 JO - Mathematical Modelling of Engineering Problems VL - 12 IS - 9 SP - 3013 EP - 3024 DO - 10.18280/mmep.120906 AB - The growing demand for digital transformation in higher education has highlighted the limitations of conventional bureaucratic systems. This study aims to develop and evaluate a structural model for implementing generative AI chatbots in campus administration, focusing on their ability to deliver sustainable service innovation. Integrating behavioral modeling and computational logic, the research adopts a mixed-methods approach. A questionnaire was distributed to 300 respondents, and data were analyzed using Partial Least Squares Structural Equation Modeling (SmartPLS). This study integrates 11 latent constructs — including AI capability, system usability, information quality, service availability, privacy and security, institutional support, user satisfaction, service experience, customer relationship management (CRM), administrative efficiency, and digital literacy (as a moderator)—into a validated structural model. The findings reveal that all primary structural paths are statistically significant (p < 0.001). Notably, customer relationship management (CRM) demonstrates a very strong effect on Administrative Efficiency (β = 0.833, p < 0.001; R2= 0.694), confirming its central role in translating satisfaction and service experience into organizational outcomes. In addition, the study introduces an operational AI algorithm and a multi-criteria optimization model that simulate trade-offs between CRM and efficiency. These computational insights provide university leaders with practical decision-making tools for aligning chatbot deployment with strategic goals such as cost savings, service scalability, and student retention. © 2025 The authors. KW - campus bureaucracy KW - chatbot KW - customer relationship management (CRM) KW - generative AI KW - SmartPLS KW - sustainable innovation CY - Indonesia ER - TY - JOUR TI - AI Capability, Digital Agility, and Strategic Innovation: The Moderating Role of Government Intervention and Competitive Intensity AU - Liu D.Y. AU - Zhang J.Z. AU - Sun J.J. AU - Dai B. PY - 2026 JO - Thunderbird International Business Review DO - 10.1002/tie.70136 AB - This study investigates how artificial intelligence (AI) capability and digital agility shape strategic innovation in firms, and how government intervention and competitive intensity condition these effects. Using survey data from 310 Chinese firms that have adopted AI technologies, we employ structural equation modeling to test the proposed hypotheses and estimate the relationships among the focal constructs. The results show that AI capability and digital agility are both positively associated with strategic innovation. Moreover, government intervention strengthens the positive effect of AI capability on strategic innovation, whereas competitive intensity amplifies the positive effect of digital agility on strategic innovation. These findings indicate the complementary roles of internal digital capabilities and external contextual forces in enabling strategic innovation in digitally intensive environments. By integrating AI capability and digital agility within a moderated framework, this study advances strategic innovation research by clarifying when and how digital capabilities translate into innovation outcomes. The study also offers actionable implications for managers and policymakers seeking to foster strategic innovation through AI deployment and organizational agility across varying institutional and competitive conditions. © 2026 Wiley Periodicals LLC. KW - AI capability KW - competitive intensity KW - digital agility KW - government intervention KW - strategic innovation CY - Australia, United States, New Zealand ER - TY - JOUR TI - The Uses of Belief: A Psychoanalytic-Feminist Critique of ‘AI Ethics' AU - Jeon W. PY - 2026 JO - Australian Feminist Studies DO - 10.1080/08164649.2026.2658028 AB - Postwar cybernetics transformed information from a measure of relation into a medium of regulation, reconfiguring communication as a problem of prediction and control. This article traces how that shift produced the modern ‘user’: a calculable figure whose rationality and adaptability were built into feedback systems linking psychology, computation, and governance. Through a close reading of Joseph Weizenbaum’s ELIZA (1966) and his reflections ‘On the Impact of the Computer on Society’ (Hamburg, 1971; Science, 1972), the paper shows how simulated dialogue substitutes plausibility for understanding, and how ethical critique becomes absorbed into the structures it seeks to address. Feminist and psychoanalytic perspectives clarify why users’ projections and affective investments sustain the appearance of machine intelligence. Contemporary AI ethics inherits these problems in translating the tension between responsibility and innovation into matters of governance and design. Recovering Weizenbaum’s ethical insight, the article argues that judgment cannot be automated without forfeiting the capacity for reflection that makes it ethical–and that avowing, rather than resolving, the contradiction between knowledge and control remains the task of critical thought in the age of AI. © 2026 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. KW - chatbots KW - design KW - Generative AI KW - language KW - large language models KW - use KW - user-interface CY - United States ER - TY - JOUR TI - Artificial Intelligence in Cyberspace: Between Danger and Innovation AU - Dumchikov M. AU - Maletova O. AU - Mishchenko T. AU - Lytvynenko Y. PY - 2025 JO - Revista de Direito, Estado e Telecomunicacoes VL - 17 IS - 1 SP - 117 EP - 142 DO - 10.26512/lstr.v17i1.53386 AB - [Purpose] The purpose of the article is to examine the impact of artificial intelligence on cybersecurity and to explore both the risks and the opportunities it presents. The article examines the primary forms of criminal use of AI in cyberspace, as well as develops effective methodologies for its application in the process of investigation, prevention, and analysis of these socially dangerous actions. [Methodology/approach/design] The authors employed an interdisciplinary approach combining methods from legal science, economics, and information technology in their work. Numerous scientific works on AI's characteristics, its role in digitization, and its use in criminal investigations have been noted. These studies offer suggestions, particularly for law enforcement agencies, financial institutions, and cybersecurity organizations. However, they are mainly theoretical and overlook new cyber threats and techniques used by cybercriminals with AI. In contrast, the authors analyzed illegal activity websites and forums, including cyberspace, using AI capabilities. They used cognitive methods to analyze how AI is used in cybercrimes, both as an auxiliary and primary tool. Content analysis methodology facilitated a systematic review of web content related to AI-enabled cybercrimes. The comparative-legal method compared AI-enabled cybercrimes to similar crimes without AI. Reviewing scientific articles, books, and conference proceedings helped understand AI, cybersecurity, and law enforcement. Case studies examined specific instances of AI in cybercrime, aiding in real-life prevention and investigation. The systematic method ensured a comprehensive examination of previous studies, identifying trends, challenges, and development prospects in AI and cybersecurity. By adopting this multifaceted and innovative approach, the authors were able to provide a more comprehensive and nuanced understanding of the emerging landscape of AI-assisted cybercrime. This research not only contributes to the academic discourse but also offers practical insights for law enforcement agencies, policymakers, and cybersecurity professionals working to combat these evolving threats. [Findings] The utilization of AI in the realm of cybercrimes unveils new prospects for the criminals themselves, as well as offers opportunities for effective combat and investigation of these crimes through AI. It is emphasized that the application of AI in crime investigations aids in refining the processes of detection and analysis of cybercriminal activities, allowing for quicker identification of anomalies and response to them. However, despite AI's significant potential, its use necessitates a cautious approach and the development of ethical and legal standards. This is essential to avoid possible negative consequences and ensure balanced development in cybersecurity. © 2025 Universidade de Brasilia. All rights reserved. KW - Artificial intelligence KW - Cybercrime KW - Cybersecurity KW - Information Technology KW - Innovation in Cybersecurity CY - Ukraine ER - TY - JOUR TI - Adoption of AI in human capital development: a multi-industry perspective AU - Behera M.K. AU - Behera R.K. AU - Bala P.K. PY - 2025 JO - Journal of Enterprise Information Management SP - 1 EP - 27 DO - 10.1108/JEIM-06-2025-0490 AB - Purpose – Employees are invaluable resources that are of significant value to a firm when it aims to perform human capital development (HCD). Eventually, any firm intending to preserve a competitive advantage over rivals must invest in HCD. Therefore, to gain a competitive edge in the digital age and to yield net benefits, this study endeavours to define the research problem, i.e. should a firm adopt artificial intelligence (AI) in HCD? For the investigation, it explores various disciplines of HCD, validates whether AI has capabilities to meet the net benefits of HCD, and measures the adoption intention. Design/methodology/approach – The source data were collected from 315 individuals through a survey with a five-point Likert-scale questionnaire. The empirical analysis is accomplished using covariance-based structural equation modelling. Findings – AI capability plays a positive role in HCD disciplines, including talent management, change management, performance management, human resource management and strategic planning. Subsequently, each AI-enabled HCD discipline positively influences net benefits. Eventually, the net benefits of AI-enabled HCD positively influence AI adoption intention. Moreover, organisational culture moderates the relationship between net benefits and AI adoption intention. Originality/value – This study demonstrates an empirical analysis of the adoption intention of AI in HCD by presenting the theoretical underpinnings of HCD disciplines, and subsequently, building and validating the structural relationships amongst HCD disciplines, net benefits and AI adoption intention with organisational culture as moderator. In this vein, the study offers multifaceted advantages of AI-enabled HCD. © 2025 Emerald Publishing Limited KW - Artificial intelligence KW - Human capital development KW - Net benefits KW - Organisational culture KW - Technology adoption CY - India ER - TY - JOUR TI - Revisiting the Six Human-Centered Artificial Intelligence Grand Challenges in the Age of Generative AI AU - Winslow B. AU - Ozmen Garibay O. AU - Goyal T. AU - Koon S. AU - Margetis G. AU - Salvendy G. AU - Shneiderman B. AU - Tayebi A. AU - Vardoulakis L. PY - 2026 JO - International Journal of Human-Computer Interaction VL - 42 IS - 7 SP - 4697 EP - 4738 DO - 10.1080/10447318.2026.2641703 AB - Generative AI (GenAI) has shifted AI capabilities from discriminative prediction to creative interaction, offering opportunities to augment productivity and innovation. However, realizing these benefits requires navigating risks where development outpaces governance. This article revisits the Six Human-Centered AI (HCAI) Grand Challenges to analyze their relevance in the generative era. Critical new requirements are identified: preserving human autonomy, ensuring operational safety against non-deterministic outputs, and navigating complex intellectual property landscapes. These findings are synthesized into an updated, actionable research agenda for each challenge, serving as a call to action to operationalize these principles. By shifting focus from risk mitigation to human empowerment, this agenda establishes human-centeredness as the organizing principle for a future where GenAI enhances human agency, dignity, and collective flourishing. © 2026 The Author(s). Published with license by Taylor & Francis Group, LLC. KW - evaluation KW - generative artificial intelligence (GenAI) KW - governance KW - Human-centered artificial intelligence (HCAI) KW - responsibility KW - Creatives KW - Deterministics KW - Evaluation KW - Generative artificial intelligence KW - Governance KW - Grand Challenge KW - Human-centered artificial intelligence KW - Operational safety KW - Property KW - Responsibility KW - Artificial intelligence CY - United States, Greece ER - TY - JOUR TI - Influence of AI on CE: underlying roles of network centrality and green product (process) innovations in manufacturing industry AU - Fawad Sharif S.M. AU - Wenping W. AU - Guo M. AU - Alghamdi O. AU - Huang Y. PY - 2026 JO - Journal of Strategy and Management DO - 10.1108/JSMA-05-2025-0152 AB - Purpose – Literature reviews unanimously report an affirmative influence of artificial intelligence (AI) capabilities on circular economy practices (CE), whereas empirical investigations, although scarce, do not align with these assertions and exhibit conflicting findings. This study aims to understand the mechanism behind the AI and CE relationship through mediation of green product (process) innovation and moderation of network centrality. Design/methodology/approach – This study integrates dynamic capability theory (DCT) with network theory to examine the moderated-mediation effect of AI on CE. The online survey raised 224 valid responses, which were examined through partial least squares structural equation modeling. Findings – Green product (process) innovation completely mediates between AI and CE. Network centrality positively moderates the mediation effects, such that mediation lowers as the firm becomes more central. Originality/value – This study views AI as a lower-order dynamic capability and integrates DCT with network theory to illustrate how higher-order capabilities, i.e. green innovation and CE, can be availed. Besides, we provide empirical support to prevailing literature reviews by presenting a novel explanation to the AI and CE relationship. © Emerald Publishing Limited KW - Artificial intelligence KW - Circular economy KW - Dynamic capability theory KW - Green innovation KW - Network theory CY - China, Saudi Arabia ER - TY - JOUR TI - What To Do About HAL-Market and Governmental Approaches to Regulating Artificial Intelligence AU - Myers G. PY - 2025 JO - Louisiana Law Review VL - 86 IS - 1 SP - 167 EP - 203 AB - This article explores the challenges and risks associated with extensive government regulation of artificial intelligence (AI), arguing that such an approach is both impractical and detrimental to innovation. AI's rapid evolution far outpaces legislative and regulatory processes, making broad command-and-control efforts ineffective, burdensome, and likely to stifle competition, particularly among startups and open-source developers. Overregulation could also hinder the United States' ability to compete globally, especially against foreign nations that aggressively invest in AI advancement and are likely to have few if any constraints on its development. Examples of expansive regulatory efforts include the European Union's AI Act, the now-repealed Biden Administration Executive Order on AI, and multiple state legislative proposals. Rather than relying on rigid government oversight, existing legal frameworks-including common law and statutory liability doctrines, consumer protection laws, and antidiscrimination statutes-can address Al-related risks without impeding progress. Additionally, industry-led standards-such as the National Institute of Standards and Technology's (NIST) and the International Organization for Standardization's AI risk management frameworks- offer adaptive, market-driven solutions for AI governance. Private ordering mechanisms like third-party audits, liability frameworks, and insurance-based oversight further ensure responsible AI development while allowing for flexibility as the technology advances. By leveraging these existing legal and industry-driven approaches, the United States can maintain its leadership in AI development, balancing safety and accountability without hindering technological progress. © 2025 The LSU Scholarly Repository. All rights reserved. CY - United States ER - TY - JOUR TI - Integrating AI and ESG in digital platforms: New profiles of platform-based business models AU - Nevi G. AU - Montera R. AU - Cucari N. AU - Laviola F. PY - 2025 JO - Journal of Engineering and Technology Management - JET-M VL - 78 SP - 101913 DO - 10.1016/j.jengtecman.2025.101913 AB - The integration of artificial intelligence (AI) into digital platforms is transforming the way businesses tackle environmental, social and governance (ESG) issues. This study investigates how AI can enable platform business models (Platform BMs) to create, deliver and capture ESG-related value, with a particular focus on the ESG rating industry. Using the Platform Business Model Canvas as a conceptual framework, and conducting a comparative analysis of six case studies, the research identifies three distinct configurations of AI-enabled Platform BMs: (1) ESG data wrangling and integration; (2) financial analysis and provision of ESG data to investors and companies; and (3) compliance and management of ESG issues in supply chains. Each configuration embeds specific mechanisms, such as predictive analytics, compliance automation and stakeholder coordination, through which AI can support ESG-oriented business innovation. Based on these findings, the study proposes four theoretical propositions that clarify the relationships between AI capabilities, data governance, and ESG value creation within platform ecosystems. The paper advances the academic understanding of the relationship between AI and sustainability and provides a typology to inform the strategic development of ESG-focused digital platforms. © 2025 KW - Artificial Intelligence KW - Business Model KW - Digital Platform KW - ESG KW - Compliant mechanisms KW - Information management KW - Predictive analytics KW - Research and development management KW - Supply chains KW - Sustainable development KW - Business innovation KW - Business models KW - Case-studies KW - Comparative analyzes KW - Conceptual frameworks KW - Digital platforms KW - Environmental, social and governance KW - Financial analysis KW - Platform business KW - Stakeholder coordination KW - Artificial intelligence CY - Italy ER - TY - JOUR TI - Complying with the EU AI Act: Innovations in explainable and user-centric hand gesture recognition AU - Seifi S. AU - Sukianto T. AU - Carbonelli C. AU - Servadei L. AU - Wille R. PY - 2025 JO - Machine Learning with Applications VL - 20 SP - 100655 DO - 10.1016/j.mlwa.2025.100655 AB - The EU AI Act underscores the importance of transparency, user-centricity, and robustness in AI systems, particularly for high-risk applications. In response, we present advancements in XentricAI, an explainable hand gesture recognition (HGR) system designed to meet these regulatory requirements. XentricAI addresses fundamental challenges in HGR, such as the opacity of black-box models using explainable AI methods and the handling of distributional shifts in real-world data through transfer learning techniques. We extend an existing radar-based HGR dataset by adding 28,000 new gestures, with contributions from multiple users across varied locations, including 24,000 out-of-distribution gestures. Leveraging this real-world dataset, we enhance XentricAI's capabilities by integrating a variational autoencoder module for improved gesture anomaly detection, incorporating user-specific dynamic thresholding. This integration enables the identification of 11.50% more anomalous gestures. Our extensive evaluations demonstrate a 97.5% success rate in characterizing these anomalies, significantly improving system explainability. Furthermore, the implementation of transfer learning techniques has shown a substantial increase in user adaptability, with an average performance improvement of at least 15.17%. This work contributes to the development of trustworthy AI systems by providing both technical advancements and regulatory compliance, offering a commercially viable solution that aligns with the EU AI Act requirements. © 2025 The Authors KW - Explainable AI (XAI) KW - Frequency-modulated continuous wave (FMCW) radar KW - Hand gesture recognition (HGR) KW - Machine learning (ML) KW - Gesture recognition KW - Image compression KW - Variational techniques KW - AI systems KW - Explainable AI (XAI) KW - Frequency-modulated-continuous-wave radars KW - Hand gesture recognition KW - Hand-gesture recognition KW - Learning techniques KW - Machine learning KW - Machine-learning KW - Transfer learning KW - User-centric KW - Transfer learning CY - Germany, Austria ER - TY - JOUR TI - Artificial intelligence, economic inequality, and the financial hurdles to sustainable peace: Navigating the interconnected challenges of the 21st century AU - Zandieh A. PY - 2026 JO - Social Sciences and Humanities Open VL - 13 SP - 102687 DO - 10.1016/j.ssaho.2026.102687 AB - Artificial intelligence (AI) is transforming economic and political systems at a pace that outstrips existing governance capacities. While AI offers significant potential for innovation, its rapid and uneven diffusion is amplifying structural inequalities within and between states. This paper develops an integrated conceptual framework to explain how AI-driven technological disruption interacts with political economy dynamics to deepen inequality, erode fiscal capacity, and weaken the foundations of sustainable peace. It argues that AI reshapes labor markets, concentrates wealth and data governance power, and reinforces global asymmetries, producing a systemic pattern of vertical and horizontal inequality. These inequalities narrow domestic tax bases, heighten debt dependency, and redirect scarce fiscal resources away from peacebuilding priorities toward technological adaptation and crisis management. As a result, in fragile contexts, institutions face diminishing ability to provide essential services, address grievances, and maintain legitimacy. By linking AI-induced inequality with fiscal fragility, the paper frames technological transformation as a potential economic, political and security threat. The analysis advances policy pathways for mitigating these risks through inclusive AI governance, equitable global cooperation, progressive fiscal reforms, and investment in local technological capacity. Ensuring that AI becomes a driver of resilience rather than a catalyst for exclusion is essential for building durable and just peace in the digital age. © 2026 The Author. KW - Artificial intelligence KW - Development finance KW - Economic inequality KW - Fiscal fragility KW - Global governance KW - Sustainable peace ER - TY - JOUR TI - Fostering responsible AI adoption in startups through entrepreneurial orientation: A sustainable approach AU - Alshibani S.M. AU - Korayim D. AU - Mehrotra A. AU - Agarwal V. PY - 2025 JO - Technological Forecasting and Social Change VL - 219 SP - 124272 DO - 10.1016/j.techfore.2025.124272 AB - In the dynamic and fluid business landscape, startups face the daunting task of maintaining their position and navigating the path of growth. Responsible AI (RAI) is technological support that promises to be a differentiator and propeller for startups. The study employed grounded theory method within a qualitative framework to analyze the themes from cross-cultural data. These themes aligned with the Entrepreneurial Orientation theory and the Responsible Innovation framework. The retrieved opinions were then divided into five sub-themes: innovativeness and reflexivity, proactiveness and anticipation, risk-taking and reflexivity, autonomy and inclusion, and competitive aggressiveness and responsiveness, which laid the foundation for understanding the drivers and adopters of RAI in startups. This research contributes to the literature on the emerging Responsible AI domain in businesses, particularly startups, by elaborating on the factors that would lead to the adoption of Responsible AI for business sustainability. © 2025 Elsevier Inc. KW - Entrepreneurial orientation theory KW - Gioia method KW - Grounded theory KW - Qualitative study KW - Responsible AI KW - Startups KW - Competition KW - Differentiators KW - Entrepreneurial orientation KW - Entrepreneurial orientation theory KW - Gioium method KW - Grounded theory KW - Grounded theory methods KW - Qualitative study KW - Responsible AI KW - Startup KW - Technological supports KW - artificial intelligence KW - entrepreneur KW - innovation KW - sustainability KW - technology adoption KW - Sustainable development CY - Saudi Arabia, India ER - TY - JOUR TI - Exploring the future of learning and the relationship between human intelligence and AI. An interview with Professor Rose Luckin AU - Luckin R. AU - Rudolph J. AU - Grünert M. AU - Tan S. PY - 2024 JO - Journal of Applied Learning and Teaching VL - 7 IS - 1 SP - 346 EP - 363 DO - 10.37074/jalt.2024.7.1.27 AB - Professor Rose Luckin, a pioneer in the integration of artificial intelligence with education, holds the position of Professor of Learner Centred Design at the UCL Knowledge Lab, University College London. Her trailblazing research has profoundly deepened our understanding of AI in education (AIEd). Rose Luckin has authored over 50 peer-reviewed articles and key works, including “Machine learning and human intelligence: The future of education for the 21st century.” As the Director of EDUCATE, she merges academic insights with ed-tech industry innovation. She is the co-founder of the Institute for Ethical AI in Education. In our interview, Rose Luckin shares her educational awakening and her personal journey into AIEd, addressing gender bias and the unique challenges faced by women in the AI field. She delves into the ethical dimensions of AI deployment in educational settings, underscoring the Institute for Ethical AI in Education’s pivotal role in fostering ethical standards. Professor Luckin advocates for AI’s potential to bolster learner-centred methodologies and stresses the critical importance of forging robust partnerships between educators and technology developers. She evaluates the impact of generative AI on assessment, learning and teaching within K-12 and higher education. She provides insights into AI’s evolving role in education and the imperative of lifelong learning. Emphasising a collaborative ethos among educators, researchers, and developers, Professor Luckin argues for AI’s integration into education within strategically crafted ethics and governance frameworks. Our interview sheds light on AIEd’s current landscape, highlighting the critical need for ongoing research and collaborative efforts in navigating its considerable dangers while seizing opportunities. © 2024. Rose Luckin, Jürgen Rudolph, Martin Grünert and Shannon Tan. KW - (human) intelligence KW - AIEd KW - Artificial intelligence (AI) KW - education KW - ethical AI KW - generative AI (GenAI) KW - higher education KW - machine learning CY - United Kingdom, United States ER - TY - JOUR TI - Enhancing green innovation through university–industry collaboration and artificial intelligence: insights from regional innovation systems in China AU - Xia S. AU - Zhou Y. AU - Wang Z. AU - He Q. AU - Parry G. PY - 2026 JO - Journal of Technology Transfer VL - 51 IS - 2 SP - 653 EP - 681 DO - 10.1007/s10961-025-10232-8 AB - Green innovation is essential for sustainable development worldwide. This study investigates how university engagement, coupled with the Artificial Intelligence (AI) capabilities of industrial actors, enhances regional green innovation performance within the framework of Regional Innovation Systems (RIS) theory. Using a longitudinal dataset of 31 Chinese provinces from 2008 to 2019 and employing a dynamic panel analysis with the GMM estimator, the results show that university embeddedness in regional innovation networks significantly increases green innovation performance. Contrary to previous studies, our research shows that within RIS, absorptive capacity plays a more critical role than AI in enhancing the effectiveness of knowledge transfer and exploitation, highlighting the primacy of human and organisational factors over technological tools alone. This research advances RIS theory by highlighting the critical role of university-embedded networks and systemic interactions among heterogeneous actors, demonstrating higher-order returns from knowledge exchange beyond dyadic partnerships, and enriching the understanding of the integration of AI into RIS frameworks. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025. KW - Absorptive capacity KW - Artificial intelligence KW - Green innovation performance KW - University–industry collaboration KW - Absorptive capacity KW - Dynamic panels KW - Embeddedness KW - GMM estimators KW - Green innovation performance KW - Green innovations KW - Innovation performance KW - Regional innovation KW - Regional innovation systems KW - University-industry collaboration CY - United Kingdom, China ER - TY - JOUR TI - A Qualitative Theory Building Research on Digital Law, Legal AI, and LegalTech AU - Qian Y. AU - Siau K.L. PY - 2025 JO - Journal of Global Information Management VL - 33 IS - 1 DO - 10.4018/JGIM.396821 AB - Artificial intelligence (AI) is transforming modern life by driving innovation and efficiency, with legal AI playing an increasingly significant role in supporting legal work. However, the growing use of AI introduces new challenges, including privacy breaches, deepfakes, ethical dilemmas, and legal uncertainty. Despite the ongoing initiatives to establish AI governance principles, research on regulating legal AI and broader AI applications remains limited. This study addresses this gap through a qualitative case study examining effective AI governance frameworks. Interviews with four senior legal experts, i.e., two judges, a law professor, and a legal researcher, identified emerging challenges of legal AI and beyond. The findings reveal pressing needs for adaptive, transparent, and equitable regulation to ensure responsible AI development and use. The study contributes theoretically by linking AI governance with legal scholarship and offers practical insights for policymakers, legal professionals, and organizations navigating AI's evolving regulatory landscape. © 2025 IGI Global. All rights reserved. KW - Artificial Intelligence (AI) KW - Case Study KW - Digital Law KW - Generative AI (GenAI) KW - Legal AI KW - LegalTech KW - Qualitative Research KW - RegTech KW - Artificial intelligence KW - Artificial intelligence KW - Case-studies KW - Digital law KW - Generative artificial intelligence KW - Legal artificial intelligence KW - Legaltech KW - Privacy breaches KW - Qualitative research KW - Regtech KW - Theory building KW - Laws and legislation CY - China, Singapore ER - TY - JOUR TI - The influence of AI capability on enterprise competitive advantage: the mediating effect of business model innovation AU - Shao S. AU - Shao Z. AU - Xiong Y. PY - 2025 JO - Journal of Enterprise Information Management SP - 1 EP - 25 DO - 10.1108/JEIM-08-2024-0453 AB - Purpose – This study seeks to examine the relationships among artificial intelligence capability (AIC), business model innovation (BMI), and the competitive advantage of enterprises (CAE) within the framework of dynamic capabilities theory. It specifically focuses on how small and medium-sized (SMEs) enterprises utilise artificial intelligence capability to foster business model innovation in a digital context, thereby attaining a sustainable competitive advantage. Design/methodology/approach – This study utilises a questionnaire survey to gather empirical data from 546 SMEs in China. Structural equation modelling was employed for quantitative analysis to examine the direct effect of artificial intelligence capabilities on competitive advantage, alongside the mediating role of business model innovation. Findings – Research indicates that three primary components of artificial intelligence capabilities, tangible resources, intangible resources, and skill resources, exert a significant positive influence on a company's competitive advantage. At the same time, business model innovation serves as a mediating factor within this relationship. Moreover, the findings underscore the necessity for firms to proactively adapt to technological advancements and to foster the synergistic development of artificial intelligence capabilities alongside business model innovation to enhance their competitiveness in a rapidly evolving environment. Originality/value – This study extends the dynamic capabilities theory from the perspective of artificial intelligence, proposing AI capability as a systemic and multidimensional dynamic capability, emphasising its transformative role in the ways small and medium-sized enterprises create and capture value. The research not only enriches the theoretical understanding in the field of artificial intelligence but also offers practical insights and policy recommendations for SMEs on how to achieve a competitive advantage through the development of AI capabilities. © 2025 Emerald Publishing Limited KW - Artificial intelligence capability KW - Business model KW - Business model innovation KW - Enterprise competitive advantage KW - Innovation CY - China, United Kingdom ER - TY - JOUR TI - Harnessing Competitive Intelligence and AI for Corporate Growth and Sustainability AU - Maune A. PY - 2025 JO - Journal of Intelligence Studies in Business VL - 15 IS - 2 SP - 6 EP - 24 DO - 10.37380/jisib.v15i2.3105 AB - This study investigates how artificial intelligence (AI) integration enhances competitive intelligence (CI) effectiveness and, in turn, drives corporate growth and sustainability performance in Zimbabwean firms. Employing a mixed methods design, the research combines a quantitative survey of 312 senior managers and strategy professionals from medium and large firms with qualitative data from 28 semi structured interviews across manufacturing, financial services, telecommunications, and retail sectors. Quantitative findings reveal that AI capability significantly predicts CI effectiveness (β = 0.62, p < .001), while CI effectiveness significantly predicts corporate growth (β = 0.51, p < .001) and sustainability performance (β = 0.47, p < .001). Mediation analysis indicates that CI effectiveness partially mediates the relationship between AI capability and both corporate growth and sustainability outcomes. Qualitative analysis using the Gioia methodology further identifies three aggregate dimensions: AI enabled competitive intelligence, strategic decision making and growth, and sustainable value creation, illustrating how AI enhances sensing, analytics, and reporting capabilities, and how these capabilities are embedded into strategic routines. The findings extend the resource based, knowledge based, and dynamic capabilities perspectives by conceptualising CI as a mediating dynamic capability that transforms AI driven data into actionable strategic knowledge. The study contributes to theory and practice by demonstrating that AI delivers strategic value only when integrated into CI processes and organisational routines, enabling firms to achieve sustainable competitive advantage in volatile emerging economy contexts. © 2025 Halmstad University. All rights reserved. KW - Artificial Intelligence KW - Competitive Intelligence KW - Corporate Growth KW - Dynamic Capabilities KW - Sustainability Performance KW - Zimbabwe CY - South Africa, Zimbabwe ER - TY - JOUR TI - Exploring the Themes of Chinese Artificial Intelligence Policy: An LDA Topic Modeling Approach AU - Gao Y. AU - Dai Q. AU - Wu G. PY - 2025 JO - Proceedings of the Association for Information Science and Technology VL - 62 IS - 1 SP - 199 EP - 206 DO - 10.1002/pra2.1248 AB - As a representative of next-generation artificial intelligence, generative AI is profoundly transforming contemporary societal structures. As a pivotal player, China serves as both a primary application market and a key innovator in AI technology, with its developmental trajectory significantly shaped by national policy frameworks. This study employs Latent Dirichlet Allocation (LDA) topic modeling to systematically analyze 78 valid and currently implemented AI policy documents in China. The research aims to identify core focus areas in China's current AI policy landscape and provide insights for sustainable development of AI. Analytical results highlight seven key policy themes: (1) technological innovation and industrial integration, (2) social governance and mechanism evaluation, (3) model training and disciplinary methodologies, (4) software algorithms and data security, (5) pilot zone construction and innovation development, (6) infrastructure and intelligent service systems, and (7) AI research project implementation. Based on these findings, the study concludes with targeted policy recommendations. Annual Meeting of the Association for Information Science & Technology | Nov. 14 – 18, 2025 | Washington, DC, USA. KW - AI Policies KW - Chinese Artificial Intelligence KW - Information Governance KW - LDA Topic Modeling KW - Computer software KW - Engineering research KW - Information systems KW - Intelligent systems KW - Public policy KW - Security of data KW - Sustainable development KW - AI policy KW - AI Technologies KW - Chinese artificial intelligence KW - Information governance KW - Latent Dirichlet allocation KW - Latent dirichlet allocation topic modeling KW - Modeling approach KW - National policy framework KW - Policy documents KW - Topic Modeling KW - Industrial research CY - China ER - TY - JOUR TI - Ten principles for responsible quantum innovation AU - Kop M. AU - Aboy M. AU - De Jong E. AU - Gasser U. AU - Minssen T. AU - Cohen I.G. AU - Brongersma M. AU - Quintel T. AU - Floridi L. AU - Laflamme R. PY - 2024 JO - Quantum Science and Technology VL - 9 IS - 3 SP - 035013 DO - 10.1088/2058-9565/ad3776 AB - This paper proposes a set of guiding principles for responsible quantum innovation. The principles are organized into three functional categories: safeguarding, engaging, and advancing (SEA), and are linked to central values in responsible research and innovation (RRI). Utilizing a global equity normative framework and literature-based methodology, we connect the quantum-SEA categories to promise and perils specific to quantum technology (QT). The paper operationalizes the responsible QT framework by proposing ten actionable principles to help address the risks, challenges, and opportunities associated with the entire suite of second-generation QTs, which includes the quantum computing, sensing, simulation, and networking domains. Each quantum domain has different technology readiness levels, risks, and affordances, with sensing and simulation arguably being closest to market entrance. Our proposal aims to catalyze a much-needed interdisciplinary effort within the quantum community to establish a foundation of quantum-specific and quantum-tailored principles for responsible quantum innovation. The overarching objective of this interdisciplinary effort is to steer the development and use of QT in a direction not only consistent with a values-based society but also a direction that contributes to addressing some of society’s most pressing needs and goals. © 2024 The Author(s). Published by IOP Publishing Ltd KW - engaging & advancing KW - quantum R&D KW - quantum safeguarding KW - quantum-AI ethics & governance KW - responsible quantum innovation KW - responsible quantum technology KW - responsible research and innovation (RRI) KW - Philosophical aspects KW - Quantum optics KW - Engaging & advancing KW - Global equities KW - Guiding principles KW - Quantum R&D KW - Quantum safeguarding KW - Quantum technologies KW - Quantum-AI ethic & governance KW - Responsible quantum innovation KW - Responsible quantum technology KW - Responsible research and innovation KW - Quantum computers CY - Canada ER - TY - JOUR TI - Harnessing artificial intelligence in shaping entrepreneurial success: Rethinking HR practices AU - Bajrami N. AU - Ahmeti F. PY - 2026 JO - Multidisciplinary Science Journal VL - 8 IS - 5 SP - e2026361 DO - 10.31893/multiscience.2026361 AB - This study examines the impact of artificial intelligence (AI) on human resource management (HRM) practices in Kosovo’s small and medium–sized enterprises (SMEs), focusing on outcomes such as accuracy, automation, computing power, and real-time analytics, to understand their effects on efficiency and cost reduction. The research employs a quantitative approach, utilizing data collected from 109 SMEs through structured surveys and in-depth interviews. Structural equation modeling (SEM) was used to analyze the relationships between AI capabilities and HRM efficiency outcomes. The results reveal that AI-driven accuracy and automation significantly enhance time efficiency and cost reduction, while real-time analytics has a moderate influence, and computational power has a limited impact within the SME context assessed. The findings suggest that SMEs should prioritize accuracy and automation tools to achieve immediate operational benefits and gradually integrate real-time analytics as the digital infrastructure improves. This study provides practical insights for SMEs on how to implement AI strategically in HRM processes, demonstrating that AI serves as an essential complement to human expertise rather than a replacement, offering a clear pathway for enhancing HR practices in developing economies. Copyright (c) 2026 The Authors. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. KW - artificial intelligence KW - digital transformation KW - entrepreneurial success KW - human resource practices KW - strategic innovation ER - TY - JOUR TI - Bridging the AI Ethics Gap: A Tripartite Framework for Accountability, Implementation, and Governance AU - Nakano M. PY - 2026 JO - International Journal of Software Innovation VL - 14 IS - 1 DO - 10.4018/IJSI.401497 AB - To address the largely unmitigated societal impact of artificial intelligence (AI) technologies, this study proposes a Tripartite Ethical Policy Framework for AI governance in global industries. The framework comprises three key components: AI ethics, technical implementation, and institutional governance. Drawing on current international standards, it integrates six core principles (i.e., human-centricity, fairness, accountability, transparency, privacy, and safety) and translates them into three actionable ethical domains: data, algorithms, and autonomy. An empirical analysis of corporate disclosures in Japan reveals a gap between stated commitments and actual implementation, highlighting the need for stronger governance and credible accountability. By embedding AI ethics in internal and external audits, the framework enhances transparency, strengthens oversight, and promotes responsible AI use. Its emphasis on adaptability provides a foundation for AI audit and responsible innovation amid rapid technological change. © 2026 Taru Publications. All rights reserved. KW - AI Audit KW - AI Ethics KW - AI Governance KW - Ethical Algorithms KW - Ethical Autonomy KW - Ethical Data Governance KW - Ethical Policies Framework KW - External Audit KW - Internal Audit KW - Technology Disclosure CY - Japan ER - TY - JOUR TI - A hybrid framework for creating artificial intelligence-augmented systematic literature reviews AU - Malik F.S. AU - Terzidis O. PY - 2025 JO - Management Review Quarterly DO - 10.1007/s11301-025-00522-8 AB - The integration of artificial intelligence (AI), particularly generative AI (GenAI) and large language models (LLMs), into systematic literature reviews (SLRs) represents a transformative advancement in research methodologies. This paper proposes a hybrid framework combining AI’s computational power with the epistemological rigor of human expertise, anchored in transparency, validity, reliability, comprehensiveness, and reflective agency. Through three interconnected phases—design, study collection, and interpretation—the framework employs AI model selection, knowledge base curation, and iterative prompt engineering to enhance scalability, uncover interdisciplinary connections, and ensure methodological integrity through robust human oversight. It addresses key SLR challenges, including handling vast datasets, ensuring reproducibility, and maintaining epistemic rigor while leveraging advanced AI capabilities. Key innovations include cyclical validation, inter-model comparisons, and sensitivity testing to enhance trustworthiness and mitigate biases. The framework aligns AI processes with ethical standards and research objectives by emphasizing domain-specific LLMs, reliability metrics, and standardized reporting protocols. It establishes SLRs as a foundation for advancing knowledge in complex, interdisciplinary research landscapes, harmonizing AI efficiency with human expertise. © The Author(s) 2025. KW - M00 KW - M1 CY - Germany ER - TY - JOUR TI - Harnessing AI capabilities for startup scalability: unlocking potential through AI-driven innovation ecosystems and AI-infrastructure readiness AU - Chotia V. AU - Sharma P. AU - Alshaghdali N.O. AU - Galgotia D. AU - Sahore N. PY - 2025 JO - European Journal of Innovation Management SP - 1 EP - 38 DO - 10.1108/EJIM-03-2025-0397 AB - Purpose – In today's rapidly changing digital world, the ability of startups to grow has become a major issue. This research looks at how AI-enhanced Human Decision-Making, AI-powered Entrepreneurial Agility and Ethical AI Governance help startups create an AI-driven Innovation Ecosystem that improves AI Infrastructure Readiness and supports both internal and external startup scalability potential. Design/methodology/approach – The Prolific platform was used to collect data from 274 decision-makers who worked for AI-intensive firms in the US. We used modified Likert-scale items from previous validated research to measure constructs. Partial Least Squares Structural Equation Modelling in SmartPLS4 was used to test the model. Both direct and indirect effects were analysed to examine the hypothesized relationships. Findings – All AI-enabled capabilities had a strong positive association with AI-driven Innovation Ecosystem. AI-driven Innovation Ecosystem significantly influenced AI Infrastructure Readiness, which in turn positively impacted both Startup Scalability Potential – Internal and Startup Scalability Potential – External. Mediation investigations showed that AI-driven Innovation Ecosystems and AI-Infrastructure Readiness serially mediate the impact of AI-enabled capabilities on startup scalability. Practical implications – Theoretically, this study builds on the Dynamic Capabilities Theory by adding AI-driven ecosystem and infrastructure preparedness as sequential mediators. This gives us a new way to look at how businesses use AI to develop quickly. In practice, the results give startup leaders and policymakers useful information on how to build AI adoption plans that are not only new but also follow the rules and fit with the infrastructure. Originality/value – This article presents a new serial mediation paradigm that connects AI capabilities to startup scalability and backs it up with real-world evidence from the US, a nation that is driven by innovation. © 2025 Emerald Publishing Limited KW - AI infrastructure readiness KW - AI-driven innovation ecosystem KW - AI-enhanced human decision-making KW - AI-powered entrepreneurial agility KW - Ethical AI governance KW - Startup scalability potential CY - India, Saudi Arabia ER - TY - JOUR TI - Ethical concerns in AI development: analyzing students’ perspectives on robotics and society AU - Ferhataj A. AU - Memaj F. AU - Sahatcija R. AU - Ora A. AU - Koka E. PY - 2025 JO - Journal of Information, Communication and Ethics in Society VL - 23 IS - 2 SP - 165 EP - 187 DO - 10.1108/JICES-08-2024-0111 AB - Purpose: The rapid advancement and integration of robotics and artificial intelligence (AI) are transforming various sectors, presenting profound ethical, economic, legal and societal challenges. This study aims to examine ethical concerns in AI development, with a specific focus on robotics, from the perspectives of university students in Albania. Design/methodology/approach: A structured questionnaire was used to collect data from 233 university students, focusing on their experiences with AI and robotics, ethical perceptions, preferences and recommendations for advancing these technologies. Hypotheses were tested at a 95% confidence interval, with data analyzed using JASP software version 0.18.3.0. Findings: The results reveal a high level of ethical awareness among students, particularly regarding transparency, liability and privacy in AI and robotics. Practical experience with robotics and understanding of AI’s ethical implications significantly shape students’ attitudes, fostering support for ethical governance. Students also advocate for robust regulatory measures to safeguard individual rights, ensure data security, promote transparency in AI decision-making and uphold privacy. Research limitations/implications: This study focuses on university students in Albania, which may limit the generalizability of its findings. Future research should explore diverse populations and cross-cultural contexts to validate and extend the proposed framework. Practical implications: Insights from this study can guide policymakers and technology developers in designing laws, regulations and practices that balance innovation with public interest, fostering trust and acceptance of AI systems. Social implications: The findings underscore the importance of Albania adopting and harmonizing its policies with the EU Civil Law Rules on Robotics, the EU AI Act and AI Strategy, supporting ethical AI integration aligned with the country’s EU accession objectives. Originality/value: This study introduces the Ethical Awareness-Trust Framework, a novel theoretical model integrating ethical literacy, experiential trust and regulatory advocacy to foster responsible AI adoption and governance. The findings address critical gaps in the literature by offering actionable recommendations for aligning national policies with European regulations and embedding ethics into AI research and education. © 2024, Emerald Publishing Limited. KW - Artificial intelligence KW - Ethics KW - Privacy KW - Regulation KW - Robotics KW - Students’ perspectives KW - Transparency CY - Albania, Canada ER - TY - JOUR TI - Harnessing artificial intelligence in the public sector: the critical role of strategic foresight in driving performance AU - Cao L.H.N. AU - Nguyen P.V. AU - Nguyen V.T.H. AU - Tran T.T. AU - Vrontis D. PY - 2025 JO - Business Process Management Journal SP - 1 EP - 23 DO - 10.1108/BPMJ-08-2025-1317 AB - Purpose – This study aims to examine how artificial intelligence (AI) capabilities influence organizational performance in the public sector, with strategic foresight as a mediating mechanism. It investigates how institutional enablers, including government incentives, regulatory support and perceived financial costs, contribute to AI capabilities and how these capabilities translate into performance outcomes. Design/methodology/approach – Drawing on the resource-based view, survey data were collected from 303 Vietnamese public officials and analyzed using partial least squares structural equation modeling. AI capabilities were conceptualized as a second-order construct encompassing AI basics, AI skills and AI proclivity, while strategic foresight comprised environmental scanning and strategic selection. Findings – Government incentives, regulatory support and cost awareness significantly enhance AI capabilities. These capabilities have both direct and indirect effects on performance through strategic foresight, which partially mediates the relationship. Although perceived financial cost strengthens AI capabilities, it does not directly affect performance. Organizational innovation shows no significant influence on AI capabilities or performance, emphasizing the greater importance of institutional support and foresight capacity. Originality/value – This study advances understanding of how AI capabilities contribute to public value creation by integrating strategic foresight into the capability and performance link. It highlights that technology adoption alone is insufficient without supportive institutional frameworks and future-oriented strategic processes, offering actionable insights for policymakers and public managers in emerging economies. © 2025 Emerald Publishing Limited KW - AI capabilities KW - Government incentives KW - Organizational context KW - Organizational performance KW - Regulatory support KW - Strategic foresight CY - Cyprus, Singapore ER - TY - JOUR TI - Unlocking AI capabilities: exploring strategic fit, innovation ambidexterity and digital entrepreneurial intent in driving digital transformation AU - Ahmad Z. PY - 2025 JO - Journal of Management Development VL - 44 IS - 2 SP - 194 EP - 218 DO - 10.1108/JMD-05-2024-0171 AB - Purpose: The insurgence of the COVID-19 pandemic insinuated that family-owned small hotels (F-OSH) should adopt AI capabilities and innovation activities and digitize their operations to survive. This study examines the potential of AI capabilities to digitally transform F-OSHs by leveraging innovation ambidexterity, preparing them for future disasters proactively. Additionally, it sheds light on how the impact of AI capabilities on innovation ambidexterity varies based on strategic fit. In addition, this research explores the influence of digital entrepreneurial intention on fostering innovation ambidexterity, essential for digital transformation in F-OSHs. Design/methodology/approach: The study collected primary data from 318 descendant entrepreneurs designated as chairpersons or managing directors in F-OSH and analyzed the data using the partial least structural equation modeling technique. Findings: This study found a positive association of AI capabilities, and digital entrepreneurial intention with the digital transformation of F-OSHs, while strategic fit does not have an association with innovation ambidexterity. Innovation ambidexterity mediates the relationship between AI capabilities and digital transformation in F-OSHs. Moreover, a strong strategic fit increases the effect of AI capabilities on innovation ambidexterity. Furthermore, a high intention for digital entrepreneurship reduces the impact of innovation ambidexterity on the digital transformation of F-OSHs. Practical implications: The combination of AI capabilities and innovation ambidexterity has transformed F-OSHs' digital transformation. This proactive approach to dealing with economic recessions such as COVID-19 is also influenced by digital entrepreneurial intention and strategic fit. Originality/value: Anchored on the dynamic capability theory, this study provides valuable insights and novel empirical evidence by investigating the mediating mechanism of innovation ambidexterity and boundary condition of strategic fit and digital entrepreneurial intention between AI capabilities and digital transformation in F-OSHs. © 2025, Emerald Publishing Limited. KW - AI capability KW - Digital entrepreneurial intention KW - Digital transformation KW - Family-owned small hotels KW - Innovation ambidexterity KW - Strategic fit CY - Malaysia ER - TY - JOUR TI - A GPT-Aided literature review process for total quality management and business excellence (2020-2023) AU - Hsueh J.-T. AU - Hsu S.-H. PY - 2024 JO - Total Quality Management and Business Excellence VL - 35 IS - 7-8 SP - 835 EP - 859 DO - 10.1080/14783363.2024.2345897 AB - ABSTRACTS: In an era of rapid technological evolution and competitive pressures, Total Quality Management (TQM) requires up-to-date literature reviews to reflect its evolving nature. This study addresses this need through a comprehensive analysis of TQM&BE journal articles from 2020 to 2023 and by introducing the LitRev-GPT framework. The research data was collected from the Scopus database, specifically targeting articles published in the TQM&BE journal from 2020 to 2023. This LitRev-GPT framework employs the Generative Pretrained Transformer (GPT) for efficient categorization and summarization of academic papers, setting new standards for reproducibility and methodological soundness. We identified emergent themes such as ‘Corporate Social Responsibility’, intertwining TQM with ethical practices, and ‘Industry 4.0’, showcasing TQM's adaptability to technological advancements. Additionally, trend analysis highlighted a sustained interest in foundational TQM themes, with a growing emphasis on innovation management. The LitRev-GPT framework demonstrates significant methodological advancements, enhancing the efficiency and depth of literature reviews beyond traditional AI capabilities. © 2024 Informa UK Limited, trading as Taylor & Francis Group. KW - Generative Pretrained Transformer (GPT) KW - Literature Review KW - Thematic Analysis KW - Topic analysis KW - Total Quality Management (TQM) CY - Taiwan ER - TY - JOUR TI - Web-Based Multimodal Deep Learning Platform with XRAI Explainability for Real-Time Skin Lesion Classification and Clinical Decision Support AU - Aksoy S. AU - Demircioglu P. AU - Bogrekci I. PY - 2025 JO - Cosmetics VL - 12 IS - 5 SP - 194 DO - 10.3390/cosmetics12050194 AB - Background: Skin cancer represents one of the most prevalent malignancies worldwide, with melanoma accounting for approximately 75% of skin cancer-related deaths despite comprising fewer than 5% of cases. Early detection dramatically improves survival rates from 14% to over 99%, highlighting the urgent need for accurate and accessible diagnostic tools. While deep learning has shown promise in dermatological diagnosis, existing approaches lack clinical explainability and deployable interfaces that bridge the gap between research innovation and practical healthcare applications. Methods: This study implemented a comprehensive multimodal deep learning framework using the HAM10000 dataset (10,015 dermatoscopic images across seven diagnostic categories). Three CNN architectures (DenseNet-121, EfficientNet-B3, ResNet-50) were systematically compared, integrating patient metadata, including age, sex, and anatomical location, with dermatoscopic image analysis. The first implementation of XRAI (eXplanation with Region-based Attribution for Images) explainability for skin lesion classification was developed, providing spatially coherent explanations aligned with clinical reasoning patterns. A deployable web-based clinical interface was created, featuring real-time inference, comprehensive safety protocols, risk stratification, and evidence-based cosmetic recommendations for benign conditions. Results: EfficientNet-B3 achieved superior performance with 89.09% test accuracy and 90.08% validation accuracy, significantly outperforming DenseNet-121 (82.83%) and ResNet-50 (78.78%). Test-time augmentation improved performance by 1.00 percentage point to 90.09%. The model demonstrated excellent performance for critical malignant conditions: melanoma (81.6% confidence), basal cell carcinoma (82.1% confidence), and actinic keratoses (88% confidence). XRAI analysis revealed clinically meaningful attention patterns focusing on irregular pigmentation for melanoma, ulcerated borders for basal cell carcinoma, and surface irregularities for precancerous lesions. Error analysis showed that misclassifications occurred primarily in visually ambiguous cases with high correlation (0.855–0.968) between model attention and ideal features. The web application successfully validated real-time diagnostic capabilities with appropriate emergency protocols for malignant conditions and comprehensive cosmetic guidance for benign lesions. Conclusions: This research successfully developed the first clinically deployable skin lesion classification system combining diagnostic accuracy with explainable AI and practical patient guidance. The integration of XRAI explainability provides essential transparency for clinical acceptance, while the web-based deployment democratizes access to advanced dermatological AI capabilities. Comprehensive validation establishes readiness for controlled clinical trials and potential integration into healthcare workflows, particularly benefiting underserved regions with limited specialist availability. This work bridges the critical gap between research-grade AI models and practical clinical utility, establishing a foundation for responsible AI integration in dermatological practice. © 2025 by the authors. KW - clinical deployment KW - deep learning KW - dermatoscopy KW - explainable artificial intelligence KW - melanoma detection KW - multimodal fusion KW - skin lesion classification KW - XRAI KW - actinic keratosis KW - adult KW - aged KW - Article KW - artificial intelligence KW - artificial neural network KW - basal cell carcinoma KW - clinical decision making KW - clinical decision support system KW - clinical practice KW - controlled study KW - deep learning KW - dermatofibroma KW - dermatoscopy KW - diagnostic test accuracy study KW - female KW - follow up KW - health care personnel KW - histopathology KW - human KW - human tissue KW - major clinical study KW - male KW - melanoma KW - middle aged KW - pigmented nevus KW - precancer KW - sensitivity and specificity KW - skin cancer KW - skin defect KW - vascular lesion CY - Germany, Turkey ER - TY - JOUR TI - Generative AI in Game Design: Enhancing Creativity or Constraining Innovation? AU - Alharthi S.A. PY - 2025 JO - Journal of Intelligence VL - 13 IS - 6 SP - 60 DO - 10.3390/jintelligence13060060 AB - Generative AI tools are increasingly being integrated into game design and development workflows, offering new possibilities for creativity, efficiency, and innovation. This paper explores the evolving role of these tools from the perspective of game designers and developers, focusing on the benefits and challenges they present in fostering creativity. Through a mixed-method study, we conducted an online survey (n = 42) with game design professionals, followed by in-depth online interviews (n = 9), to investigate how generative AI influences the creative process, decision-making, and artistic vision. Our findings reveal that while generative AI accelerates ideation, enhances prototyping, and automates repetitive tasks, it also raises concerns about originality, creative dependency, and undermine of human-authored content. Future work will aim to address these challenges by investigating strategies to balance leveraging AI’s capabilities with preserving the integrity of human creativity. This includes developing collaborative human-AI workflows that maintain human oversight, designing systems that support rather than replace creative decision-making, and establishing ethical guidelines to ensure transparency, accountability, and authorship in AI-assisted content creation. By doing so, we aim to contribute to a more nuanced understanding of generative AI’s role in creative practices and its implications for the game design and development lifecycle. © 2025 by the author. KW - creativity KW - game design KW - games KW - generative AI KW - user experiece CY - Saudi Arabia ER - TY - JOUR TI - Evaluating chatbot architectures for public service delivery: balancing functionality, safety, ethics, and adaptability AU - Papadopoulos T. AU - Alexopoulos C. AU - Charalabidis Y. PY - 2025 JO - Frontiers in Political Science VL - 7 SP - 1601440 DO - 10.3389/fpos.2025.1601440 AB - The increasing integration of AI-driven interfaces into public service channels has catalyzed a vibrant discourse on the interplay between technological innovation and the traditional values of public governance. This discussion invites a critical exploration of how emerging chatbot architectures can be aligned with ethical principles and resilient public sector practices. While there is research assessing the potential benefits of integrating chatbots in service delivery, existing evaluation approaches often lack specificity to the unique context of public administration, failing to adequately balance technical performance with crucial ethical considerations, safety requirements, and core public service principles like transparency, fairness, and accountability. This research addresses this critical gap by developing and applying a structured evaluation framework specifically designed for assessing diverse chatbot architectures within the public sector. The methodology offers actionable insights to guide the selection and implementation of chatbot solutions that enhance citizen engagement, streamline government services, and uphold key public service values. A key contribution is the introduction of fifteen pre-assessed evaluation criteria, encompassing areas such as input understanding, error handling, legal compliance, safety, and personalization, which are applied to four distinct chatbot architectures. Our findings indicate that while no single architecture is universally optimal, hybrid retrieval-augmented generation (RAG) systems emerge as the most balanced approach, effectively mitigating the risks of pure generative models while retaining their adaptability. Ultimately, this work provides actionable guidance for policymakers and researchers, supporting informed decisions on the responsible use of chatbots and emphasizing the critical balance between innovation and public trust. Copyright © 2025 Papadopoulos, Alexopoulos and Charalabidis. KW - AI ethics KW - chatbots KW - evaluation framework KW - LLMs KW - politics of technology KW - public service delivery CY - Greece ER - TY - JOUR TI - Harmonizing innovation and regulation: The EU Artificial Intelligence Act in the international trade context AU - REN Q. AU - DU J. PY - 2024 JO - Computer Law and Security Review VL - 54 SP - 106028 DO - 10.1016/j.clsr.2024.106028 AB - The European Union's Artificial Intelligence Act focuses on establishing harmonized rules across EU Member States so that AI systems are safe, transparent, and respectful of existing laws and fundamental rights. It introduces a risk-based regulatory approach, classifying AI applications by risk levels and imposing stringent compliance requirements on high-risk applications. The paper critically examines the Act's provisions, including its prohibitions on certain AI practices, requirements for high-risk AI systems, and mandates for transparency and human oversight. The paper examines the implications of the Act for international trade and technological regulation, particularly in the context of the World Trade Organization's Technical Barriers to Trade (TBT) Agreement. It addresses the Act's potential impact on developing countries, highlighting concerns that the Act's uniform standards could potentially exacerbate the digital divide and create barriers in global AI innovation and trade. The paper suggests incorporating flexibility and differential standards in the Act, enhancing technical assistance for developing countries, and advocating the EU's active participation in global standard-setting. © 2024 Elsevier Ltd KW - Developing countries and AI compliance KW - EU Artificial Intelligence Act KW - International trade regulation KW - Risk-based AI regulation KW - Technical barriers to trade agreement KW - Artificial intelligence KW - International trade KW - Regulatory compliance KW - AI systems KW - Developing country and AI compliance KW - EU artificial intelligence act KW - International trade regulation KW - Risk-based KW - Risk-based AI regulation KW - Technical barrier to trade agreement KW - Technical barriers to trade KW - Trade agreements KW - Trade regulations KW - Developing countries CY - China, United Kingdom ER - TY - JOUR TI - Enhancing Supply Chain Innovation via Generative AI: Mediating Effects of Knowledge Sharing and Supply Chain Learning AU - Yongsheng L. AU - Zhaoxia Z. PY - 2026 JO - Journal of Information and Knowledge Management SP - 2650007 DO - 10.1142/S0219649226500073 AB - As generative AI applications in supply chain management become increasingly thorough, systematic studies on how it could promote enterprise innovation are yet to come to light. This paper takes 298 manufacturing enterprises in Zhejiang Province as samples, uses questionnaire surveys and PLS-SEM methods to investigate how generative AI exerts its influence on supply chain innovation, and tests the role of knowledge sharing and supply chain learning as a mediator. Research has found that generative AI capabilities can significantly enhance knowledge sharing and supply chain learning levels. Knowledge sharing not only promotes supply chain learning but also has a direct driving effect on supply chain innovation, playing a key mediating role between generative AI capabilities and innovation. In contrast, the hypothesised mediation of supply chain learning did not receive statistical support. This indicates that the impact of generative AI on supply chain innovation does not depend on supply chain learning. The results reveal the transmission path of generative AI in supply chain innovation, emphasising the core position of knowledge sharing in the process of transforming technological capabilities into innovative results. This paper provides new empirical evidence to understand AI-driven innovation and provides reference practice to promote digital transformation and collaborative innovation among manufacturing enterprises. © 2026 World Scientific Publishing Co. KW - Generative AI KW - knowledge sharing KW - PLS-SEM KW - supply chain innovation KW - supply chain learning KW - Collaborative learning KW - Engineering research KW - Knowledge acquisition KW - Knowledge management KW - Knowledge transfer KW - Supply chains KW - AI applications KW - Chain management KW - Generative AI KW - Knowledge supply KW - Knowledge-sharing KW - Manufacturing enterprise KW - Mediating effect KW - PLS-SEM KW - Supply chain innovations KW - Supply chain learning KW - Supply chain management CY - China ER - TY - JOUR TI - Human-AI Intersection: Understanding the Ethical Challenges, Opportunities, and Governance Protocols for a Changing Data-Driven Digital World AU - Mujtaba B.G. PY - 2025 JO - Business Ethics and Leadership VL - 9 IS - 1 SP - 109 EP - 126 DO - 10.61093/bel.9(1).109-126.2025 AB - Artificial intelligence (AI), in this data-driven digital world, is revolutionizing modern life with far-reaching implications for individuals, teams, organizations, and society. Using comments from 126 undergraduate students in South Florida, this theoretical paper highlights concepts and concerns regarding AI challenges related to cheating, plagiarizing, and biased information. The worries about the impact of AI are analogous to what the internet was three decades ago. People were using the internet as it was being developed, fine-tuned, and improved; it felt like walking over a long and tall bridge as it was being built, and the same is true for the growth of AI. Drawing parallels with the internet’s transformative impact over the past three decades, this paper emphasizes that AI is poised to drive similar positive changes, fostering increased productivity, transparency, accountability, and ethics, but at a much faster pace. In the meantime, due to the availability of data and digital content, the virtual world increased misinformation, disinformation, bias, and prejudiced speech, which AI can easily exacerbate. While AI adoption may cause process-related disruptions, its integration into everyone’s daily life is inevitable. As a natural extension of the information superhighway, AI will usher in a new wave of innovation, ultimately and perpetually transforming the fabric of our personal and professional lives. Drawing on literature and recent trends forecasted by experts, this theoretical manuscript provides an overview of AI uses, its history, challenges, and ethical implications for us all. The conceptual paper ends with recommendations for educators, managers, entrepreneurs, and human resources professionals to create awareness regarding the benefits of this new endemic technology, to ease people’s anxiety, and to reduce or mitigate hallucinations so AI tools can be used to enhance everyone’s effectiveness and efficiency. © 2025 by the author. KW - AI and sustainability KW - AI ethics KW - AI implications KW - AI training KW - Artificial intelligence KW - biases in AI KW - generative-AI KW - hallucinations of AI CY - United States ER - TY - JOUR TI - Artificial Intelligence in Higher Education: A State-of-the-Art Overview of Pedagogical Integrity, Artificial Intelligence Literacy, and Policy Integration AU - Adamakis M. AU - Rachiotis T. PY - 2025 JO - Encyclopedia VL - 5 IS - 4 SP - 180 DO - 10.3390/encyclopedia5040180 AB - Artificial Intelligence (AI), particularly Generative AI (GenAI) and Large Language Models (LLMs), is rapidly reshaping higher education by transforming teaching, learning, assessment, research, and institutional management. This entry provides a state-of-the-art, comprehensive, evidence-based synthesis of established AI applications and their implications within the higher education landscape, emphasizing mature knowledge aimed at educators, researchers, and policymakers. AI technologies now support personalized learning pathways, enhance instructional efficiency, and improve academic productivity by facilitating tasks such as automated grading, adaptive feedback, and academic writing assistance. The widespread adoption of AI tools among students and faculty members has created a critical need for AI literacy—encompassing not only technical proficiency but also critical evaluation, ethical awareness, and metacognitive engagement with AI-generated content. Key opportunities include the deployment of adaptive tutoring and real-time feedback mechanisms that tailor instruction to individual learning trajectories; automated content generation, grading assistance, and administrative workflow optimization that reduce faculty workload; and AI-driven analytics that inform curriculum design and early intervention to improve student outcomes. At the same time, AI poses challenges related to academic integrity (e.g., plagiarism and misuse of generative content), algorithmic bias and data privacy, digital divides that exacerbate inequities, and risks of “cognitive debt” whereby over-reliance on AI tools may degrade working memory, creativity, and executive function. The lack of standardized AI policies and fragmented institutional governance highlight the urgent necessity for transparent frameworks that balance technological adoption with academic values. Anchored in several foundational pillars (such as a brief description of AI higher education, AI literacy, AI tools for educators and teaching staff, ethical use of AI, and institutional integration of AI in higher education), this entry emphasizes that AI is neither a panacea nor an intrinsic threat but a “technology of selection” whose impact depends on the deliberate choices of educators, institutions, and learners. When embraced with ethical discernment and educational accountability, AI holds the potential to foster a more inclusive, efficient, and democratic future for higher education; however, its success depends on purposeful integration, balancing innovation with academic values such as integrity, creativity, and inclusivity. © 2025 by the authors. KW - academic integrity policies KW - artificial intelligence literacy KW - cognitive debt KW - generative artificial intelligence KW - large language models KW - pedagogical innovation CY - Greece ER - TY - JOUR TI - From user needs to AI solutions: a human-centered design approach for AI-powered virtual teamwork competency training AU - Hu W. AU - Chan C.K.Y. PY - 2025 JO - International Journal of Educational Technology in Higher Education VL - 22 IS - 1 SP - 52 DO - 10.1186/s41239-025-00551-z AB - This study develops a human-centered design (HCD) approach to create a GenAI trainer that addresses critical gaps in virtual teamwork training for engineering students. While virtual teamwork competency is increasingly essential, current programs often prioritize task completion over competency development. Leveraging generative AI's capabilities for personalized interaction, scenario simulation, and tailored feedback, we employ a three-phase HCD approach: (1) identifying unmet user needs through stakeholder interviews, revealing key challenges in instructional support, training formats, feedback mechanisms, and teamwork awareness; (2) co-designing solutions with instructors and students to create an AI trainer that combines Socratic questioning and scenario-based learning; and (3) testing the system and obtaining feedback from engineering students. Results demonstrate significant improvements across multiple dimensions: transforming passive learning into active experiences, delivering real-time actionable feedback, enhancing conceptual understanding and awareness of virtual teamwork, and developing practical virtual teamwork skills through authentic scenarios. Participant feedback also identified future improvements for enhanced personalization and immersion. This study contributes both theoretically and practically by illustrating how HCD can effectively integrate AI capabilities with pedagogical needs, while providing a replicable model for developing competency-based training tools that balance technological innovation with educational effectiveness. © The Author(s) 2025. KW - Generative AI KW - Human-centered design KW - Virtual teamwork competency ER - TY - JOUR TI - Dynamic capabilities perspective on innovation ecosystem of China’s universities in the age of artificial intelligence: Policy-based analysis AU - Qu C. AU - Kim E. PY - 2022 JO - Journal of Infrastructure, Policy and Development VL - 6 IS - 2 SP - 1661 DO - 10.24294/jipd.v6i2.1661 AB - Universities play a key role in university-industry-government interactions and are important in innovation ecosystem studies. Universities are also expected to engage with industries and governments and contribute to economic development. In the age of artificial intelligence (AI), governments have introduced relevant policies regarding the AI-enabled innovation ecosystem in universities. Previous studies have not focused on the provision of a dynamic capabilities perspective on such an ecosystem based on policy analysis. This research work takes China as a case and provides a framework of AI-enabled dynamic capabilities to guide how universities should manage this based on China’s AI policy analysis. Drawing on two main concepts, which are the innovation ecosystem and dynamic capabilities, we analyzed the importance of the AI-enabled innovation ecosystem in universities with governance regulations, shedding light on the theoretical framework that is simultaneously analytical and normative, practical, and policy-relevant. We conducted a text analysis of policy instruments to illustrate the specificities of the AI innovation ecosystem in China’s universities. This allowed us to address the complexity of emerging environments of innovation and draw meaningful conclusions. The results show the broad adoption of AI in a favorable context, where talents and governance are boosting the advance of such an ecosystem in China’s universities. © by author(s). KW - artificial intelligence KW - dynamic capability KW - innovation ecosystem KW - policy instruments KW - policy stakeholders KW - text analysis KW - universities CY - Japan, China ER - TY - JOUR TI - The dynamics of AI capability and its influence on public value creation of AI within public administration AU - van Noordt C. AU - Tangi L. PY - 2023 JO - Government Information Quarterly VL - 40 IS - 4 SP - 101860 DO - 10.1016/j.giq.2023.101860 AB - Artificial Intelligence (AI) technologies in public administration are gaining increasing attention due to the potential benefits they can provide in improving governmental operations. However, translating technological opportunities into concrete public value for public administrations is still limited. One of the factors hindering this progress is the lack of AI capability within public organisations. The research found that various components of AI capability are essential for successfully developing and using AI technologies, including tangible, intangible, and human-related factors. There is a distinction between the AI capability to develop and the AI capability to implement AI technologies, with more administrations capable of the former but finding difficulties in the latter. A lack of in-house technical expertise to maintain and update the AI systems, legal challenges in deploying developed AI systems, and the capability to introduce changes in the organisation to ensure the system remains operational and used by relevant end-users are among the most critical limiting factors for long-term use of AI by public administrations. The research underlines the strong complementarity between historical eGovernment developments and the capability to deploy AI technologies. The study suggests that funding alone may not be enough to acquire AI capability, and public administrations need to focus on both the capability to develop and implement AI technologies. The research emphasizes that human skillsets, both technical and non-technical, are essential for the successful implementation of AI in public administration. © 2023 The Authors KW - AI-capability KW - Artificial intelligence KW - Digital government KW - Digital government transformation KW - Emerging technologies KW - Public sector innovation CY - Estonia, Italy ER - TY - JOUR TI - Artificial Intelligence Capabilities and R&D Leaps: An Analysis of the Key Factors of Enterprise Innovation Transformation AU - Li J. AU - Pongtornkulpanich A. PY - 2024 JO - Pakistan Journal of Life and Social Sciences VL - 22 IS - 2 SP - 5952 EP - 5965 DO - 10.57239/PJLSS-2024-22.2.00443 AB - This research seeks to assess how AI capabilities and improvements in research and developmental technology impact the generation of innovative transformations in Chinese firms. This paper evaluates the impact of AI and R &D integration on innovation performance using survey data from 500 firms cutting across different industries. In the context of the research study, PLS-SEM was used to demonstrate the impact of AI talents on operation efficiency and decision-making in improving R&D outcomes, thus promoting product innovation and process improvement. Consequently, the research and development undertakings done within commercial organizations will be bound to change the approaches to innovations, with artificial intelligence needed to increase the speed at which such exercises are accomplished. The current study assists policymakers and managers in understanding how to improve innovation performance with AI and R&D. © (2023), (Elite Scientific Publications). All Rights Reserved. KW - Artificial Intelligence KW - Chinese Enterprises KW - Enterprise KW - Innovation Transformation KW - PLS-SEM KW - R&D CY - Thailand ER - TY - JOUR TI - Integrating Innovation in Healthcare: The Evolution of 'CURA's' AI-Driven Virtual Wards for Enhanced Diabetes and Kidney Disease Monitoring AU - Aljaafari M. AU - El-Deep S.E. AU - Abohany A.A. AU - Sorour S.E. PY - 2024 JO - IEEE Access VL - 12 SP - 126389 EP - 126414 DO - 10.1109/ACCESS.2024.3451369 AB - The healthcare sector faces intricate challenges that demand innovative solutions to enhance patient outcomes and streamline operations. The advent of Artificial Intelligence (AI) has unleashed groundbreaking potential in numerous healthcare domains, including diagnostics, patient care, and disease management. This study explores the incorporation of AI-driven methodologies for the advanced monitoring of diabetes and kidney diseases. It underscores the development of predictive models that utilize six Machine Learning (ML) and four deep learning (DL) models: Our comprehensive data analysis and rigorous model evaluation showcase AI's capability to significantly enhance clinical practices, fostering a proactive healthcare environment marked by precision, personalization, and predictive care. Our results demonstrate substantial enhancements in the accuracy of disease monitoring. For diabetes prediction, the Gradient Boosting (GB) and Random Forest (RF) models achieved up to 89.61% accuracy, while the hybrid LSTM-CNN model outperformed other DL models with an accuracy of 89.7%. For kidney disease prediction, the RF model reached 97.5% accuracy, and the LSTM-CNN model demonstrated a remarkable accuracy of 98.9%. These findings underscore the transformative potential of AI in healthcare, fostering a proactive environment characterized by precision, personalization, and predictive care. Integrating AI within CURA's virtual wards facilitates earlier disease detection and timely interventions and enables more tailored treatment plans, ultimately optimizing healthcare delivery and patient management. © 2013 IEEE. KW - artificial intelligence (AI) KW - chronic kidney disease (CKD) KW - CURA KW - deep learning (DL) KW - diabetes disease KW - Healthcare KW - kidney disease KW - LSTM-CNN KW - machine learning (ML) KW - virtual wards KW - Diagnosis KW - Diseases KW - Patient treatment KW - Personalized medicine KW - Accuracy KW - Artificial intelligence KW - Chronic kidney disease KW - CURA KW - Deep learning KW - Diabetes disease KW - Healthcare KW - Kidney KW - Kidney disease KW - LSTM-CNN KW - Machine-learning KW - Medical services KW - Predictive models KW - Virtual ward KW - Deep learning CY - Egypt, Saudi Arabia ER - TY - JOUR TI - The impact of AI capability on breakthrough technological innovation in China: a perspective of value co-creation within innovation ecosystems AU - Xu X. AU - Yuan H. PY - 2025 JO - Asia Pacific Business Review DO - 10.1080/13602381.2025.2496511 AB - This study aims to uncover the mechanism through which artificial intelligence (AI) capability influences breakthrough technological innovation within the context of innovation ecosystems. Based on 201 survey responses collected from Chinese enterprises, the research model was tested using partial least squares structural equation modelling (PLS-SEM). The findings demonstrate that AI capability positively influences both high-end and low-end breakthrough technological innovations, with ecosystem value co-creation serving as a mediating mechanism. These results illuminate the black box of how AI capability affects breakthrough technological innovation and provide new insights for open innovation research in the AI era. © 2025 Informa UK Limited, trading as Taylor & Francis Group. KW - AI capability KW - Artificial intelligence KW - breakthrough technological innovation KW - China KW - innovation ecosystem KW - value co-creation CY - China ER - TY - JOUR TI - A design framework for operationalizing trustworthy artificial intelligence in healthcare: Requirements, tradeoffs and challenges for its clinical adoption AU - Moreno-Sánchez P.A. AU - Del Ser J. AU - van Gils M. AU - Hernesniemi J. PY - 2026 JO - Information Fusion VL - 127 SP - 103812 DO - 10.1016/j.inffus.2025.103812 AB - Artificial Intelligence (AI) holds great promise for transforming healthcare, particularly in disease diagnosis, prognosis, and patient care. The increasing availability of digital medical data, such as images, omics data, biosignals, and electronic health records, combined with advances in computing, has enabled AI models to approach expert-level performance. However, widespread clinical adoption remains limited, primarily due to challenges beyond technical performance, including ethical concerns, regulatory barriers, and lack of trust. To address these issues, medical AI systems must align with the principles of Trustworthy AI (TAI), which emphasize human agency and oversight, algorithmic robustness, privacy and data governance, transparency, bias and discrimination avoidance, and accountability. Yet, the complexity of healthcare processes (e.g., screening, diagnosis, prognosis, and treatment) and the diversity of stakeholders (clinicians, patients, providers, regulators) complicate the integration of TAI principles. To bridge the gap between TAI theory and practical implementation, this paper proposes a design framework to support developers in embedding TAI principles into medical AI systems. Thus, for each stakeholder identified across various healthcare processes, we propose a disease-agnostic collection of requirements that medical AI systems should incorporate to adhere to the principles of TAI. Additionally, we examine the challenges and tradeoffs that may arise when applying these principles in practice. To illustrate the discussion, we focus on cardiovascular diseases, which is a field marked by both high prevalence and active AI innovation, and demonstrate how TAI principles have been applied and where key obstacles persist. © 2025 The Author(s). KW - AI fairness KW - AI safety KW - Design framework KW - Explainable AI KW - Health stakeholders KW - Healthcare KW - Human agency and oversight KW - Medical AI KW - Privacy KW - Trustworthy AI KW - Artificial intelligence KW - Diseases KW - Electronic health record KW - Ethical aspects KW - Medical computing KW - Medical imaging KW - Medical information systems KW - Patient treatment KW - Artificial intelligence fairness KW - Artificial intelligence safety KW - Design frameworks KW - Explainable artificial intelligence KW - Health stakeholder KW - Healthcare KW - Human agency KW - Human oversight KW - Medical artificial intelligence KW - Privacy KW - Trustworthy artificial intelligence KW - Diagnosis CY - Finland, Spain ER - TY - JOUR TI - Empowering AI-Driven Proactive and Reactive Green Innovation: Exploring Knowledge Management, Trust and Sustainability AU - Abdulmuhsin A.A. AU - Alkhwaldi A.F. AU - Rehman S.U. AU - Thabit S.M.M. AU - Hussein H.D.H. AU - Dbesan A.H. PY - 2026 JO - Knowledge and Process Management DO - 10.1002/kpm.70026 AB - This study examines the integration of artificial intelligence (AI) and knowledge management (KM) processes and their role in fostering proactive and reactive green innovation (GI) in the Iraqi oil industry. It also explores the moderating effects of trust in technologies and sustainability orientation. Using a cross-sectional design, data were collected from 612 middle-level managers in Iraqi oil companies through a structured questionnaire. The data were analysed using SmartPLS v3.9 and SPSS v26 to assess measurement validity, reliability and the hypothesised relationships. The findings indicate that AI has a significant positive effect on both KM processes and GI. KM processes play a crucial mediating role by transforming AI capabilities into proactive and reactive GI outcomes. While trust in technologies and sustainability orientation moderate these relationships, their effects are relatively modest. Theoretically, the study underscores the importance of integrating AI and KM to enhance environmental performance. It contributes original empirical evidence from a challenging and underexplored context, offering insights into the conditions enabling GI in traditional industries. © 2026 John Wiley & Sons Ltd. KW - artificial intelligence KW - green innovation KW - knowledge management KW - oil industry KW - sustainability KW - trust in technology CY - Iraq, Jordan, United States, Malaysia ER - TY - JOUR TI - From Synergy to Strain: Exploring the Psychological Mechanisms Linking Employee–AI Collaboration and Knowledge Hiding AU - Li Y.-B. AU - Liao T.-H. AU - Tsai C.-H. AU - Wu T.-J. PY - 2026 JO - Behavioral Sciences VL - 16 IS - 1 SP - 13 DO - 10.3390/bs16010013 AB - As artificial intelligence (AI) becomes an integral part of organizational operations, collaboration between humans and AI is transforming employees’ work experiences and behavioral patterns. This study examines the psychological challenges and coping responses associated with such collaboration. Drawing on Cognitive Appraisal Theory, we construct and test a theoretical framework that connects employee–AI collaboration to knowledge hiding via job insecurity, while considering AI trust as a moderating variable. Data were collected through a three-wave time-lagged survey of 348 employees working in knowledge-intensive enterprises in China. The empirical results demonstrate that (1) employee–AI collaboration elevates perceptions of job insecurity; (2) job insecurity fosters knowledge-hiding behavior; (3) job insecurity mediates the link between collaboration and knowledge hiding; and (4) AI trust buffers the positive effect of collaboration on job insecurity, thereby reducing its indirect impact on knowledge hiding. These findings reveal the paradoxical role of AI collaboration: although it enhances efficiency, it may also provoke defensive reactions that inhibit knowledge exchange. By highlighting the role of AI trust in shaping employees’ cognitive appraisals, this study advances understanding of how cognitive appraisals influence human adaptation to intelligent technologies. Practical insights are offered for managers aiming to cultivate trust-based and psychologically secure environments that promote effective human–AI collaboration and organizational innovation. © 2025 by the authors. KW - AI trust KW - Cognitive Appraisal Theory KW - employee–AI collaboration KW - job insecurity KW - knowledge hiding KW - adult KW - Article KW - artificial intelligence KW - cognitive appraisal KW - conceptual framework KW - descriptive research KW - employee KW - female KW - human KW - job insecurity KW - knowledge KW - male KW - psychological functioning KW - social problem KW - workplace CY - China, Taiwan ER - TY - JOUR TI - DEVELOPING EMPATHY AS A STRATEGIC AND TACTICAL SKILL IN THE CONTEXT OF INNOVATING FOR TRANSGENDER CONSUMERS AU - Braig B.M. AU - Witt H. PY - 2024 JO - Marketing Education Review VL - 34 IS - 1 SP - 60 EP - 76 DO - 10.1080/10528008.2023.2226124 AB - We begin with the premise that empathy adds value as a strategic marketing skill. Getting into the heads and hearts of consumers enables tailored offerings and tactics that meet the unique, richly contextualized needs of a given target audience segment. The advent of marketing automation and artificial intelligence (AI) is predicted to make human-centered skills even more critical to develop, as AI’s capabilities stop short of emotional connection and interpretation. As a result, we ask how empathy can be cultivated and applied, and if so, will empathy toward a given market segment result in attitude or behavior change? This paper details a three-stage project that gives students the opportunity to practice using empathy toward a specific market segment–transgender consumers. Students leverage empathy to ultimately develop concrete innovations across a broad range of marketing tactics. We also discuss the impact of the project on students. Results indicate that students completing the project embraced empathy as a strategic skill, and in the process, many also expressed intended support behaviors for the transgender community, although attitudes toward the transgender community did not change. © 2023 Society for Marketing Advances. CY - United States ER - TY - JOUR TI - The impact of artificial intelligence capabilities on servitization: The moderating role of absorptive capacity-A dynamic capabilities perspective AU - Abou-Foul M. AU - Ruiz-Alba J.L. AU - López-Tenorio P.J. PY - 2023 JO - Journal of Business Research VL - 157 SP - 113609 DO - 10.1016/j.jbusres.2022.113609 AB - The advent of artificial intelligence (AI)-based technologies has opened new opportunities for manufacturers to maintain their technological edge and address pressing societal challenges. This research investigates the nature of the relationships between AI capabilities, servitization, and the role of absorptive capacity. Building on dynamic capabilities literature, we developed and empirically tested a model using structural equation modeling (SEM) and further applied a fuzzy-set qualitative comparative analysis (fsQCA). Through the construct of AI capabilities and its four sub-dimensions, we find supportive evidence from our model estimates employing data from 185 manufacturing firms in the US and EU. The study findings highlight the positive impact of AI capabilities on servitization; this relationship is positively moderated by absorptive capacity. Furthermore, the road to servitization is through advancing AI capabilities related to internal process and resource optimization coupled with AI for social innovation services. The study's theoretical and pragmatic implications are discussed. © 2022 Elsevier Inc. KW - Artificial intelligence KW - Dynamic capabilities KW - Fuzzy set qualitative comparative analysis (fsQCA) KW - Servitization KW - Social innovation CY - Palestine, United Kingdom, Spain ER - TY - JOUR TI - KEY COMPETENCES for the ADOPTION of AI-BASED INNOVATIONS in ORGANISATIONS AU - Baumgartner M. AU - Horvat D. AU - Kinkel S. AU - Kick E. PY - 2024 JO - International Journal of Innovation Management VL - 28 IS - 10 SP - 2440002 DO - 10.1142/S1363919624400024 AB - Successfully adopting AI and realising its full innovation potential requires different competences within a company. We identified five clusters, namely, AI decision-making, AI utilisation, AI foundational, AI development and leadership & moderation competences, as the basis for our AI competence framework, combining 35 individual competences. Based on a quantitative survey of 215 companies, we determined the importance of these competences for the successful adoption of AI innovations and their current availability within companies. According to our findings, AI foundational competences play a particularly critical role compared to the other competence clusters, which are considered important but comparatively rarely available. Furthermore, our analyses show that companies with higher levels of AI foundational, AI development, and AI utilisation competences have significantly higher AI innovation capabilities. Again, in particular AI foundational competences seem to fertilize the capabilities to identify appropriate AI use cases, to make decisions about AI innovation adoption, to successfully integrate AI into internal processes, and to use the AI innovation effectively within the organisation. Our findings thus enrich the theoretical discourse on competences for organisational adoption of AI innovations and guide practitioners in taking action to develop the necessary competences. © 2024 World Scientific Publishing Europe Ltd. KW - AI capabilities KW - artificial intelligence KW - company KW - Competences KW - digital competences KW - framework KW - future skills KW - innovation adoption KW - organisation KW - survey CY - Germany ER - TY - JOUR TI - The green transition journey: How digital platforms and AI capabilities drive green ambidexterity innovation, circular economy and sustainable performance AU - Duong C.D. PY - 2026 JO - World Development Sustainability VL - 8 SP - 100296 DO - 10.1016/j.wds.2026.100296 AB - Despite growing attention to sustainability in SMEs, limited research explains the capability-building mechanism through which digital and AI capabilities enable green transition and circular economy outcomes under resource constraints. Drawing on Dynamic Capability Theory, this study examines how digital platform capability and AI capability foster green transition and ambidextrous green innovation, thereby enhancing circular economy practices and sustainable performance. Using survey data from 286 Vietnamese SMEs and applying hierarchical regression and polynomial regression with response surface analysis, the findings show that digital capability exerts a stronger effect on green transition than AI capability. Both exploratory and exploitative green innovation significantly improve sustainability outcomes, though through distinct pathways. Balanced innovation maximizes circular economy practices, whereas imbalance may weaken circularity but improve performance. The study advances dynamic capability theory by clarifying the innovation-mediated pathway linking digital transformation to sustainability in emerging-economy SMEs. © 2026 The Author(s). KW - AI capabilities KW - Circular economy practices KW - Digital platform capabilities KW - Green ambidexterity innovation KW - Green transition KW - Sustainable performance ER - TY - JOUR TI - Subthalamic nucleus or globus pallidus internus deep brain stimulation for the treatment of parkinson's disease: An artificial intelligence approach AU - Shin D. AU - Tang T. AU - Carson J. AU - Isaac R. AU - Dinh C. AU - Im D. AU - Fay A. AU - Isaac A. AU - Cho S. AU - Brandt Z. AU - Nguyen K. AU - Shaffrey I. AU - Yacoubian V. AU - Taka T.M. AU - Spellicy S. AU - Lopez-Gonzalez M.A. AU - Danisa O. PY - 2025 JO - Journal of Clinical Neuroscience VL - 138 SP - 111393 DO - 10.1016/j.jocn.2025.111393 AB - Background: Generative artificial intelligence (AI) in deep brain stimulation (DBS) is currently unvalidated in its content. This study sought to analyze AI responses to questions and recommendations from the 2018 Congress of Neurological Surgeons (CNS) guidelines on subthalamic nucleus and globus pallidus internus DBS for the treatment of patients with Parkinson's Disease. Methods: Seven questions were generated from CNS guidelines and asked to ChatGPT 4o, Perplexity, Copilot, and Gemini. Answers were “concordant” if they highlighted all points provided by the CNS guidelines; otherwise, answers were considered “non-concordant” and sub-categorized as either “insufficient” or “overconclusive.” AI responses were evaluated for readability via the Flesch-Kincaid Grade Level, Gunning Fog Index, Simple Measure of Gobbledygook (SMOG) Index, and Flesch Reading Ease tests. Results: ChatGPT 4o showcased 42.9% concordance, with non-concordant responses classified as 14.3% insufficient and 42.8% over-conclusive. Perplexity displayed a 28.6% concordance rate, with 14.3% insufficient and 57.1% over-conclusive responses. Copilot showed 28.6% concordance, with 28.6% insufficient and 42.8% over-conclusive responses. Gemini demonstrated 28.6% concordance, with 28.6% insufficient and 42.8% over-conclusive responses. The Flesch-Kincaid Grade Level scores ranged from 14.44 (Gemini) to 18.94 (Copilot), Gunning Fog Index scores varied between 17.9 (Gemini) and 22.06 (Copilot), SMOG Index scores ranged from 16.54 (Gemini) to 19.67 (Copilot), and all Flesch Reading Ease scores were low, with Gemini showing the highest score of 30.91. Conclusion: ChatGPT 4o displayed the most concordance, Perplexity displayed the highest over-conclusive rate, and Copilot and Gemini showcased the most insufficient answers. All responses showcased complex readability. Despite the possible benefits of future developments and innovation in AI capabilities, AI requires further improvement before independent clinical usage in DBS. © 2025 Elsevier Ltd KW - Artificial intelligence KW - Chatgpt KW - Deep brain stimulation KW - Neurosurgery KW - Parkinson's disease KW - Artificial Intelligence KW - Deep Brain Stimulation KW - Globus Pallidus KW - Humans KW - Parkinson Disease KW - Subthalamic Nucleus KW - Article KW - artificial intelligence KW - artificial intelligence chatbot KW - brain depth stimulation KW - ChatGPT KW - clinical outcome KW - clinical practice guideline KW - Copilot KW - Gemini KW - globus pallidus KW - globus pallidus internus KW - human KW - neurosurgery KW - Parkinson disease KW - Perplexity KW - reading KW - reliability KW - subthalamic nucleus KW - Unified Parkinson Disease Rating Scale KW - Parkinson disease KW - procedures KW - therapy CY - United States ER - TY - JOUR TI - Merging two revolutions: A human-artificial intelligence method to study how sustainability and Industry 4.0 are intertwined AU - Calabrese A. AU - Costa R. AU - Tiburzi L. AU - Brem A. PY - 2023 JO - Technological Forecasting and Social Change VL - 188 SP - 122265 DO - 10.1016/j.techfore.2022.122265 AB - Industry 4.0 is an important contributor to industrial innovation and sustainability. Nevertheless, few studies empirically analyse how it acts as a binding force of both business practices. This study examines 1501 sustainability reports using a mixed human-artificial intelligence method based on Python's text mining libraries. This method takes advantage of AI's capabilities to extract information from large samples of data and of human critical thinking to find patterns in those data. Specifically, the method is used to evaluate the adoption of Industry 4.0 technologies, analyse how they are deployed worldwide, and investigate their sustainability outcomes. In terms of overall frequency, robots and cybersecurity are the most often reported technologies. Broken down by the firm's region, Asian firms have the highest rate of adoption, while African firms are lagging. Regarding the themes, Industry 4.0 is mainly adopted to improve production processes and customer experience. A small percentage of firms, particularly in Europe and North America, utilize Industry 4.0 to reduce the environmental footprint of their operations. Furthermore, results indicate that Industry 4.0 and sustainability are following two routes. Some firms have massively adopted Industry 4.0 to increase operational efficiency and reaped environmental gains as an indirect consequence of improved operations. Others have chosen to balance the adoption of technologies aimed to increase productivity with innovations whose explicit aim is the reduction of their operations' environmental footprint, such as additive manufacturing. Eastern firms tend to follow the first route while western firms the second. African and South American firms are still at a very early stage in their Industry 4.0 and sustainability journey. At the global level, Industry 4.0 is still far from being utilized as a catalyst to develop sustainability-driven business models. © 2023 Elsevier Inc. KW - GRI KW - Industry 4.0 KW - Innovation KW - Sustainability KW - Text mining KW - Europe KW - North America KW - Artificial intelligence KW - Data mining KW - Environmental technology KW - Industry 4.0 KW - Artificial intelligence methods KW - Binding forces KW - Business practices KW - Environmental footprints KW - GRI KW - Industrial innovation KW - Industrial sustainability KW - Innovation KW - Sustainability report KW - Text-mining KW - artificial intelligence KW - empirical analysis KW - industrial development KW - innovation KW - sustainability KW - Sustainable development CY - Italy, Germany, Denmark ER - TY - JOUR TI - Experiential learning and governance in the socio-technical era: Modeling responsible AI performance via explainability and adaptability AU - Liu M. AU - Almugren I. AU - Chotia V. AU - Sahore N. AU - Kurucz A. PY - 2026 JO - Technological Forecasting and Social Change VL - 227 SP - 124624 DO - 10.1016/j.techfore.2026.124624 AB - The concept of artificial intelligence (AI) is altering the way organizations operate. AI systems will deliver more intelligent results in a shorter period of time, starting with decision-making up to innovation. However, the more it is adopted, the more issues to do with fairness, transparency, and accountability are raised. Most organizations are finding it difficult to reconcile innovation and ethical responsibility. This study discusses the role of internal capabilities in making firms govern AI responsibly. The study proposes a model linking four key organizational capabilities, i.e., explainable AI capability, contextual learning adaptability, experiential learning orientation, and organizational ethical alignment to responsible AI performance. The impact of these capabilities on user interpretability and trust, responsible AI governance maturity, and decision transparency is also examined in this study. The results show that explainable AI capability and learning adaptability enhance user trust, while experiential learning orientation and organizational ethical alignment significantly improve governance maturity. Governance maturity and decision transparency lead to stronger responsible AI performance. Interestingly, not all expected paths held as user interpretability trust and governance maturity did not directly predict decision transparency. The findings show that building technical and cultural capabilities inside firms is essential not just to deploy AI effectively, but to do it responsibly. For leaders, this means moving beyond checklists and toward meaningful governance rooted in learning, transparency, and ethical alignment. © 2026 Elsevier Inc. KW - Contextual learning adaptability KW - Decision transparency KW - Experiential learning orientation KW - Explainable AI capability KW - Organizational ethical alignment KW - Responsible AI governance maturity KW - Responsible AI performance KW - User interpretability trust KW - Alignment KW - Artificial intelligence KW - Decision making KW - Ethical technology KW - Learning systems KW - Contextual learning KW - Contextual learning adaptability KW - Decision transparency KW - Experiential learning KW - Experiential learning orientation KW - Explainable artificial intelligence capability KW - Interpretability KW - Learning orientation KW - Organisational KW - Organizational ethical alignment KW - Performance KW - Responsible artificial intelligence governance maturity KW - Responsible artificial intelligence performance KW - User interpretability trust KW - artificial intelligence KW - ethics KW - governance approach KW - learning KW - performance assessment KW - Transparency CY - China, Saudi Arabia, India, Hungary ER - TY - JOUR TI - AI-Augmented Design Thinking: Potentials, Challenges, and Mitigation Strategies of Integrating Artificial Intelligence in Human-Centered Innovation Processes AU - Polster L. AU - Bilgram V. AU - Gortz S. PY - 2025 JO - IEEE Engineering Management Review VL - 53 IS - 5 SP - 193 EP - 214 DO - 10.1109/EMR.2024.3512866 AB - The integration of artificial intelligence (AI) into innovation management has expanded into creative domains such as design thinking (DT), yet its role within complex, collaborative innovation frameworks remains underexplored. This article addresses this gap by investigating how professionals perceive and utilize AI in DT workshops. Using affordance theory as a conceptual lens, we conducted observational studies and semistructured interviews with DT experts to identify AI's action potentials, constraints, and mitigation strategies in this context. Our findings highlight four key affordances of AI in DT workshops: enhanced creativity, support for analytical tasks, facilitation of task initiation, and acceleration of processes. However, these benefits are tempered by challenges, including reduced team collaboration, diminished ownership of AI-generated outputs, and disruptions to workshop flow. The article reveals distinct human–AI interaction archetypes, underscoring the dynamic interplay between human expertise and AI capabilities. To mitigate constraints, we propose strategies such as prepreparing AI-generated content, defining clear roles for AI and human inputs, and fostering collaborative reflection on AI outputs. By illuminating AI's potential and limitations within DT, this article contributes to the innovation management literature and offers actionable insights for practitioners seeking to integrate AI into hybrid innovation processes. © 1973-2011 IEEE. KW - AI-augmented design thinking KW - artificial intelligence (AI) KW - ChatGPT KW - design thinking (DT) KW - generative AI (GenAI) KW - ideation workshop KW - innovation KW - large language models KW - Ai-augmented design thinking KW - Chatgpt KW - Design thinking KW - Generative artificial intelligence KW - Ideation workshop KW - Innovation KW - Language model KW - Large language model KW - Mitigation strategy CY - Germany ER - TY - JOUR TI - AI Legislation, Private International Law and the Protection of Human Rights in the European Union AU - Malacka M. PY - 2024 JO - European Studies: The Review of European Law, Economics and Politics VL - 11 IS - 1 SP - 122 EP - 151 DO - 10.2478/eustu-2024-0006 AB - The emergence of artificial intelligence challenges existing legal frameworks, notably in civil liability, cross-border regulation, and the protection of fundamental rights. The European Union has developed the AI Regulation and AI Liability Directive to address these issues, emphasizing transparency, accountability, and consumer protection while promoting innovation. This regulatory framework categorizes AI systems by risk levels and mandates strict compliance for high-risk applications, ensuring alignment with fundamental EU values. Additionally, the Council of Europe AI Convention complements these efforts by focusing on human rights, democracy, and the rule of law, offering a broader international perspective. Both frameworks present complementary yet distinct approaches to AI governance, with the EU focusing on market harmonization and innovation, and the Convention prioritizing ethical and social dimensions. The interplay between these instruments underscores the EU's ambition to set a global standard for AI regulation while addressing the complexities of private international law and cross-border liability. The success of this legal framework will depend on its flexibility, coherence, and ability to adapt to rapid technological developments. © 2024 Michal Malacka, published by Sciendo. KW - Accountability KW - AI Liability Directive KW - Artificial Intelligence (AI) KW - Civil Liability KW - Council of Europe's AI Convention KW - Cross-border Regulation KW - EU AI Regulation KW - Human Rights KW - Innovation KW - Private International Law (PIL) KW - Transparency CY - Czech Republic ER - TY - JOUR TI - Role of AI capabilities in sustainable firm performance: mediating role of innovative work behavior and the moderating effect of environmental dynamism AU - Shahid M.K. AU - Mahmood K. AU - Altayyar R.S. AU - Abdullah H. PY - 2026 JO - International Journal of Productivity and Performance Management VL - 75 IS - 2 SP - 651 EP - 671 DO - 10.1108/IJPPM-02-2025-0114 AB - Purpose – This study assesses the role of organizational AI capabilities (AIC) in framing employees' innovative work behavior (IWB) and their subsequent effect on sustainable firm performance (SFP). In addition, the role of environmental dynamism (ED) was also assessed on the relationship between AIC and IWB pursuant to SFP. Design/methodology/approach – We collected data from managerial-level employees working in the pharmaceutical industry across Pakistan. A validated survey questionnaire was circulated using snowball sampling technique, and partial least square structural equation modeling (PLS-SEM) was applied to the data collected from 366 respondents. After confirming the reliability and validity of the model, we assessed the robustness of the results through linearity, endogeneity and unobserved heterogeneity tests. Findings – The results of the study indicate that AIC significantly influences IWB and SFP. The moderation-mediation analysis confirms the importance of both the direct and indirect effects of ED on the relationship between AIC and SFP, as well as the role of ED in enhancing IWB among employees in the pharmaceutical industry. Originality/value – This study provides significant insights in advancing sustainable development through technological innovation, considering ED among the pharmaceutical industry within the developing economies having identical technological, sectoral and environmental concerns. © 2025 Emerald Publishing Limited KW - AI capabilities KW - Environmental dynamism KW - Innovative work behavior KW - PLS-SEM KW - Sustainable firm performance CY - Pakistan, Malaysia, Saudi Arabia ER - TY - JOUR TI - Command Redefined: Neural-Adaptive Leadership in the Age of Autonomous Intelligence AU - Riti R.I. AU - Abrudan C.I. AU - Bacali L. AU - Bâlc N. PY - 2025 JO - AI (Switzerland) VL - 6 IS - 8 SP - 176 DO - 10.3390/ai6080176 AB - Artificial intelligence has taken a seat at the executive table and is threatening the fact that human beings are the only ones who should be in a position of power. This article gives conjectures on the future of leadership in which managers will collaborate with learning algorithms in the Neural Adaptive Artificial Intelligence Leadership Model, which is informed by the transformational literature on leadership and socio-technical systems, as well as the literature on algorithmic governance. We assessed the model with thirty in-depth interviews, system-level traces of behavior, and a verified survey, and we explored six hypotheses that relate to algorithmic delegation and ethical oversight, as well as human judgment versus machine insight in terms of agility and performance. We discovered that decisions are made quicker, change is more effective, and interaction is more vivid where agile practices and good digital understanding exist, and statistical tests propose that human flexibility and definite governance augment those benefits as well. It is single-industry research that contains self-reported measures, which causes research to be limited to other industries that contain more objective measures. Practitioners are provided with a practical playbook on how to make algorithmic jobs meaningful, introduce moral fail-safes, and build learning feedback to ensure people and machines are kept in line. Socially, the practice is capable of minimizing bias and establishing inclusion by visualizing accountability in the code and practice. Filling the gap between the theory of leadership and the reality of algorithms, the study provides a model of intelligent systems leading in organizations that can be reproduced. © 2025 by the authors. KW - AI–human governance KW - algorithmic decision-making KW - engineering management innovation KW - ethical leadership in AI KW - neural-adaptive leadership CY - Romania ER - TY - JOUR TI - Unveiling the transformative role of artificial intelligence in improving business process performance AU - Sharma P. AU - Dang G.P. PY - 2025 JO - Journal of Manufacturing Technology Management SP - 1 EP - 21 DO - 10.1108/JMTM-04-2025-0308 AB - Purpose – The present study intends to examine the role of AI in enhancing the process performance of manufacturing entities in India. It aims to explore factors that measure the business process performance, which are influenced by the adoption of AI within various business processes. Design/methodology/approach – The research conducted an empirical survey on Indian Manufacturing organizations using a questionnaire-based survey method on those businesses that have adopted AI within their business processes. For this, the study targeted C-level technology managers in the select manufacturing businesses in India. The structural equation modelling (SEM) technique was applied for data analysis. Findings – The findings of the study indicate that the adoption of AI has a significant positive role in improving the business process performance of manufacturing firms. It can bring manifold advantages to business firms, including accuracy, speed, operational cost reduction, improved quality, enhanced productivity and efficiency. These benefits can help organizations in India to gain a competitive advantage in the changing business world. Originality/value – The present article explores the transformative role of AI in the manufacturing sector of a rapidly developing economy like India. It provides empirical evidence on certain crucial benefits of AI, including quality enhancement, cost efficiency and productivity enhancement. This research offers valuable insights for both business leaders and policymakers on leveraging AI to drive industrial growth and competitiveness. It contributes to the limited literature on the practical implications of AI in emerging markets, particularly within the Indian context. © 2025 Emerald Publishing Limited KW - AI capabilities KW - Business process performance KW - Infrastructure flexibility KW - Management capabilities KW - Manufacturing entities KW - Personnel expertise KW - Administrative data processing KW - Competition KW - Cost reduction KW - Efficiency KW - AI capability KW - Business Process KW - Business process performance KW - Design/methodology/approach KW - Empirical surveys KW - Infrastructure flexibility KW - Management capabilities KW - Manufacturing entities KW - Personnel expertise KW - Process performance KW - Industrial research CY - India ER - TY - JOUR TI - The critical role of HRM in AI-driven digital transformation: a paradigm shift to enable firms to move from AI implementation to human-centric adoption AU - Fenwick A. AU - Molnar G. AU - Frangos P. PY - 2024 JO - Discover Artificial Intelligence VL - 4 IS - 1 SP - 34 DO - 10.1007/s44163-024-00125-4 AB - The rapid advancement of Artificial Intelligence (AI) in the business sector has led to a new era of digital transformation. AI is transforming processes, functions, and practices throughout organizations creating system and process efficiencies, performing advanced data analysis, and contributing to the value creation process of the organization. However, the implementation and adoption of AI systems in the organization is not without challenges, ranging from technical issues to human-related barriers, leading to failed AI transformation efforts or lower than expected gains. We argue that while engineers and data scientists excel in handling AI and data-related tasks, they often lack insights into the nuanced human aspects critical for organizational AI success. Thus, Human Resource Management (HRM) emerges as a crucial facilitator, ensuring AI implementation and adoption are aligned with human values and organizational goals. This paper explores the critical role of HRM in harmonizing AI's technological capabilities with human-centric needs within organizations while achieving business objectives. Our positioning paper delves into HRM's multifaceted potential to contribute toward AI organizational success, including enabling digital transformation, humanizing AI usage decisions, providing strategic foresight regarding AI, and facilitating AI adoption by addressing concerns related to fears, ethics, and employee well-being. It reviews key considerations and best practices for operationalizing human-centric AI through culture, leadership, knowledge, policies, and tools. By focusing on what HRM can realistically achieve today, we emphasize its role in reshaping roles, advancing skill sets, and curating workplace dynamics to accommodate human-centric AI implementation. This repositioning involves an active HRM role in ensuring that the aspirations, rights, and needs of individuals are integral to the economic, social, and environmental policies within the organization. This study not only fills a critical gap in existing research but also provides a roadmap for organizations seeking to improve AI implementation and adoption and humanizing their digital transformation journey. © The Author(s) 2024. KW - AI knowledge KW - AI leadership KW - AI policies KW - AI tools KW - Behavioral science KW - HRM KW - Humanizing AI KW - Organizational culture KW - Behavioral research KW - Data handling KW - Environmental protection KW - Metadata KW - Artificial intelligence knowledge KW - Artificial intelligence leadership KW - Artificial intelligence policy KW - Artificial intelligence tools KW - Behavioral science KW - Digital transformation KW - Human resources management KW - Human-centric KW - Humanizing artificial intelligence KW - Organizational cultures KW - Human resource management CY - United Kingdom ER - TY - JOUR TI - A Review of Generative AI's Impact on Workforce Transformation and Future Skill Requirements AU - Oyetade K. AU - Zuva T. PY - 2025 JO - OIDA International Journal of Sustainable Development VL - 18 IS - 12 SP - 187 EP - 196 AB - The Fourth Industrial Revolution (4IR) is transforming industries and workforce structures through rapid advancements in artificial intelligence (AI), automation, and digital technologies. Among these innovations, Generative AI (GAI) has emerged as a disruptive force capable of autonomously producing text, images, and code, thereby redefining traditional job roles and skills requirements. While GAI boosts productivity and creativity in a variety of industries, it also poses issues such as job displacement, skill mismatches, and ethical concerns. This study reviews 46 peer-reviewed journal articles, conference papers, and policy reports published between 2018 and 2025 to examine GAI’s impact on workforce transformation and the evolving demand for future skills. Using a qualitative literature review approach and thematic analysis, the study identifies recurring themes such as technological disruption, job displacement, skill mismatches, and the emergence of AI-driven professions. To ensure validity and minimize internal bias from third-party sources, the analysis applied triangulation, source credibility checks, and cross-disciplinary comparison, ensuring that findings were grounded in verified evidence. The results emphasize the growing need for continuous learning, reskilling, and integration of AI-related competencies, particularly digital literacy, critical thinking, creativity, and emotional intelligence, within education and professional development programs. Policymakers and industries must collaborate to develop inclusive strategies that promote equitable workforce adaptation, ethical AI governance, and resilience in the face of automation. This study contributes to the discussion on responsible AI adoption by providing insights into workforce evolution, skill adaptation, and policy directions in the era of 4IR. © Authour(s) OIDA International Journal of Sustainable Development, Ontario International Development Agency, Canada. KW - AI Policy and Ethical Governance KW - Fourth Industrial Revolution (4IR) KW - Future Skills Development KW - Generative Artificial Intelligence (GAI) KW - Workforce Transformation CY - South Africa ER - TY - JOUR TI - Adoption of robust business analytics for product innovation and organizational performance: the mediating role of organizational data-driven culture AU - Chaudhuri R. AU - Chatterjee S. AU - Vrontis D. AU - Thrassou A. PY - 2024 JO - Annals of Operations Research VL - 339 IS - 3 SP - 1757 EP - 1791 DO - 10.1007/s10479-021-04407-3 AB - In the present digital environment, a data-driven organizational culture has become a vital emerging driver of organizational growth. This data-driven culture has assumed an advanced shape due to adoption of artificial intelligence (AI) integrated business analytics tools in the organization. Data-driven culture in the organization could considerably impact product innovation strategy as well as organizational process alteration. In this context, the aim of this study is to investigate how an organization’s data-driven culture impacts process performance and product innovation that led to enhanced organizational overall performance and higher business value. Methodologically, supported by relevant extant literature and inputs from the resource-based view and dynamic capability theories (organizational context), a conceptual model and a set of hypotheses are initially developed. These are subsequently statistically validated through a survey involving 513 usable responses from employees of different organizations using business analytics tools embedded with AI capability. The findings demonstrate that an organizational data-driven culture has considerable moderating impact on product innovation and process improvement, which ultimately enhance business value through improved organizational overall performance. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021. KW - Business analytics KW - Business value KW - Data acquisition KW - Data-driven culture KW - Organizational performance KW - Product innovation CY - India, Cyprus ER - TY - JOUR TI - Addressing brain drain and strengthening governance for advancing government readiness in artificial intelligence (AI) AU - Socol A. AU - Iuga I.C. PY - 2024 JO - Kybernetes VL - 53 IS - 13 SP - 47 EP - 71 DO - 10.1108/K-03-2024-0629 AB - Purpose: This study aims to investigate the impact of brain drain on government AI readiness in EU member countries, considering the distinctive governance characteristics, macroeconomic conditions and varying levels of ICT specialists. Design/methodology/approach: The research employs a dynamic panel data model using the System Generalized Method of Moments (GMM) to analyze the relationship between brain drain and government AI readiness from 2018 to 2022. The study incorporates various control variables such as GDP per capita growth, government expenditure growth, employed ICT specialists and several governance indicators. Findings: The results indicate that brain drain negatively affects government AI readiness. Additionally, the presence of ICT specialists, robust governance structures and positive macroeconomic indicators such as GDP per capita growth and government expenditure growth positively influence AI readiness. Research limitations/implications: Major limitations include the focus on a specific region of countries and the relatively short period analyzed. Future research could extend the analysis with more comprehensive datasets and consider additional variables that might influence AI readiness, such as the integration of AI with emerging quantum computing technologies and the impact of governance reforms and international collaborations on AI readiness. Practical implications: The theoretical value of this study lies in providing a nuanced understanding of how brain drain impacts government AI readiness, emphasizing the critical roles of skilled human capital, effective governance and macroeconomic factors in enhancing AI capabilities, thereby filling a significant gap in the existing literature. Originality/value: This research fills a significant gap in the existing literature by providing a comprehensive analysis of the interaction between brain drain and government AI readiness. It uses control variables such as ICT specialists, governance structures and macroeconomic factors within the context of the European Union. It offers novel insights for policymakers to enhance AI readiness through targeted interventions addressing brain drain and fostering a supportive environment for AI innovation. © 2024, Adela Socol and Iulia Cristina Iuga. KW - Artificial intelligence (AI) KW - Brain drain KW - EU member countries KW - Governance KW - ICT specialists KW - Macroeconomic indicators KW - Inflation KW - Artificial intelligence KW - Control variable KW - EU member country KW - Governance KW - Governance structures KW - Government expenditure KW - ICT specialist KW - Macroeconomic indicators KW - Member countries KW - Per capita KW - Artificial intelligence CY - Romania ER - TY - JOUR TI - Did the COVID-19 pandemic propel usage of AI in pharmaceutical innovation? New evidence from patenting data AU - Rathi S. AU - Majumdar A. AU - Chatterjee C. PY - 2024 JO - Technological Forecasting and Social Change VL - 198 SP - 122940 DO - 10.1016/j.techfore.2023.122940 AB - It is now much discussed that Artificial Intelligence (AI) as a General-Purpose Technology (GPT) can resolve the efficiency problems of industries, including in pharmaceutical markets where productivity challenges continue in costs and time for new drug discovery. But did the COVID-19 pandemic inadvertently accelerate the pace of AI adoption in pharmaceutical innovation? We answer this question using novel data on pharmaceutical patents. We use two different databases to analyze abstracts of pharmaceutical patents applied in the USA. Topic modeling was used to identify patents with technical artifacts and classify them as treated group AI-adopting patents. An AI dictionary is used to match AI-related keywords in the patent abstracts. Subsequently, using a difference-in-differences research design we observe that both presence and count of AI keywords in pharmaceutical patents have increased with pandemic. An increase in AI is also related to reduced time taken from application to publication of a patent suggesting innovation efficiencies in the industry. Finally, we find that results are driven by firms that have already built AI capability in the past. Our results remain consistent with various robustness checks, and we conclude by discussing managerial and policy implications of our findings. © 2023 KW - AI KW - Innovation management KW - Pandemic KW - Patents KW - Pharmaceutical industry KW - United States KW - Abstracting KW - Patents and inventions KW - Public policy KW - Drug discovery KW - General purpose technologies KW - Innovation management KW - Pandemic KW - Patent KW - Pharmaceutical industry KW - Pharmaceutical innovations KW - Pharmaceutical market KW - Technical artifacts KW - Topic Modeling KW - artificial intelligence KW - COVID-19 KW - data KW - epidemic KW - innovation KW - pharmaceutical industry KW - Efficiency CY - India, United States ER - TY - JOUR TI - CAPABILITIES PATHWAY TO FIRM PERFORMANCE: MODERATING ROLE OF ENVIRONMENTAL DYNAMISM IN THE FOOD MANUFACTURING FIRMS OF PAKISTAN AU - Naz S. AU - Ul Haq N. AU - Nasir S. PY - 2022 JO - International Journal of Innovation Management VL - 26 IS - 4 SP - 2250030 DO - 10.1142/S136391962250030X AB - This study examines the significant influence of entrepreneurial orientation (EO), big data analytics capabilities (BDACs), and artificial intelligence capabilities (AICs) on the firm performance (FP) of manufacturing industries of Pakistan using environmental dynamism (ED) as a moderator. For this purpose, we developed a model based on the dynamic capabilities (DCs) view of enterprises and contingency theory (CT) that describes EO's role in adopting big data analytics, artificial intelligence, and FP. The data of 240 respondents were collected and analysed using SPSS and Smart PLS software from the survey of Pakistani food manufacturing firms. The results of our study extend the DC perspective and CT to provide a clearer understanding of the organisation's DCs while also giving managers theoretically supported counsel on matching their EO with their firms' technology capabilities. The findings discovered that EO, big data analytics, and AICs were positively related to FP. Moreover, it illustrates that ED does not moderate the relationship between BDACs, AICs and FP. The findings of this study have important implications for the manufacturing industry in terms of improving an effective method and organisation performance through EO. © 2022 World Scientific Publishing Europe Ltd. KW - artificial intelligence capabilities KW - big data analytics capabilities KW - Entrepreneurial orientation KW - environmental dynamism KW - firm performance CY - Pakistan ER - TY - JOUR TI - Achieving green competitive advantage through generative AI: the mediating roles of organisational creativity and green innovation ambidexterity in manufacturing AU - Ruangkanjanases A. AU - Chen S.-C. AU - Sivarak O. AU - Khan A. PY - 2025 JO - International Journal of Logistics Research and Applications DO - 10.1080/13675567.2025.2573664 AB - This study examines the implementation pathway of Generative AI Capabilities (GAIC) in manufacturing organisations, focusing on the roles of organisational creativity and green innovation ambidexterity in achieving a green competitive advantage. Drawing on the Technology-Organisation-Environment (TOE) framework, this research develops and empirically tests an integrated model using data collected from 297 senior and middle-level managers of manufacturing firms in Taiwan. The results indicate that both technological and organisational contexts have a significant influence on GAIC implementation, whereas the environmental context shows no significant impact. Furthermore, GAIC demonstrates significant positive effects on both organisational creativity and green innovation ambidexterity, which in turn enhances green competitive advantage. This study addresses critical research gaps by making several contributions by extending the TOE framework to encompass GAIC, thereby advancing the understanding of human-AI collaborative creativity. The findings provide novel insights into how manufacturing organisations can strategically implement GAIC to achieve a green competitive advantage. © 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. KW - environmental context KW - Generative AI capabilities KW - green competitive advantage KW - green innovation ambidexterity KW - organisational context KW - technological context CY - Thailand, Taiwan ER - TY - JOUR TI - From Chat To Cheat: the Disruptive Effects of ChatGPT and Academic Integrity in Hong Kong Higher Education AU - Lo N. AU - Chan S. PY - 2025 JO - SN Computer Science VL - 6 IS - 8 SP - 993 DO - 10.1007/s42979-025-04532-x AB - The rapid adoption of AI-powered conversational agents such as ChatGPT is transforming the landscape of higher education in Hong Kong, offering both unprecedented opportunities for personalszed learning and complex challenges to academic integrity. This study investigates the perceptions and experiences of students across eight Hong Kong universities, employing a quantitative design that analyses questionnaire data from 200 students. Results reveal a pronounced polarisation: while students—particularly those with lower GPAs—appreciate ChatGPT’s capacity to clarify complex concepts and facilitate research, their counterparts with higher GPAs express deep concerns about dependency, plagiarism, and the erosion of critical thinking skills. The findings underscore the urgent need for Hong Kong universities to implement comprehensive policies, advanced AI-detection tools, and targeted educational initiatives that foster a culture of integrity and responsible AI use. This research contributes to ongoing debates about the integration of generative AI in higher education, advocating for localised, policy-driven solutions that balance innovation with ethical stewardship. © The Author(s) 2025. KW - Academic integrity KW - Artificial intelligence KW - Higher education KW - Hong kong KW - Perceptions CY - United Kingdom ER - TY - JOUR TI - Curtain call for AI: Transforming theatre through technology AU - Horváth D. PY - 2025 JO - Sustainable Futures VL - 9 SP - 100747 DO - 10.1016/j.sftr.2025.100747 AB - The creative and cultural industries, including theatre, are significantly affected by the development of artificial intelligence (AI). In the theatre sector, there is a need for a deeper understanding of the impact of AI in this area, but the amount of research on this topic is extremely limited. The aim of this paper was to explore, within a complex framework, the applications of AI in the operational, support and artistic areas of theatre. The study also sought to understand the concerns of theatre practitioners regarding the application of AI and to formulate recommendations for its effective integration. To address the research questions, a series of 24 semi-structured interviews were conducted, employing grounded theory methodology with theatre practitioners who already actively utilising AI in their work were or exploring its potential impact. The findings of the study indicate that the adoption of AI-based solutions in operational and support areas is predominantly a bottom-up initiative, primarily in marketing, audience management and sales. In contrast, experimentation with AI is more prevalent in independent theatres and contemporary productions within the artistic domain. However, opinions on the utilisation of AI remain divided. The study emphasises the significance of human creativity and the necessity for a nuanced exploration of the role of AI in theatre. It advocates for transparency, collaboration, education, regulation and policy advocacy to ensure responsible AI integration. © 2025 KW - Artificial intelligence KW - Culture KW - Digital transformation KW - Grounded theory KW - Innovation KW - Theatre CY - Hungary ER - TY - JOUR TI - Artificial Intelligence capabilities in Digital Servitization: Identifying digital opportunities for different service types AU - Ayala N.F. AU - Rodrigues da Silva J. AU - Cannarozzo Tinoco M.A. AU - Saccani N. AU - Frank A.G. PY - 2025 JO - International Journal of Production Economics VL - 284 SP - 109604 DO - 10.1016/j.ijpe.2025.109604 AB - The advancement of digital technologies and the pursuit of higher-value solutions have driven companies to expand their portfolios with smart products and digital services, resulting in the innovation known as 'digital servitization.' This concept merges servitization —integrating services with products— and digitization —enhancing operations through digital technologies. While previous research has examined digital servitization and smart technologies, a gap remains in understanding how Artificial Intelligence (AI) specifically supports various types of digital servitization across both back-office and front-office activities. This study addresses this gap by investigating how AI enhances digital servitization through six case studies of companies implementing AI-driven servitized solutions. Adopting a capability theoretical perspective, we analyze the application of AI in both back-office and front-office activities for the delivery of base, intermediate, and advanced services. Our findings reveal that AI's role varies by service type, affecting customer interactions and operational tasks differently. We present a theoretical framework with five propositions that elucidate how AI capabilities enhance digital servitization. This framework gives scholars a refined understanding of AI's roles beyond the generalized black box approach and offers practitioners practical insights on leveraging AI for digital transformation. © 2025 Elsevier B.V. KW - Artificial intelligence KW - Capabilities KW - Case study KW - Digital servitization KW - Servitization KW - Back office KW - Capability KW - Case-studies KW - Different services KW - Digital opportunity KW - Digital servitization KW - Digital technologies KW - Servitization KW - Smart products CY - Brazil, Mexico, Italy ER - TY - JOUR TI - A COMPARATIVE ANALYSIS OF ARTIFICIAL INTELLIGENCE REGULATION IN ASEAN AND THE EUROPEAN UNION AU - Lu Y. AU - Tie F.H. PY - 2025 JO - Journal of Governance and Regulation VL - 14 IS - 4 special issue SP - 401 EP - 411 DO - 10.22495/jgrv14i4siart16 AB - This study conducts a comparative analysis of artificial intelligence (AI) regulation in the European Union (EU) and the Association of Southeast Asian Nations (ASEAN), examining their governance frameworks, enforcement mechanisms, and regulatory impact. The EU AI Act (EU, 2024) establishes a legally binding, centralized regulatory model that prioritizes risk-based AI classification, strict compliance obligations, and human rights protections (Huang et al., 2024). In contrast, ASEAN follows a decentralized, voluntary governance approach, promoting flexibility, innovation, and industry self-regulation (Putra, 2024). The analysis highlights the trade-offs between regulatory stringency and innovation flexibility. The EU’s strict enforcement model ensures accountability and consumer protection but poses compliance burdens for businesses, potentially slowing AI adoption. Conversely, ASEAN’s market-driven approach fosters rapid AI deployment but raises concerns about regulatory fragmentation, ethical risks, and cross-border governance inconsistencies. These findings are crucial for policymakers and businesses navigating AI governance complexities. As AI continues to evolve globally, harmonizing regulatory approaches and establishing mutual recognition mechanisms between regions could enhance AI accountability while supporting innovation, shaping a more cohesive global AI governance landscape. © 2025 The Authors. KW - AI Regulation KW - ASEAN AI Governance KW - Cross-Border AI Governance KW - EU AI Act KW - Regulatory Compliance CY - Malaysia ER - TY - JOUR TI - Building trust through Technology: AI, public service perception, and citizen satisfaction in Abu Dhabi policing AU - Alhefaity S.R.S.A. AU - Mohamad E. AU - Jamli M.R. AU - Ito T. AU - Larasati A. AU - Mohamad N.A. PY - 2026 JO - Multidisciplinary Science Journal VL - 8 IS - 4 SP - e2026277 DO - 10.31893/multiscience.2026277 AB - This study examines the impact of artificial intelligence (AI) implementation, perceptions of public services, and trust in government on citizen satisfaction within the Abu Dhabi Police, offering insights into the role of emerging technologies in public administration. Guided by Public Value Theory, Expectation-Confirmation Theory, and IT Assimilation Theory, the research develops a conceptual framework to examine both direct and mediated relationships among these constructs. A purposive sample of 500 police employees from AI-enabled, operational, and administrative units was surveyed using a structured questionnaire, from which 365 valid responses were analyzed to assess the relationships among AI implementation, perception of public services, trust in government, and citizen satisfaction. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) in SmartPLS, enabling a robust assessment of the measurement and structural models. The findings reveal that AI implementation significantly enhances citizens’ satisfaction, particularly when services are transparent, efficient, and aligned with public expectations. Perceptions of public service quality also emerged as a critical determinant of satisfaction, reflecting the importance of accessibility, responsiveness, and fairness in shaping positive citizen experiences. Trust in government was found to play a crucial mediating role, strengthening the link between AI-enabled services, public service perceptions, and satisfaction outcomes. Importantly, the results indicate partial mediation, suggesting that while AI and service quality directly influence satisfaction, their effects are amplified when mediated by trust. These findings highlight the dual importance of technological advancement and institutional credibility in fostering citizen satisfaction. The study contributes theoretically by integrating three complementary frameworks to explain how AI influences service outcomes, and practically by providing evidence-based recommendations for policymakers, AI developers, and law enforcement agencies. Emphasizing transparency, accountability, and ethical AI use can further enhance public trust and maximize satisfaction. This research aligns with the UAE’s Vision 2031 of positioning the nation as a global leader in safety, innovation, and smart governance, while also offering a model for other countries seeking to integrate AI into public service delivery. Copyright (c) 2025 The Authors This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. KW - artificial intelligence KW - citizen satisfaction KW - perception of public services KW - trust in government CY - Malaysia, Japan, Indonesia ER - TY - JOUR TI - The EU AI Act: a proactive framework for comprehensive AI regulation AU - Mahmutovic A. PY - 2025 JO - International Journal of Law and Information Technology VL - 33 SP - eaaf028 DO - 10.1093/ijlit/eaaf028 AB - The rapid advancement of artificial intelligence (AI), enabling autonomous decision-making and selflearning, is transforming legal systems and societal structures, while presenting significant governance challenges. Regulators grapple with the Collingridge Dilemma, uncertainty hinders early intervention, and entrenchment complicates later action, exacerbated by the pacing problem, where AI’s exponential growth outpaces static laws, rendering traditional command-and-control regulation inadequate. The European Union’s AI Act (Regulation (EU) 2024/1689) introduces proactive tools like regulatory sandboxes and data governance, yet struggles to balance innovation with ethical oversight. This paper explores proactive law, an anticipatory and collaborative approach emphasizing stakeholder engagement and adaptive governance, as a viable paradigm for AI regulation. Through doctrinal, comparative, and socio-legal analysis of the EU AI Act alongside USA and Chinese strategies, focusing on transparency mandates and codes of practice, the study discusses a refined proactive model to mitigate risks, foster trust, and sustain innovation, advancing global responsible AI governance. © The Author(s) 2025. Published by Oxford University Press. All rights reserved. KW - AI governance KW - China KW - EU AI Act KW - European Union KW - pacing problem KW - proactive law KW - USA CY - Saudi Arabia ER - TY - JOUR TI - Linking Psychological Safety Climate to Dual Innovation Through AI-Enabled Dynamic Capabilities AU - Tao K. AU - Tan C.C. PY - 2025 JO - Emerging Science Journal VL - 9 IS - 6 SP - 3268 EP - 3287 DO - 10.28991/ESJ-2025-09-06-022 AB - Objective: This study develops and empirically validates an integrated model that explains how the psychological safety climate influences dual innovation through AI-enabled dynamic capabilities in Chinese design organizations. Methods: A cross-sectional survey was conducted among 281 designers from industry design firms and departments. Data analysis employed partial least squares-structural equation modeling, including mediation bootstrapping analysis, importance-performance map analysis, necessary condition analysis, and quadratic effect analysis. Findings: All hypotheses received strong empirical support. The psychological safety climate has a significant influence on AI-enabled dynamic capabilities, with a path coefficient of 0.452 at p <0.001, and on dual innovation, with a coefficient of 0.383 at p < 0.001. AI-enabled dynamic capabilities have a positive impact on dual innovation, with a coefficient of 0.384 at p < 0.001, and significant mediation effects, indicating an indirect effect of 0.174 at p < 0.001. The model explains 42.7% of the variance in dual innovation. Importance-performance analysis reveals a psychological safety climate as highly important but moderately performing, indicating strategic opportunities for improvement for organizations. Necessary condition analysis confirms both constructs as essential requirements for innovation outcomes. The findings demonstrate that psychological safety climate, as a higher-order cultural resource, enables lower-order AI-enabled dynamic capabilities, supporting socio-technical systems structure for dual innovation. Organizations should prioritize investments in psychological safety while maintaining their AI capabilities. Novelty: This research introduces AI-enabled dynamic capabilities as a second-order formative construct and establishes the meta-capability role of psychological safety climate in AI-enabled dynamic capabilities and dual innovation, thereby extending the resource-based view and dynamic capabilities theories through micro-foundational perspectives. © 2025 by the authors. Licensee ESJ, Italy. KW - AI-Enabled Dynamic Capability KW - Dual Innovation KW - Industrial Design KW - Psychological Safety Climate KW - Resource-Based View CY - Thailand, China ER - TY - JOUR TI - Policing in service innovation: The role of artificial intelligence and big data capabilities and organizational impact AU - Khalid K. AU - Ahmad S.Z. AU - Abu Bakar A.R. PY - 2026 JO - Police Journal DO - 10.1177/0032258X261442402 AB - This study examines the relationships between artificial intelligence capability (AIC), big data capability (BDC), and organizational impact on service innovation (SI) in policing, with a focus on how organizational impact mediates these relationships. Data from 266 police managers in Abu Dhabi and Dubai were analyzed using structural equation modeling. The findings reveal that AIC and BDC positively influence SI and organizational impact, with organizational impact also playing a mediating role. This research addresses a gap in empirical studies by demonstrating how AIC and BDC serve as essential digital capabilities driving SI within police forces. © The Author(s) 2026 KW - artificial intelligence capability KW - big data capability KW - organizational impact KW - police forces KW - public sector KW - service innovation CY - United Arab Emirates, Saudi Arabia ER - TY - JOUR TI - Artificial intelligence ethics in authoritarian Vietnam: governance, trust, and societal tensions AU - Tran H.T. AU - Dang B.H. AU - Nguyen M.T.T. AU - Pham Q.T.T. AU - Nguyen P.V. PY - 2025 JO - Policy Design and Practice VL - 8 IS - 4 SP - 427 EP - 441 DO - 10.1080/25741292.2025.2529625 AB - This study investigates the ethical challenges of artificial intelligence (AI) deployment in Vietnam, driven by the need to understand how authoritarian governance and public trust shape technology’s societal impact in a rapidly modernizing state. The objective is to analyze the governance-trust interplay, identifying ethical dilemmas and their implications in Vietnam’s single-party context. Employing a qualitative approach, the research draws on policy documents, semi-structured interviews with policymakers, developers, and citizens, social media analysis, and case studies of facial recognition in Hanoi and AI in public health. Results reveal that Vietnam’s centralized governance enables swift AI adoption but falters in ethical flexibility and accountability, evident in privacy concerns and unclear responsibility for errors. Public trust varies, supported by state narratives in urban areas yet weakened by opacity and rural digital divides. The governance-trust dynamic shows transparency deficits undermining confidence, countered by societal resistance prompting modest policy adjustments. The study concludes that AI in Vietnam is a socio-political process with ethical stakes, offering a non-Western perspective to global debates and insights for aligning innovation with societal well-being. © 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. KW - AI ethics KW - authoritarian governance KW - public trust KW - societal tensions KW - Vietnam ER - TY - JOUR TI - The Barcelona principles: An agreement on the use of human donated tissue for ocular transplantation, research, and future technologies PY - 2018 JO - Cornea VL - 37 IS - 10 SP - 1213 EP - 1217 DO - 10.1097/ICO.0000000000001675 AB - Preamble The Barcelona Principles: An Agreement on the use of human donated tissue for ocular transplantation, research, and future technologies (Agreement) is a global bioethical framework (GBF), developed by the eye bank and ophthalmic communities, to inform on the management of altruistic and voluntary donations; their subsequent utility within ophthalmology and research; their retention as a public resource for the shared benefit of all; and their accessibility by waiting recipients. The Agreement is the result of global sector engagement over a 12-month period-led by the Global Alliance of Eye Bank Associations. Its aim is to provide leadership, guidance and recommendations that inform and support sound policy, and sector wide strategic planning and implementation at local, national, regional, and international levels. Inspired by the Declaration of Istanbul on Organ Trafficking and Transplant Tourism, this Agreement affirms the importance of the missions of the United Nations Sustainable Development Goals (Transforming our World: the 2030 Agenda for Sustainable Development); Universal Declaration of Human Rights; World Medical Association's Declaration of Helsinki-Ethical Principles for Medical Research Involving Human Subjects, and their Statement on Organ and Tissue Donation; The Council for International Organizations of Medical Science's International Ethical Guidelines for Health-related Research Involving Humans 2016; and accords with the World Health Organization's 2010 Guiding principles on human cell, tissue, and organ transplantation -WHA63.22. With millions waiting for a corneal transplant at any given moment1-and the majority residing in lower resource locations, a significant component of this Agreement promotes equitable allocation systems for waiting recipients, and the development of self-sufficient services. It recognises important instruments, such as the International Council of Ophthalmology 2017 Position Statement: Donation, Processing, Allocation, Advocacy, and Legislation Supporting Human Corneal Tissue for Ocular Transplant; the World Health Organization's Universal Eye Health-Global Action Plan 2014 to 2019, and the mission of the International Agency for the Prevention of Blindness. Future biological innovations/technologies are also addressed within the Agreement, promoting research and development that seek to improve donation utility, reduce burden, and improve therapeutic options for recipients, without ethical compromise. The Agreement has been developed by the Global Alliance of Eye Bank Associations in conjunction with the International Council of Ophthalmology, International Agency for the Prevention of Blindness, The Corneal Society, Asian Eye Bank Association, European Eye Bank Association, Eye Bank Association of America, Eye Bank Association of Australia and New Zealand, Eye Bank Association of India, the Pan American Association of Eye Banks, and in countries and regions without eye bank organizations, their ophthalmic societies-such as the Ophthalmological Society of the West Indies, and the Pacific Eye Care Society. Finally, we endorse the current international consensus that prohibits trafficking and transplant tourism. Copyright © 2018 The Global Alliance of Eye Bank Associations, Inc. KW - Bioethics KW - Biomedical Research KW - Eye KW - Eye Banks KW - Humans KW - International Cooperation KW - Organ Transplantation KW - Tissue and Organ Procurement KW - Article KW - bioethics KW - cornea transplantation KW - donor by donated tissue KW - eye bank KW - human KW - leadership KW - organ transplantation KW - organizational policy KW - priority journal KW - research KW - strategic planning KW - sustainable development KW - World Health Organization KW - bioethics KW - ethics KW - eye KW - international cooperation KW - medical research KW - organization and management KW - transplantation ER - TY - JOUR TI - The power of personalization: Generation Z's emotional response to AI food marketing under the EU AI act AU - Jackson K.M. AU - Kiss H. AU - Bergman M.E. PY - 2025 JO - British Food Journal SP - 1 EP - 18 DO - 10.1108/BFJ-05-2025-0727 AB - Purpose – AI-driven marketing is transforming how food brands connect with younger audiences, especially Generation Z (Gen Z). Artificial intelligence has evolved from a trend into a structural norm that influences personalization and persuasion in food marketing. This paper examines how Gen Z engages with AI versus traditional food ads, mapping psychological pathways and their relevance to the EU AI Act. Design/methodology/approach – An online survey of 982 participants (ages 18–27) from Hungary, Germany and Spain compared responses to a traditional Lidl ad and an AI-generated Heinz ad. Engagement was measured using a shortened Multimedia Ad Exposure Scale (MMAES) alongside established psychological scales for social media addiction, compulsive buying, flourishing and eating style. Analyses included t-tests, correlations and clustering. Two new measures, the Impulse Susceptibility Score (ISS) and the Flourishing Engagement Delta (FED), were introduced as scalable risk assessment tools. Findings – The AI-generated ad elicited significantly higher engagement than the traditional ad. Impulsivity-related traits correlated with elevated engagement, indicating susceptibility to persuasive content. Unexpectedly, participants with high well-being (flourishers) also showed stronger engagement, revealing a “flourishing paradox”. Clustering identified two pathways aligning with EU AI Risk categories. The ISS and FED detected that about 28% of participants fall into a high-risk group. Originality/value – The study shows how AI-generated ads activate dual psychological pathways: impulsive and reflective. By introducing ISS and FED, it offers scalable tools for assessing digital risk profiles and supports responsible innovation in AI food marketing by exposing its algorithmic influence on consumer behaviour and engagement. © 2025 Emerald Publishing Limited KW - AI food marketing KW - Digital vulnerability KW - Emotional flourishing KW - EU AI act KW - Generation Z KW - Personalized content CY - Hungary ER - TY - JOUR TI - Influence of artificial intelligence (AI) on firm performance: the business value of AI-based transformation projects AU - Wamba-Taguimdje S.-L. AU - Fosso Wamba S. AU - Kala Kamdjoug J.R. AU - Tchatchouang Wanko C.E. PY - 2020 JO - Business Process Management Journal VL - 26 IS - 7 SP - 1893 EP - 1924 DO - 10.1108/BPMJ-10-2019-0411 AB - Purpose: The main purpose of our study is to analyze the influence of Artificial Intelligence (AI) on firm performance, notably by building on the business value of AI-based transformation projects. This study was conducted using a four-step sequential approach: (1) analysis of AI and AI concepts/technologies; (2) in-depth exploration of case studies from a great number of industrial sectors; (3) data collection from the databases (websites) of AI-based solution providers; and (4) a review of AI literature to identify their impact on the performance of organizations while highlighting the business value of AI-enabled projects transformation within organizations. Design/methodology/approach: This study has called on the theory of IT capabilities to seize the influence of AI business value on firm performance (at the organizational and process levels). The research process (responding to the research question, making discussions, interpretations and comparisons, and formulating recommendations) was based on a review of 500 case studies from IBM, AWS, Cloudera, Nvidia, Conversica, Universal Robots websites, etc. Studying the influence of AI on the performance of organizations, and more specifically, of the business value of such organizations’ AI-enabled transformation projects, required us to make an archival data analysis following the three steps, namely the conceptual phase, the refinement and development phase, and the assessment phase. Findings: AI covers a wide range of technologies, including machine translation, chatbots and self-learning algorithms, all of which can allow individuals to better understand their environment and act accordingly. Organizations have been adopting AI technological innovations with a view to adapting to or disrupting their ecosystem while developing and optimizing their strategic and competitive advantages. AI fully expresses its potential through its ability to optimize existing processes and improve automation, information and transformation effects, but also to detect, predict and interact with humans. Thus, the results of our study have highlighted such AI benefits in organizations, and more specifically, its ability to improve on performance at both the organizational (financial, marketing and administrative) and process levels. By building on these AI attributes, organizations can, therefore, enhance the business value of their transformed projects. The same results also showed that organizations achieve performance through AI capabilities only when they use their features/technologies to reconfigure their processes. Research limitations/implications: AI obviously influences the way businesses are done today. Therefore, practitioners and researchers need to consider AI as a valuable support or even a pilot for a new business model. For the purpose of our study, we adopted a research framework geared toward a more inclusive and comprehensive approach so as to better account for the intangible benefits of AI within organizations. In terms of interest, this study nurtures a scientific interest, which aims at proposing a model for analyzing the influence of AI on the performance of organizations, and at the same time, filling the associated gap in the literature. As for the managerial interest, our study aims to provide managers with elements to be reconfigured or added in order to take advantage of the full benefits of AI, and therefore improve organizations’ performance, the profitability of their investments in AI transformation projects, and some competitive advantage. This study also allows managers to consider AI not as a single technology but as a set/combination of several different configurations of IT in the various company’s business areas because multiple key elements must be brought together to ensure the success of AI: data, talent mix, domain knowledge, key decisions, external partnerships and scalable infrastructure. Originality/value: This article analyses case studies on the reuse of secondary data from AI deployment reports in organizations. The transformation of projects based on the use of AI focuses mainly on business process innovations and indirectly on those occurring at the organizational level. Thus, 500 case studies are being examined to provide significant and tangible evidence about the business value of AI-based projects and the impact of AI on firm performance. More specifically, this article, through these case studies, exposes the influence of AI at both the organizational and process performance levels, while considering it not as a single technology but as a set/combination of the several different configurations of IT in various industries. © 2020, Emerald Publishing Limited. KW - Artificial intelligence KW - Business value KW - Cases studies KW - Firm performance KW - Process innovation CY - Cameroon, France ER - TY - JOUR TI - Governing the machine: leadership priorities for industry 5.0 in emerging digital economies AU - Abdallah Y.O. AU - Elnazer A.A. PY - 2026 JO - Cogent Business and Management VL - 13 IS - 1 SP - 2656023 DO - 10.1080/23311975.2026.2656023 AB - The transition to Industry 5.0 is reshaping leadership expectations as organisations seek to balance advanced technologies with human-centred and sustainable priorities. This study examines the leadership competencies most critical for guiding this transition in developing digital economies. A structured assessment was conducted using the Best–Worst Method (BWM), a multi-criteria decision-making technique that reduces cognitive burden, enhances internal consistency, and enables robust prioritisation of expert judgements. Fifteen senior experts from industry, academia, and policy across the Middle East evaluated five leadership domains: technological and AI capability; strategic foresight and innovation; adaptability and learning agility; relational and people-oriented skills; and ethical governance and sustainability stewardship. The findings indicate a clear prioritisation of governance-related competencies, particularly transparency, accountability, and the ability to interpret and communicate algorithmic decisions. Interpersonal competence and sound judgement were also rated highly, while visionary innovation and technical implementation skills received comparatively lower emphasis. Overall, the results suggest that leadership in digitally intensive contexts is becoming increasingly ethics- and governance-oriented. The study contributes to the leadership and digital transformation literature by offering an empirically grounded hierarchy of Industry 5.0 leadership competencies situated within the socio-technical realities of the Middle East. © 2026 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. KW - BWM KW - digital leadership KW - emerging economy KW - Ethical governance KW - Human-centric leadership KW - Industry 5.0 CY - United Kingdom, Egypt, Saudi Arabia ER - TY - JOUR TI - The transformative power of AI and its impact on business strategy, financial operations, and marketing decision-making: a case study method AU - Gabelaia I. AU - Hendieh J. PY - 2025 JO - International Journal of Innovation Science SP - 1 EP - 21 DO - 10.1108/IJIS-02-2025-0051 AB - Purpose – While artificial intelligence (AI) is widely integrated into modern enterprises, its concrete impacts on strategic agility, financial accuracy and marketing personalization remain under-researched. The purpose of this study is to bridge that gap by empirically assessing AI’s transformative role across business strategy, finance and marketing functions. Design/methodology/approach – Drawing on a systematic literature review, the authors developed three hypotheses and examined them through a combination of case study analyses and a survey conducted within three small and medium-sized enterprises (SMEs). This approach clarifies how AI transforms these areas and what this means for the future of business continuity. Findings – The results of this study demonstrate that integrating AI leads to more data-driven decision-making, thereby enhancing strategic planning and execution. Moreover, businesses with advanced AI capabilities display greater agility and adaptability. In financial terms, AI technologies simplify processes, reduce operational costs and improve efficiency. In addition, AI-driven models improve the accuracy of financial forecasting and risk management. Finally, AI technologies such as chatbots and recommendation systems enhance customer experience and satisfaction. Research limitations/implications – The authors acknowledge several limitations. First, the case study analysis was limited to three SMEs and may not be generalizable to all industries or regions; however, it still provided substantial experiential insights. Second, the survey, despite a substantial sample size, relies on self-reported data, which may be subject to bias. This study assumes that the selected SMEs and their employees provided accurate and truthful information about their AI adoption and perceived business outcomes. It further assumes that the observed patterns and themes reflect broader trends in AI adoption across industries. Practical implications – In financial terms, AI technologies simplify financial processes, reduce operational costs and improve efficiency. In addition, AI-driven models improve the accuracy of financial forecasting and risk management. Social implications – From a societal perspective, this research supports a future in which AI facilitates more informed and efficient business practices. Economically, this study paves the way for innovations that foster productivity and profitability across industries, driving overall market competitiveness and progress. Originality/value – This research offers valuable insights into AI’s transformative capabilities, emphasizing its significant impact on business strategy, financial operations and marketing decision-making. The findings are practical for businesses, offering recommendations for integrating AI technologies effectively to achieve a competitive advantage and pursue sustainable business growth. © 2025 Emerald Publishing Limited KW - AI adoption KW - Artificial intelligence KW - Business continuity KW - Business strategy KW - Decision-making KW - Financial management KW - G32 KW - G32 KW - G32 KW - JEL Code M15 KW - L25 KW - L25 KW - L25 KW - L81 KW - L81 KW - L81 KW - M15 KW - M15 KW - M21 KW - M21 KW - M21 KW - Marketing analytics KW - SMEs KW - Behavioral research KW - Commerce KW - Costs KW - Customer satisfaction KW - Efficiency KW - Financial data processing KW - Forecasting KW - Strategic planning KW - Artificial intelligence adoption KW - Business continuity KW - Business strategy KW - Decisions makings KW - Financial managements KW - G32 KW - JEL code m15 KW - JEL codes KW - L25 KW - L81 KW - M15 KW - M21 KW - Marketing analytic KW - Small and medium-sized enterprise KW - Decision making CY - Lithuania, France ER - TY - JOUR TI - The Evolution of Generative AI: Trends and Applications AU - Trigka M. AU - Dritsas E. PY - 2025 JO - IEEE Access VL - 13 SP - 98504 EP - 98529 DO - 10.1109/ACCESS.2025.3574660 AB - Generative artificial intelligence (AI) has revolutionized AI by enabling high-fidelity content creation across text, images, audio, and structured data. This survey explores the core methodologies, advancements, applications, and ongoing challenges of generative AI, covering key models such as Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), Diffusion Models, and Transformer-based architectures. These innovations have driven breakthroughs in healthcare, scientific computing, Natural Language Processing (NLP), computer vision, and autonomous systems. Despite its progress, generative AI faces challenges in bias mitigation, interpretability, computational efficiency, and ethical governance, necessitating research into scalable architectures, explainability, and AI safety mechanisms. Integrating Reinforcement Learning (RL), multi-modal learning, and self-supervised techniques enhances controllability and adaptability in generative models. Additionally, as AI reshapes industrial automation, digital media, and scientific discovery, its societal and economic implications demand robust policy frameworks. This survey provides a comprehensive analysis of generative AI’s current state and future directions, highlighting innovations in efficient generative modelling, AI-driven scientific reasoning, adversarial robustness, and ethical deployment. By consolidating theoretical insights and real-world applications, it offers a structured foundation for researchers, industry professionals, and policymakers to navigate the evolving landscape of generative AI. © 2013 IEEE. KW - ethical AI and governance KW - Generative AI KW - multi-modal AI KW - multi-modal learning KW - Generative adversarial networks KW - Multi-task learning KW - Natural sciences computing KW - Supervised learning KW - Text processing KW - Content creation KW - Ethical artificial intelligence and governance KW - Generative artificial intelligence KW - Generative model KW - High-fidelity KW - Multi-modal KW - Multi-modal artificial intelligence KW - Multi-modal learning KW - Text data KW - Text images KW - Reinforcement learning CY - Greece ER - TY - JOUR TI - The Role of Artificial Intelligence in Healthcare: A Critical Analysis of Its Implications for Patient Care AU - Mashabab M.F. AU - Sheniff M.S.A. AU - Alsharief M.S. AU - Yami M.A.A.A. AU - Matnah H.N.M. AU - Abbas A.M.A. AU - Shenief H.Y.M.A. AU - Abbas D.A.A.A.A. AU - Raseen F.M.S.A. AU - Kulayb A.H.A.A. PY - 2024 JO - Journal of Ecohumanism VL - 3 IS - 7 SP - 597 EP - 604 DO - 10.62754/joe.v3i7.4228 AB - Artificial Intelligence (AI) is rapidly transforming healthcare, with significant implications for patient care. This article critically analyzes AI's role in improving healthcare delivery, focusing on diagnostic accuracy, personalized treatments, and system efficiency. It highlights key benefits such as enhanced decision-making, reduced human error, and the potential for better patient outcomes through AI-driven tools like predictive analytics and robotic surgery. However, the article also addresses challenges including ethical concerns, algorithmic bias, data privacy issues, and the need for clear regulations and accountability structures. The study explores how AI affects healthcare professionals, reshaping their roles and requiring new skill sets. Through case studies, the article illustrates both the successes and limitations of AI in clinical applications. Ultimately, this critical analysis emphasizes that while AI holds promise in improving patient care, responsible implementation is necessary to address ethical, legal, and technical challenges. © 2024, Creative Publishing House. All rights reserved. KW - Ai Ethics KW - Algorithmic Bias KW - Artificial Intelligence KW - Data Privacy KW - Diagnostic Accuracy KW - Healthcare KW - Healthcare Innovation KW - Healthcare Professionals KW - Patient Care KW - Personalized Medicine KW - Predictive Analytics KW - Robotic Surgery CY - Saudi Arabia ER - TY - JOUR TI - Artificial intelligence in sport management education: A students' perspective AU - López-Carril S. AU - Alguacil M. AU - Gregori-Faus C. AU - Anagnostopoulos C. PY - 2026 JO - International Journal of Management Education VL - 24 IS - 3 SP - 101433 DO - 10.1016/j.ijme.2026.101433 AB - Artificial intelligence (AI) is transforming both higher education and the sport industry. This study qualitatively explores the perceptions of sport management students regarding AI's integration into academic contexts and the sport industry, guided by the Uses and Gratifications Theory (U&G) and the Disruptive Innovation Theory (DIT). Seventy-nine undergraduates from a Spanish university completed an open-ended questionnaire after a classroom debate on AI. Thematic analysis revealed that, consistent with U&G, students use AI to fulfil cognitive needs (e.g., acquiring and clarifying information, generating ideas) and instrumental needs (e.g., improving efficiency, solving problems quickly). Reported benefits included rapid access to information, enhanced learning support, and time savings, while concerns focused on plagiarism, reduced creativity, overreliance, and unreliable outputs. From a DIT perspective, students viewed AI as a potentially disruptive force capable of transforming sport management education and industry practices, generating opportunities for innovation but also posing risks such as job displacement and over-automation. Overall sentiment was cautiously positive, with calls for ethical guidelines, targeted training, and balanced adoption that leverages AI's advantages without eroding essential human skills. This situated perspective provides practical and theoretical insights for integrating AI into sport management curricula and informs future quantitative or mixed-method research. © 2026 The Authors KW - AI KW - Disruptive Innovation Theory KW - Higher education KW - Student perceptions KW - Technology adoption KW - Uses and Gratifications Theory CY - Spain, Qatar ER - TY - JOUR TI - Artificial intelligence-driven innovation for enhancing supply chain resilience and performance under the effect of supply chain dynamism: an empirical investigation AU - Belhadi A. AU - Mani V. AU - Kamble S.S. AU - Khan S.A.R. AU - Verma S. PY - 2024 JO - Annals of Operations Research VL - 333 IS - 2-3 SP - 627 EP - 652 DO - 10.1007/s10479-021-03956-x AB - Supply chain resilience (SCRes) and performance have become increasingly important in the wake of the recent supply chain disruptions caused by subsequent pandemics and crisis. Besides, the context of digitalization, integration, and globalization of the supply chain has raised an increasing awareness of advanced information processing techniques such as Artificial Intelligence (AI) in building SCRes and improving supply chain performance (SCP). The present study investigates the direct and indirect effects of AI, SCRes, and SCP under a context of dynamism and uncertainty of the supply chain. In doing so, we have conceptualized the use of AI in the supply chain on the organizational information processing theory (OIPT). The developed framework was evaluated using a structural equation modeling (SEM) approach. Survey data was collected from 279 firms representing different sizes, operating in various sectors, and countries. Our findings suggest that while AI has a direct impact on SCP in the short-term, it is recommended to exploit its information processing capabilities to build SCRes for long-lasting SCP. This study is among the first to provide empirical evidence on maximizing the benefits of AI capabilities to generate sustained SCP. The study could be further extended using a longitudinal investigation to explore more facets of the phenomenon. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021. KW - Artificial intelligence KW - Digital transformation KW - organizational information processing theory KW - Supply chain performance KW - Supply chain resilience CY - Morocco, France, China, Denmark ER - TY - JOUR TI - Exploring the Impact of AI Capabilities on Employee Well-Being: A Mediated Moderation Analysis AU - Bibi M. AU - Tan T.G. AU - Yao H. PY - 2025 JO - SAGE Open VL - 15 IS - 3 DO - 10.1177/21582440251361981 AB - Around the globe, technological advancements such as artificial intelligence (AI) are reshaping workplaces affecting employee wellbeing (EWB). To understand the AI-EWB link, a conceptual model is developed to explore the link between AI-driven capabilities and employee wellbeing (EWB), with cybernetic thinking (CT) as a mediator. Furthermore, organizational ambidexterity (OA) is introduced as a moderating factor between CT and EWB grounded on integrated dynamic capabilities with resource-based theory in the context of a developing country like Pakistan. Data were collected from 490 doctors working in private sector hospitals across two major cities of Pakistan—Karachi & Islamabad and data analysis was performed using PLS-SEM 4.0. Results indicate that AI-driven capabilities significantly relate to EWB. Furthermore, CT explains the relationship between tangible, human resources, intangible-driven AI capabilities, and EWB. In addition, OA moderates the link between CT and EWB. Hence, mediated moderation is established. To remain resilient, this study offers theoretical as well as practical insights into how healthcare practitioners can harness AI through integrating organizational factors like CT can help reduce stress and improve EWB through adopting a balanced approach to manage innovation. Policy implications along with directions for studies to be conducted by researchers are also provided. © The Author(s) 2025. This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage). KW - AI capabilities KW - cybernetic thinking KW - dynamic capabilities KW - employee wellbeing KW - mediated moderation KW - organizational ambidexterity KW - resource-based theory CY - Malaysia, Pakistan, China ER - TY - JOUR TI - Developing artificial intelligence (AI) capabilities for data-driven business model innovation: Roles of organizational adaptability and leadership AU - Ghosh S. PY - 2025 JO - Journal of Engineering and Technology Management - JET-M VL - 75 SP - 101851 DO - 10.1016/j.jengtecman.2024.101851 AB - More and more industrial businesses leverage AI to optimize operations and introduce new and innovative business models for competitive advantage. Businesses are collecting huge amounts of data from their business processes and plan to utilize them for better customer experience and insights. However, industrial managers are still determining the AI capabilities they need to analyze the data and develop customer-centric business models. Based on the discussions with twenty-five elite informants from five large industrial businesses, in this study, we propose a set of AI capabilities that can help managers develop data-driven business models. We offer a conceptual framework of AI capabilities and their influence on data-driven business model innovation that can guide managers in their transformation journeys. © 2024 KW - Advanced technologies KW - Artificial intelligence KW - Data-driven business model innovation KW - Digital servitization KW - Digital transformation KW - Advanced technology KW - Business model innovation KW - Business models KW - Competitive advantage KW - Data driven KW - Data-driven business model innovation KW - Digital servitization KW - Digital transformation KW - Organizational adaptabilities KW - Servitization CY - United States ER - TY - JOUR TI - A framework and exemplars for ethical and responsible use of AI Chatbot technology to support teaching and learning AU - Chauncey S.A. AU - McKenna H.P. PY - 2023 JO - Computers and Education: Artificial Intelligence VL - 5 SP - 100182 DO - 10.1016/j.caeai.2023.100182 AB - The aim of this paper is to investigate the ethical and responsible use of AI chatbots in education in support of critical thinking, cognitive flexibility and self-regulation in terms of their potential to enhance and motivate teaching and learning in contemporary education environments. AI chatbots such as ChatGPT by OpenAI appear to be improving in conversational and other capabilities and this paper explores such advances using version 4. Based on a review of the research literature, a conceptual framework is formulated for responsible use of AI chatbots in education supporting cognitive flexibility in AI-rich learning environments. The framework is then operationalized for use in this paper through the development of exemplars for math, english language arts (ELA), and studying with ChatGPT to close learning gaps in an effort to foster more ethical and responsible approaches to the design and development of AI chatbots for application and use in teaching and learning environments. This paper extends earlier foundational work on cognitive flexibility and AI chatbots as well as work on cognitive flexibility in support of creativity and innovation with AI chatbots in urban civic spaces. © 2023 The Authors KW - AI ethics KW - AI responsibility KW - AI-Rich learning environments KW - Cognitive flexibility KW - Critical thinking KW - Self-regulation KW - Computer aided instruction KW - Deregulation KW - Ethical technology KW - AI ethic KW - AI responsibility KW - AI-rich learning environment KW - Chatbots KW - Cognitive flexibility KW - Conceptual frameworks KW - Critical thinking KW - Learning environments KW - Self regulation KW - Teaching and learning KW - Teaching CY - United States, Canada ER - TY - JOUR TI - Is AI a ‘substance driver’ or a ‘fiction booster’? The impact of AI application on corporate green innovation bubbles AU - Zhao K. AU - Liu X. PY - 2026 JO - Finance Research Letters VL - 91 SP - 109100 DO - 10.1016/j.frl.2025.109100 AB - This study investigates whether artificial intelligence (AI) acts as a "substance driver" that promotes genuine green innovation or a "fiction booster" that facilitates sophisticated greenwashing. Analyzing panel data from Chinese A-share listed manufacturing companies spanning 2013 to 2023, fixed effects regression results reveal that AI application significantly suppresses green innovation bubbles, demonstrating a substantial reduction effect at the sample mean. However, this effect is highly contingent on external institutional and competitive contexts. Stringent environmental regulation amplifies AI's bubble-suppressing effect by channeling AI capabilities toward substantive compliance-oriented innovation. Additionally, intense market competition amplifies this effect, as competitive pressure disciplines firms to deploy AI toward substantive innovations that generate genuine competitive advantages rather than superficial green credentials. Heterogeneity analyses reveal AI's effectiveness is significantly stronger among non-state-owned enterprises and non-heavy-polluting firms, while SOEs and heavy-polluting firms show insignificant effects. Firms located in AI innovation pilot zones exhibit stronger bubble-suppressing effects compared to those outside. These findings contribute an integrated "institution-market" contingency framework to technology adoption literature, introduce an objective patent-based measure of innovation quality, and demonstrate that AI's role in corporate sustainability is neither technologically deterministic nor universally beneficial but critically depends on the alignment of institutional pressures and market incentives. The results remain robust to instrumental variable analysis, propensity score matching, Heckman correction, and multiple robustness checks. © 2025 Elsevier Inc. All rights are reserved. KW - Artificial intelligence KW - Environmental regulation KW - Green innovation bubbles KW - Greenwashing KW - Market competition CY - China ER - TY - JOUR TI - Navigating the Tech Turn: A Bibliometric Analysis of Decision-Making Trends in 21st Century Education AU - Prasad R.D. AU - Pek L.S. AU - Yob F.S.C. AU - Von W.Y. AU - Magulod G.C., Jr. AU - Adom D. PY - 2025 JO - International Journal of Learning, Teaching and Educational Research VL - 24 IS - 11 SP - 297 EP - 313 DO - 10.26803/ijlter.24.11.14 AB - This bibliometric analysis illustrates how, between 2020 and 2024, technology has impacted educational decisions. Using the Web of Science Core Collection, 371 English-language publications in the field of education and educational research were analysed. In VOSviewer, assessments of performance, co-citation, and keyword co-occurrence were carried out. Six thematic clusters emerged: (1) qualitative research and pedagogical frameworks; (2) technology acceptance and behavioral theories; (3) e-learning, learning analytics, and pandemic adaptation; (4) artificial intelligence, ethics, and mixed-method evaluation; (5) active learning, diffusion of innovations, and learning efficacy; and (6) social cognitive and motivational perspectives on STEM pathways. The corpus is worldwide in scope and has strong ties to analytics-informed leadership, policy responsiveness, and teacher practice. The findings demonstrate a growing interest in evidence-based education, data-driven leadership, and AI governance – all of which are consistent with Sustainable Development Goal 4 (Quality Education). This study's thorough intellectual map connects adoption, pedagogy, analytics, and governance while offering helpful recommendations for institutional and policy decision-making on curriculum, funding, and capacity building. The bibliometric analysis updates to track this rapidly evolving topic and spot a clear research gap: the requirement for multi-theoretical, equity-sensitive models that integrate analytics and artificial intelligence with institutional decision-making processes. © Authors. KW - AI in education KW - digital transformation KW - educational technology KW - inclusive education KW - technology adoption CY - Malaysia, Philippines, Ghana ER - TY - JOUR TI - AI Capability and Sustainable Performance: Unveiling the Mediating Effects of Organizational Creativity and Green Innovation with Knowledge Sharing Culture as a Moderator AU - Gazi M.A.I. AU - Rahman M.K.H. AU - Masud A.A. AU - Amin M.B. AU - Chaity N.S. AU - Senathirajah A.R.B.S. AU - Abdullah M. PY - 2024 JO - Sustainability (Switzerland) VL - 16 IS - 17 SP - 7466 DO - 10.3390/su16177466 AB - The purpose of this study is to investigate the role of AI capability (AIC) on organizational creativity (OC), green innovation (GI), and sustainable performance (SP). It also aims to investigate the mediating roles of OC and GI, as well as the moderating role of knowledge sharing culture (KNC). This study used quantitative methodology and utilized a survey to collect data from 421 employees in different organizations in Bangladesh. We used the structural equation modeling (SEM) technique to analyze the data. This study finds that AI capability significantly influences OC, GI, and SP. OC and GI work as mediators, and KNC serves as a moderator among the suggested relationships. This study is notable for its novelty in examining multiple unexplored aspects in the current body of research. This research also provides valuable insights for policymakers and practitioners regarding the effective integration of AI to enhance organizational competitiveness. © 2024 by the authors. KW - AI capability KW - green innovation KW - knowledge sharing culture KW - organizational creativity KW - sustainable development KW - sustainable performance KW - Bangladesh KW - artificial intelligence KW - competitiveness KW - cultural influence KW - industrial performance KW - innovation KW - knowledge KW - planning method KW - sustainable development CY - China, Malaysia, Bangladesh, Hungary ER - TY - JOUR TI - The AI Revolution in Higher Education: Transforming Teaching and Research AU - Flückiger Y. PY - 2025 JO - Journal of Higher Education Policy and Leadership Studies VL - 6 IS - 4 SP - 30 EP - 44 DO - 10.61882/johepal.6.4.30 AB - The rapid integration of Artificial Intelligence (AI) into higher education is profoundly transforming both teaching and research. This article explores how AI-driven technologies enable personalized and adaptive learning, empowering educators to shift from content delivery to mentorship and creativity. By tailoring instruction to individual needs, AI fosters inclusivity and enhances student engagement through adaptive platforms, virtual tutors, and chatbots. Case studies from leading universities demonstrate tangible improvements in learning outcomes, retention, and student support, while also underscoring ethical and pedagogical challenges. Beyond education, AI is revolutionizing research practices by accelerating data analysis, generating hypotheses and promoting interdisciplinary collaboration. From genomics to computational social sciences, AI expands the capacity of researchers to address complex global challenges. However, these opportunities raise pressing ethical issues, including data privacy, algorithmic bias, transparency and equity. The article concludes by emphasizing the need for responsible AI governance, institutional investment, and international collaboration. When integrated thoughtfully, AI can enhance learning experiences, broaden access, and accelerate innovation for the benefit of society. © This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International https://creativecommons.org/licenses/by-nc/4.0/ (CC BY-NC 4.0) which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format for noncommercial purposes only, and only so long as attribution is given to the creator. KW - AI in Higher Education KW - Educational Technology KW - Ethical Challenges of AI KW - Interdisciplinary Collaboration KW - Personalized & Adaptive Learning CY - Switzerland ER - TY - JOUR TI - Fostering Tech Innovation: Exploring TRIZ and ChatGPT Integration for Developer and Entrepreneur Challenges AU - Shin W.-S. AU - Lee S.-H. AU - Sue H.-J. PY - 2024 JO - Tehnicki Glasnik VL - 18 IS - 4 SP - 588 EP - 597 DO - 10.31803/tg-20231212081808 AB - This study aimed to interpret the value of integration of TRIZ (Teoriya Resheniya Izobretatelskih Zadach; Theory of Inventive Problem Solving) and ChatGPT(Chat Generative Pre-trained Transformer) to enhance technological and entrepreneurial problem-solving and decision-making. TRIZ offers a structured approach to innovation, while ChatGPT excels in generating diverse and innovative responses through advanced natural language processing. From the combination of them, we tried to discover synergies that promote innovation in highly competitive business areas. Through the analysis of case studies, including "Imperfect Waterproof Zipper" and "Drilling a Hole in a Thin-Walled Tube", we discovered that this integration not only aligns with actual problem-solving outcomes but also enhances the quality of solutions, particularly benefiting developers with limited TRIZ knowledge. We identified that leveraging ChatGPT enables developers and entrepreneurs to approach challenges with enhanced creativity, yielding practical and innovative solutions through these case studies. Our approach, focusing on real-world applications, demonstrates the study's contribution by providing a novel strategy for combining structured problem-solving with AI capabilities. The primary motivation behind this research was to ascertain whether AI can amplify the problem-solving framework of TRIZ, thereby extending its utility beyond traditional domains. The findings underline the importance of AI in creative problem-solving, suggesting that even those unfamiliar with TRIZ can apply its principles effectively with the aid of ChatGPT. This research adds to the existing knowledge by showcasing how AI can be a powerful ally in the creative process, offering new avenues for problem-solving and strategic decision-making. In conclusion, our study demonstrates that the collaboration between TRIZ and ChatGPT not only elevates creativity but also equips developers and entrepreneurs with competitive strategies, emphasizing the role of AI in driving forward human innovation and creativity. © 2024 University North. All rights reserved. KW - AI-driven insights KW - ChatGPT KW - decision-making KW - entrepreneurial strategy KW - problem-solving KW - technological innovation KW - TRIZ CY - South Korea ER - TY - JOUR TI - Geoscience in the era of generative artificial intelligence (Geo[AI]-LSM): understanding the potential benefits of Google Gemini in producing landslide susceptibility mapping AU - Sahin E.K. AU - Demir S. AU - Ozturk M. AU - Duzce M.S. PY - 2026 JO - Advances in Space Research VL - 77 IS - 3 SP - 3061 EP - 3085 DO - 10.1016/j.asr.2025.11.048 AB - In recent years, many technological innovations have marked the 21st century. One of the most rapid and unpredictable is the Artificial Intelligence (AI) revolution. The integration of AI systems, particularly generative AI, has just started manifesting itself in geoscience applications. This study investigates the potential benefits and limitations of the state-of-the-art generative AI framework, Google-Gemini, in improving the accuracy and efficiency of landslide susceptibility maps (LSMs). The research aims to shed light on the efficacy of Gemini AI and its implications for enhancing geoscience applications beyond LSM through empirical trials and comparative analysis. Furthermore, a web-based, user-friendly interface called Geo[AI]-LSM has been produced and is freely available to all users for producing LSMs. In the proposed framework, two distinct tools play critical roles: the Data Preparation tool, which prepares the landslide conditioning factor dataset, and the Geo[AI]-LSM tool, which constructs model architecture based on the provided prompt, applies the model training strategies, displays the accuracy values, and finally plots the LSM. In this study, Geo[AI]-LSM is employed to estimate the landslide susceptibility of Mudurnu district in Bolu Province, Türkiye to demonstrate the generative AI’s capabilities. The current work develops models using various machine learning (ML) pipelines, each more sophisticated than the previous one. For this purpose, five alternative prompts (i.e., Prompts [1], [2], [3], [4], [5]) ranging from relatively simple to complex, were employed to generate ML models using the well-known Random Forest (RF) algorithm. The findings are evaluated using various performance metrics, including accuracy, Kappa, precision, recall, and F1 statistics. Experiments with datasets from the study area showed that the proposed Geo[AI]-LSM approach achieved an accuracy of about 89 % for the Prompt [5] model. Ultimately, it is believed that this research’s findings will make a substantial contribution to the current conversation about using AI technology to address geoscience challenges and improve landslide risk assessment and management. © 2025 COSPAR. Published by Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies. KW - Artificial intelligence (AI) KW - Google Gemini KW - Landslide susceptibility mapping KW - Large language model (LLM) KW - Machine learning (ML) KW - Data mining KW - Learning systems KW - Machine learning KW - Mapping KW - Artificial intelligence KW - Google geminus KW - Google+ KW - Landslide susceptibility KW - Landslide susceptibility mapping KW - Language model KW - Large language model KW - Machine learning KW - Machine-learning KW - Susceptibility maps KW - Landslides CY - Turkey ER - TY - JOUR TI - Leveraging generative AI capabilities for competitive advantage: A moderated mediation analysis of environmental dynamism and service innovation AU - Li L. AU - Xu C. AU - Zhang Q. AU - Liu Y. AU - Li Q. PY - 2025 JO - Industrial Marketing Management VL - 128 SP - 10 EP - 20 DO - 10.1016/j.indmarman.2025.05.007 AB - As generative AI increasingly transforms industrial markets, B2B firms face the imperative to strategically leverage it to sustain competitive advantage. Grounded in dynamic capabilities theory, this study establishes a novel framework to examine the interconnections among generative AI capabilities, service innovation, and competitive advantage under varying levels of environmental dynamism. Analyzing data from 260 Chinese firms, this study reveals that the three dimensions of service innovation—service concept, service process, and customer experience—serve as key mediators between generative AI capabilities and competitive advantage. Furthermore, a counterintuitive discovery is that environmental dynamism negatively moderates the mediation effects of service concept innovation and customer experience innovation on the relationship between generative AI capabilities and competitive advantage. These findings contribute to the literature on AI integration in B2B contexts by elucidating the conditions under which generative AI capabilities translate into competitive advantage, offering practical insights for firms navigating dynamic industrial landscapes. © 2024 KW - Competitive advantage KW - Dynamic capabilities theory KW - Environmental dynamism KW - Generative AI capabilities KW - Service innovation CY - China, France ER - TY - JOUR TI - Startup category membership and boundary expansion in the field of artificial intelligence AU - Truong Y. PY - 2024 JO - International Journal of Entrepreneurial Behaviour and Research VL - 30 IS - 2-3 SP - 398 EP - 420 DO - 10.1108/IJEBR-08-2022-0773 AB - Purpose: An important but neglected area of investigation in digital entrepreneurship is the combined role of both core and peripheral members of an emerging technological field in shaping the symbolic and social boundaries of the field. This is a serious gap as both categories of members play a distinct role in expanding the pool of resources of the field. I address this gap by exploring how membership category is related to funding decisions in the emerging field of artificial intelligence (AI). Design/methodology/approach: The first quantitative study involved a sample of 1,315 AI-based startups which were founded in the period of 2011–2018 in the United States. In the second qualitative study, the author interviewed 32 members of the field (core members, peripheral members and investors) to define the boundaries of their respective role in shaping the social boundaries of the AI field. Findings: The author finds that core members in the newly founded field of AI were more successful at attracting funding from investors than peripheral members and that size of the founding team, number of lead investors, number of patents and CEO approval were positively related to funding. In the second qualitative study, the author interviewed 30 members of the field (core members, peripheral members and investors) to define their respective role in shaping the social boundaries of the AI field. Research limitations/implications: This study is one of the first to build on the growing literature in emerging organizational fields to bring empirical evidence that investors adapt their funding strategy to membership categories (core and peripheral members) of a new technological field in their resource allocation decisions. Furthermore, I find that core and peripheral members claim distinct roles in their participation and contribution to the field in terms of technological developments, and that although core members attract more resources than peripheral members, both actors play a significant role in expanding the field’s social boundaries. Practical implications: Core AI entrepreneurs who wish to attract funding may consider operating in fewer categories in order to be perceived as core members of the field, and thus focus their activities and limited resources to build internal AI capabilities. Entrepreneurs may invest early in filing a patent to signal their in-house AI capabilities to investors. Social implications: The social boundaries of an emerging technological field are shaped by a multitude of actors and not only the core members of the field. The author should pay attention to the role of each category of actors and build on their contributions to expand a promising field. Originality/value: This paper is among the first to build on the growing literature in emerging organizational fields to study the resource acquisition strategies of entrepreneurs in a newly establishing technological field. © 2023, Emerald Publishing Limited. KW - Financing KW - Innovation KW - Small firm/new venture strategy KW - Technology CY - France ER - TY - JOUR TI - The Impact of Artificial Intelligence on Business Performance in Saudi Arabia: The Role of Technological Readiness and Data Quality AU - Alarefi M. PY - 2024 JO - Engineering, Technology and Applied Science Research VL - 14 IS - 5 SP - 16802 EP - 16807 DO - 10.48084/etasr.7871 AB - This study aims to examine the impacts of Machine Learning (ML) and Artificial Intelligence (AI) capabilities on Business Performance (BP) of technology enterprises in the Kingdom of Saudi Arabia (KSA). Building on established theories such as the Resource-Based View (RBV) and the Technology Organization Environment (TOE) framework, the study proposes that AI and ML capabilities impact business performance. Their effects are anticipated to be mediated by Technological Readiness (TR) and moderated by Data Quality (DQ). A total of 190 executives and IT professionals in KSA participated in this study. Smart PLS 4 was used to analyze the data. The findings showed that AI and ML capabilities positively affected business performance. Technological readiness acted as a mediator in the relationship between AI and ML capabilities, and BP. Data quality significantly increased the impact of AI capabilities on BP. The business performance of enterprises in KSA will increase with the presence of efficient AI and ML capabilities as well as the development of a high level of technological readiness and data quality. © by the authors. KW - artificial intelligence capability KW - business performance KW - data quality KW - machine learning KW - technological readiness CY - Saudi Arabia ER - TY - JOUR TI - Inception, development and evolution of guidelines for AI in parliaments AU - Fitsilis F. AU - von Lucke J. AU - De Vrieze F. PY - 2025 JO - Theory and Practice of Legislation VL - 13 IS - 3 SP - 405 EP - 428 DO - 10.1080/20508840.2025.2474791 AB - The rapid rise of Artificial Intelligence (AI) is transforming numerous sectors, including governance. As AI technologies become increasingly integrated into governance frameworks, several issues arise, ranging from integration with legacy systems to ethical considerations. Parliaments worldwide face similar challenges while planning to adopt AI tools for the purpose of strengthening their institutional processes such as automated transcription, legislative support tools and AI-powered public engagement. Therefore, the development of AI guidelines for parliaments is a necessary step to manage technological evolution while upholding democratic values and principles. They outline a democratic approach to AI governance and the role of parliaments in its implementation. This article argues that parliaments must develop AI governance capacities and presents the inception, development and evolution of a comprehensive set of 40 guidelines published in July 2024 designed to facilitate the integration and utilisation of AI in the parliamentary workspace. Apart from a brief outline of the guidelines, the article offers a detailed analysis of their development process, during a course of over one and a half years, from late 2022 till mid-2024. It first highlights the methodological approach used for capturing diverse perspectives of an international expert team composed of scholars and practitioners. Furthermore, it explores the composition of the expert team, its interdisciplinarity, as well as the strategies used to address critical aspects such as how to ensure global applicability amidst international and cultural differences. The article also discusses the agile development process and the potential of these guidelines to promote innovation and adaptability within parliaments. By examining these factors, the article contributes to a deeper understanding of how parliaments around the world can strategically deploy AI while balancing ethical, practical and cultural complexities. The findings provide valuable insights for parliamentarians, administrators and civic stakeholders, thus offering a structured approach for AI governance. © 2025 Informa UK Limited, trading as Taylor & Francis Group. KW - Artificial Intelligence KW - democratic values KW - ethics KW - governance KW - guidelines KW - institutional resilience KW - international collaboration KW - parliaments KW - technological integration CY - Greece, Germany, United Kingdom ER - TY - JOUR TI - ARTIFICIAL INTELLIGENCE APPLICATION IN HUMAN RESOURCES MANAGEMENT AU - Tairov I. AU - Stefanova N. AU - Aleksandrova A. PY - 2024 JO - Business Management VL - 2024 IS - 3 SP - 72 EP - 88 DO - 10.58861/tae.bm.2024.3.05 AB - In the contemporary landscape marked by the pervasive influence of artificial intelligence (AI), technological innovations continue to reshape conventional practices across various domains. Within the realm of human resources management, the intricate process of decision-making has long posed challenges in terms of analytical elucidation. However, the advent of AI technologies has ushered in a new era, offering unprecedented opportunities to augment and refine HR administration practices. This paper delves into the transformative potential of AI applications within human resources management, shedding light on how diverse AI modalities, including narrow and general AI, are revolutionizing traditional approaches. Through a comprehensive review of literature sourced from esteemed databases such as Scopus and Google Scholar, this study identifies key advancements poised to drive future research endeavors. Beyond the realm of recruitment, AI presents a myriad of possibilities spanning talent acquisition, employee training and development, performance assessment, compensation management, engagement initiatives, and even employee well-being programs. The synergy between human capabilities and AI integration emerges as a cornerstone for achieving enhanced outcomes, often serving as a determinant for competitive advantage within organizations while also impacting broader societal dynamics. By exploring the symbiotic relationship between human ingenuity and AI capabilities, this research seeks to elucidate the pathways through which AI-driven innovations can foster organizational excellence and societal progress. © 2024, Dimitar A Tsenov Academy of Economics. All rights reserved. KW - General artificial intelligence KW - Human resources management KW - Narrow artificial intelligence CY - Bulgaria ER - TY - JOUR TI - An Analytical Framework for Evaluating the Impact of Digital Transformation Technologies on Business Performance: A Natural Language Processing Approach AU - Vanani I.R. AU - Yalpanian M.A. AU - Taghavifard M.T. AU - Tahmaseby Y. PY - 2025 JO - Journal of Information Technology Management VL - 17 IS - 3 SP - 41 EP - 88 DO - 10.22059/jitm.2025.384662.3872 AB - Extensive technological advancements have highlighted the importance of digital transformation in improving business performance. While prior research on this topic has been done in the information systems and business management domains, it has been limited to specific areas. Therefore, it is crucial to evaluate the impact of digital transformation comprehensively. This research aims to systematically identify critical themes, significant opics, main concepts, and trend priorities. The study involved the analysis of 474 research papers from 2015 to 2024 from reputable databases such as SCOPUS, Web of Science, and IEEE Xplore. First, thematic analysis identified the main themes and interpreted their relationships. Identified themes refer to technological changes at the operational and strategic levels through data analytics, digitalization, collaborative learning, and digital interaction. Realizing that digital transformation leads to value creation, improved service quality, customer experience, and long-term communication in digital ecosystems. These findings were related to dynamic capability theory concepts and compared with theory constructs like sensing, seizing, and transforming. Next, text mining techniques were used for deeper investigation, including word cloud, topic modeling (Latent Dirichlet Allocation), and text clustering (K-means). Findings were categorized into three perspectives: business, customer, and systemic, highlighting the influential role of digital technologies, particularly artificial intelligence (AI) capabilities. Moreover, trend analysis presented research priorities using VOSviewer. Finally, research innovation involved designing thematic networks and examining the relevance of significant topics as a research artifact with subtle differences compared to the conducted research. This novel approach provides five targeted propositions to audiences for future research. © 2025 University of Tehran. All rights reserved. CY - Iran ER - TY - JOUR TI - Governance of medical AI in the greater Bay Area in Southern China: regulatory rule of law and AI sovereignty AU - Ho C.W.-L. PY - 2026 JO - Policy Studies VL - 47 IS - 2 SP - 306 EP - 337 DO - 10.1080/01442872.2025.2578435 AB - This paper examines what the regulatory rule of law means in relation to the governance of medical devices with artificial intelligence capability (AIMDs) in China, and by extension, the Guangdong-Hong Kong-Macao Greater Bay Area (GBA). It explains how rules and processes in device-based and data-based legal regimes administered by two super-regulators (i.e. the National Medical Products Administration and the Cyberspace Administration of China) support participatory governance, which is enabled through the delineation of national sovereignty and security concerns, specification of public / societal interests and private rights and obligations. The recent shift in emphasis from technological sovereignty to more specifically AI sovereignty on the mainland could elevate the importance of the GBA in AIMD development and innovation. The links with Hong Kong SAR and Macao SAR enable the provincial government of Guangdong to collate and curate high-quality and diverse forms of data from overseas sources. This could re-enact Guangdong's historical role as a gateway to the rest of China. For the GBA to be effective as a technological and data intermediary, the regulatory rule of law on AIMDs should be strengthened by drawing on the flexibilities that the “one country, two systems and three jurisdictions” policy arrangement affords. © 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. KW - Artificial intelligence KW - China KW - data KW - governance KW - Greater Bay Area KW - medical device KW - participatory KW - regulatory KW - rule of law KW - sovereignty CY - Australia, United Kingdom ER - TY - JOUR TI - Leveraging Artificial Intelligence Capability and Open Innovation to Optimize Agility: Is Generative AI Outmatching Human Expertise? AU - Arias-Pérez J. AU - Vélez-Jaramillo J. AU - Callegaro-de-Menezes D. PY - 2026 JO - Journal of the Knowledge Economy VL - 17 IS - 2 SP - 3635 EP - 3662 DO - 10.1007/s13132-025-02799-2 AB - Artificial intelligence (AI) will be performing 30% of creative and knowledge-intensive tasks by the year 2030. The use of affordable user-friendly generative AI, such as ChatGPT, has consequently experienced a significant surge within the corporate sector. Concurrently, it is assumed that human expertise, which refers to the business and technical knowledge in AI of individuals, is gradually losing its significance. However, until recently, human expertise was regarded as the primary catalyst for the positive effect of open innovation on organizational agility. Hence, this study aims to examine the mediating effect of AI capability on the relationship between open innovation and organizational agility, particularly in the presence of human expertise. The moderated mediation was tested with survey data. The main finding reveals that 93% of the variance of agility is explained by the effect of open innovation that is transmitted by the mediator. Moreover, human expertise only moderates the pathway between AI capability and organizational agility. The study offers a realistic understanding of the role of individuals in the context of increased use of AI in firms, in contrast to prior research that predicted an abrupt substitution of personnel with AI. AI capability, particularly generative AI (Gen-AI), is essential for the efficient generation of innovation ideas and prototypes, as well as the identification of unconventional commercial exploitation routes by leveraging data from external sources. Nevertheless, human expertise is essential to extract more accurate and contextually relevant outcomes from Gen-AI. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025. KW - Agility KW - AI capability KW - Digital transformation KW - Generative AI KW - Human expertise KW - Open innovation CY - Colombia, Brazil ER - TY - JOUR TI - Model Construction and Strategies for AI-enabled University Library Services to Facilitate Scientific and Technological Achievement Transformation; [AI赋能高校图书馆服务科技成果转化的 模式构建与策略研究] AU - Guo H. AU - Zeng M. AU - Feng Y. PY - 2026 JO - Journal of Library and Information Science in Agriculture VL - 38 IS - 2 SP - 56 EP - 65 DO - 10.13998/j.cnki.issn1002-1248.25-0568 AB - [Purpose/Significance] Against the backdrop of national innovation-driven development strategies and the pressing need to enhance the efficiency with which scientific and technological achievements are transformed within universities, university libraries are undergoing a critical transition. They are shifting from being traditional, passive information providers to becoming proactive, embedded partners in the research and innovation value chain. However, this transition is often hampered by inherent limitations in traditional service models. This study, therefore, posits artificial intelligence (AI) as a pivotal enabler and investigates the specific mechanisms through which AI technologies can empower university libraries to achieve deep, systemic integration into the entire lifecycle of technology transfer. The research aims to provide a comprehensive theoretical framework for understanding this transformation and offer actionable, evidence-based practical pathways for academic libraries to redefine their functional boundaries and substantially strengthen the institutional support ecosystem for university technology transfer. [Method/Process] This research employs a qualitative multi-case study design, underpinned by an analytical framework constructed around the four critical, sequential stages of the technology transfer lifecycle: 1) research topic selection and project initiation, 2) research and development, 3) project conclusion and evaluation, and 4) marketization and industrialization of outcomes. Case selection followed purposive sampling criteria to ensure representation across diverse contexts, including domestic and international universities, as well as varied library types. The primary data comprised detailed case descriptions from published academic literature, institutional reports, and official service platforms. Within this staged framework, the analysis focuses on two intertwined dimensions at each phase: the evolution of the library’s core service functions and the transformative impact of AI empowerment. Through a comparative cross-case analysis, this study examines how specific AI technologies augment traditional services, fundamentally changing the role and value proposition of libraries. [Results/Conclusions] The results show that through intelligent information analysis, knowledge association, data mining, and precise matching, AI can promote university libraries to shift from resource supply-oriented support to collaborative services that run through the entire lifecycle of technology transfer. This transformation manifests across the four-stage lifecycle as a shift: from providing literature to forecasting opportunities at the initiation phase; from offering patent data to navigating R&D pathways and risks during development; from archiving outputs to assessing value and potential at conclusion; and from disseminating information to intelligently brokering industry partnerships at the commercialization phase. Synthesizing these stage-specific transformations, this study constructs a novel, integrated service framework. This framework explicitly links specific AI capabilities with the redefined core functions of the library at each stage, illustrating the transition from a linear support model to a dynamic, AI-augmented ecosystem wherein the library serves as a central intelligence node. Meanwhile, this study reveals practical challenges in current practices, including ambiguous organizational boundaries, insufficient professional capabilities, and imperfect evaluation mechanisms oriented toward technology transfer. Correspondingly, it proposes strategies such as clarifying collaborative positioning, strengthening the construction of AI-empowered service capabilities, and improving technology transfer-oriented evaluation mechanisms to promote the sustainable development of AI-empowered research services in university libraries. © 2026, Agricultural Information Institute, Chinese Academy of Agricultural Sciences KW - artificial intelligence KW - case study KW - full-lifecycle services KW - technology transfer KW - university libraries CY - China ER - TY - JOUR TI - Green Leadership and AI-Enabled Innovation Pathways Toward Circular Supply Chain Practices AU - Hou G. AU - Zhang J. AU - Gao X. AU - Yu J. PY - 2026 JO - Corporate Social Responsibility and Environmental Management DO - 10.1002/csr.70487 AB - The study examines how AI-enabled capabilities, innovation ambidexterity, and green leadership affect the circular supply-chain practices in the Chinese furniture industry. The current research is motivated by increasing environmental pressures, regulatory expectations, and the strategic necessity of manufacturing firms to unite digital technologies with sustainability-oriented ventures. Based on the Resource based View and the Dynamic Capability Theory, the paper develops a framework in which dynamic routines (sensing, coordinating, learning, integrating, and reconfiguring) supplement AI-mediated capabilities of firms, enhancing both exploratory and exploitative innovation. The responses of 387 managers working in production, operations, supply-chain, and sustainability departments were collected and analyzed using Partial Least Squares Structural Equation Modelling (PLS-SEM). The results show that AI-driven functions have a significant influence on both types of innovation that, in turn, stimulate the introduction of circular supply-chain practices. The mediation analysis also shows that innovation is the important component by which AI-enabled capabilities contributes to sustainable supply-chain. Further, green leadership has a positive direct impact on circular practice and increases the impact of innovation on sustainability performance. These results support the idea that technological resources, balanced innovation strategies, and strong leadership commitment all accelerate firms to shift towards the practice of a circular economy, therefore providing a more accurate understanding of how AI-enabled capabilities are translated into measurable sustainability results. The combination of AI capabilities, innovation ambidexterity, and green leadership into a single system leads to a scientific impact on sustainability and digital transformation literature, as well as providing practical solutions to industry practitioners and policymakers that aim to encourage the deployment of circular supply chains. © 2026 ERP Environment and John Wiley & Sons Ltd. KW - AI-enabled capabilities KW - circular supply chain KW - exploitative innovation KW - exploratory innovation KW - green leadership CY - China ER - TY - JOUR TI - Imagining AI at work: the impact of polarized imaginaries on AI use in human resources management AU - Ecclesia S. PY - 2025 JO - Journal of Workplace Learning SP - 1 EP - 15 DO - 10.1108/JWL-06-2025-0162 AB - Purpose – The purpose of this study is to investigate how polarized imaginaries about the future of artificial intelligence (AI) for work impact AI practices in Human Resources Management (HRM). In doing so, this study provides insights into the influence replacement narratives have on AI use, showing that AI imaginaries can be understood as social practice imaginaries emerging from the present. Design/methodology/approach – The study is based on 20 interviews with HRM practitioners in Italy and two organizational observations with companies using AI in the recruitment process. The use of interviews and observations together has allowed to gather information about both HR practitioners’ expectations and emerging practices. Findings – The results show that HR practitioners negotiate replacement narratives against the backdrop of their own experience in using AI. Learning is adopted as a strategy to protect themselves from replacement, while popular fears and hopes are delayed as the current AI capabilities do not fulfill their promises. AI becomes a useful tool allowing them to dedicate themselves to meaningful tasks contributing to their self-actualization. From these emerging practices stems an alternative imaginary of AI, positioning workers’ wellbeing at the center of automation. Originality/value – This study challenges narratives on the future of AI for work by providing empirical evidence of their negotiation by workers and intersecting them with research on social practices. By doing so, it proposes an alternative imaginary of the future of AI in the workplace that moves away from traditional expectations about replacement and invites reflection on workers’ needs. © 2025 Emerald Publishing Limited KW - Artificial intelligence KW - Human resources KW - Imaginaries KW - Replacement KW - Social practices KW - Workplace innovation CY - Norway ER - TY - JOUR TI - Configurational pathways to smart city AI Adoption: Evidence from local governments in Australia, Hong Kong, Saudi Arabia, Spain, and the United States AU - Yigitcanlar T. AU - Liu K. AU - Senadheera S. AU - Marasinghe R. AU - David A. AU - Cheong P.H. AU - Corchado J. PY - 2026 JO - Cities VL - 175 SP - 107117 DO - 10.1016/j.cities.2026.107117 AB - Despite increasing policy attention and technological progress, AI adoption in smart city governance and local governments remains uneven. While previous studies have identified individual drivers of adoption, limited research has examined how multiple factors interact to enable or constrain implementation. Drawing on the technology-community-policy framework, this study employs fuzzy-set qualitative comparative analysis to investigate configurational pathways leading to AI-enabled smart city adoption across eleven local governments in five countries, Australia, Hong Kong, Saudi Arabia, Spain, and the United States. The findings reveal three different equifinal configurations leading to high AI adoption. First, the technology-driven pathway shows that robust smart city infrastructure and data capability can offset limited regulatory preparedness. Second, the balanced pathway integrates technological readiness, policy awareness, and organisational attention to community considerations to support adoption holistically. Third, the policy-driven pathway demonstrates that strong institutional mandates can compensate for weaker technical capacity. Across all pathways, perceived implementation constraints emerge as a core enabling condition, suggesting that recognition of challenges can stimulate proactive adoption strategies. The findings highlight substitutability between technological and policy dimensions, offering strategic flexibility for municipalities with differing resource endowments. This study advances configurational thinking in smart city and public sector innovation research and provides actionable insights for context-sensitive, resource-appropriate AI governance in local governments. © 2026 The Authors. KW - AI adoption KW - AI governance KW - Local government KW - Organisational configuration analysis KW - Public sector innovation KW - Smart city KW - Australia KW - China KW - Hong Kong KW - Saudi Arabia KW - Spain KW - United States KW - artificial intelligence KW - innovation KW - local government KW - smart city KW - technology adoption CY - South Africa ER - TY - JOUR TI - How Do AI Capabilities Affect Ambidextrous Green Innovation? A Mechanistic Analysis Based on Green Knowledge Management and Human–Organization–Technology Fit AU - Zhao P. AU - Cao Y. AU - Liu W. PY - 2026 JO - Systems VL - 14 IS - 4 SP - 357 DO - 10.3390/systems14040357 AB - Although artificial intelligence (AI) capabilities have emerged as a critical driver of corporate innovation in the contemporary business landscape, how they facilitate ambidextrous green innovation (AGI) during the manufacturing sector’s green transition—and under what conditions these benefits are most pronounced—remains unclear. Drawing on the Resource-Based View (RBV) and Knowledge-Based View (KBV), this study investigates the mechanism by which AICs foster AGI through the mediating role of green knowledge management (GKM), while further examining how Human–Organization–Technology (HOT) fit moderates these pathways. An analysis of survey data from 238 Chinese manufacturing firms using PLS-SEM reveals that AICs significantly drive AGI, with GKM playing a pivotal mediating role. Furthermore, the study confirms that Human–Organization–Technology (HOT) fit acts as a boundary condition, moderating the impact of AICs on GKM. These findings clarify the underlying mechanisms and boundary conditions of AICs, offering actionable insights for manufacturers seeking to boost green innovation capabilities by optimizing HOT alignment and leveraging green knowledge management systems. © 2026 by the authors. KW - AI capabilities KW - ambidextrous green innovation KW - green knowledge management KW - human–organization–technology fit KW - Knowledge acquisition KW - Knowledge organization KW - Ambidextrous green innovation KW - Artificial intelligence capability KW - Condition KW - Corporate innovation KW - Green innovations KW - Green knowledge management KW - Human–organization–technology fit KW - Manufacturing sector KW - Mechanistic analysis KW - Mediating roles KW - Green manufacturing CY - China ER - TY - JOUR TI - The impact of artificial intelligence capabilities on the sustainability with the mediating role of green innovation in the Jordanian hotels sector AU - Rawash H.N. AU - Alkawaja M. AU - Albadarneh M. AU - Jahmani K. AU - Salah A. PY - 2025 JO - Journal of Project Management (Canada) VL - 10 IS - 3 SP - 549 EP - 562 DO - 10.5267/j.jpm.2025.3.007 AB - The hotel industry in Jordan plays a crucial role in stimulating economic expansion by attracting tourists and creating job prospects. The industry can benefit from the use of Artificial Intelligence (AI) to improve sustainability through the promotion of green innovation, efficient resource utilization, and reduction of environmental harm. Hence, this study designs a model to enhance the environmental, economic, and social sustainability in the Jordanian hotels Sector. The study aimed to examine the impact of the AI Capabilities (tangible, intangible and human) on social, economic, and environmental sustainability with the mediating effect of the green innovation. The population of this study is all employees in 19 eco-friendly hotels in Jordan, they were 18,850 distributed over four Jordanian regions (Amman, Aqaba, Dead Sea and Petra). A total of 377 questionnaires distributed to respondents using stratified sampling. The study used SEM with SMART-PLS 4 to analyze the data collected. The measurement model applied to analyze the reliability and reliability of the model, the path coefficient in the structural equation model used to test the study hypotheses. The results of this study supported most of the study’s hypotheses, as it supported the impact of tangible and human capabilities on the sustainability, while the study did not find any direct impact of the intangible capabilities on the sustainability in the hotel sector in Jordan. The results show significant direct impact of the three AI capabilities; tangible, intangible and human on the green innovation, also the study found significant impact of the green innovation on the sustainability. The study confirms the three mediation hypotheses of the green innovation on the impact of the AI capabilities on the sustainability in the Jordanian hotel sector. The study provides important implications to the managers in the Jordanian hotel sector to enhance their environmental, economic and social sustainability by improving AI capabilities and innovation. © 2025 by the authors; licensee Growing Science, Canada. KW - AI capabilities KW - Hotel Sector KW - Human Capabilities KW - Intangible Capabilities KW - Sustainability KW - Tangible Capabilities CY - Jordan ER - TY - JOUR TI - Survey and Tutorial on Hybrid Human-Artificial Intelligence AU - Shi F. AU - Zhou F. AU - Liu H. AU - Chen L. AU - Ning H. PY - 2023 JO - Tsinghua Science and Technology VL - 28 IS - 3 SP - 486 EP - 499 DO - 10.26599/TST.2022.9010022 AB - The growing computing power, easy acquisition of large-scale data, and constantly improved algorithms have led to a new wave of artificial intelligence (AI) applications, which change the ways we live, manufacture, and do business. Along with this development, a rising concern is the relationship between AI and human intelligence, namely, whether AI systems may one day overtake, manipulate, or replace humans. In this paper, we introduce a novel concept named hybrid human-Artificial intelligence (H-AI), which fuses human abilities and AI capabilities into a unified entity. It presents a challenging yet promising research direction that prompts secure and trusted AI innovations while keeping humans in the loop for effective control. We scientifically define the concept of H-AI and propose an evolution road map for the development of AI toward H-AI. We then examine the key underpinning techniques of H-AI, such as user profile modeling, cognitive computing, and human-in-The-loop machine learning. Afterward, we discuss H-Al's potential applications in the area of smart homes, intelligent medicine, smart transportation, and smart manufacturing. Finally, we conduct a critical analysis of current challenges and open gaps in H-AI, upon which we elaborate on future research issues and directions. © 1996-2012 Tsinghua University Press. KW - artificial intelligence (AI) KW - hybrid human-Artificial intelligence (H-AI) KW - Internet of Things (IoT) KW - Artificial intelligence KW - Automation KW - Intelligent buildings KW - Artificial intelligence KW - Artificial intelligence systems KW - Computing power KW - Human intelligence KW - Human-in-the-loop KW - Hybrid human-artificial intelligence KW - Improved * algorithm KW - Internet of thing KW - Large scale data KW - Novel concept KW - Internet of things CY - China, United Kingdom ER - TY - JOUR TI - Future-ready AI: A framework for ethical and sustainable adoption AU - Kittipanya-ngam P. AU - Tan K.H. AU - Cavite H.J. PY - 2025 JO - Technology in Society VL - 83 SP - 102993 DO - 10.1016/j.techsoc.2025.102993 AB - The rise of artificial intelligence (AI) is transforming industries through automation, data-driven decision-making, and innovation. However, its adoption also poses challenges, including high implementation costs, limited technical capacity, and growing concerns around ethical and sustainable practices. While research on AI adoption continues to grow, the intersection of ethics, sustainability, and small and medium-sized enterprises (SMEs) remains underexplored. This study addresses that gap by investigating the dynamics of AI adoption among SMEs in Thailand—a key Southeast Asian economy—through multiple in-depth case studies. Within-case and cross-case analyses reveal that AI adoption presents both opportunities and challenges across the technological, organizational, and environmental (TOE) framework. Key factors include job security, data protection, and cost savings, while user education, mental well-being, and financial access emerge as critical concerns. The study further explores how TOE dimensions interact with sustainability and ethical considerations, conceptualized as ESG + E (Environmental, Social, Governance, and Economic). This expanded lens offers a more comprehensive understanding of responsible AI adoption. A novel integrative framework is proposed, providing actionable insights for SMEs, policymakers, and technology providers. The findings contribute to the broader discourse on AI adoption by advancing a sustainability- and ethics-oriented perspective relevant to emerging economies. © 2025 KW - Adoption KW - Artificial intelligence KW - Challenges and opportunities KW - Ethics KW - Sustainability KW - Decision making KW - Economics KW - Ethical technology KW - Sustainable development KW - Adoption KW - Challenge and opportunity KW - Data driven decision KW - Decisions makings KW - Ethical practices KW - Implementation cost KW - Organisational KW - Small and medium-sized enterprise KW - Sustainable practices KW - Technical capacity KW - artificial intelligence KW - ethics KW - small and medium-sized enterprise KW - sustainability KW - technology adoption KW - Artificial intelligence CY - Thailand, United Kingdom ER - TY - JOUR TI - Artificial Intelligence Capability and Firm Performance: A Sustainable Development Perspective by the Mediating Role of Data-Driven Culture AU - Fosso Wamba S. AU - Queiroz M.M. AU - Pappas I.O. AU - Sullivan Y. PY - 2024 JO - Information Systems Frontiers VL - 26 IS - 6 SP - 2189 EP - 2203 DO - 10.1007/s10796-023-10460-z AB - Artificial Intelligence (AI) tools, applications, and capabilities have received tremendous attention from industry practitioners, scholars, and policymakers. Despite the substantial progress of the literature on AI, there is a considerable scarcity of research investigating the effects of AI capability, considering the importance of a data-driven culture and whether a data-driven culture truly mediates the relationship between AI capability and firm performance from a sustainable development perspective. Anchored by the resource-based theory (RBT), we developed a high-order model of AI capability and its resources (tangible, intangible, and human). We used a two-stage approach, with PLS-SEM in the first and fsQCA in the second. The findings from the first step suggest that AI capability directly impacts firm performance and that data-driven culture mediates the relationship between AI capability and firm performance. The results from the second step indicated that different configurations of AI resources could be considered for firms to achieve high performance but that AI infrastructure is a crucial resource. Our study advances the literature on AI capability and sustainable development goals. Similarly, it contributes to moving the RBT theory forward by suggesting that AI capability is a paramount variable that substantially influences firm performance. Simultaneously, it is harmoniously connected with SDG 9 (industry, innovation, and infrastructure) and SDG 12 (responsible consumption and production). © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. KW - Artificial intelligence capability KW - Data-driven culture KW - Firm performance KW - SDG 12 KW - SDG 9 KW - Sustainable development KW - Artificial intelligence KW - Artificial intelligence capability KW - Artificial intelligence tools KW - Data driven KW - Data-driven culture KW - Firm Performance KW - Mediating roles KW - Resource-based theory KW - SDG 12 KW - SDG 9 KW - Tool applications KW - Sustainable development CY - France, Brazil, Norway, United States ER - TY - JOUR TI - How AI capabilities enable business model innovation: Scaling AI through co-evolutionary processes and feedback loops AU - Sjödin D. AU - Parida V. AU - Palmié M. AU - Wincent J. PY - 2021 JO - Journal of Business Research VL - 134 SP - 574 EP - 587 DO - 10.1016/j.jbusres.2021.05.009 AB - Artificial intelligence (AI) is predicted to radically transform the ways manufacturing firms create, deliver, and capture value. However, many manufacturers struggle to successfully assimilate AI capabilities into their business models and operations at scale. In this paper, we explore how manufacturing firms can develop AI capabilities and innovate their business models to scale AI in digital servitization. We present empirical insights from a case study of six leading manufacturers engaged in AI. The findings reveal three sets of critical AI capabilities: data pipeline, algorithm development, and AI democratization. To scale these capabilities, firms need to innovate their business models by focusing on agile customer co-creation, data-driven delivery operations, and scalable ecosystem integration. We combine these insights into a co-evolutionary framework for scaling AI through business model innovation underscoring the mechanisms and feedback loops. We offer insights into how manufacturers can scale AI, with important implications for management. © 2021 KW - Artificial intelligence KW - Business model innovation KW - Digital servitization KW - Digital transformation KW - Digitalization KW - Platform CY - Sweden, Norway, Switzerland, Finland ER - TY - JOUR TI - Balancing innovation and integrity: AI in tax administration and taxpayer rights AU - Guglyuvatyy E. PY - 2025 JO - Humanities and Social Sciences Communications VL - 12 IS - 1 SP - 1818 DO - 10.1057/s41599-025-06099-7 AB - Artificial intelligence (AI) is transforming tax administration by improving efficiency, compliance, and decision-making. However, this shift raises critical concerns about transparency, accountability, and taxpayer rights. This paper examines how AI-driven systems impact legal fairness, due process, and the integrity of tax procedures. It highlights risks such as algorithmic bias, opacity, and weakened procedural safeguards, while acknowledging AI’s potential to streamline enforcement. To safeguard taxpayer rights, the paper proposes an independent AI oversight mechanism to explain and review tax decisions. This system would enhance transparency, reinforce trust, and ensure legal accountability. The paper calls for regulatory frameworks that embed oversight, uphold public trust, and balance innovation with fundamental legal protections. © The Author(s) 2025. CY - Malaysia, Australia ER - TY - JOUR TI - Inside-out AI strategy at Microsoft: From capability building to commercialization AU - Durairaj M. AU - Bagilesh K. AU - Sathyamoorthy A. AU - Shanmugam K. PY - 2025 JO - Journal of Information Technology Teaching Cases SP - 20438869251383032 DO - 10.1177/20438869251383032 AB - As organizations adjust to the changing landscape of artificial intelligence (AI), a critical strategic question arises: Should companies prioritize internal transformation or lead with customer-driven innovation? This case investigates the ‘inside-out’ approach to AI adoption, where internal transformation functions as the foundation for market-facing solutions. The case illustrates how internal AI tool deployments, such as Microsoft 365 Copilot and Dynamics 365, enhanced operational efficiency and led to the development of scalable products for commercial clients, with Microsoft Corporation serving as the central organization. With quantifiable gains in efficiency and productivity, Microsoft must now decide how quickly to expand its AI products worldwide. The study highlights internal improvements such as enhanced operational reliability, alongside external milestones including Azure AI’s role in retail optimization and the widespread adoption of GitHub Copilot across enterprise clients. In comparing Microsoft’s strategy to rivals such as Google, Amazon, and Salesforce, the case highlights market potential as well as ethical, pricing, and regulatory issues. This case offers a framework for analysing commercialization of AI, capability building, and innovation strategy, while drawing on organizational theories such as the Resource-Based View, dynamic capabilities, and responsible innovation to evaluate Microsoft’s long-term strategic options. © Association for Information Technology Trust 2025 KW - AI capability KW - AI commercialization KW - azure AI KW - competitive strategy KW - digital transformation KW - enterprise AI adoption KW - inside-out strategy KW - Microsoft 365 Copilot KW - responsible AI CY - India ER - TY - JOUR TI - HOW DOES AI CAPABILITY ENABLE DIGITAL PRODUCT INNOVATION? A MIXED METHODS DESIGN AU - Li S. AU - Liu J. AU - Yang X. PY - 2025 JO - International Journal of Innovation Management VL - 29 IS - 03n04 SP - 2550017 DO - 10.1142/S1363919625500173 AB - Despite the fact that Artificial Intelligence (AI) in innovation management has been a topic of interest for several decades, little is known throughout the literature about how and why AI capability creates product value. In this work, we proposed a dual process model to explore the effects of AI capability on digital product innovation and tested it using quantitative and qualitative methods. In quantitative analysis, based on AI-Open Innovation matrix and dynamic capability theory, we tested the model using a total of 314 managers from 127 firms in the Chinese mainland. We found that AI capability enables digital product innovation by enhancing online value co-creation (a process of external asset) and digital resilience (a process of internal asset). Moreover, socio-cognitive sensemaking strengthens the mediation process of digital resilience but has no significant moderating effect on the mediation process of online value co-creation. The qualitative analysis enables us to better interpret the reasons why sensemaking plays different roles in mediation processes and suggests that it strengthens the effects of online value co-creation and digital resilience on digital product innovation through the external loop (time effect) and the internal loop (interactive effect), respectively. Our findings provide insights into how firms can scale digital product innovation using AI, with important implications for management. © 2025 World Scientific Publishing Europe Ltd. KW - AI capability KW - digital product innovation KW - digital resilience KW - online value co-creation KW - socio-cognitive sensemaking CY - China ER - TY - JOUR TI - Artificial Intelligence in Bioanalytical Chemistry: A Review of Algorithms, Applications, and Future Prospects AU - Garugu S. AU - Saxena K. AU - Zare K.B. AU - Kumar M. AU - Mukherjee B. AU - Shenoy A.V. AU - Rani S.K. PY - 2025 JO - Journal of Applied Bioanalysis VL - 11 IS - 4 SP - 661 EP - 677 DO - 10.53555/jab.v11i4.410 AB - The emergence of artificial intelligence (AI) as a data-centric tool is redefining bioanalytical chemistry by offering enhanced capabilities for automation, precision, and intelligent decision-making. As analytical laboratories face increasing demands for higher throughput, greater reproducibility, and real-time data processing, AI offers a strategic framework to address these challenges across diverse platforms, including spectroscopy, chromatography, and mass spectrometry. This review critically explores the current landscape of AI applications in bioanalytical workflows, encompassing key algorithmic approaches such as supervised and unsupervised learning, deep learning, and explainable AI (XAI). Emphasis is placed on practical implementations for spectral deconvolution, chromatographic peak detection, retention time prediction, and mass spectral interpretation. The review highlights AI-driven innovations in sample preparation, quality assurance, and method development optimization, supported by automation and Internet of Things (IoT) integration. Emerging concepts such as digital twins, self-learning adaptive systems, and blockchain-enabled traceability are also discussed as future enablers of smart analytical laboratories. While AI demonstrates transformative potential, significant challenges remain in areas of data quality, model reproducibility, interpretability, and regulatory alignment. Addressing these gaps through cross-disciplinary collaboration and the development of standardized validation frameworks is essential for ensuring trustworthy and compliant AI adoption in bioanalytical settings. This review provides a comprehensive synthesis of AI’s capabilities, limitations, and innovation directions, offering valuable insights for researchers, analysts, and regulatory professionals seeking to navigate the evolving intersection of artificial intelligence and analytical chemistry. © 2025, Green Publication. All rights reserved. KW - Artificial Intelligence KW - Bioanalytical Chemistry KW - Chromatography KW - Machine Learning KW - Spectroscopy CY - India ER - TY - JOUR TI - Technologies of improving the university efficiency by using artificial intelligence: Motivational aspect AU - Vinichenko M.V. AU - Melnichuk A.V. AU - Karácsony P. PY - 2020 JO - Entrepreneurship and Sustainability Issues VL - 7 IS - 4 SP - 2696 EP - 2714 DO - 10.9770/jesi.2020.7.4(9) AB - The aim of this study was to identify the most appropriate technologies to improve the university efficiency by using the motivational artificial intelligence (AI). Methods of the study were as follows: the questionnaire survey by using the Google Chrome electronic service, content analysis, methods of statistical analysis, and a focus group. The authors’ version of the questionnaire was made by using the Likert methodology taking into account indicators of the QS World University Rankings rating system. The data obtained during the three stages were generalized and analyzed by using the descriptive statistics. The regression analysis was used to study the relationship between the motives of the academic staff (AS) and the nature of the stimulating effect of the university authorities on the staff of the university. Results: The discrepancy between the AS motivation structure and the range of stimulation methods applied by the university authorities, a continuous increase in the burden from introduced innovations, and the formal style for employees to fulfill new tasks have been revealed. The analysis of the results on using the techniques and methods by the university authorities to motivate and stimulate the staff has shown the need in new combinatorics, an innovative system that harmoniously combines the advantages of natural and artificial intelligence to motivate the AS in training HR for the digital economy of the 21st century. The new system should be universal and flexibly respond to constant changes in the socio-economic environment. It is important to timely eliminate the contradictions in needs and teachers’ opinion on the ideal assessment system of their activities and offered forms of stimulation by universities authorities. The vectors of their activities must be constantly coordinated, based on the AI capabilities. The introduction of AI in the activities of universities improves the competitiveness of promising, innovative teachers and has positive impact on the image, efficiency, academic reputation, and citation index of universities. The authors for the first time ever have studied the problems of using the AI in the motivational system of the university’s AS and offered technologies to improve the efficiency of universities by using the motivational AI. The practical importance of solving the problem is related to the real possibility of applying the offered technologies by the university authorities that strive to improve their efficiency and competitiveness in the educational market. The main advantage of the work is related to the advanced solutions of the emerging problems on using the AI in motivating the university staff identified during the three-stage study. The interdisciplinary nature of the study and the offered technologies can serve as the basis for the further study and an additional element that expands the views, approaches, and the framework of categories and concepts of the world science. Conclusion: The most suitable technologies for the university that strives to be efficient include the elimination of the imbalance in the system of staff motives – incentives of the university (employer) authorities, the harmonious use of the AI in educational activities and the system of motivation and stimulation of staff where the natural intelligence prevails, and the improvement of the staff’s publication and grant activities by using the AI with a synergistic effect due to efficient team building. © 2020 by author(s) and VsI Entrepreneurship and Sustainability Center. KW - Artificial intelligence KW - Efficiency KW - Higher education KW - Motivation and stimulation KW - Technologies CY - Slovakia ER - TY - JOUR TI - Exploring the artificial intelligence integration in top management team decision-making: an empirical analysis AU - Bevilacqua S. AU - Ferraris A. AU - Kozel R. AU - Vincurova Z. PY - 2025 JO - Business Process Management Journal VL - 31 IS - 5 SP - 1763 EP - 1784 DO - 10.1108/BPMJ-07-2024-0659 AB - Purpose Drawing on upper echelons theory (UET), this study empirically explores how artificial intelligence (AI) has influenced the top management team’s (TMT) decision-making process in business management. Design/methodology/approach This article is based on 21 semi-structured interviews with top managers leading AI integration in their organizations. It adopts an inductive approach and applies the Gioia methodology. Findings The research identifies four primary areas of impact of AI for TMTs in managing digital business processes: (1) hybrid decision-making process, (2) AI’s ethical implications, (3) TMT governance through AI, and (4) AI-driven competitive advantage. Also, a framework has been developed that provides an initial understanding of how integrating AI in organizations affects the TMT’s decision-making process. Practical implications The study provides practical insights for the TMT leveraging AI technologies to enhance decision-making in managing business processes. Additionally, it offers helpful guidance for organizations to stay at the forefront of innovation and adaptability in an ever-evolving world. Originality/value Our findings highlight the critical role of TMT’s decision-making in managing business processes transformed by AI. Moreover, the study extends the UET, highlighting how the integration of AI influences the TMT’s decision-making process and how ethical implications impact these decisions and business management. © 2025 Emerald Publishing Limited KW - Artificial intelligence KW - Decision-making KW - Leadership KW - Top management team KW - Upper echelons theory CY - Slovakia ER - TY - JOUR TI - From discovery to delivery: Governance of AI in the pharmaceutical industry AU - Pasas-Farmer S. AU - Jain R. PY - 2025 JO - Green Analytical Chemistry VL - 13 SP - 100268 DO - 10.1016/j.greeac.2025.100268 AB - Artificial Intelligence (AI) is revolutionizing the pharmaceutical industry, significantly enhancing drug discovery, patient care, and operational efficiency. Key AI technologies like machine learning, deep learning, natural language processing, and computer vision are transforming pharmaceutical practices. Despite the promising potential, AI implementation faces numerous challenges such as technical complexity, ethical concerns, regulatory hurdles, and a shortage of skilled professionals. Governance frameworks are essential to ensure AI technologies are ethically developed and deployed, balancing innovation with safety and transparency. Key components of AI governance include regulatory compliance, data governance, algorithm transparency, and continuous system monitoring. However, the fast pace of technological advancements, global regulatory discrepancies, and the need for stakeholder collaboration present ongoing challenges. Best practices for AI governance, such as promoting transparency, fostering multidisciplinary collaboration, and adhering to robust data management standards, are critical for ensuring the ethical and effective use of AI. Addressing these challenges will enable the pharmaceutical industry to fully harness the power of AI, ensuring patient safety and promoting innovation in healthcare. © 2025 KW - AI governance KW - Analytical chemistry KW - Artificial intelligence KW - Ethical considerations KW - Pharmaceutical industry KW - Regulatory challenges KW - Transparency ER - TY - JOUR TI - AI Startup Business Models: Key Characteristics and Directions for Entrepreneurship Research AU - Weber M. AU - Beutter M. AU - Weking J. AU - Böhm M. AU - Krcmar H. PY - 2022 JO - Business and Information Systems Engineering VL - 64 IS - 1 SP - 91 EP - 109 DO - 10.1007/s12599-021-00732-w AB - We currently observe the rapid emergence of startups that use Artificial Intelligence (AI) as part of their business model. While recent research suggests that AI startups employ novel or different business models, one could argue that AI technology has been used in business models for a long time already—questioning the novelty of those business models. Therefore, this study investigates how AI startup business models potentially differ from common IT-related business models. First, a business model taxonomy of AI startups is developed from a sample of 100 AI startups and four archetypal business model patterns are derived: AI-charged Product/Service Provider, AI Development Facilitator, Data Analytics Provider, and Deep Tech Researcher. Second, drawing on this descriptive analysis, three distinctive aspects of AI startup business models are discussed: (1) new value propositions through AI capabilities, (2) different roles of data for value creation, and (3) the impact of AI technology on the overall business logic. This study contributes to our fundamental understanding of AI startup business models by identifying their key characteristics, common instantiations, and distinctive aspects. Furthermore, this study proposes promising directions for future entrepreneurship research. For practice, the taxonomy and patterns serve as structured tools to support entrepreneurial action. © 2021, The Author(s). KW - Artificial intelligence KW - Business model KW - Entrepreneurship KW - Machine learning KW - Pattern KW - Taxonomy CY - Germany ER - TY - JOUR TI - UNLOCKING AI POTENTIAL: EFFORT EXPECTANCY, SATISFACTION, AND USAGE IN RESEARCH AU - Izhar N.A. AU - Teh W.V.Y. AU - Adnan A. PY - 2025 JO - Journal of Information Technology Education: Innovations in Practice VL - 24 SP - 5 DO - 10.28945/5450 AB - Aim/Purpose This study investigates the key factors influencing the adoption and use of artificial intelligence (AI) applications among researchers, focusing on effort expectancy, satisfaction, perceived ease of use, and perceived usefulness, which shaped attitudes and drove AI adoption as a research assistant. Background AI tools have rapidly become game-changers in academic research, transforming tasks such as literature retrieval, writing, editing, and data analysis. Despite their potential, barriers like high effort expectancy, inconsistent user satisfaction, and ethical concerns regarding over-reliance and plagiarism continue to hinder widespread adoption. A pressing gap exists in understanding how AI impacts the efficiency and integrity of academic research workflows. Methodology A quantitative approach using structural equation modeling (SEM) was employed. Data was collected from 120 active researchers who use AI tools for academic tasks, including literature reviews, writing support, and data visualization. Contribution This study contributes to the understanding of how key factors, such as effort expectancy and satisfaction, affect AI adoption in academic research. It emphasizes the importance of reducing cognitive load and improving user satisfaction to promote widespread AI adoption. It also underscores the importance of intuitive AI design and institutional support in shaping researchers’ engagement with AI tools, which could enhance productivity and research outcomes. Findings The findings reveal that effort expectancy, satisfaction, perceived ease of use, and perceived usefulness significantly influence attitude and actual use of AI tools, with attitude serving as a key mediator. The model demonstrated moderate to high explanatory power (R2 = 0.409 to 0.459) and predictive relevance (Q2 = 0.171 to 0.409), highlighting the substantial role of effort expectancy and satisfaction in shaping perceived ease of use and usefulness. These findings emphasize the importance of reducing cognitive load and improving user satisfaction to encourage the adoption of AI tools in research. Recommendations Institutions and AI developers should focus on reducing the learning curve of for Practitioners AI tools by enhancing their intuitiveness and providing targeted training and technical support. Ethical AI use should also be promoted to address concerns about over-reliance and plagiarism. Institutions should foster a culture that normalizes AI integration in research practices. Recommendations Researchers should be informed of the long-term effects of AI adoption on for Researchers research quality and integrity and how institutional support can foster positive attitudes toward AI tools in academic research. Impact on Society The broader adoption of AI tools in academic research could enhance productivity and efficiency, leading to more breakthroughs in various fields and benefiting society by accelerating research and innovation. Additionally, AI can democratize access to research resources, particularly for underfunded institutions and early-career researchers, by enabling broader participation in cutting-edge research and fostering equity and diversity in academic contributions. Future Research Future studies should focus on the role of user experience in AI adoption, particularly how different user groups interact with AI tools. Longitudinal studies could provide insights into how attitudes toward AI change as users become more familiar with the tools. © (2025), (Informing Science Institute). All rights reserved. KW - academic research KW - AI adoption KW - artificial intelligence KW - effort expectancy KW - perceived ease of use KW - research assistance KW - satisfaction KW - technology acceptance CY - Malaysia ER - TY - JOUR TI - Multi-Stakeholder Agile Governance Mechanism of AI Based on Credit Entropy AU - Cheng L. AU - Chen W. AU - Li R. AU - Zhang C. PY - 2025 JO - Sustainability (Switzerland) VL - 17 IS - 20 SP - 9196 DO - 10.3390/su17209196 AB - Driven by the rapid evolution of AI technology, compatible management mechanisms have become a systematic project involving the participation of multiple stakeholders. However, constrained by the rigidity and lag of traditional laws, the “one-size-fits-all” regulatory model will exacerbate the vulnerability of the complex system of AI governance, hinder the sustainable evolution of the AI ecosystem that relies on the dynamic balance between innovation and responsibility, and ultimately fall into the dilemma of “chaos when laissez-faire, stagnation when over-regulated”. To address this challenge, this study takes the multi-stakeholder collaborative mechanism co-established by governments, enterprises, and third-party technical audit institutions as its research object and centers on the issue of “strategic fluctuations” caused by key factor disturbances. From the perspective of the full life cycle of technological development, the study integrates the historical compliance performance of stakeholders and develops a nonlinear dynamic reward and punishment mechanism based on Credit Entropy. Through evolutionary game simulation, it further examines this mechanism as a realization path to promote the transformation from passive campaign-style AI supervision to agile governance of AI, which is characterized by rapid response and minimal intervention, thereby laying a foundation for the sustainable development of AI technology that aligns with long-term social well-being, resource efficiency, and inclusive growth. Finally, the study puts forward specific governance suggestions, such as setting access thresholds for third-party institutions and strengthening their independence and professionalism, to ensure that the iterative development of AI makes positive contributions to the sustainability of socio-technical systems. © 2025 by the authors. KW - collaborative governance KW - credit entropy KW - dynamic reward and punishment KW - full life cycle KW - tripartite evolutionary game KW - compliance KW - governance approach KW - life cycle analysis KW - sustainability KW - sustainable development CY - China ER - TY - JOUR TI - Legislation on the Use of Artificial Intelligence in European Union Countries AU - Kolomoiets T. AU - Velykanova M. AU - Kravets M. AU - Blinova H. AU - Posykaliuk O. PY - 2026 JO - Nusantara: Journal of Law Studies VL - 5 IS - 1 SP - 269 EP - 287 DO - 10.66325/nusantaralaw.v5i1.233 AB - This study analyzes the legislative framework governing the use of artificial intelligence (AI) in the European Union, focusing on patterns of legal convergence and divergence, as well as the governance challenges arising from its implementation. The research aims to examine how the EU constructs a harmonized yet flexible regulatory regime capable of addressing the multifaceted risks of AI while promoting innovation. Methodologically, this study employs a qualitative approach through doctrinal legal analysis and policy review, drawing on primary legal instruments, including the EU AI Act, as well as secondary sources such as policy reports and academic literature. The findings indicate that the EU adopts a risk-based regulatory model that classifies AI systems into low, medium, and high-risk categories. While most AI applications fall into low-or medium-risk categories, high-risk systems—particularly those used in sensitive sectors such as healthcare, justice, employment, and finance—pose significant legal and ethical challenges. The study identifies key risks, including algorithmic bias, data privacy violations, and a lack of transparency, alongside broader concerns about accountability and the protection of fundamental rights. Furthermore, although legal convergence is evident in the establishment of uniform EU standards, divergence persists in national implementation, enforcement practices, and institutional readiness across member states. This study contributes to the existing literature by providing a comprehensive analysis of the interplay between harmonization and fragmentation in EU AI regulation. It also highlights the need for adaptive governance mechanisms that balance regulatory consistency with contextual flexibility. Ultimately, the research underscores that effective AI legislation must strengthen accountability, ensure ethical compliance, and foster public trust, thereby aligning technological development with the core values of the European Union. © 2026, PT. Islamic Research Publisher. All rights reserved. KW - Administrative Legal Protection KW - Civil Legal Protection KW - Intellectual Property Law KW - Liability KW - Object of Legal Relations CY - Ukraine ER - TY - JOUR TI - From automation to cognition: a contextual design approach for enhancing elderly patron services with proactive and memory-aware library robots AU - Yueh H.-P. AU - Lin W. AU - Chen C.-H. AU - Chang F.-H. AU - Fu L.-C. PY - 2025 JO - Electronic Library SP - 1 EP - 23 DO - 10.1108/EL-06-2025-0244 AB - Purpose – This study aims to explore the feasibility and effectiveness of integrating a cognitively-enabled robot into public library services specifically tailored for elderly patrons. It aims to demonstrate how a user-centred and contextually designed robotic system can meet the multifaceted needs of this demographic, moving beyond traditional automated functions to provide more interactive and personalized information services. Design/methodology/approach – Adopting a user-centred design research approach, this study developed “OREO”, a cognitive library robot featuring three key innovations: proactive engagement capabilities, intelligent navigation and recommendation and memory-aware dialogue built upon multiple visits. A field study was conducted in the senior zone of a national public library with elderly patrons, and both quantitative usability test data and qualitative interview data were collected and analysed. Findings – The cognitive robot, OREO, successfully demonstrated its designed functions in a real-world library setting. Three main functions targeted for verification – proactive greeting, intelligent guidance and memory-aware dialogue – were all confirmed with positive evaluation feedback. Originality/value – This study offers novel empirical insights into the deployment of a cognitive embodied robot that integrates advanced artificial intelligence (AI) capabilities of proactive engagement, intelligent navigation and memory-aware dialogue into a public library’s elderly services. It uniquely uses a user-centred and humanity-centred design approach within an authentic field setting, providing first-hand evidence and a crucial understanding of human-robot interaction in community contexts. The findings demonstrate the transformative potential of moving library robots from mere automation to intelligent, socially aware agents, offering actionable design principles for future AI-driven information services. © 2025 Emerald Publishing Limited KW - Artificial intelligence KW - Cognitive robotics KW - Elderly patrons KW - Generative AI KW - Human-robot interaction KW - Information access KW - Library services KW - Public libraries KW - User-centred design CY - Taiwan ER - TY - JOUR TI - EXPLORING RESPONSIBLE INNOVATION WITH PRIVACY PRESERVATION: FEDERATED LEARNING POLICIES FOR DIGITAL FINANCE SERVICES IN ASIA AU - Tam P. AU - Corrado R. AU - Pham T.T. PY - 2025 JO - Asia Pacific Sustainable Development Journal VL - 32 IS - 2 SP - 215 EP - 241 DO - 10.18356/26178419-32-2-10 AB - Advancements in artificial intelligence (AI) and financial technologies (fintech) are transforming digital finance with innovations in personalized products, fraud detection, accessibility and risk management. However, these innovations require sensitive customer data, raising privacy and security concerns. Federated learning (FL) offers a solution by enabling institutions to train AI models locally, sharing only model updates and minimizing data-sharing risks. This paper contains an exploration of how FL can advance AI-driven innovation while ensuring privacy compliance, in particular in Asia, by analysing FL key use cases, including personalized recommendations, fraud detection and credit scoring. We then propose frameworks for FL platform assessments and stakeholder analysis for policy recommendations to enhance data security, regulatory compliance and ethical guidelines for responsible innovation in digital finance. © 2025, UNESCAP. All rights reserved. KW - Asia KW - digital finance KW - federated learning KW - responsible innovation KW - stakeholder analysis CY - Cambodia, Netherlands ER - TY - JOUR TI - Flipping the odds of AI-driven open innovation: The effectiveness of partner trustworthiness in counteracting interorganizational knowledge hiding AU - Arias-Pérez J. AU - Huynh T. PY - 2023 JO - Industrial Marketing Management VL - 111 SP - 30 EP - 40 DO - 10.1016/j.indmarman.2023.03.005 AB - This paper aims to analyze the negative effect of knowledge hiding on the relationship between artificial intelligence (AI) capability and open innovation (inbound and outbound) when partner trustworthiness (benevolence, integrity, and ability) is high. Structural equations were used to test this three-way interaction with survey data from a sample of 229 firms, mainly from highly digitalized sectors. The findings indicate that interorganizational knowledge hiding affects only the relationship between AI capability and outbound open innovation and that partner ability is the only factor that will counteract this negative effect. Therefore, co-exploitation of AI-based solutions with external allies is the sole scenario to encourage knowledge hiding by increasing employees' perceptions of the likelihood of AI negatively impacting their personal interests at work. Moreover, when trustworthiness is at the forefront of the intraorganizational discussion, the findings downplay the significance of benevolence and integrity as traits that significantly reduce knowledge hiding. In contrast, at the interorganizational level, knowledge hiding is lessened only when employees perceive that co-exploitation with external partners represents an opportunity to learn and capture crucial AI knowledge. © 2023 KW - Artificial intelligence capability KW - Digital transformation KW - Knowledge hiding KW - Open innovation KW - Partner ability KW - Partner trustworthiness CY - Colombia, United Kingdom ER - TY - JOUR TI - The more capability, the better behavioural intention? Empirical evidence on the relation between institutes’ artificial intelligence capability and pre-service teachers’ behavioural intentions to design artificial intelligence assisted teaching AU - Wang K. AU - Shen L. AU - Duan B. AU - Zhang C. AU - Yuan X. PY - 2026 JO - Asia Pacific Journal of Education DO - 10.1080/02188791.2026.2625132 AB - The field of education has witnessed a rapid expansion in the utilization of Artificial Intelligence (AI) technologies, fundamentally transforming classroom instruction. Thus, it is critical for pre-service teachers to implement AI-powered technology in their future teaching. This study was conducted in six higher education institutions (HEIs) in China and is grounded in resource-based theory, the technology acceptance model (TAM), and relevant literature. SmartPLS 4.0 was utilized to develop a partial least squares structural equation model (PLS-SEM) to examine the relationships among AI capability (AIC), creativity, self-efficacy, Technological Pedagogical Content Knowledge (TPACK), and pre-service teachers’ behavioural intentions towards AI-assisted teaching. The findings indicated that HEIs’ AIC is a significant predictor of pre-service teachers’ behavioural intentions towards designing AI-assisted teaching. It also predicts their creativity, self-efficacy, and TPACK. Furthermore, creativity, self-efficacy, and TPACK were found to mediate the relationships between HEIs’ AIC and pre-service teachers’ behavioural intentions. These findings suggest that HEIs should support the development of pre-service teachers by enhancing AIC, including resources (data and technology) and awareness (reform and innovation), providing insights into AI integration in higher education within the Asia-Pacific context. © 2026 National Institute of Education, Singapore. KW - AI-assisted teaching KW - Artificial intelligence capability KW - behavioural intention KW - higher education institute KW - pre-service teachers CY - China, Belgium ER - TY - JOUR TI - Towards a Multi-Stakeholder process for developing responsible AI governance in consumer health AU - Rozenblit L. AU - Price A. AU - Solomonides A. AU - Joseph A.L. AU - Srivastava G. AU - Labkoff S. AU - deBronkart D. AU - Singh R. AU - Dattani K. AU - Lopez-Gonzalez M. AU - Barr P.J. AU - Koski E. AU - Lin B. AU - Cheung E. AU - Weiner M.G. AU - Williams T. AU - Thuy Bui T.T. AU - Quintana Y. PY - 2025 JO - International Journal of Medical Informatics VL - 195 SP - 105713 DO - 10.1016/j.ijmedinf.2024.105713 AB - Introduction: AI is big and moving fast into healthcare, creating opportunities and risks. However, current approaches to governance focus on high-level principles rather than tailored recommendations for specific domains like consumer health. This gap risks unintended consequences from generic guidelines misapplied across contexts and from providing answers before agreeing on the questions. Objective: Our objective is to explore pragmatic multi-stakeholder approaches to govern consumer-facing health AI. The aims are to (1) establish an approach tailored for consumer health AI governance and (2) identify key constraints and desirable model characteristics. Methods: This paper synthesizes insights informed by a 4-month multidisciplinary expert consensus process with nearly 200 participants. The deliberations provided guidance for the development of the proposed governance models in consumer health AI. Results: (1) A Shared View of Consensus: A process for consumer health AI governance should limit the scope and incorporate multi-stakeholder perspectives centered on patient needs. Desirable model characteristics include adaptability, patient empowerment, and transparency. (2) Recommended Collaborative Process: A pathway for effective governance should begin by forming a Health AI Consumer Consortium (HAIC2) representing patients and aligning incentives across stakeholders. Conclusions: While examples focus on the United States healthcare system, core themes around incorporating consumer voices, enabling transparency, and balancing innovation with thoughtful oversight while avoiding overambitious scope will have relevance globally. As consumer AI spreads worldwide, the multi-stakeholder alignment and patient empowerment principles proposed here may offer productive ways to ensure AI for consumers is safe, effective, equitable, and trustworthy (SEET). © 2024 KW - Artificial Intelligence KW - Consumer Health Informatics KW - Humans KW - Stakeholder Participation KW - 'current KW - Consumer healths KW - Governance models KW - Key constraints KW - Multi-stakeholder KW - Multi-stakeholder approach KW - Multi-stakeholder perspectives KW - Patient empowerments KW - Stakeholder process KW - Unintended consequences KW - adult KW - article KW - clinical practice guideline KW - consensus KW - consumer KW - female KW - health care system KW - human KW - patient empowerment KW - practice guideline KW - responsible artificial intelligence KW - artificial intelligence KW - consumer health informatics KW - stakeholder participation KW - Electronic health record CY - United States, United Kingdom, Canada ER - TY - JOUR TI - Polycentric Power Plays: Gulf Agency and the Dynamics of China’s AI Diplomacy AU - Tran E. AU - Gulrez T. PY - 2026 JO - Communication and the Public DO - 10.1177/20570473261438564 AB - This article offers the first systematic, comparative analysis of China’s AI diplomacy across all six Gulf Cooperation Council (GCC) states – Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates – foregrounding Gulf governments’ agency in negotiating the intersection of Chinese digital infrastructure and global AI governance. Challenging portrayals of the region as a monolithic or passive recipient of external influence, the study advances a polycentric negotiation framework that integrates theories of norm diffusion, strategic hedging, and co-production to reveal how Gulf states selectively absorb, filter, or recalibrate Chinese technological offerings and regulatory models. Drawing on systematic documentary analysis and comparative case studies, the research uncovers significant intra-GCC divergence: while some states exhibit high infrastructure dependence with limited normative alignment, others compartmentalize Chinese participation and prioritize regulatory convergence with Western frameworks. The findings highlight Gulf states’ strategic use of digital sovereignty and alignment flexibility to maximize autonomy amid intensifying US–China competition. Ultimately, the analysis demonstrates how Gulf agency and institutional innovation shape the global diffusion and localization of AI norms, providing a replicable model for understanding digital diplomacy in other geopolitical contexts. © The Author(s) 2026. This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage). KW - China-GCC AI diplomacy KW - digital sovereignty KW - norm diffusion and localization KW - polycentric negotiation KW - strategic hedging CY - Qatar ER - TY - JOUR TI - Legitimating entrepreneurship through generative AI: The reproduction of visual stereotypes AU - Hormiga E. AU - Jonckers G. AU - Urbano D. PY - 2026 JO - Technology in Society VL - 86 SP - 103278 DO - 10.1016/j.techsoc.2026.103278 AB - Generative artificial intelligence (AI) tools are transforming visual production across multiple domains, including entrepreneurship. However, their influence on constructing cultural imaginaries and legitimating symbols remains insufficiently examined. This study analyzes how text-to-image systems visually represent entrepreneurship and success, using a focused sample of 24 images generated with the Midjourney platform. Through critical visual discourse analysis, the research identifies recurring aesthetic codes, symbolic patterns, and temporal framings that demonstrate how generative AI replicates established cultural narratives of entrepreneurship. The results indicate a consistent depiction of solitary, self-assured individuals who embody control, ambition, and transcendence. In contrast, representations of collaboration, diversity, and social contribution are largely absent. The visual grammar emphasizes formality, isolation, and monumental composition, while temporal orientations favor immediacy and permanence rather than process and collective effort. These algorithmic representations reinforce narrow ideals of entrepreneurial legitimacy and perpetuate masculine-coded notions of success and authority. To synthesize these findings, the study introduces a conceptual model of the sociotemporal automation of legitimacy in generative AI entrepreneurial imaginaries. This model connects algorithmic infrastructures, aesthetic and temporal representations, and cultural circulation through the feedback loop of human-AI co-production. The paper contributes to understanding the aesthetic mechanisms by which generative AI consolidates dominant entrepreneurial ideals and considers implications for critical visual literacy, responsible AI deployment, and inclusive innovation policy. © 2026 The Authors. KW - Algorithmic bias KW - Diversity and inclusion KW - Entrepreneurship KW - Generative artificial intelligence KW - Legitimation KW - Sociotechnical imaginaries KW - Visual stereotypes KW - Vision KW - Algorithmic bias KW - Algorithmics KW - Diversity and inclusion KW - Entrepreneurship KW - Generative artificial intelligence KW - Imaginaries KW - Legitimation KW - Sociotechnical KW - Sociotechnical imaginarie KW - Visual stereotype KW - artificial intelligence KW - entrepreneur KW - innovation KW - Artificial intelligence CY - Spain, Belgium ER - TY - JOUR TI - Transforming early childhood education in Saudi Arabia: AI’s impact on emotional recognition and personalized learning AU - Aldhilan D. AU - Rafiq S. PY - 2025 JO - International Journal of Evaluation and Research in Education VL - 14 IS - 4 SP - 2473 EP - 2486 DO - 10.11591/ijere.v14i4.32660 AB - Artificial intelligence (AI) technologies are increasingly integrated into early childhood education (ECE) worldwide, promising to revolutionize learning experiences for young children. ECE in Saudi Arabia faces challenges in addressing diverse learning needs and fostering socio-emotional development. This qualitative study investigates the role of AI in enhancing emotional recognition, promoting socio-emotional development, and addressing associated challenges in the context of Saudi Arabian schools. A total of 55 ECE teachers in Jeddah were interviewed using purposive sampling, with data saturation achieved at 50 interviews. Themes emerging from the data highlight AI’s effectiveness in personalizing learning experiences based on individual needs and learning styles, fostering empathy and social interaction among children, and enhancing classroom management. Challenges identified include data privacy concerns, cultural adaptation of AI tools, and ensuring equitable access to technology. The study highlights the importance of comprehensive teacher training, ethical guidelines, and robust policy frameworks to support responsible AI integration in Saudi Arabian education. Implications for practice include enhancing educational practices through AI while emphasizing the human role of educators, and the need for ongoing research to inform future innovations in ECE. © 2025, Institute of Advanced Engineering and Science. All rights reserved. KW - AI technologies KW - Data privacy KW - Early childhood education KW - Personalized learning KW - Socio-emotional development CY - Saudi Arabia, Pakistan ER - TY - JOUR TI - AI FOR ACCESSIBILITY IN DIGITAL MEDIA EDUCATION AU - Babu M.R.N. AU - Tungoe C. AU - Vasanthan R. AU - Pimple J. AU - Khandare K.S. AU - Kalyani L.K. PY - 2025 JO - ShodhKosh: Journal of Visual and Performing Arts VL - 6 IS - 2s SP - 67 EP - 77 DO - 10.29121/shodhkosh.v6.i2s.2025.6704 AB - Artificial Intelligence (AI) is transforming the digital media education field to be more accessible and inclusive to various learners. This paper discusses how AI based technologies can be implemented in digital media learning environment to help students with various types of physical, cognitive and sensory disabilities. The study is based on the principles of Universal Design of Learning (UDL) and the author is investigating the possibility of breaking down the barriers to content delivery and participation through the use of adaptive systems (-speech recognition, text to speech (TTS) and image recognition) that assist students with disabilities in their learning process. The study assesses the practical benefits and disadvantages of AI in educational accessibility using a mixed-method approach, which is a combination of classroom observation and comparative study of the AI (accessibility tools) and non-AI (accessibility tools). The results point to the role played by AI-driven applications in ensuring fair interaction whereby they can be used to create more personalized learning experience, enhance understanding and promote communication between students and teachers. This paper highlights that it is necessary to have open AI systems that consider fairness data protection and inclusivity in the design of education. The current study can be added to the current discussion about inclusive pedagogy in which responsible AI involves can improve access, as well as creativity and innovation in digital media education. The paper ends with policy suggestions on how policy makers, educators and technologists can come up with sustainable AI access models in future learning environments. © 2025 The Author(s). KW - Accessibility KW - Artificial Intelligence (AI) KW - Design for Learning (UDL) KW - Digital Media Education KW - Inclusive Technology KW - Universal CY - India ER - TY - JOUR TI - AI, Ethics, and Human-Centered Policy in Albanian Education AU - Hodaj A. AU - Laçka S. PY - 2025 JO - Interdisciplinary Journal of Research and Development VL - 12 IS - 3 SP - 156 EP - 163 DO - 10.56345/ijrdv12n318 AB - The integration of Artificial Intelligence (AI) into education is reshaping pedagogical practices, policy frameworks, and ethical standards worldwide. This paper explores how AI is transforming educational policy and teaching in Albania, a country still adapting to the post-pandemic digital era. Drawing upon comparative policy analysis and global frameworks from UNESCO, the European Union, and the OECD, the study identifies significant gaps between Albania’s current legal structures and international standards. While European education systems emphasize ethical governance, teacher AI literacy, and digital inclusion, Albania’s higher education law (Law No. 80/2015) remains silent on online learning, AI ethics, and data protection. The COVID-19 crisis accelerated technological adoption but revealed the absence of systemic readiness. The paper argues for a human-centered AI policy that links innovation to ethics, proposing reforms for 2025–2030 to align with European directives. Additionally, the research highlights the critical role of teachers as ethical mediators in AI-supported classrooms, emphasizing that technological integration must be accompanied by moral awareness and cultural adaptation. The findings also suggest that Albania’s alignment with the EU Artifi cial Intelligence Act (2024) could foster a coherent framework for accountability, transparency, and fairness in digital education. Through a multidisciplinary approach, this study provides both analytical and policy-oriented insights into how developing educational systems can balance innovation with human dignity, ensuring that Artificial Intelligence becomes a catalyst for inclusive, equitable, and ethically grounded education. © 2025 Albana Hodaj and Senada Laçka. KW - Albania KW - Artificial Intelligence KW - Digital Transformation KW - Educational Policy KW - Ethics KW - Higher Education KW - Teacher Training CY - Albania ER - TY - JOUR TI - AI capability, knowledge integration, and cognitive barriers: Innovation pathways for circular economy practices in construction AU - Soomro M.A. AU - Khan A.N. AU - Khahro S.H. AU - Javed Y. PY - 2026 JO - Journal of Innovation and Knowledge VL - 14 SP - 100948 DO - 10.1016/j.jik.2026.100948 AB - The transformative potential of artificial intelligence (AI) is prompting construction organizations to redefine the concept of progress. However, the transition from digital capability to a higher-level sense of innovation is not inevitable. This research delves into the effect of AI capability on innovation-driven circular economy (CE) practices, providing evidence that technological advances alone do not drive change without an enabling cognitive and organizational environment. We adopt the lens of sociotechnical systems (STS) theory to conceptualize knowledge integration as the mechanism by which AI capability leads to CE practice adoption and cognitive rigidity as the inhibitor that reduces the positive effect of knowledge integration. Structural equation modeling and moderated mediation tests are applied to a survey of 414 construction professionals. Results suggest that AI capability promotes new ways of working and CE practices indirectly through its influence on knowledge integration; however, this influence is attenuated when cognitive rigidity hampers knowledge integration. The research extends STS theory into a domain of innovation management by integrating its cognitive, technical, and organizational elements. Our findings offer practical implications for industry practitioners, suggesting that building CE capacity goes beyond adopting digital technology; it also involves fostering cognitive agility and robust knowledge exchange mechanisms to enable those technologies to translate into innovation. Copyright © 2026. Published by Elsevier B.V. KW - AI capability KW - Circular economy practice KW - Cognitive rigidity KW - Construction industry KW - Knowledge integration KW - Sociotechnical systems CY - China, Saudi Arabia ER - TY - JOUR TI - Revisiting patent law paradigms: legal, economic, and ethical implications of AI-driven inventions in the biosciences: introducing the universal model of augmented invention AU - Levy H.V. PY - 2025 JO - Law, Ethics and Technology VL - 2 IS - 2 SP - 0006 DO - 10.55092/let20250006 AB - The rapid advancements in artificial intelligence (AI) are transforming numerous sectors, particularly biotechnology, where AI systems now play an active role in autonomous research and invention generation. This paper introduces, for the first time, the universal model of augmented invention—a hybrid legal category recognizing patentable outputs co-produced by human agents and AI under meaningful human oversight—and examines its implications for patent law in the biosciences. Focusing on inventorship, ownership, and patentability, the paper highlights the need to adapt patent frameworks to reflect AI’s accelerating impact on research timelines and innovation processes. It analyzes the economic incentives and ethical dimensions of AI-generated inventions, including equity, access, and accountability challenges, and proposes targeted reforms: statutory recognition of augmented inventorship, AI-specific disclosure requirements, and adaptive compulsory licensing triggers. These reforms aim to ensure that patent law both fosters technological progress and safeguards the public interest. ©2025 by the authors. Published by ELSP. KW - AI-driven inventions KW - augmented inventorship KW - compulsory licensing KW - drug discovery and development KW - economic incentives KW - enablement and written description (patent disclosure) KW - ethical AI governance (biomedicine) KW - patent law KW - patentability KW - technology transfer and licensing CY - Israel ER - TY - JOUR TI - ChatGPT’s crystal ring: simulating auditors’ use of machine learning in stock price prediction AU - Arabiat O. AU - Alshurafat H. PY - 2024 JO - Journal of Decision Systems DO - 10.1080/12460125.2024.2371670 AB - This study investigates the influence of technological factors on the intent to use Machine Learning (ML) tools such as Python for the purpose of predicting stock prices. Further, it investigates the moderate impact of Artificial Intelligence (AI) models usage, in particular ChatGPT, on these associations. The outcomes of a simulation involving 400 auditors, accounting for the heterogeneity of their competencies, were obtained through code utilisation based on the Python programming language. The technological factors drawn from diffusion of innovation theory (DOI), including relative advantages, Complexity, compatibility, observability, and triability, all showed positive associations with behavioural intent. The use of ChatGPT significantly fortified these connections. These results suggest a fruitful symbiotic outcome may be achieved by combining AI capabilities with these variables. The findings underscore the significance of planning for the adoption of AI in financial decision-making and auditing and also illustrate the potential of AI in these areas. © 2024 Informa UK Limited, trading as Taylor & Francis Group. KW - auditor KW - ChatGPT KW - DOI KW - Python KW - simulation KW - stock prices KW - Computer simulation languages KW - Computer software KW - Costs KW - Crystals KW - Decision making KW - Electronic trading KW - Financial markets KW - Machine learning KW - Problem oriented languages KW - Auditor KW - ChatGPT KW - Crystal rings KW - Diffusions of innovation theories KW - Learning tool KW - Machine-learning KW - Simulation KW - Stock price KW - Stock price prediction KW - Technological factors KW - Python CY - Jordan ER - TY - JOUR TI - Vulnerabilities and Defenses: A Monograph on Comprehensive Analysis of Security Attacks on Large Language Models AU - Balakrishnan P. AU - Leema A.A. PY - 2025 JO - Indian Journal of Information Sources and Services VL - 15 IS - 2 SP - 442 EP - 467 DO - 10.51983/ijiss-2025.IJISS.15.2.54 AB - This research mainly focused on highly developed natural language processing capabilities, such as large language models (LLMs), which can generate code and power chatbots, among many other uses. Their growing use, though, has put them under many security risks. This work thoroughly investigates LLM vulnerabilities, including adversarial attacks, data poisoning, prompt injection, privacy leaking, and model exploitation via jailbreak. Though there is an increasing corpus of defensive tactics, most still have limited reach, potency, or adaptability. The paper lists ideas for the following studies and emphasizes the requirement for strong, generalizable, explainable security solutions. Creating uniform evaluation standards, adaptive defense mechanisms, more transparent models, automated threat detection, and frameworks for ethical integration are all part of the approach. Ensuring LLMs calls for a multidisciplinary strategy that strikes a compromise between responsible government and technology innovation. © The Research Publication, www.trp.org.in. KW - AI Governance KW - Data poisoning KW - Defense Mechanism KW - Explainability KW - Jailbreaking KW - large Language models KW - LLM Security KW - Model Robustness KW - prompt Injection CY - India ER - TY - JOUR TI - Why AI Adoption Fails to Create Digital Green Innovation: The Transformative Role of Knowledge-Based Dynamic Capabilities AU - Ji Z. AU - Tian F. PY - 2026 JO - Sustainability (Switzerland) VL - 18 IS - 5 SP - 2560 DO - 10.3390/su18052560 AB - Environmental challenges, such as climate change, resource scarcity, and pollution, increasingly demand organizational strategies that integrate artificial intelligence (AI) into sustainable innovation. This study examines how employee-level artificial intelligence capabilities (AIC) enable digital green innovation, a strategic approach that leverages AI-powered digital technologies to enhance green product development, green processes, and sustainable supply chains. Drawing on knowledge-based view (KBV) and the dynamic capability view (DCV), this study develops a theoretical framework linking AIC, knowledge-based dynamic capabilities (KBDC), and digital green innovation. Using survey data from 299 employees in Chinese High-Tech firms, results show that higher employee AIC strengthens KBDC, which in turn facilitates effective digital green innovation. The findings contribute theoretically by extending the antecedents of digital green innovation to the individual level and clarifying the multilevel mechanism through which AIC translates into organizational environmental performance, thereby enhancing both theories’ explanatory power in digital environments. Practically, the study highlights the importance for environmental managers of strengthening employee AIC and organizational KBDC to implement AI-driven sustainability strategies more effectively. © 2026 by the authors. KW - artificial intelligence capability KW - digital green innovation KW - dynamic capability view KW - knowledge-based dynamic capabilities KW - knowledge-based view KW - artificial intelligence KW - climate change KW - green economy KW - innovation KW - knowledge KW - strategic approach KW - sustainability CY - Australia ER - TY - JOUR TI - Strategic green entrepreneurship for business sustainability of Batik SMEs in Indonesia: the role of knowledge and ambidextrous innovation AU - Dewi Anjaningrum W. AU - Sudiro A. AU - Setiawan M. AU - Aisjah S. PY - 2025 JO - International Journal of Business Innovation and Research VL - 38 IS - 10 SP - 1 EP - 20 DO - 10.1504/IJBIR.2025.151278 AB - Batik SMEs in today’s highly competitive landscape encounter numerous obstacles concerning their sustainability. This study employs an approach that integrates green knowledge management (GKM) and ambidextrous green innovation (AGI) as sequential mediating factors to investigate the link between green entrepreneurial orientation (GEO) and sustainable performance (SP). We employed an accidental-purposive sampling method to collect quantitative data, specifically selecting 401 respondents from 253 batik SMEs in Indonesia that are actively engaged in green entrepreneurship and innovation. A second-order PLS-SEM analysis yielded multiple findings. Initially, we found that GEO had an insignificant direct effect on SP. Furthermore, GKM and AGI have been established as sequential mediators in this relationship. Lastly, the dimensional analysis indicated that batik SMEs appear to prioritise exploitative green innovation over exploratory innovation. Future research should examine the relationships among these dimensions for more profound insights and investigate how AI capabilities can enhance the GKM process. Copyright © 2025 Inderscience Enterprises Ltd. KW - GEO KW - GKM KW - green business KW - green entrepreneurial orientation KW - green innovation KW - green knowledge management KW - Indonesia KW - sustainability performance CY - Indonesia ER - TY - JOUR TI - AI characteristics and competitive advantage: the moderating role of resource allocation AU - Jabbouri R. AU - Issa H. AU - Truong Y. PY - 2025 JO - International Journal of Entrepreneurial Behaviour and Research SP - 1 EP - 23 DO - 10.1108/IJEBR-08-2024-0814 AB - Purpose – This research explores how artificial intelligence's (AI’s) distinct capabilities (interactivity, autonomy, inscrutability and abstraction), manifested as unique characteristics, impact decision-making in resource-limited social entrepreneurship, assessing their effect on competitive advantage with resource allocation as a key moderator. By analyzing such tensions, this research aims to bridge critical gaps in understanding how emerging technologies influence decisions in social entrepreneurship. Design/methodology/approach – By adopting a dual theoretical framework (next-generation perceived characteristics of innovations [PCI] and resource-based view), this research employs a quantitative empirical approach by gathering data through e-surveys (n = 269) from a professional database and two prominent conferences in AI and social entrepreneurship. Findings – Linear and nonlinear relationships among AI characteristics and decision-making emerge, with the potential for moderation effects influenced by resource allocation. Originality/value – This research makes four key contributions: empirically examining how distinct AI capabilities, manifested through unique characteristics, influence decision-making in the social entrepreneurship context; conceptually introducing “abstraction” as a novel AI capability; theoretically integrating the next-generation PCI framework with the resource-based view for a novel theoretical lens and practically developing a calibration graph as a “prototype” tool to quantify AI abstraction for resource-limited social entrepreneurship, thus potentially enabling optimal decision-making and consequently competitive advantage. © 2025 Emerald Publishing Limited KW - Abstraction KW - AI KW - Next-generation PCI KW - Resource-based view KW - Social entrepreneurship CY - United Arab Emirates, France ER - TY - JOUR TI - Can artificial intelligence bridge the diversity, equity, and inclusion gap for a sustainable future? A PRISMA systematic review AU - Chedrawi C. AU - Haddad G. AU - Haddad G. AU - ElAli R. PY - 2026 JO - Journal of Innovation and Knowledge VL - 15 SP - 100986 DO - 10.1016/j.jik.2026.100986 AB - This study addresses a critical gap in understanding how artificial intelligence (AI) adoption influences workplace diversity, equity, and inclusion (DEI) initiatives in advancing organizational sustainability. Through a systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, we synthesize and analyze 59 peer-reviewed articles (2019–2025) to examine the intersection of AI implementation and DEI practices. This study reveals how AI technologies can systematically address DEI challenges through algorithmic decision-making frameworks that mitigate unconscious bias, enhance representational parity, and foster inclusive organizational cultures. We develop an integrative theoretical framework that delineates the mechanisms through which AI adoption builds organizational capabilities across technical, managerial, and cultural dimensions while identifying key contingencies that influence DEI outcomes. These findings advance theory by conceptualizing AI-enabled DEI transformation as a dynamic process rather than a static outcome, contributing to the technology adoption and organizational sustainability literature streams. This analysis provides evidence-based insights for implementing AI-driven DEI initiatives while highlighting critical considerations regarding algorithmic fairness and ethical deployment. This study offers significant insights for organizational theorists examining technological innovation in social sustainability contexts and practitioners seeking to leverage AI capabilities for advancing workplace equity and inclusion. © 2026 The Authors. KW - Artificial intelligence adoption KW - Equity and inclusion (DEI) KW - PRISMA Systematic review KW - Social sustainability KW - Workplace diversity CY - Lebanon, Cyprus, France ER - TY - JOUR TI - Overview of the Application of Generative Artificial Intelligence in Film Production: Algorithms, Tools, and Future Trends AU - Li L. AU - Mat Desa M.A.B. AU - Li T. AU - Li W. PY - 2026 JO - Studies in Media and Communication VL - 14 IS - 2 SP - 22 EP - 39 DO - 10.11114/smc.v14i2.8095 AB - The rapid evolution of generative artificial intelligence (GenAI) is transforming the film industry. This article reviews key GenAI algorithms, including GANs, VAEs, diffusion models, and transformer-based architectures, and explores their application across various stages of film production, from scriptwriting to post-production. Through case studies such as The Frost and Netflix's AI-assisted projects, the study illustrates GenAI's creative potential and workflow innovations. It also addresses critical ethical and legal concerns, including authorship disputes, deepfakes, and algorithmic bias. Finally, the paper outlines future directions, such as multimodal model integration and AI-human co-creation, advocating for a responsible and human-centered implementation of these technologies. Copyright for this article is retained by the author(s), with first publication rights granted to the journal. This is an open-access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/4.0/). KW - AI in cinema KW - creative automation KW - diffusion models KW - ethical AI KW - film production KW - generative AI KW - multimodal models CY - Malaysia, China ER - TY - JOUR TI - Revolutionizing dentistry: the integration of artificial intelligence and robotics AU - Veseli E. PY - 2024 JO - Khyber Medical University Journal VL - 16 IS - 4 SP - 352 EP - 353 DO - 10.35845/KMUJ.2024.23729 AB - Technology is rapidly transforming traditional practices in modern healthcare. One area that stands out is the convergence of Artificial Intelligence (AI) and robotics, revolutionizing dentistry. This powerful combination enhances precision, efficiency, and patient outcomes in oral health care while reducing potential errors. AI, with its ability to analyze large amounts of data and identify intricate patterns, has found a place in dentistry. Its applications range from diagnostic tools and treatment planning to personalized medicine and patient management.1 By utilizing advanced imaging techniques, AI assists in the early detection of oral diseases,2 enabling proactive intervention and improving prognosis. The integration of AI into orthodontics and endodontics has radically transformed the field of dental care. In orthodontics, AI and Machine Learning systems support orthodontists in making informed decisions, particularly regarding tooth extraction. AI-driven custom orthodontic treatments minimize subjectivity and improve decision-making processes by utilizing neural networks to predict the extraction outcomes. AI is used throughout orthodontic procedures, from diagnosis to personalized treatment planning, utilizing 3D scans and virtual models to assess abnormalities, produce aligners, and optimize tooth removal strategies.3 Similarly, in endodontics, AI enhances root canal therapy by enabling precise anatomical analysis, lesion detection, fracture identification, stem cell viability prediction, and assessment of treatment efficacy.4 The contributions of AI in both orthodontics and endodontics have resulted in increased efficiency, accuracy, and improved patient outcomes, showcasing significant advancements in dental healthcare. AI also plays a critical role in posttreatment patient monitoring, ensuring timely intervention, and improving recovery. Through continuous data analysis and feedback, AI facilitates long-term oral health management, empowering patients and practitioners with proactive insights into sustained well-being. By integrating AI, dental experience is enhanced by combining cutting-edge technology with personalized care that redefines standards in dental health and treatment protocols. Furthermore, AI algorithms streamline administrative processes, optimize scheduling, and enhance patient experience, thereby improving the overall operational efficiency.5 The capabilities of robotics in dentistry have complemented those of AI, unlocking new frontiers in precision and minimally invasive procedures.6 Robotics provide unparalleled dexterity and control during surgery, resulting in superior outcomes and quicker recovery times for patients. These technological marvels not only enhance the skill set of dental professionals, but also expand access to care in remote or underserved areas. Advancements in technology and computer science have pushed the integration of robotics into navigational surgery in various medical fields. This progress is now being extended to dentistry, where innovative technologies are revolutionizing traditional dental procedures. Robotics-assisted dentistry, employing nanomaterials, nanorobots, and advanced diagnostic tools, is evolving to address the complex procedures necessary for oral healthcare maintenance and lesion removal. These advanced systems are reshaping conventional practices in dentistry, particularly implant therapy, challenging existing paradigms, and expanding the capabilities of practitioners.7 One notable development in robot-assisted dentistry is the creation of micro robots (MR), designed to enhance the precision and efficiency of endodontic treatments, specifically root canal therapy. These advanced robots autonomously perform tasks such as drilling, cleaning, shaping, and filling the root canal system under the supervision of cutting-edge computer-assisted technologies. By integrating various components, such as micro position controllers, sensors, and automated tools, the MR ensures error-free procedures, reduces discomfort for dentists, and enhances treatment outcomes with unparalleled accuracy.8 Furthermore, nanomaterials and nanorobots play a crucial role in enabling the creation of nanorobots for various dental applications such as tooth repair, drug delivery, orthodontic adjustments, and cavity treatments. These minuscule robots offer swift and precise dental care interventions, illustrating their potential to revolutionize traditional dental practices. Additionally, robotic applications in oral and maxillofacial surgery enhance surgical precision by allowing surgeons to program robots for specific tasks, such as bone surgeries and plate positioning.9 As technology continues to advance, the integration of robotics into dentistry promises to reshape the field, offering new possibilities for enhanced patient care and treatment outcomes. The fusion of robotics with AI algorithms holds promise for a future in which complex dental procedures are conducted with unprecedented accuracy and safety. Although the potential benefits of AI and robotics in dentistry are immense, their integration is not devoid of challenges. Ensuring data security, maintaining patient privacy, and addressing ethical concerns surrounding autonomy and decision making are crucial considerations in this rapidly evolving landscape. With appropriate regulations and ethical guidelines in place, the dental community can harness the full potential of these technologies, while upholding the highest standards of patient care and professional integrity. As we stand on the cusp of a new era in oral healthcare, characterized by the symbiotic relationship between human expertise and technological prowess, it is imperative for stakeholders to embrace innovation responsibly.10 Collaborative efforts among researchers, clinicians, technologists, and policymakers will be vital in harnessing the transformative power of AI and robotics to chart a course towards a future where dental treatments are not only effective but also personalized, efficient, and accessible to all. The integration of AI and robotics in dentistry heralds a paradigm shift in the delivery and reception of oral healthcare services. By leveraging these cutting-edge technologies thoughtfully and ethically, the dental community can elevate standards of care, expand treatment options, and improve patient outcomes, as well as redefine the future of dentistry. © 2024, Khyber Medical University. All rights reserved. KW - Artificial intelligence KW - dentistry KW - robotics CY - India ER - TY - JOUR TI - Executives’ perspectives on the impact of generative AI in business: a qualitative study of strategic, ethical and organizational transformations AU - Varouchas E. PY - 2026 JO - Journal of Science and Technology Policy Management SP - 1 EP - 34 DO - 10.1108/JSTPM-10-2025-0493 AB - Purpose – Generative Artificial Intelligence (GenAI) is rapidly transforming business strategy, innovation processes and governance practices. While prior research has focused primarily on technological implementation and performance outcomes, limited attention has been paid to how senior executives interpret, frame and adapt to GenAI as a strategic and ethical phenomenon. This study aims to explore executives’ sensemaking and adaptive responses to GenAI and to develop a conceptual model that captures this process. Design/methodology/approach – The study adopts a qualitative exploratory design based on semi-structured interviews with 15 senior executives from diverse industries in Greece. Data were analyzed using thematic analysis to identify recurring patterns in executives’ perceptions, decision rationales and ethical considerations related to GenAI adoption and integration. Findings – The analysis reveals four interrelated dimensions shaping executive adaptation: strategic integration and decision-making, business value and innovation, human–AI collaboration and workforce transformation and ethics, governance and adoption barriers. Cross-thematic synthesis indicates that executives perceive GenAI as a decision-support and innovation amplifier rather than an autonomous decision-maker. These findings inform an Emergent Integrative Model of Executive Adaptation to GenAI, conceptualized as a dynamic cycle comprising strategic sensemaking (Curate), operational experimentation (Create) and ethical consolidation (Consolidate). Research limitations/implications – Several executives cited technical and resource limitations as obstacles to effective GenAI implementation. Challenges include legacy systems, lack of skilled artificial intelligence (AI) engineers and limited integration between AI platforms and enterprise software. Practical implications – This model advances understanding of executive cognition and adaptive intelligence in the AI era, positioning leadership as a process of continuous learning, sensemaking and ethical stewardship. Practically, the research offers a roadmap for organizations and policymakers for aligning GenAI-driven innovation with responsible governance and leadership development. Social implications – By highlighting the role of executive stewardship, the study underscores how ethical leadership in GenAI adoption influences public trust, workforce well-being and organizational legitimacy. Originality/value – Existing research on AI in business has predominantly focused on technological implementation, efficiency gains and economic outcomes. Studies emphasize measurable benefits such as productivity enhancement, improved decision-making speed and customer experience optimization. However, fewer studies explore how executive cognition and strategic reasoning shape the trajectory of AI adoption – particularly regarding GenAI technologies that introduce new forms of creative automation. The study advances leadership and sensemaking research by shifting the focus from GenAI adoption outcomes to executive cognition and ethical stewardship. It offers a novel integrative model that explains how strategy, innovation and governance co-evolve in the GenAI era. © 2026 Emerald Publishing Limited KW - Adaptive leadership KW - AI governance KW - Digital transformation KW - Executive cognition KW - Generative artificial intelligence KW - Responsible innovation CY - Greece ER - TY - JOUR TI - The Impact of Artificial Intelligence on Corporate Governance AU - Kalkan G. PY - 2024 JO - Journal of Corporate Finance Research VL - 18 IS - 2 SP - 17 EP - 25 DO - 10.17323/j.jcfr.2073-0438.18.2.2024.17-25 AB - The advent of artificial intelligence (AI) marks a pivotal shift in the landscape of corporate governance, catalyzing a reevaluation of traditional frameworks and necessitating a forward-looking approach to decision-making, risk management, and ethical considerations. This study explores the multifaceted impact of AI on corporate governance, offering a nuanced analysis of how AI technologies are transforming the operational, strategic, and ethical dimensions of organizations. The research underscores the potential of AI to enhance decision-making processes, optimize operational efficiencies, and foster innovation by providing advanced analytical capabilities and predictive insights. However, it concurrently highlights the emergence of unprecedented challenges, including data privacy concerns, algorithmic bias, and the need for robust regulatory frameworks to mitigate risks associated with AI deployment. The article advocates for a proactive stance in redefining corporate governance models to accommodate the disruptive nature of AI, emphasizing the integration of ethical considerations and transparency in AI applications. It calls for a collaborative effort among corporate leaders, policymakers, and stakeholders to develop governance structures that not only leverage AI’s potential but also safeguard against its inherent risks. The study’s recommendations include the establishment of ethical AI guidelines, the adoption of transparent AI practices, and the continuous monitoring of AI systems to ensure their alignment with corporate governance objectives and societal values. However, it is important to note that the approach and methods used in this study are based on a qualitative literature review and, therefore, the generalization of the findings across different sectors and corporate governance frameworks may be limited. Additionally, the rapidly evolving nature of AI technologies poses inherent challenges to keeping up with emerging trends and potential risks. © 2024, National Research University, Higher School of Econoimics. All rights reserved. KW - artificial intelligence KW - corporate governance KW - decision-making KW - digital transformation KW - ethical considerations KW - legal and regulatory challenges KW - transparency CY - Turkey ER - TY - JOUR TI - Governance of Personal Information Security in the Iteration of Generative AI: From the Perspective of the Technological Evolution of Large Models; [生成式人工智能迭代中的个人信息安全治理：基于大模型技术演进视角] AU - An L. PY - 2026 JO - Journal of Library and Information Science in Agriculture VL - 38 IS - 4 SP - 61 EP - 70 DO - 10.13998/j.cnki.issn1002-1248.25-0750 AB - [Purpose/Significance] The rapid advancement of generative artificial intelligence (AI) is driving societal digital transformation, yet it simultaneously poses unprecedented systemic risks to personal information security due to the large-scale, automated, and complex nature of its data processing. Previous research has lacked exploration of governance pathways that consider endogenous technological evolution and specific model iterations. This paper takes the technological evolution of mainstream, large-scale generative AI models, both domestically and internationally as a starting point, and systematically reveals the impact of generative AI on personal information protection principles across the stages of data collection, model operation, and content generation. The focus is on analyzing how technological innovations in China's DeepSeek, including open-source traceability, decision transparency, and flexible deployment, lay the groundwork for risk-graded governance. This study not only broadens the theoretical perspective on AI governance and promotes the formation of a "technology-institution" collaborative governance paradigm, but also offers innovative and actionable insights for building an agile and effective personal information protection system in China amidst the rapid adoption of generative AI. [Method/Process] This study employs a comparative analysis and inductive research approach. First, it systematically compares the core technological differences among mainstream generative AI models, both domestic and international, across three dimensions: model ecosystem, model capabilities, and deployment methods. Through this comparison, it analyzes the challenges generative AI poses to personal information protection at various stages, including data collection, model operation, and content generation. Second, the study systematically examines the differentiated impacts brought about by DeepSeek's technological iterations on personal information security governance. Building on this foundation, the research proposes a comprehensive governance strategy centered on the principles of inclusiveness and prudence, guided by risk grading, and covering all operational stages of generative AI. This strategy emphasizes the critical role of DeepSeek's technical characteristics in supporting the implementation of this framework. [Results/Conclusions] The research indicates that constructing a risk-graded governance system based on the sensitivity of personal information is an effective approach to balancing security and innovation in generative AI. This system emphasizes distinguishing between sensitive and general information during data collection, achieving traceability and purpose control during model operation, and implementing differentiated security safeguards during content generation. With its technical advantages, including open-source traceability, decision transparency, and flexible deployment, DeepSeek provides technical validation and practical possibilities for graded governance. This facilitates the protection of sensitive personal information in high-risk scenarios while simultaneously fostering technological iteration and application innovation in medium- to low-risk contexts. Future research should further incorporate multi-dimensional governance elements such as industry self-regulation, social coordination, and international collaboration. Empirical analysis should also be conducted to test the applicability and effectiveness of the governance framework, thereby gradually developing a well-rounded personal information security governance scheme that adapts to the dynamic evolution of technology. © 2026, Agricultural Information Institute, Chinese Academy of Agricultural Sciences. All rights reserved. KW - deepseek KW - generative artificial intelligence KW - personal information security KW - risk classification CY - China ER - TY - JOUR TI - The Convergence of Artificial Intelligence and Sustainability Reporting: A Systematic Review of Applications, Challenges and Future Directions AU - Mustafa F. AU - Smolarski J. AU - Elamer A. PY - 2025 JO - Business Strategy and the Environment VL - 34 IS - 8 SP - 9761 EP - 9784 DO - 10.1002/bse.70090 AB - This research examines the potential of artificial intelligence (AI) to improve sustainability reporting, particularly in relation to environmental, social and governance (ESG) issues. Despite growing interest in the field, the integration of AI in sustainability remains underexplored, especially in terms of its impact on data accuracy, transparency and sustainability reporting effectiveness. This study conducts a systematic literature review (SLR) of 135 peer-reviewed articles to identify significant research gaps and presents a comprehensive framework that integrates AI technologies, such as machine learning, Industry 4.0 innovations and decision support systems (DSS), with sustainability reporting practices. The findings support the need for stronger theoretical and practical frameworks to effectively leverage AI's capabilities in sustainability reporting. The originality of this study is found in its innovative approach to connecting AI technologies with sustainability reporting, a field characterised by fragmentation and underdevelopment in research. This study introduces a broad framework and takes a critical look at the unintended externalities of AI, such as increased inequality and environmental costs. It does this by challenging existing sustainability frameworks, like the GRI and SASB, to change with the times and keep up with new technologies. The emphasis on both the advantages and possible drawbacks of AI in sustainability reporting substantiates the study's publication, providing fresh insights into AI's role in enhancing ethical, transparent and effective ESG disclosures. The study offers recommendations for managers and policymakers aimed at improving the accuracy, transparency and credibility of ESG disclosures via AI-driven solutions, thereby promoting more effective sustainability practices. This paper provides a framework for future research and practical application of AI in sustainability reporting, with the goal of enhancing academic knowledge and real-world practices in the pursuit of sustainable development. © 2025 The Author(s). Business Strategy and the Environment published by ERP Environment and John Wiley & Sons Ltd. KW - artificial intelligence KW - decision support systems KW - environmental impact KW - innovation KW - machine learning KW - sustainability reporting KW - artificial intelligence KW - decision support system KW - environmental economics KW - environmental impact KW - literature review KW - machine learning KW - sustainability KW - sustainable development CY - Egypt ER - TY - JOUR TI - MANAGEMENT STRATEGIES FOR AI-INTEGRATED CRAFT INDUSTRIES AU - Kale C.D. AU - Kaur G. AU - Sule B. AU - Jarad R.S. AU - Prabha D. AU - Bawa D.S. AU - Priyadharshini K. PY - 2026 JO - ShodhKosh: Journal of Visual and Performing Arts VL - 7 IS - 1s SP - 305 EP - 314 DO - 10.29121/shodhkosh.v7.i1s.2026.7086 AB - Traditional craft industry is crucial in preserving culture, rural livelihoods, and creative economies but continues to encounter the same issues with fragmentation of value chains, lack of market confidence, loss of skills and the inability to be sustainable. Artificial intelligence (AI) brings fresh possibilities to overcome these issues, but there is a need to use it in craft ecosystems mindfully, so as to prevent a degradation of culture and marginalization of artisans. This paper looks at AI implementation in craft industries and how to manage these industries, presenting the argument that the main challenge of implementing AI in craft industry is management and governance-related, and not a technological issue. The paper suggests a conceptual model that places strategic management in the mediating role between the traditional craft foundations and the AI capabilities. In the systematic discussion on the strategic alignment, human and AI work, operational integration, and ethical governance, the study proves that AI could be used to improve coordination, quality assurance, market responsiveness, and sustainability without compromising cultural authenticity. The operational mappings and pictorial performance analyses also indicate that the balanced improvement of supply-chain functions through the use of phased and collaborative AI adoption models is still possible without losing handcrafted variability. The results reported add up to strategic management and creative industry publications by reshaping AI as a supplementary technology that enhances the power of artisans instead of eliminating them. The article also brings out the significance of governance systems in respect to intellectual property, ownership of data and representation of cultures. Further studies must apply the suggested frameworks to different craft settings empirically and research involved design strategies of participatory AI development to create inclusive innovation. © 2026 The Author(s). This work is licensed under a Creative Commons Attribution 4.0 International License. KW - Artificial Intelligence KW - Craft Industries KW - Cultural Heritage Preservation KW - Human–AI Collaboration KW - Strategic Management KW - Supply Chain Management CY - India ER - TY - JOUR TI - Artificial Intelligence in Smart City Governance: Case Studies of Singapore and Barcelona AU - Zheng H. PY - 2026 JO - Journal of Urban Technology DO - 10.1080/10630732.2026.2644136 AB - Gen AI is transforming city and urban governance in a way that enhances decision-making, management of infrastructure, and delivery of services to the population. This article considers the role of Gen AI in Singapore and Barcelona and determines that AI has a positive effect in Singapore in terms of traffic movement and healthcare and improving waste management and energy consumption in Barcelona. Governance models have a significant impact on the efficiency of AI: a centralized governmental body in Singapore would allow achieving the desired effect more quickly, and the participatory benefit of Barcelona can decelerate its implementation. Using the Diffusion of Innovations (DOI) Theory, the adoption behaviors and ethical consequences of AI were determined. Although the efficiency gains, which are made by AI, are impressive, there are instances where its impact is excessive to the extent of putting issues of trust in AI and fairness. Some of the recommendations are to enhance AI risk assessment, empower the people and hold the algorithms accountable in order to bring future smart city projects to democratic values and social justice. © 2026 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. KW - generative AI KW - responsible innovation KW - smart cities KW - urban governance CY - China ER - TY - JOUR TI - Advancing Human-AI Collaboration in Small and Medium-Sized Enterprises: A Systems Engineering Approach AU - Ortolano L.F. AU - Gallegos E.E. PY - 2026 JO - Systems Engineering VL - 29 IS - 3 SP - 477 EP - 494 DO - 10.1002/sys.70031 AB - The integration of Artificial Intelligence (AI) into organizational processes presents unique challenges for Small and Medium-sized Enterprises (SMEs), particularly in fostering effective human-AI collaboration. Unlike large corporations with extensive resources for AI adoption, SMEs require adaptable frameworks tailored to their specific constraints and operational needs. This paper introduces the novel Human-AI Collaboration Maturity Model (HAIC-MM), which is a systems engineering framework designed to assess, guide, and enhance AI integration within SMEs. Developed through the synthesis of AI maturity models, digital transformation frameworks, and human-machine teaming research, HAIC-MM identifies seven dimensions and 32 capabilities across five maturity levels that are essential for successful AI adoption in SME contexts. Empirical validation through survey analysis (N = 100) confirmed the model's robustness. Subsequent focus group analyses (N = 10, repeated across five sessions) further validated HAIC-MM's practical utility and alignment with the operational realities of SMEs, emphasizing its relevance to everyday challenges faced by these organizations. Pilot testing with industry practitioners (N = 3) confirmed the usability and usefulness of the final HAIC-MM tool. HAIC-MM provides SME leaders with a structured, human-centered, and systematic approach to evaluate and cultivate human-AI collaboration, addressing key areas such as resource optimization, workforce empowerment, ethical AI oversight, and adaptive organizational culture. This research contributes to AI-enabled systems engineering by offering a practical framework for harmonizing human and AI capabilities within resource-constrained environments, ultimately supporting SMEs in achieving sustainable and ethically grounded AI integration across the organization. Summary: This paper introduces the Human-AI Collaboration Maturity Model (HAIC-MM), a framework designed to address the unique AI adoption challenges faced by Small and Medium-sized Enterprises (SMEs). The model identifies critical dimensions and capabilities needed to foster effective collaboration between humans and AI systems. The model also defines five maturity levels within each capability, allowing a granular assessment within the holistic framework. HAIC-MM provides a practical, step-by-step guide to assess and enhance AI integration for SMEs. The model emphasizes ethical AI oversight, workforce empowerment, and adaptive organizational culture, while addressing key challenges like resource constraints. HAIC-MM represents a significant contribution to the fields of systems engineering and organizational behavior, offering researchers investigating socio-technical systems, AI integration processes, and SME innovation strategies a rigorous framework for both theoretical advancement and practical implementation. With its focus on real-world application, HAIC-MM equips practitioners with actionable insights to build trust, optimize collaboration between human and AI capabilities, and achieve sustainable, ethically sound AI adoption, ensuring their organizations remain competitive in an increasingly digital economy. © 2025 The Author(s). Systems Engineering published by Wiley Periodicals LLC. KW - digital transformation KW - human-AI teaming KW - human-machine KW - maturity model KW - trust in AI KW - Artificial intelligence KW - Ethical aspects KW - Integration KW - Personnel KW - Digital transformation KW - Human-artificial intelligence teaming KW - Human-machine KW - Intelligence integration KW - Intelligence oversight KW - Maturity levels KW - Maturity model KW - Organizational cultures KW - Small and medium-sized enterprise KW - Trust in artificial intelligence KW - Man machine systems CY - United States ER - TY - JOUR TI - ARTIFICIAL INTELLIGENCE REVOLUTION IN INDONESIAN ISLAMIC HIGHER EDUCATION: HOW IT AFFECTS STUDENTS’ SELF-EFFICACY, CREATIVITY, AND LEARNING PERFORMANCE AU - Megawati S. AU - Alfarizi M. AU - Wahyuni J. PY - 2025 JO - Journal of Educators Online VL - 22 IS - 4 DO - 10.9743/JEO.2025.22.4.11 AB - Higher education plays a crucial role in society through research and innovation, but research indi-cates there is resistance to adopting new technologies. The presence of artificial intelligence (AI) offers an innovative approach to learning, information retrieval, and decision-making. AI has garnered global attention for its ability to generate output based on external stimuli. Islamic Higher Education Institutions (IHEIs) also adopt AI for personalized learning and operational efficiency. While international research focuses on AI trends, there is a need for further research on the capacity of AI in education. Literature has yet to explore the role of AI in, and its relationship to, student creativity and learning performance, particularly in IHEIs. This study aimed to analyze AI components at IHEIs and its implications for student creativity and learning performance. The study developed a theoretical model built on the resource-based view theory and tested it through a survey involving 373 faculty members from IHEIs. The data analysis used Partial Least Squares-Structural Equation Modeling (PLS-SEM). Results indicate that the resources and skills possessed by IHEIs and their faculty can predict AI capability. The impact of AI capability on student creativity and self-efficacy is significant and a factor in enhancing learning performance. However, there is no significant impact on student learning performance, highlighting the need for IHEIs to integrate AI more effectively. These findings encourage IHEIs to develop holistic strategies to maximize AI potential for student needs and higher education management efficiency. © 2025, Grand Canyon University. All rights reserved. KW - artificial intelligence KW - capability KW - IHEIs KW - performanc CY - Indonesia ER - TY - JOUR TI - Perceptions of health data commodification in AI-driven healthcare systems in Saudi Arabia AU - Al Qwaid M. PY - 2025 JO - Frontiers in Artificial Intelligence VL - 8 SP - 1559302 DO - 10.3389/frai.2025.1559302 AB - Introduction: Artificial Intelligence (AI) is transforming healthcare service delivery through predictive analytics, precision medicine, and advanced diagnostics. However, the commodification of health data introduces complex ethical and social challenges related to privacy, ownership, and consent. This study explores perceptions of health data commodification within AI-driven healthcare systems, focusing on Saudi Arabia’s rapidly evolving digital healthcare landscape. Methods: A mixed-methods approach was employed, combining quantitative surveys and in-depth qualitative interviews. The study included 42 patients, 8 healthcare professionals, 3 insurance representatives, and 4 AI experts. Data were collected across three main themes: data privacy, perceived benefits of AI, and attitudes toward data commodification. Quantitative data were analyzed descriptively, while qualitative responses were examined thematically. Results: Findings reveal that 61.9% of patients consider health data a form of personal property, while 59.5% feel they have limited control over how their data are used. A significant trust deficit was observed, with 50% expressing low confidence in AI systems’ ability to protect privacy, particularly among older participants. Financial incentives strongly influenced willingness to share data, with 81% agreeing to share their data if compensated. Furthermore, 64.3% supported the sale of anonymized data by healthcare providers to technology companies, provided adequate safeguards are in place. Discussion: These insights underscore the urgent need for robust regulatory frameworks emphasizing informed consent, transparency, and ethical governance in AI healthcare systems. The study highlights the importance of patient-centered policies, equitable compensation mechanisms, and enhanced training and awareness programs to build public trust and ensure responsible AI adoption. By addressing these ethical and governance challenges, policymakers can align technological innovation with equity, privacy, and the principles of ethical healthcare delivery. Copyright © 2025 Al Qwaid. KW - AI KW - AI-driven healthcare KW - digital healthcare KW - health data commodification KW - trust in AI systems CY - Saudi Arabia ER - TY - JOUR TI - Nurse educators' experiences integrating artificial intelligence in teaching and practice: A descriptive phenomenological study AU - Kulintang M.B.M. AU - Ngo A.D. AU - Salas C.J.C. AU - Sumaoy K.C. AU - Alquwez N. PY - 2026 JO - Nurse Education Today VL - 162 SP - 107050 DO - 10.1016/j.nedt.2026.107050 AB - Backgrounds: Artificial intelligence (AI) is rapidly reshaping nursing education by transforming teaching strategies, student engagement, and clinical learning. Despite its growing influence, there is limited evidence exploring how nurse educators experience and navigate the integration of AI, particularly within the Philippine academic context. Aim: To explore the lived experiences of nurse educators integrating AI in classroom and clinical instruction, focusing on adaptation processes, perceived benefits, ethical considerations, and evolving professional roles. Design: Descriptive phenomenological study grounded in Husserlian philosophy. Methods: Thirteen nurse educators with direct experience in AI-related teaching were purposively selected from nursing schools across the Philippines. Semi-structured interviews were conducted face-to-face and online. Data were analyzed using Colaizzi's seven-step method. Trustworthiness was ensured through bracketing, member checking, reflexive journaling, and audit trails. Results: Six themes emerged: (1) navigating AI adoption and readiness; (2) transforming pedagogy through AI; (3) reimagining nurse educators' identity; (4) adaptive practices and institutional support; (5) ethical stewardship and nursing values; and (6) human–technology partnership for the future. Educators perceived AI as a transformative yet ethically sensitive tool that enhances teaching efficiency, supports personalized learning, strengthens student engagement, and reshapes their professional roles. However, they emphasized the need for institutional readiness, faculty development, and clear guidelines to ensure the responsible and value-aligned use of AI. Conclusions: AI integration redefines nursing education by fostering innovation, adaptability, and reflective teaching. Responsible adoption necessitates human-centered approaches, robust ethical safeguards, and organizational support. Implications for nursing education: Developing structured AI policies, providing continuous faculty training, and aligning technological integration with nursing values may promote safe, ethical, and effective AI-enhanced pedagogy in both classroom and clinical settings. © 2026 Elsevier Ltd KW - Artificial Intelligence KW - Digital Pedagogy KW - Ethics KW - Nurse Educators KW - Nursing Education KW - Phenomenology KW - Adult KW - Artificial Intelligence KW - Education, Nursing, Baccalaureate KW - Faculty, Nursing KW - Female KW - Humans KW - Interviews as Topic KW - Male KW - Middle Aged KW - Philippines KW - Qualitative Research KW - Teaching KW - article KW - artificial intelligence KW - clinical audit KW - clinical practice guideline KW - female KW - human KW - male KW - nurse KW - nursing education KW - pedagogics KW - personal experience KW - phenomenology KW - Philippines KW - practice guideline KW - professional standard KW - semi structured interview KW - student engagement KW - teaching KW - trustworthiness KW - adult KW - interview KW - middle aged KW - procedures KW - psychology KW - qualitative research CY - Saudi Arabia, Philippines ER - TY - JOUR TI - AI-driven framework for enhancing water quality engineering experimentation AU - Bai F. AU - Liu S. AU - Yuan Y. AU - Zhang Y. AU - Zhou J. PY - 2026 JO - Results in Engineering VL - 30 SP - 110732 DO - 10.1016/j.rineng.2026.110732 AB - Artificial Intelligence (AI) holds transformative potential for cultivating high-quality talent in higher education, particularly in engineering experimental pedagogy. However, traditional water quality engineering courses face challenges in dynamic responsiveness, personalized learning, and data-intensive experimental workflows. This study addresses these gaps by establishing an integrated AI-enhanced framework for water quality engineering experimental courses. A mixed-methods approach was employed across eight laboratory modules. The framework combined project-based learning, Bloom’s taxonomy, and AI-driven tools (SHAP-optimized Extreme Trees/Random Forest/Decision Tree algorithms, NLP/image recognition, closed-loop feedback systems). Undergraduate cohorts using AI-integrated methods (n = 64) were compared with conventional method (CM) cohorts (n = 67). Metrics included technical proficiency, data accuracy, innovation capability, engagement, error rates, and teacher workload. AI integration significantly enhanced pedagogical efficacy, evidenced by a 41% increase in experimental design efficiency alongside 33.8% (p < 0.01) and 35.6% (p < 0.01) improvements in hypothesis formulation and data interpretation accuracy, respectively. Data processing accuracy exceeded 85%, accompanied by a 57.9% reduction in processing time, while innovation capability rose by 28.3% (p < 0.01) and operational errors decreased by 40%. Concurrently, student engagement increased by 62.5% (p < 0.05) with significant metacognitive skill gains (Cohen’s d = 0.72), and teacher workload declined by 37.1%, freeing 3.1 weekly hours per instructor. These outcomes were driven primarily by real-time closed-loop feedback and personalized learning pathways. This study provides a replicable “AI + Experimental Courses” paradigm that synergizes human expertise with AI capabilities to overcome data robustness and emotional intelligence challenges. It advances sustainable AI-education integration, offering a scalable model for engineering education reform aligned with sustainable development goals. Copyright © 2026. Published by Elsevier B.V. KW - Artificial intelligence KW - Closed-loop feedback KW - Experimental pedagogy KW - Personalized learning pathways KW - Water quality engineering KW - Curricula KW - Data accuracy KW - Data integration KW - Engineering education KW - Learning systems KW - Students KW - Teaching KW - Technical presentations KW - Water quality KW - Closed-loop feedback KW - Experimental course KW - Experimental pedagogy KW - Innovation capability KW - Learning pathway KW - Personalized learning KW - Personalized learning pathway KW - Quality engineering KW - Teachers' KW - Water quality engineering KW - Artificial intelligence CY - China ER - TY - JOUR TI - AI Insights for Wind Speed Retrieval From GNSS Reflectometry AU - Xiao T. AU - Wickert J. AU - Asgarimehr M. PY - 2026 JO - IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing VL - 19 SP - 13693 EP - 13713 DO - 10.1109/JSTARS.2026.3681975 AB - Artificial intelligence (AI) models developed for Global Navigation Satellite System Reflectometry (GNSS-R) observations have demonstrated competitive performance in estimating geophysical parameters, especially ocean surface wind speeds. However, the transition from transparent physical scattering models to complex deep learning architectures raises concerns regarding reduced model transparency and trust. Understanding the decision-making processes of these 'black-box' models is essential for assessing model behavior, detecting anomalies, and ensuring reliability in AI-based Earth observations. In this study, we investigate the role of explainable artificial intelligence (XAI) in addressing the transparency gap for hybrid deep learning models designed for GNSS-R observations. Focusing on ocean wind speed retrieval as a well-characterized benchmark, our study is structured around three primary objectives: first, assessing the robustness and efficiency of XAI explainers, second, interpreting a benchmark hybrid model trained using a manually selected feature set with Shapley additive explanations (SHAP) and gradient-weighted class activation mapping (Grad-CAM), which provide quantitative branchwise attribution and qualitative spatial saliency, and third, proposing an XAI-based feature selection pipeline that leverages SHAP-based ranking and exclusion, comparing its efficacy against conventional statistical methods. The results demonstrate that SHAP is effective not only for model interpretation but also for supporting computationally efficient feature selection and model debugging. Meanwhile, Grad-CAM offers complementary spatial interpretability by highlighting salient regions in the delay-Doppler map inputs. This study demonstrated the potential of integrating XAI as a diagnostic and validation tool into the model development cycle, enabling more transparent, robust, and trustworthy AI models for upcoming GNSS-R missions and future applications. © 2026 IEEE. KW - Deep learning KW - explainable artificial intelligence (XAI) KW - feature engineering KW - Global Navigation Satellite System Reflectometry (GNSS-R) KW - ocean wind speed Al for Remote Sensing KW - Behavioral research KW - Benchmarking KW - Communication satellites KW - Decision making KW - Doppler effect KW - Feature extraction KW - Global positioning system KW - Learning systems KW - Navigation KW - Oceanography KW - Reflection KW - Reflectometers KW - Salinity measurement KW - Transparency KW - Wind KW - Deep learning KW - Explainable artificial intelligence KW - Feature engineerings KW - Global navigation satellite system reflectometry KW - Global Navigation Satellite Systems KW - Ocean wind speed KW - Ocean winds KW - Reflectometry KW - Shapley KW - Wind speed KW - Deep learning CY - Germany ER - TY - JOUR TI - A framework for AI-powered service innovation capability: Review and agenda for future research AU - Akter S. AU - Hossain M.A. AU - Sajib S. AU - Sultana S. AU - Rahman M. AU - Vrontis D. AU - McCarthy G. PY - 2023 JO - Technovation VL - 125 SP - 102768 DO - 10.1016/j.technovation.2023.102768 AB - Artificial intelligence (AI)-powered service innovation (e.g., OpenAI's ChatGPT, Google's Bard and Microsoft's Sydney) has become one of the most significant determinants of firms' success in the Fourth Industrial Revolution. However, extant studies on this topic show that research studies hitherto have been ad-hoc, lacking a conceptual framework for the strategic management of AI-powered service innovation capability in dynamic markets. Thus, this study synthesises the current body of knowledge, proposes a framework, and develops an agenda to advance our knowledge. The findings reveal: (1) AI-market capability relates to customer orientation, industry orientation, and cross-functional orientation; (2) AI-infrastructure capability relates to data, business models, and ecosystem; and (3) AI-management capability relates to AI-orientation, organisational learning, and AI ethics which are crucial determinants of forming AI-powered service innovation capability. These capabilities for the strategic management of AI-powered service innovations play an essential role in achieving organizational agility and competitive advantage. © 2023 Elsevier Ltd KW - AI ethics KW - AI infrastructure Capability KW - AI management Capability KW - AI market Capability KW - AI-Powered service innovation KW - Innovation capability KW - Organizational agility KW - Sustainable competitive advantage KW - Commerce KW - Philosophical aspects KW - Strategic planning KW - Artificial intelligence ethic KW - Artificial intelligence infrastructure capability KW - Artificial intelligence management capability KW - Artificial intelligence market capability KW - Artificial intelligence-powered service innovation KW - Innovation capability KW - Intelligence management KW - Management capabilities KW - Organizational agility KW - Service innovation KW - Sustainable competitive advantages KW - Competition CY - United Arab Emirates ER - TY - JOUR TI - A stochastic production frontier model for evaluating the performance efficiency of artificial intelligence investment worldwide AU - Sun Y.-C. AU - Cosgun O. AU - Sharman R. AU - Mulgund P. AU - Delen D. PY - 2024 JO - Decision Analytics Journal VL - 12 SP - 100504 DO - 10.1016/j.dajour.2024.100504 AB - As artificial intelligence (AI) begins to take center stage in technological innovations, it is essential to understand the business value of AI innovation efforts and investments. While some early work at the firm level exists, there is a shortage of literature that takes a larger country-level perspective. This study investigated the effect of AI innovation efforts on production efficiency across countries using stochastic production frontier approaches. In addition, our model also included the traditional economic inputs of capital and labor. We used both the Cobb–Douglas function and Constant Elastic Substitution model specifications. The significant findings of this study are as follows: Innovation efforts in AI measured by the number of AI-related patents and capital investment in AI have a substantial effect on economic output. The significance of AI investments indicates the need for a robust digital infrastructure as a prerequisite for harnessing AI capabilities. The complementary relationship between labor and AI-related patents implies that high-skilled labor is often necessary to incorporate AI inputs into production. However, as AI capabilities develop, they weaken the effect on labor input. The study also distinguishes between AI innovation (research and development activities indicated by AI patents) and the production efficiency of AI investments (return on every dollar invested), highlighting that more AI innovation does not always translate into better production efficiency. The findings indicate that while the United States leads innovation in AI, the UK has the best production efficiency. China ranked fourth in AI innovation and has the lowest production efficiency among the countries included in the study. © 2024 The Author(s) KW - Artificial intelligence KW - Cobb–Douglas function KW - Innovation KW - Production efficiency KW - Stochastic production frontier CY - United States, Turkey ER - TY - JOUR TI - Artificial intelligence for information-driven resilience: Enhancing strategic adaptation and entrepreneurial success AU - Wang S. AU - Zhang H. PY - 2026 JO - International Journal of Information Management VL - 89 SP - 103057 DO - 10.1016/j.ijinfomgt.2026.103057 AB - This study investigates how artificial intelligence (AI) innovation capability—the systematic integration, management, and application of AI-driven information—enhances strategic resilience and entrepreneurial success under conditions of market volatility and resource constraints. We conceptualize information-driven resilience as the organizational capacity to convert AI-enabled information flows into adaptive responses that sustain competitive functioning. Drawing on dynamic capability theory, we examine how early-stage ventures transform AI capabilities into adaptive capacity and competitive advantage. Using a multi-method approach, we collected two-wave survey data from 357 early-stage ventures in China and Europe, complemented by importance-performance map analysis (IPMA), fuzzy-set qualitative comparative analysis (fsQCA), and semi-structured interviews. Our convergent multi-method design provides complementary evidence: PLS-SEM establishes the mediation hypothesis, IPMA identifies actionable managerial priorities, fsQCA reveals equifinal configurational pathways to high performance, and interviews illuminate the underlying mechanisms. Specifically, AI innovation capability is significantly associated with strategic resilience, which is positively related to entrepreneurial success. The mediating effect of strategic resilience is robust and fully accounts for the AI capability–success relationship, indicating that the mechanism through which AI investments generate performance returns is fundamentally organizational rather than technological in nature. The value derived from AI is unlocked by first building organizational capacity for adaptive learning and agile decision-making. The study contributes to information management literature by clarifying how AI-enabled information processing capabilities enhance adaptive responses in uncertain environments, while providing practitioners with an actionable framework for developing information-driven resilience through strategic AI implementation. © 2026 Elsevier Ltd KW - Artificial intelligence KW - Digital transformation KW - Entrepreneurial performance KW - Information management KW - Knowledge management KW - Strategic resilience KW - Competition KW - Decision making KW - Investments KW - Knowledge management KW - Uncertainty analysis KW - Adaptive response KW - Digital transformation KW - Entrepreneurial performance KW - Entrepreneurial success KW - Innovation capability KW - Map analysis KW - Organisational KW - Performance KW - Performance maps KW - Strategic resilience KW - Artificial intelligence CY - China ER - TY - JOUR TI - Modeling AI-driven inequality and adaptive governance: A system dynamics approach to U.S. Socioeconomic futures AU - Moosavihaghighi M. PY - 2026 JO - Sustainable Futures VL - 11 SP - 101746 DO - 10.1016/j.sftr.2026.101746 AB - Artificial Intelligence is reshaping socioeconomic systems by enhancing productivity while intensifying concerns about inequality, unemployment, and policy responsiveness. This study employs a System Dynamics model to simulate the U.S. socioeconomic landscape from 2000 to 2035, focusing on the interdependencies between AI investment, income distribution, and adaptive policy design. Given data constraints, AI investment is modeled as a uniform labor market driver, with international competition introduced via the DeepSeek stress test. The model integrates political feedback loops linking wealth concentration to reform inertia. Three policy scenarios are evaluated: (1) baseline U.S. AI adoption, (2) competitive pressure from a low-cost foreign platform under varying regulations, and (3) adaptive reforms coupling AI taxation and redistribution to real-time inequality and unemployment metrics. Results reveal that while AI-driven productivity may reduce unemployment and cost of production initially, it exacerbates inequality without responsive governance. Adaptive mechanisms, such as dynamic reskilling and AI-linked fiscal tools, outperform static interventions in promoting equity and competitiveness. However, entrenched political influence constrains reform unless public dissatisfaction crosses critical thresholds. These findings highlight the urgent need for anticipatory, adaptive policy frameworks that align technological innovation with inclusive and sustainable socioeconomic outcomes. © 2026 The Author. Published by Elsevier Ltd. This is an open access article under the CC BY license. http://creativecommons.org/licenses/by/4.0/ KW - Adaptive Governance KW - AI Governance KW - Artificial Intelligence KW - Income Inequality KW - Labor Market KW - System Dynamics CY - Iran ER - TY - JOUR TI - The Impact of VAT Credit Refunds on Enterprises’ Sustainable Development Capability: A Socio-Technical Systems Theory Perspective AU - She J. AU - Sun M. AU - Yan H. PY - 2025 JO - Systems VL - 13 IS - 8 SP - 669 DO - 10.3390/systems13080669 AB - We investigate whether China’s Value-Added Tax (VAT) Credit Refund policy influences firms’ sustainable development capability (SDC), which reflects innovation-driven growth and green development. Exploiting the 2018 implementation of the VAT Credit Refund policy as a quasi-natural experiment, we employ a difference-in-differences (DID) approach and find causal evidence that the policy significantly enhances firms’ SDC. This suggests that fiscal instruments like VAT refunds are valued by firms as drivers of long-term sustainable and high-quality development. Our mediating analyses further reveal that the policy promotes firms’ SDC by strengthening artificial intelligence (AI) capabilities and facilitating intelligent transformation. This mechanism “AI Capability Building—Intelligent Transformation” aligns with the socio-technical systems theory (STST), highlighting the interactive evolution of technological and social subsystems in shaping firm capabilities. The heterogeneity analyses indicate that the positive effect of VAT Credit Refund policy on SDC is more pronounced among small-scale and non-high-tech firms, firms with lower perceived economic policy uncertainty, higher operational diversification, lower reputational capital, and those located in regions with a higher level of marketization. We also find that the policy has persistent long-term effects, with improved SDC associated with enhanced ESG performance and green innovation outcomes. Our findings have important implications for understanding the SDC through the lens of STST and offer policy insights for deepening VAT reform and promoting intelligent and green transformation in China’s enterprises. © 2025 by the authors. KW - AI KW - socio-technical systems theory KW - sustainable development capability KW - value-added tax KW - Green development KW - Sustainable development KW - System theory KW - Taxation KW - Difference-in-differences KW - Differences-in-differences KW - Intelligent transformations KW - Natural experiment KW - Refund policies KW - S values KW - Sociotechnical systems theory KW - Sustainable development capability KW - Tax credits KW - Value-added tax KW - Artificial intelligence CY - China ER - TY - JOUR TI - Ethics dumping in artificial intelligence AU - Bélisle-Pipon J.-C. AU - Victor G. PY - 2024 JO - Frontiers in Artificial Intelligence VL - 7 SP - 1426761 DO - 10.3389/frai.2024.1426761 AB - Artificial Intelligence (AI) systems encode not just statistical models and complex algorithms designed to process and analyze data, but also significant normative baggage. This ethical dimension, derived from the underlying code and training data, shapes the recommendations given, behaviors exhibited, and perceptions had by AI. These factors influence how AI is regulated, used, misused, and impacts end-users. The multifaceted nature of AI’s influence has sparked extensive discussions across disciplines like Science and Technology Studies (STS), Ethical, Legal and Social Implications (ELSI) studies, public policy analysis, and responsible innovation—underscoring the need to examine AI’s ethical ramifications. While the initial wave of AI ethics focused on articulating principles and guidelines, recent scholarship increasingly emphasizes the practical implementation of ethical principles, regulatory oversight, and mitigating unforeseen negative consequences. Drawing from the concept of “ethics dumping” in research ethics, this paper argues that practices surrounding AI development and deployment can, unduly and in a very concerning way, offload ethical responsibilities from developers and regulators to ill-equipped users and host environments. Four key trends illustrating such ethics dumping are identified: (1) AI developers embedding ethics through coded value assumptions, (2) AI ethics guidelines promoting broad or unactionable principles disconnected from local contexts, (3) institutions implementing AI systems without evaluating ethical implications, and (4) decision-makers enacting ethical governance frameworks disconnected from practice. Mitigating AI ethics dumping requires empowering users, fostering stakeholder engagement in norm-setting, harmonizing ethical guidelines while allowing flexibility for local variation, and establishing clear accountability mechanisms across the AI ecosystem. Copyright © 2024 Bélisle-Pipon and Victor. KW - accountability KW - AI ethics KW - AI governance KW - artificial intelligence KW - ethical guidelines KW - ethics dumping CY - Canada ER - TY - JOUR TI - Enhancing university education with AI: a Telegram bot leveraging RAG and external APIs for secure knowledge retrieval AU - Bashurov V. AU - Safonov P. PY - 2025 JO - Issues in Information Systems VL - 26 IS - 3 SP - 413 EP - 420 DO - 10.48009/3_iis_2025_2025_133 AB - This paper presents a novel AI-powered Telegram bot designed to enhance university information services by securely integrating external AI capabilities with institutional private data. The system leverages Retrieval-Augmented Generation (RAG) to transform structured university data (faculty profiles, schedules, lecture notes) into vectorized embeddings, which are dynamically retrieved and combined with responses from a general-purpose AI API (e.g., GPT-4). This hybrid approach ensures accurate, context-aware answers while preserving data privacy — raw institutional information is never exposed directly to third-party systems. Implemented at Comtrade University, the bot demonstrates significant outperforming standalone AI models for domain-specific questions. Key innovations include a scalable pipeline for embedding private data, seamless Telegram-based access, and cost-efficient prompt engineering via RAG. The solution addresses critical challenges in educational technology: balancing AI augmentation with data security and providing 24/7 conversational access to institutional knowledge. We discuss architectural decisions, privacy safeguards, and empirical results, offering a replicable framework for other universities. © 2025 International Association for Computer Information Systems. All rights reserved. KW - educational chatbots KW - hybrid AI systems KW - LLM vector embeddings KW - privacy in EdTech KW - retrieval-augmented generation (RAG) KW - telegram API CY - Serbia, United States ER - TY - JOUR TI - Generative AI-driven sustainability in supply chains: A micro foundation of dynamic capability towards a socially responsible supply chain to achieve greater societal change AU - Yadav S. AU - Samadhiya A. AU - Kumar A. AU - Pandey K.K. AU - Luthra S. AU - El jaouhari A. PY - 2026 JO - Technological Forecasting and Social Change VL - 229 SP - 124726 DO - 10.1016/j.techfore.2026.124726 AB - The application of Gen AI (Generative AI) across multiple sectors like manufacturing and service domains, shows transformative effects to improve socially responsible decision-making and collaborative efforts. Yet it remains insufficiently investigated in the context of a socially responsible supply chain (SRSC) towards sustainable supply chain management (SSCM) in a wider context. Gen AI enables faster reporting and adaptive responses to enhance decision-making, which together improve supply chain flexibility while promoting social responsibility. Although previous research recognizes Gen AI's contribution to social functionality within a supply chain, it does not provide a full theoretical structure for analyzing how Gen AI solutions develop and function in SSCM. Prior research stresses the importance of making people and communities central elements in SSCM from the outset. To address this gap, this research conducts a rigorous qualitative study by analyzing 82 exemplary SSCM cases from manufacturing and service sectors through content analysis. The research explores how organizations can leverage dynamic capability theory (DCT) to adopt and integrate Gen AI systems. The findings demonstrate the stakeholder role in SSCM: 1) NGOs and universities provide essential knowledge and skills together with resources which support sustainable practices; 2) active collaboration with external stakeholders creates competitive benefits while promoting wider implementation of sustainability efforts through imitation. This research delivers a conceptual framework, showing how dynamic supply chain capabilities enabled by Gen AI affect stakeholder alignment towards sustainability goals while mobilizing stakeholders towards SSCM practices; this creates positive effects for wider communities in dynamically evolving Gen AI based SC systems. Our study utilizes micro-foundations of dynamic capabilities to deliver actionable recommendations for managers and outlines future research paths for expanding sustainability practices across multiple dimensions using Gen AI. This study provides helpful insights for professionals, researchers, and leaders to achieve Sustainable Development Goals (SDGs). © 2026 Elsevier Inc. KW - Capability reconfiguration KW - Digital sustainability transition KW - Responsible AI adoption KW - Social change KW - Socio-technical innovation KW - Supply chain resilience KW - Behavioral research KW - Competition KW - Decision making KW - Economic and social effects KW - Social aspects KW - Supply chain management KW - Supply chains KW - Sustainable development KW - Sustainable development goals KW - Capability reconfiguration KW - Digital sustainability transition KW - Responsible AI adoption KW - Social changes KW - Socio-technical innovation KW - Sociotechnical KW - Supply chain resiliences KW - Sustainability transition KW - Sustainable supply chains KW - Technical innovation KW - Industrial research CY - India, United Kingdom, Morocco ER - TY - JOUR TI - Advancements in Artificial Intelligence-based prescriptive and cognitive analytics for business performance: a special issue editorial AU - Charles V. AU - Emrouznejad A. AU - Kunz W.H. PY - 2025 JO - Journal of Business Research VL - 200 SP - 115576 DO - 10.1016/j.jbusres.2025.115576 AB - The rapid advancement of Artificial Intelligence (AI) is transforming business decision-making across industries. AI-based prescriptive and cognitive analytics offer significant potential to enhance decision-making, optimise performance, and create new avenues for value creation. This special issue explores the state-of-the-art advancements in these analytics and their business implications. We introduce the Analytics Onion as a conceptual foundation, comprising three interrelated layers: Perspective Analytics, Responsible Analytics, and the Descriptive-Diagnostic-Predictive-Prescriptive-Cognitive Analytics framework. The Analytics Onion captures the interplay between human judgment, ethics, analytical rigour, and AI techniques. The featured papers exemplify these layers through various topics, namely humanoid service robots, business location optimisation, ESG evaluation, energy efficiency, customer churn, prediction-led prescription, innovation culture, user satisfaction with AI, responsible AI in business models, and executives’ emotions influencing firm value. We highlight emerging opportunities and challenges and offer a forward-looking research agenda to guide future developments in this evolving field. © 2025 Elsevier Inc. KW - Artificial intelligence KW - Business performance KW - Cognitive analytics KW - Decision-making KW - Perspective analytics KW - Prescriptive analytics KW - Responsible analytics CY - United Kingdom, United States ER - TY - JOUR TI - Responsible Artificial Intelligence Attention and Firm Innovation: An Attention-Based View AU - Xiong M. AU - Xu H. AU - Ji J. AU - Zuo R. AU - Wang Y. AU - Olya H. PY - 2026 JO - Journal of Product Innovation Management VL - 43 IS - 1 SP - 186 EP - 214 DO - 10.1111/jpim.70015 AB - Academic Summary: This article draws on the attention-based view (ABV) to examine whether, how, and under what conditions top management team (TMT) attention to responsible artificial intelligence (AI) influences firm innovation. We developed a 480-word responsible AI dictionary grounded in 155 academic sources and 527 corporate case descriptions, and applied it to 2452 S&P 500 earnings call transcripts (2011–2021) using natural language processing (NLP) and large language model (LLM) techniques, yielding 2670 firm-year observations. Linking these measures to US patent data, we find that greater responsible AI attention predicts more and higher-impact patents. The effect is stronger in low-technology industries and under short-term investor pressure, while the presence of a chief technology officer (CTO) does not amplify it. Mechanism analyses reveal that responsible AI attention fosters innovation by increasing investment in AI-relevant human capital and mitigating innovation risk. Theoretically, this article enriches the AI and innovation management literature by positioning responsible AI attention as a dynamic strategic asset that mobilizes resources, reduces risk, and enables contextual adaptation. Practically, findings suggest that firms can strengthen innovation by prioritizing managerial attention to responsible AI, distributing responsibility beyond technical specialists, balancing ethical safeguards with strategic flexibility, and aligning governance with investor and industry conditions. Managerial Summary: This article examines how managerial attention to responsible artificial intelligence (AI) can enhance firm innovation. Using text analytics on 2452 earnings call transcripts from S&P 500 firms (2011–2021) and a panel of 2670 firm-year observations linked to patent outcomes, we show that firms whose top management teams (TMT) devote greater attention to responsible AI produce more and higher-impact patents. This effect is stronger in low-technology industries and when firms face short-term investor pressure; it is not amplified by having a chief technology officer (CTO). In practice, sustained attention to responsible AI tends to build AI-related skills and reduce project risk, thereby supporting a more reliable innovation pipeline. Executives should treat responsible AI as a strategic priority rather than a compliance task by establishing cross-functional governance, investing in role-based governance training, and sharing accountability across the C-suite. Innovation managers can embed ethics checkpoints (bias audits, design reviews) into project workflows to enhance stability and organizational learning. Policymakers can reinforce responsible innovation by providing clear regulatory frameworks and incentives that align ethical safeguards with competitiveness. Together, these actions can help build more durable organizational capability for responsible innovation and support long-term performance and adaptation to ongoing technological change. © 2025 The Author(s). Journal of Product Innovation Management published by Wiley Periodicals LLC on behalf of Product Development & Management Association. KW - attention-based view (ABV) KW - firm innovation KW - large language model (LLM) KW - natural language processing (NLP) KW - responsible artificial intelligence (AI) KW - Investments KW - Natural language processing systems KW - Patents and inventions KW - Product development KW - Attention-based view KW - Attention-based views KW - Condition KW - Firm innovation KW - Language model KW - Language processing KW - Large language model KW - Natural language processing KW - Natural languages KW - Responsible artificial intelligence KW - Human resource management CY - United Kingdom, China, South Korea ER - TY - JOUR TI - Governing AI virtual anchors in China's live streaming E-commerce ecosystem: Policy challenges and global implications AU - Meng Y. PY - 2026 JO - Telecommunications Policy VL - 50 IS - 2 SP - 103109 DO - 10.1016/j.telpol.2025.103109 AB - The rapid advancement of generative artificial intelligence (AI) has fundamentally reshaped the traditional media value chain, transforming the processes of content production, distribution, and consumption. Among these developments, AI virtual anchors have significantly reduced operational costs and enabled the large-scale creation of content. However, their widespread adoption has also raised complex legal, ethical, and regulatory challenges. This paper investigates the governance of AI virtual anchors from three key dimensions. First, it examines how AI technologies are restructuring the media ecosystem, particularly in the realm of live-streaming e-commerce, by displacing human labour and creating new market dynamics. Second, it examines the associated legal and ethical concerns, including intellectual property disputes, the under-recognized rights of “ghost performers”, risks of misinformation, and consumer protection issues. Third, it evaluates China's evolving governance responses, highlighting both proactive regulatory innovations and ongoing challenges. Starting from platform governance theories, this paper develops a China-specific regulatory narrative and identifies a multi-tiered governance system that involves the government, platforms, and public participation, and reveals the underlying logic that redefines platform roles in China's digital governance architecture. This paper argues that China's evolving governance of AI virtual anchors illustrates a distinct institutional model and aims to situate this experience within global discussions, offering comparative reference points for AI governance, particularly regarding platform responsibility, adaptive regulation, and public participation. © 2025 The Author KW - AI KW - China KW - Live streaming KW - Media regulation KW - Value chain KW - Chains KW - Consumer protection KW - Ecosystems KW - Electronic commerce KW - Government data processing KW - Media streaming KW - Philosophical aspects KW - Public policy KW - China KW - Content consumption KW - Content distribution KW - Content production KW - E-commerce ecosystems KW - Live streaming KW - Medium regulation KW - Production distribution KW - Public participation KW - Value chains KW - Artificial intelligence CY - China ER - TY - JOUR TI - Ethical Leadership Challenges in the Age of Artificial Intelligence: An In-depth Analysis AU - Bannor F.O. AU - Baysah J.O. PY - 2025 JO - Pan-African Journal of Education and Social Sciences VL - 6 IS - 2 SP - 76 EP - 88 DO - 10.56893/pajes2025v06i02.06 AB - Artificial intelligence (AI) is rapidly transforming decision-making across various sectors, introducing both opportunities and ethical challenges for leadership. While AI enhances efficiency and innovation, concerns, such as algorithmic bias, transparency deficits, and accountability gaps, pose significant risks to governance. This study examines these ethical dilemmas through real world cases, including Amazon’s recruiting tool, Olay’s algorithmic audit, IBM Watson for Oncology, and predictive policing via COMPAS, to assess their impact on leadership frameworks and the necessity for proactive ethical oversight. Through a comprehensive interdisciplinary analysis, this paper explores traditional ethical leadership models alongside emerging AI governance frameworks, notably the Ethical Management of Artificial Intelligence (EMMA) model. By synthesizing research across ethics, psychology, and management, this study demonstrates how leaders must integrate technical expertise with ethical sensitivity to align AI adoption with organizational values and societal expectations. These findings underscore the crucial need for explainable AI (XAI), bias audits, and transparent accountability structures to promote trust in AI systems. To address these challenges, this study recommends a multi-stakeholder approach that prioritizes interdisciplinary collaboration, continuous ethical monitoring, and enforceable AI governance policies. Ethical AI leadership necessitates adaptive oversight to ensure that AI innovation benefits humanity without perpetuating systemic biases or ethical blind spots. © 2025, Adventist University of Africa. All rights reserved. KW - accountability KW - AI ethics KW - bias KW - ethical leadership KW - governance KW - transparency CY - Liberia ER - TY - JOUR TI - The impact of design thinking and artificial intelligence capabilities on performance: The role of new product development decision-making agility AU - Kyriakopoulos N. AU - Kim E. AU - Hultink E.J. AU - Santema S. PY - 2025 JO - Journal of Business Research VL - 200 SP - 115633 DO - 10.1016/j.jbusres.2025.115633 AB - Design thinking and artificial intelligence (AI) capabilities are gaining prominence in today's dynamic markets. However, research gaps remain regarding their influence on the outcomes of new product development (NPD), such as decision-making agility, and the structural conditions facilitating or impeding their effective implementation. Considering design thinking as a dynamic capability and AI capabilities as technology-driven innovation enablers, this study examines their impact on NPD performance via NPD decision-making agility. An empirical investigation using data collected from 230 U.S. firms shows that design thinking and AI capabilities positively influence agility, which in turn drives NPD performance. This study also uncovers that the moderating role of organizational formalization attenuates the impact of design thinking on NPD decision-making agility but strengthens the impact of AI capabilities on NPD decision-making agility. These findings provide NPD managers with insights into using these capabilities to enhance agility and improve NPD performance in the organizational context. © 2025 The Author(s) KW - Agility KW - Artificial intelligence KW - Design thinking KW - NPD performance KW - Organizational formalization CY - Netherlands ER - TY - JOUR TI - Artificial intelligence (AI) for social innovation in health education: promoting health literacy through personalized ai-driven learning tools – a systematic review AU - Tbaishat D.M. AU - Elfadel M.W. PY - 2026 JO - BMC Medical Education VL - 26 IS - 1 SP - 123 DO - 10.1186/s12909-025-08462-3 AB - Background: Artificial Intelligence (AI) is transforming health education by enabling personalized, adaptive, and scalable approaches that may enhance aspects of health literacy. Despite rapid adoption, comprehensive synthesis of AI tools’ impact on health literacy as social innovation is limited. Understanding these effects guides educators, developers, and policymakers in designing potentially effective, inclusive, and ethical AI interventions. This review examines generative AI models, chatbots, and adaptive learning systems in supporting health literacy globally. Methods: A systematic review was conducted following PRISMA guidelines. Literature was identified primarily through PubMed/Medline, Scopus, and ScienceDirect. Connectedpapers.com was used exclusively as a citation chasing tool, performing both backward and forward reference searches to identify thematically linked studies not captured by database searches. All records retrieved via Connected Papers were subjected to the same eligibility criteria as database-sourced studies, covering publications from 2000–2025. A total of 75 peer-reviewed empirical and theoretical studies focusing on AI tools for health literacy and social innovation were included. Titles, abstracts, keywords, and full texts were screened using predefined criteria. Data were managed and de-duplicated using Zotero. Screening and eligibility decisions were recorded in Excel spreadsheets. Thematic synthesis was conducted manually. PRISMA 2020 and PRISMA-S checklists were used to ensure transparent reporting. Results: AI research in health education was minimal until 2020 but rose sharply from 2021, peaking in 2023–2024 with generative AI (e.g., ChatGPT). Of the 75 included studies, 68 (90.7%) were co-authored by two or more researchers, 54 (72.0%) were published as Open Access, and review articles dominated with 41 studies (54.7%), while empirical research was limited, highlighting moderate to weak evidence. Research focused on personalized AI tools and learning effectiveness, with limited exploration of ethics, technical barriers, or social innovation. Findings suggest that AI interventions may improve readability, metacognitive engagement, cultural accessibility, and learner autonomy in the short term, particularly when multifaceted. However, evidence for long-term behavior change and real-world impact is sparse, indicating caution in generalizing results. Challenges include algorithmic bias, digital inequity, and lack of transparency, emphasizing the need for inclusive, equity-driven AI strategies. Conclusion: AI-powered tools have potential to support health literacy and learner-centered innovation, while contributing to social impact. Multifaceted, adaptive interventions may offer greater benefits than single-tool approaches. Findings provide preliminary guidance for standardized training, AI literacy integration, and policy frameworks, while acknowledging the current limitations in evidence, generalizability, and long-term outcomes. © The Author(s) 2025. KW - Artificial intelligence (AI) KW - Digital health education KW - Health education KW - Health literacy promotion KW - Personalized learning tools KW - Systematic literature review KW - Artificial Intelligence KW - Health Education KW - Health Literacy KW - Humans KW - artificial intelligence KW - health education KW - health literacy KW - human KW - procedures CY - United Arab Emirates, Jordan ER - TY - JOUR TI - Transforming Healthcare in India: The Role of Artificial Intelligence and Regulatory Frameworks for Sustainable Growth AU - Ghosh A. AU - Saini A. AU - Barad H. PY - 2025 JO - World Medical and Health Policy VL - 17 IS - 3 SP - 475 EP - 490 DO - 10.1002/wmh3.70015 AB - Artificial intelligence (AI) has quickly emerged as a game changer in healthcare, providing innovative ways to improve patient care, enhance processes, and reduce expenses. AI could solve important healthcare issues in India, including increasing service demand, a lack of trained medical personnel, and notable geographical differences, especially in rural areas. AI can aid in addressing this gap by offering scalable, affordable regulatory framework that enhance diagnosis, treatment planning, and suitable resource allocation. This paper examines the impact of AI on healthcare, considering its benefits, challenges, and ethical implications for improving the healthcare delivery system. The paper also explores global regulatory frameworks and their implications for AI in healthcare, focusing on the roles of the United States, United Kingdom, European Union, India, and other prominent organizations. Additionally, the paper explores the opportunity to develop a robust AI policy framework for healthcare in India, drawing from global approaches. The study emphasizes ethical, interoperable AI and outlines a roadmap for India's healthcare sector-integrating risk-based regulations, enhanced digital infrastructure, ethical AI policies, and private entrepreneurship via public–private partnerships—to position India as a leader in AI-driven healthcare regulation. As India continues to invest in digital health infrastructure, a comprehensive, ethically sound regulatory framework will be crucial in ensuring that AI-powered healthcare is accessible, affordable, and inclusive for all citizens. By learning from global best practices and focusing on equitable healthcare, India can lead the way in AI-driven healthcare innovation. © 2025 Policy Studies Organization. KW - affordable healthcare KW - AI regulatory framework KW - artificial intelligence (AI) KW - digital health infrastructure KW - article KW - artificial intelligence KW - best practice KW - diagnosis KW - digital health KW - disease management KW - entrepreneurship KW - European Union KW - health care KW - health care cost KW - health care delivery KW - health infrastructure KW - human KW - India KW - infrastructure KW - medical personnel KW - patient care KW - pharmacoeconomics KW - public-private partnership KW - resource allocation KW - rural area KW - sustainable growth KW - treatment planning KW - United Kingdom KW - United States CY - India ER - TY - JOUR TI - Advancements in Information Technology and Tourism Management: Four decades on the Internet AU - Li K.Q. AU - Zhang K. AU - Law R. PY - 2026 JO - Journal of Smart Tourism VL - 6 IS - 1 SP - 29 EP - 43 DO - 10.1177/27652157251393805 AB - The swift advancement of information and communication technologies (ICTs) over the past four decades has fundamentally reshaped tourism, transforming business models, consumer behavior, and value creation. Adopting a sociotechnical perspective, this paper analyzes the historical trajectory and socio-economic implications of technological shifts in tourism across three phases: technological penetration, data reciprocity, and algorithmic dominance. By conceptualizing technology as a socially constructed phenomenon, it highlights the interplay between consumers, businesses, and technology (C-B-T). The findings reveal a shift from technology-driven control to algorithmic governance, redefining supply–demand relationships and operational strategies while raising ethical concerns. To address these challenges, the study proposes a dynamic power equilibrium framework underpinned by governance mechanisms such as consumer transparency, algorithmic accountability, and business auditing. This framework aims to balance value creation, power distribution, and responsibility allocation, offering strategic insights for sustainable smart tourism development. © The Author(s) 2025 KW - information and communication technologies KW - smart tourism KW - sociotechnical perspective KW - technological evolution KW - value creation CY - China ER - TY - JOUR TI - Driving Sustainable Entrepreneurship Through AI and Knowledge Management: Evidence from SMEs in Emerging Economies AU - Alshammakhi Q.M. AU - Sheikh R.A. PY - 2025 JO - Sustainability (Switzerland) VL - 17 IS - 24 SP - 10928 DO - 10.3390/su172410928 AB - This study investigates how artificial intelligence (AI) capabilities shape sustainable entrepreneurship (SE) among small and medium-sized enterprises (SMEs) in emerging economies. Focusing on knowledge management (KM) as a mediator, entrepreneurial orientation (EO) as a moderator, and government policy support (GPS) as an enabler, the research draws upon the Knowledge-Based View, Dynamic Capabilities Theory, and Institutional Theory. Using data from Saudi Arabian SMEs operating within the Vision 2030 agenda, the structural model demonstrates that AI primarily influences sustainability when firms possess robust KM systems capable of translating digital insights into actionable practices. Both EO and GPS strengthen the conversion of knowledge into sustainable outcomes, where EO fosters innovation and proactivity, and GPS provides essential resources and legitimacy. Nevertheless, excessive reliance on policy incentives may divert firms toward compliance rather than substantive transformation. Conceptually, this paper situates KM at the core of sustainability transformation, with policy support shaping the institutional context. The findings offer actionable guidance for SME managers and policymakers seeking to advance the United Nations Sustainable Development Goals (SDGs) through strategic engagement with AI and KM. © 2025 by the authors. KW - artificial intelligence KW - entrepreneurial orientation KW - government policy support KW - knowledge management KW - sustainable entrepreneurship KW - vision 2030 KW - artificial intelligence KW - economic analysis KW - entrepreneur KW - government relations KW - GPS KW - innovation KW - Sustainable Development Goal KW - United Nations CY - Saudi Arabia ER - TY - JOUR TI - Efficacy of Intelligent Consulting Services in Libraries at Home and Abroad under the Background of AI Large Model Driving; [AI大模型驱动背景下国内外图书馆智能咨询服务效能研究] AU - Song L. AU - Zhang X. PY - 2026 JO - Journal of Library and Information Science in Agriculture VL - 38 IS - 4 SP - 99 EP - 111 DO - 10.13998/j.cnki.issn1002-1248.25-0524 AB - [Purpose/Significance] This study investigates the operational practices and strategic development pathways of intelligent consultation services in libraries globally, specifically under the impetus of artificial intelligence (AI) large language models (LLMs). By conducting a systematic analysis of representative case studies, we examine the applied technologies, emerging service models, and measurable efficacy of these AI-enhanced services. The research holds significance in offering actionable insights for the effective implementation of AI within the library sector. It aims to guide the evolution of intelligent consultation toward greater innovation and cultural-contextual adaptability, thereby providing both theoretical underpinning and practical guidance for the localized development of smart library ecosystems. [Method/Process] Employing a comparative case study methodology, this research selected 30 representative libraries from diverse international and domestic contexts as its subjects. Data were primarily gathered through structured online surveys and content analysis of publicly available service interfaces, systematically capturing the scope, functionality, and operational status of their intelligent consultation services. The analysis focused on characterizing technological applications-identifying core LLM integrations, typical functionalities, and architectural highlights. It further integrated findings to compare and contrast prevailing service models and implementation variances. Subsequently, the study conducted a multidimensional comparative assessment of the practical service effectiveness enabled by AI large models, evaluating performance across four key areas: service response efficiency and accuracy; capabilities in resource organization and structured knowledge management; tangible improvements in user service experience; and degree of service model innovation. [Results/Conclusions] The findings indicate that AI large model-driven intelligent consulting services exhibit pronounced advantages in key operational metrics, including enhanced response efficiency, superior knowledge synthesis and management capabilities, enriched user interaction experiences, and the facilitation of novel service paradigms. However, a comparative analysis reveals significant disparities among libraries concerning the depth of technological integration, the sophistication of service offerings, and the level of cultural and linguistic adaptation achieved. In response, the study proposes targeted strategic recommendations from three interrelated perspectives: nuanced technological application, user-centered service design, and collaborative ecosystem construction. It advocates for libraries to prioritize the synergistic balance between technological capability and humanistic service values, to achieve deeper integration with localized and institutional knowledge repositories, and to institute mechanisms for continuous service evaluation and iterative optimization. These approaches are essential for fostering more efficient, inclusive, and sustainable development of intelligent consultation services. Future research directions should encompass longitudinal studies on service effectiveness, the integration of multimodal interactive capabilities, and the formulation of ethical guidelines and governance frameworks for AI deployment in library services. © 2026, Agricultural Information Institute, Chinese Academy of Agricultural Sciences. All rights reserved. KW - AI large model-driven KW - efficiency research KW - intelligent consulting services KW - library CY - China ER - TY - JOUR TI - Big data artificial intelligence to promote new product performance: the role of electronic-supply chain collaboration in B2B firms AU - Tao Y. AU - Muhammad Muneeb F. AU - Wanke P.F. AU - Tan Y. PY - 2025 JO - Journal of Business and Industrial Marketing SP - 1 EP - 14 DO - 10.1108/JBIM-09-2024-0706 AB - Purpose – This study aims to examine how big data analytical intelligence (BDAI) assimilation promotes new product performance (NPP) in business-to-business (B2B) manufacturing firms. This study tests the intermediary role of artificial intelligence (AI) capabilities and the contingency impact of electronic supply chain collaboration (ESCC) in the relationship between BDAI assimilation and NPP. Design/methodology/approach – Drawing on the dynamic capabilities theory (DCT), this study tests the moderated-mediation model using multi-wave, multi-source data collected from 291 Chinese B2B manufacturing firms. Structural equation modeling was applied to test the proposed hypotheses. Findings – The results demonstrate that BDAI assimilation significantly promotes NPP, and AI capabilities act as an intermediary bridge – empowering B2B firms to convert BDAI assimilation into enhanced NPP. This study further found that this mediation model is strengthened through the contingency impact of ESCC and increases its indirect effect on NPP. Practical implications – This study suggests that B2B managers and policy architects should recognize that investment in BDAI assimilation is not sufficient. However, building AI capabilities might fully support BDAI assimilation to gain innovation outcomes such as NPP. This study further suggests the practical implications of ESCC in achieving higher returns through AI capabilities in the B2B context. Originality/value – This study has threefold contributions to the existing literature on big data innovation and B2B firms. This study contributes to extend DCT by emphasizing AI capabilities as an intermediary channel and ESCC as a vital contingency – strengthening the relationship between BDAI assimilation and NPP. We contributed and understand how Chinese B2B firms strategically used big data and AI strategies to reach firms’ competitive advantage. © 2025 Emerald Publishing Limited KW - Artificial intelligence capabilities KW - Big data analytical intelligence KW - Electronic supply chain collaboration KW - New product performance CY - China, Brazil, United Kingdom ER - TY - JOUR TI - Reaching new frontiers in nanoelectronics through artificial intelligence AU - Sivasubramani S. AU - Prodromakis T. PY - 2025 JO - Frontiers in Nanotechnology VL - 7 SP - 1627210 DO - 10.3389/fnano.2025.1627210 AB - Artificial Intelligence (AI) is revolutionizing industries worldwide, delivering unprecedented productivity gains across diverse sectors, from healthcare to manufacturing. Recent advances in generative AI models have particularly accelerated innovation, enabling more efficient execution of complex tasks such as drug discovery, autonomous driving, and predictive maintenance. In the areas of electronics manufacturing: a sector crucial to the advancement of modern technologies, the impact of AI is profound, with the potential to transform every stage of the supply chain. This perspective investigates the role of AI in reshaping the electronics and semiconductor industries, exploring how it integrates into various stages of production and development. The approach to AI integration is structured and methodical, addressing both challenges and opportunities across five key nanotechnology areas: materials discovery, device design, circuit and system design, testing/verification, and modeling. In materials discovery, AI aids in identifying new, more efficient and sustainable materials. In device design, it enhances the functionality and integration of components. AI’s capabilities in circuit and system design enables more complex and precise electronic systems. During the testing and verification stage, AI contributes to more rigorous and faster testing processes, ensuring reliability before market release. Finally, in modeling, AI’s predictive capabilities allow for accurate simulations, crucial for anticipating performance under various scenarios. Each pillar of this electronics supply chain underscores AI’s ability to accelerate processes, optimize performance, and reduce costs. Supported by case studies of AI-driven breakthroughs, this perspective provides a comprehensive review of current AI applications across the entire electronic supply chain, illustrating improvements in yield and sustainable manufacturing practices. Copyright © 2025 Sivasubramani and Prodromakis. KW - artificial intelligence KW - electronics supply chain KW - nanoelectronics manufacturing KW - nanotechnology applications KW - semiconductor design KW - sustainable engineering CY - United Kingdom ER - TY - JOUR TI - Artificial Intelligence and Digital Marketing: Ethical Challenges of Digital Influence on Public Perception and Consumer Behavior in the Law of the UAE AU - Yas H. AU - Abdalaziz M.M.O. AU - Dafri W. AU - Al-Falahi Q. AU - Kashmoola B. AU - Allouzi A.S. PY - 2025 JO - Research Journal in Advanced Humanities VL - 6 IS - 3 DO - 10.58256/5hjmrw16 AB - This paper discusses the ethical and legal considerations of artificial intelligence (AI) in digital marketing in the fast-changing regulatory environment of the United Arab Emirates (UAE). Through the secondary research methodology, the article examines 85 documents comprising academic publications, government reports, and legal texts, providing a thorough review of the nexus between AI capabilities and regulatory frameworks in the UAE. The results show that although the use of AI has increased at a faster pace, especially among various generational groups of consumers, there is a need to transform the legal and ethical framework to address the arising risks. Ethical AI governance is based on the main regulations, including the UAE Personal Data Protection Law, Cybercrime Law, Consumer Protection Law, and Digital Commerce Law. Such laws are focused on transparency, consent, and accountability in AI-driven marketing activities. Additionally, explainability and fairness are key factors that make consumers trustful, but AI is usually too technical to provide meaningful transparency. The paper concludes that a moderate solution is the key, which is to incorporate technological innovation with moral governance. It demands enhanced regulation enforcement, industry self-regulation, and cultural change within organizations to guarantee responsible AI use. The future of ethical digital marketing and consumer protection in the era of intelligent automation will be influenced by the changing legal framework of the UAE. © 2025 The Author(s). KW - AI Ethics KW - Artificial Intelligence (AI) KW - Consumer Protection KW - Data Privacy KW - Digital Marketing KW - Ethical Governance KW - Law KW - Legal Framework CY - Malaysia, United Arab Emirates ER - TY - JOUR TI - From Triumph to Uncertainty: The Journey of Software Engineering in the AI Era AU - Mastropaolo A. AU - Escobar-Velásquez C. AU - Linares-Vásquez M. PY - 2025 JO - ACM Transactions on Software Engineering and Methodology VL - 34 IS - 5 SP - 131 DO - 10.1145/3709360 AB - Over the last 10 years, the realm of AI has experienced an explosion of revolutionary breakthroughs, transforming what seemed like a far-off dream into a reality that is now deeply embedded in our everyday lives. AI’s widespread impact is revolutionizing virtually all aspects of human life, and software engineering (SE) is no exception. As we explore this changing landscape, we are faced with questions about what the future holds for SE and how AI will reshape the roles, duties, and methodologies within the field. The introduction of these groundbreaking technologies highlights the inevitable shift toward a new paradigm, suggesting a future where AI’s capabilities may redefine the boundaries of SE, potentially even more than human input. In this article, we aim at outlining the key elements that, based on our expertise, are vital for the smooth integration of AI into SE, all while preserving the intrinsic human creativity that has been the driving force behind the field. First, we provide a brief description of SE and AI evolution. Afterward, we delve into the intricate interplay between AI-driven automation and human innovation, exploring how these two components can work together to advance SE practices to new methods and standards. © 2025 Association for Computing Machinery. All rights reserved. KW - AI4SE KW - Artificial Intelligence KW - History KW - LLM4Code KW - Software engineering KW - Application programs KW - Embedded software KW - Human engineering KW - Software design KW - Software packages KW - Software quality KW - Verification KW - AI4SE KW - Driving forces KW - Human creativity KW - Human lives KW - Key elements KW - Llm4code KW - Software engineering practices KW - Two-component KW - Uncertainty KW - Computer operating systems CY - United States, Colombia ER - TY - JOUR TI - Harnessing AI capabilities and green entrepreneurial orientation for sustainable SME performance using SEM analysis approach AU - Alwakid W.N. AU - Dahri N.A. PY - 2025 JO - Technology in Society VL - 83 SP - 103007 DO - 10.1016/j.techsoc.2025.103007 AB - The growing focus on sustainability and technological innovation has encouraged small and medium-sized enterprises (SMEs) to adopt artificial intelligence (AI) capabilities and a green entrepreneurial orientation as central drivers of sustainable performance. This research investigates the contribution of AI capabilities in promoting green innovations and creativity in SMEs, which collectively lead to long-term sustainability. The study is based on the Resource-Based View (RBV) theory, which offers a theoretical framework for investigating how AI-based competencies and green entrepreneurial strategies can promote SME performance. The research model comprises infrastructure, business integration, and a proactive attitude influencing AI capabilities, affecting SME creativity and green innovations. Moreover, green risk-taking, innovativeness, and proactiveness significantly contribute to green entrepreneurial orientation, affecting SME creativity and green innovations, ultimately resulting in sustainable performance. A quantitative research design was applied, utilizing survey data gathered from SME managers and business owners functioning in the manufacturing and service sectors in Saudi Arabia. 250 SMEs were examined using “Structural Equation Modeling (SEM)” analysis to validate the proposed hypotheses and evaluate these relationships. The results indicate that AI capabilities significantly impact SME creativity and green innovations. Moreover, green entrepreneurial orientation positively influences SME creativity and green innovations, which in turn facilitate sustainable performance. This research identifies the importance of SMEs allocating resources to AI infrastructure, proactive business strategies, and entrepreneurial risk-taking to foster green innovation and sustainability. Moreover, policymakers must facilitate AI-based green initiatives by implementing incentives and regulatory policies to promote sustainable development. By integrating AI capabilities and green entrepreneurship, SMEs can achieve a competitive advantage while achieving world sustainability goals. © 2025 Elsevier Ltd KW - AI capabilities KW - Green entrepreneurial orientation KW - Green innovations KW - SME creativity KW - Sustainable performance KW - Saudi Arabia KW - Green development KW - Green manufacturing KW - Industrial research KW - Sustainable development KW - Sustainable development goals KW - Artificial intelligence capability KW - Enterprise performance KW - Entrepreneurial orientation KW - Green entrepreneurial orientation KW - Green innovations KW - Modeling analyzes KW - Small and medium-sized enterprise KW - Small and medium-sized enterprise creativity KW - Structural equation models KW - Sustainable performance KW - artificial intelligence KW - business KW - entrepreneur KW - green economy KW - industrial performance KW - infrastructure KW - innovation KW - small and medium-sized enterprise KW - sustainability KW - Competition CY - Saudi Arabia, Malaysia, Oman ER - TY - JOUR TI - AIoT-enabled platform urbanism for smart city management: a demonstration of building footprint extraction AU - Hossain S.T. AU - Yigitcanlar T. AU - Ye X. PY - 2026 JO - Computational Urban Science VL - 6 IS - 1 SP - 30 DO - 10.1007/s43762-026-00267-4 AB - Urban environments are increasingly complex, dynamic, and data-intensive, requiring advanced spatial intelligence to support proactive, evidence-based governance. Current smart city and urban informatics platforms are limited by static datasets, siloed architectures, and underutilised AI capabilities. This study proposes and demonstrates a novel AIoT-enabled platform architecture for built environment mapping and spatial decision support. Anchored in platform urbanism, the architecture integrates high-resolution imagery, pretrained deep learning models from the ArcGIS Living Atlas, iterative workflows in ArcGIS Pro, and interactive dissemination via ArcGIS Experience Builder. The platform is demonstrated through building footprint detection in three Brisbane suburbs using the Building Footprint Extraction Australia model. Suburb-level processing enhances computational efficiency, while analytical extensions support footprint change detection, flood exposure assessment, and land-use zoning overlays. Results indicate that the platform transforms manual, fragmented processes into automated, reproducible, and dynamic workflows directly applicable to urban planning. Although demonstrated for building footprints, the architecture is scalable to other urban features, including roads, parcels, and solar panels. Limitations include dependence on high-resolution imagery and pretrained models, highlighting opportunities for future work in multi-model integration, real-time data streams, and developing AI models tailored to diverse urban contexts. By bridging cutting-edge AI innovation with operational governance needs, the proposed platform offers a replicable pathway for embedding AI-enabled spatial intelligence into smart city management. © The Author(s) 2026. KW - Artificial-intelligence-of-things KW - Brisbane KW - Built environment KW - Platform urbanism KW - Smart city KW - Urban analytics KW - Architecture KW - Automation KW - Computational efficiency KW - Computer aided software engineering KW - Deep learning KW - Iterative methods KW - Land use KW - Smart city KW - Urban planning KW - Artificial-intelligence-of-thing KW - Brisbane KW - Building footprint KW - Built environment KW - City management KW - High resolution imagery KW - Platform urbanism KW - Spatial intelligence KW - Urban analytic KW - Urban environments KW - Extraction CY - Australia, South Africa, United States ER - TY - JOUR TI - Research on the Integration and Innovation Path of Artificial Intelligence and the Real Economy AU - Qinghua L.I. AU - Meiyan S.U.I. AU - Aining L.I. AU - Zhang Y. AU - Hongmei X.U. PY - 2026 JO - Tehnicki Vjesnik VL - 33 IS - 2 SP - 719 EP - 731 DO - 10.17559/TV-20250612002741 AB - This study aims to explore the impact of artificial intelligence (AI) integration on economic performance, focusing on the roles of frugal innovation and business model innovation as mediators. Utilizing a quantitative research design, data were collected from secondary sources, including industry reports and databases like the World Bank and OECD. The sample comprised 177 firms across various sectors, with a focus on small and medium-sized enterprises (SMEs). The findings reveal that AI-driven innovation significantly enhances economic performance, both directly and through its positive impact on real economy innovation. AI Integration Readiness (AIR) amplifies the AIDI→REI link yet shows a non-significant total moderation on EP (p = 0.15; BF10 = 1.8, anecdotal evidence for H0). Challenges such as the negative impacts of AI readiness on performance highlight the need for targeted support for SMEs. The study concludes that fostering AI capabilities and readiness is crucial for overcoming bottlenecks and achieving optimal economic outcomes, emphasizing the importance of supportive policies and infrastructure for broad-based AI adoption. These insights provide valuable implications for policymakers and business leaders aiming to leverage AI for sustainable economic growth and innovation. © 2026 Strojarski Facultet. All rights reserved. KW - AI integration readiness KW - artificial intelligence KW - business model innovation KW - economic performance KW - frugal innovation KW - medium-sized enterprises (SMEs) KW - small KW - Economic analysis KW - Industrial economics KW - Integration KW - Artificial intelligence integration readiness KW - Business model innovation KW - Economic performance KW - Frugal innovations KW - Integration readiness KW - Intelligence integration KW - Medium sized enterprise KW - Medium-sized enterprise KW - Medium-sized enterprise (small and medium-sized enterprise) KW - Small KW - Small and medium-sized enterprise KW - Artificial intelligence CY - China ER - TY - JOUR TI - Artificial Intelligence, Lean Startup Method, and Product Innovations AU - Wang X. AU - Wu L. PY - 2025 JO - Management Science VL - 72 IS - 1 SP - 756 EP - 782 DO - 10.1287/mnsc.2022.03905 AB - Although artificial intelligence (AI) has the potential to drive significant business innovation, many firms struggle to realize its benefits. We investigate why some firms succeed in using AI for innovation, whereas others fail, focusing on the organizational support necessary for leveraging AI in both novel and incremental innovation. Specifically, we examine how the lean startup method (LSM) influences the impact of AI on product innovation in startups. Analyzing data from 1,800 Chinese startups between 2011 and 2020, alongside policy shifts by the Chinese government in encouraging AI adoption, we find that companies with strong AI capabilities produce more innovative products. Moreover, our study reveals that AI investments complement LSM in innovation, with effectiveness varying by the type of innovation and AI capability. We differentiate between discovery-oriented AI, which reduces uncertainty in novel areas of innovation, and optimization-oriented AI, which refines and optimizes existing processes. Within the framework of LSM, we further distinguish between prototyping—focused on developing minimum viable products—and controlled experimentation—focused on rigorous testing such as A/B testing. We find that LSM complements discovery-oriented AI by utilizing AI to expand the search for market opportunities and employing prototyping to validate these opportunities, thereby reducing uncertainties and facilitating the development of the first release of products. Conversely, LSM complements optimization-oriented AI by using A/B testing to experiment with the universe of input features and using AI to streamline iterative refinement processes, thereby accelerating the improvement of iterative releases of products. As a result, when firms use AI and LSM for product development, they are able to generate more high-quality products in less time. These findings, applicable to both software and hardware development, underscore the importance of treating AI as a heterogeneous construct because different AI capabilities require distinct organizational processes to achieve optimal outcomes. © 2025 INFORMS KW - artificial intelligence KW - innovation KW - lean startup method KW - product development KW - startup KW - Investments KW - Iterative methods KW - Reactor startup KW - Business innovation KW - Incremental innovation KW - Innovation KW - Lean startup method KW - Optimisations KW - Organizational support KW - Policy shift KW - Product innovation KW - Startup KW - Uncertainty KW - Artificial intelligence KW - Product development CY - United States ER - TY - JOUR TI - Measuring Customer Experience in AI Contexts: A Scale Development AU - Li C. AU - Hao R. AU - Li N. AU - Zhang C. PY - 2025 JO - Journal of Theoretical and Applied Electronic Commerce Research VL - 20 IS - 1 SP - 31 DO - 10.3390/jtaer20010031 AB - With the advent of the digital intelligence era and the rapid evolution of emerging technologies, Artificial Intelligence (AI) is fundamentally transforming the way consumers and businesses interact, gradually becoming one of the primary tools for companies to continuously improve customer experience and maintain competitiveness. However, existing research on customer experience largely overlooked the disruptive changes brought by the widely applied AI technologies. Therefore, this paper focuses on customer AI experience in the new context, using a mixed research method combining qualitative and quantitative approaches to explore the connotation, measurement, formation mechanism, and related action mechanisms of this construct. This study finds the following: (1) the customer AI experience is an intrinsic and subjective response generated by customers after interacting with AI capabilities, mediated by AI. It specifically includes five dimensions: social experience, intellectual experience, classification experience, exploitation experience, and service experience; (2) its formation and development is a cyclical model comprising three stages: expectation, realization, and reflection, corresponding to the mechanisms of contact, interaction, and comparison; (3) the perceived innovative characteristics of AI technology help customers to have a better AI experience, thereby stimulating customer engagement behavior. This provides certain guidance and reference for enterprises to better understand and utilize AI’s innovative characteristics to improve the customer experience, promote customer engagement, seize opportunities in AI technology development, and maintain a competitive advantage. © 2025 by the authors. KW - customer AI experience KW - customer engagement KW - digital interaction platform KW - perceived AI innovative characteristics CY - China ER - TY - JOUR TI - Empirical Validation of Measurement Scales: AI Capabilities, Cybernetic Thinking, Organizational Ambidexterity, and Employee Wellbeing AU - Bibi M. AU - Tan T.G. AU - Yao H. PY - 2026 JO - SAGE Open VL - 16 IS - 1 DO - 10.1177/21582440251382640 AB - In the technological era, changes are happening around the globe at a fast rate. In this regard, healthcare organizations are implementing changes to improve their process. Hence, to manage implemented changes, there is a need to assess AI capabilities, cybernetic thinking (CT), organizational ambidexterity (OA), and employee wellbeing (EWB). However, no validated scale exists specifically to measure the aspects mentioned earlier in the context of healthcare organizations (HCO). Accordingly, our study attempted to validate existing scales of AI capabilities, CT, OA, and EWB in the context of HCO. Besides, to attain this purpose, a pilot study was led on a sample of 150 doctors employed in private sector hospitals in Pakistan, and data were analyzed using CB-SEM. This study confirms the validity and reliability of the refined scale in the context of a Pakistani healthcare setting. From the practical context, healthcare organizations can use the validated scale to assess their capacity towards adopting emerging technologies. These scales can be used to formulate strategies for managing technological change from both organizational and employee perspectives in healthcare settings. In addition, this study offers a multidimensional perspective by integrating diffusion of innovation theory (DOIT) with AI capabilities, EWB, CT, and OA to specify how innovation diffuses across complex systems, such as healthcare settings. © The Author(s) 2026. This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage). KW - AI capabilities KW - cybernetic thinking KW - diffusion of innovation theory KW - employee wellbeing KW - organizational ambidexterity KW - scales validation CY - Malaysia, Pakistan, China ER - TY - JOUR TI - The Implications of Artificial Intelligence for Small and Medium-Sized Enterprises’ Sustainable Development in the Areas of Blockchain Technology, Supply Chain Resilience, and Closed-Loop Supply Chains AU - Khan S.A.R. AU - Sheikh A.A. AU - Shamsi I.R.A. AU - Yu Z. PY - 2025 JO - Sustainability (Switzerland) VL - 17 IS - 1 SP - 334 DO - 10.3390/su17010334 AB - In today’s fast-paced business settings, the metaverse as a shared marketplace has gained popularity and is helping businesses to develop crucial business strategies in their pursuit of sustainable performance. However, a lack of understanding and knowledge about the effectiveness of the metaverse and its related technologies creates a barrier. Therefore, the current study fills this gap and uses organizational information-processing theory to develop the theoretical framework to examine metaverse-related technologies (artificial intelligence and blockchain technology—BCT) and their direct and indirect effects on sustainable business performance, which no other study has examined. Using purposive sampling, the sample data from 326 SMEs were gathered and analyzed using a partial least square structural equation modeling (PLS-SEM). This study’s findings revealed that AI capabilities are vital for information gathering, analyzing, and decision-making in the metaverse context. BCT facilitates ensuring a transparent, visible, traceable, and immutable supply chain, which helps make it more resilient and improves the closed-loop supply chain (CLSC) system with positive technological advancements and significant effects on increasing sustainable business performance (SBP). This study’s findings help organizations understand the potential benefits of AI-enabled SMEs’ presence in the metaverse. The current investigation provides a strategy for managers to gain a competitive advantage, make the supply chain more robust, and enhance overall business performance. © 2025 by the authors. KW - adaptive capabilities KW - artificial intelligence KW - blockchain technology KW - closed-loop supply chain KW - metaverse KW - supply chain resilience KW - sustainable business performance KW - artificial intelligence KW - business KW - decision making KW - numerical model KW - performance assessment KW - small and medium-sized enterprise KW - supply chain management KW - sustainable development CY - China, Oman ER - TY - JOUR TI - Corporate AI Living Labs: A Structured Approach to Accelerating AI Adoption and Transforming Towards AI-Empowered Employees for Operational Excellence AU - Son A. AU - Apachite C. AU - Petcu A. AU - Schuurman D. PY - 2024 JO - Journal of Innovation Management VL - 12 IS - 3 SP - XV EP - XX DO - 10.24840/2183-0606_012.003_L002 AB - The integration of artificial intelligence (AI) into corporate environments becomes essential for enhancing efficiency, decision-making, and fostering innovation. While AI adoption has successfully optimized specific operational processes, the holistic deployment of AI aimed at empowering employees remains underdeveloped. This letter introduces the concept of the “AI Living Lab” within corporate environments, designed to accelerate AI adoption, foster innovation, and enhance employee productivity and satisfaction. The concept appeared as a response in Continental Automotive to the problem of faster adoption time and scaling of AI-Empowered Employee solutions inside the company. Through a current observation on the state of the art, Continental Automotive’s AI Living Lab as a case study, and identification of existing gaps, this letter suggests future research areas for a scalable AI Living Lab framework in corporate settings. © 2024 Universidade do Porto - Faculdade de Engenharia. All rights reserved. KW - academia-industry Collaboration KW - AI adoption KW - AI-empowered employees KW - automotive industry KW - co-creation with employees KW - corporate AI living labs KW - employee empowerment KW - employee-driven AI development KW - ethical AI governance KW - framework development for scalability KW - impact assessment metrics KW - knowledge transfer models KW - operational transformation KW - real-world experimentation KW - scalability of AI solutions CY - Romania, Germany, Belgium ER - TY - JOUR TI - From smart infrastructure to regenerative destinations: a tri-country study of tourism digital capabilities, innovation and ethical AI in Southeast Asia AU - Ali Mari M. AU - Ahmad W. PY - 2026 JO - Tourism Review SP - 1 EP - 22 DO - 10.1108/TR-11-2025-1289 AB - Purpose – Tourism destinations increasingly pursue digital transformation, yet most initiatives remain efficiency-focused. Existing research provides a limited empirical explanation of how digital capability, environmental literacy and ethical artificial intelligence (AI) jointly enable regeneration beyond sustainability. To address this gap, this study aims to develop and test a smart regenerative tourism transformation model explaining how digital readiness, organizational capability and ethical governance support net-positive destination renewal. Design/methodology/approach – Survey data were collected from 543 tourism managers in Malaysia, Singapore and Thailand. Partial least squares structural equation modeling (PLS-SEM) was used to test eight hypothesized relationships linking smart tourism infrastructure, digital accessibility and inclusion, eco-literacy and net-zero commitment and regenerative destination governance to tourism digital transformation capability (TDTC), regenerative tourism innovation and regenerative destination transformation, with AI and data-ethics climate as a moderator. Findings – The results indicate that smart infrastructure, inclusivity and governance strengthen TDTC, which, in turn, supports regenerative tourism innovation and regenerative destination transformation. A strong AI and data-ethics climate amplifies these relationships. These findings are based on managerial perceptions and suggest, rather than confirm, destination-level regenerative progress. Research limitations/implications – This study’s cross-sectional design limits causal inference, as relationships remain correlational despite procedural and statistical checks. Future research should adopt longitudinal, experimental or panel data approaches to track TDTC over time. Additionally, incorporating objective indicators, such as AI ethics audits and digital investment records, can enhance validity. Expanding the sample to include diverse cultural contexts and adopting multi-stakeholder approaches will provide richer insights. Finally, dynamic-system modeling can better capture feedback loops between digitalization, governance and regeneration, advancing the understanding of regenerative tourism in evolving destinations. Practical implications – This study provides evidence for advancing regenerative digital transformation in Southeast Asian tourism. It confirms that smart tourism infrastructure, digital accessibility, eco-literacy and regenerative governance collectively enhance digital transformation outcomes. AI and data ethics climate play a crucial moderating role, ensuring ethical digital progress. Tourism boards must invest in inclusive digital ecosystems, ensuring that technology empowers local communities and businesses. Governments should integrate eco-literacy and sustainability into programs, while transparent AI frameworks should be adopted to ensure fairness. The findings support the creation of an Association of Southeast Asian Nations (ASEAN) regenerative tourism data network to align digital standards and promote sustainable, inclusive tourism. Social implications – This study highlights the role of digital transformation in fostering social inclusion and community well-being. By ensuring that technology enhances accessibility and empowers local communities, destinations can reduce socioeconomic inequalities and promote equitable growth. Regenerative governance frameworks and AI ethics are crucial for building trust and accountability in digital tourism, ensuring that innovations benefit all stakeholders. The integration of eco-literacy and sustainability practices further supports societal regeneration, encouraging active participation in conservation efforts and sustainable tourism practices. The proposed ASEAN regenerative tourism data network offers a model for fostering inclusive, responsible digital engagement across tourism destinations. Originality/value – This study offers a tri-country empirical examination of how ethical AI conditions the transformation of digital capability into regenerative value creation. It advances tourism theory by positioning digital transformation as a morally governed organizational capability that supports socio-ecological renewal rather than efficiency or harm reduction alone. © 2026 Emerald Publishing Limited KW - Digital transformation KW - Ethical artificial intelligence KW - Gobernanza inteligente KW - Inteligencia artificial ética KW - Regenerative tourism KW - Smart governance KW - Southeast Asia KW - Sudeste Asiático KW - Transformación digital KW - Turismo regenerativo KW - 东南亚 KW - 伦理人工智能 KW - 再生型旅游 KW - 数字化转型 KW - 智慧治理 CY - Malaysia ER - TY - JOUR TI - Are Universities Becoming Obsolete in the Age of Artificial Intelligence? AU - Mili K. AU - Abdelaziz K. PY - 2026 JO - TEM Journal VL - 15 IS - 1 SP - 828 EP - 841 DO - 10.18421/TEM151-76 AB - This paper examines whether traditional university education faces displacement by artificial intelligence technologies. As AI systems democratize knowledge access, personalize learning, and offer scalable skills training, universities' core value proposition is challenged. The analysis explores technological, economic, and social drivers behind this potential shift while acknowledging aspects resistant to replication. While universities historically dominated higher learning, research, and credentialing, AI technologies fundamentally alter how knowledge is accessed, created, and validated. Many core functions can now be achieved through AI-powered alternatives, potentially rendering conventional models obsolete for many learners. However, this analysis examines which functions might be better served by emerging technologies versus which unique values universities continue to provide. Findings reveal AI displacement potential varies substantially across university functions: administrative tasks face 75-80% disruption risk while mentorship and social development remain largely human-dependent at 25-30% substitutability. Knowledge transmission shows 75-80% AI substitutability, while research literature synthesis demonstrates 70-75% automation potential. Conversely, critical thinking development and ethical reasoning cultivation retain 70-75% human centrality. The transformation requires governments to redesign accreditation frameworks and quality assurance mechanisms. Workforce development systems need lifelong learning infrastructure and dynamic credentialing for continuous reskilling. Societally, knowledge democratization through AI may reduce educational inequality yet risk exacerbating digital divides and eroding universities' social mobility function. The analysis provides strategic recommendations emphasizing hybrid models integrating AI capabilities while preserving irreplaceable human elements. Successful adaptation requires neither wholesale abandonment of traditional models nor uncritical technological embrace, but deliberate institutional redesign balancing technological innovation with preservation of core academic values. © 2026 Khaled Mili & Khaled Abdelaziz; published by UIKTEN. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 License. KW - Artificial Intelligence KW - education KW - higher education KW - obsolescence KW - universities CY - Saudi Arabia, Tunisia ER - TY - JOUR TI - A.I. INTO FASHION PROCESSES LAYING THE GROUNDWORK AU - Rizzi G. AU - Casciani D. PY - 2023 JO - Fashion Highlight VL - 2023 IS - 2 SP - 12 EP - 20 DO - 10.36253/fh-2490 AB - The article aims to provide a comprehensive understanding of Artificial Intelligence (AI) and its integration into fashion processes, focusing on the research, design, development, and manufacturing stages. First, it offers an overview of AI evolution, from its early developments to the contemporary advanced Machine and Deep learning models, attempting to tackle the challenge of ambiguous terminology and aiming to deal with the different interpretations of AI capabilities. Subsequently, a review of the perspectives on the integration of AI tools within fashion processes will be presented. This overview will underscore the growing need for industries to undergo a conscious technological transformation, adopting AI toward a more sustainable and responsible fashion evolution. © 2023, Firenze University Press. All rights reserved. KW - Artificial Intelligence KW - Fashion Processes Transformation KW - Sustainable Fashion KW - Technological Innovation CY - Italy ER - TY - JOUR TI - Digitalisation and AI adoption as drivers of market share in GCC banking AU - Albaker Y. AU - Khalaf B.A. PY - 2025 JO - Journal of Asian Scientific Research VL - 16 IS - 1 SP - 146 EP - 160 DO - 10.55493/5003.v16i1.5869 AB - This study investigates the impact of digitalization and AI adoption on the market share of banks operating in the Gulf Cooperation Council's (GCC) region, drawing upon the resource-based view (RBV) and dynamic capabilities theory (DCT). In the current context of digital transformation and AI-driven innovation reshaping the banking sector, it is crucial to understand the role of these technologies in driving competitive advantage. The study constructs novel composite indices for digitalization and AI adoption using secondary data from 400 bank-year observations across five GCC countries between 2015-2024. Employing a dynamic panel estimation technique, the analysis reveals that both digitalization and AI adoption significantly and positively influence bank market share, even after controlling for profitability, bank size, and macroeconomic conditions. These results hold strong across different models, supporting the idea that improving and adapting technological skills is key to enhancing the market share of banks. The study offers theoretical contributions by operationalizing digital and AI capabilities as strategic resources and practical implications for bank executives and policymakers aiming to strengthen digitalization in the financial sector. It also provides one of the first empirical validations of the digitalization–market share nexus in the GCC context, thereby filling an important gap in the literature on technology-enabled market performance. © 2026 AESS Publications. All Rights Reserved. KW - Artificial intelligence KW - Banking sector KW - Digital transformation KW - Digitalisation KW - Dynamic capabilities KW - GCC KW - Market share KW - Resource-based view KW - System GMM CY - Qatar ER - TY - JOUR TI - Artificial intelligence capabilities for circular business models: Research synthesis and future agenda AU - Madanaguli A. AU - Sjödin D. AU - Parida V. AU - Mikalef P. PY - 2024 JO - Technological Forecasting and Social Change VL - 200 SP - 123189 DO - 10.1016/j.techfore.2023.123189 AB - This study explores the interlink between AI capabilities and circular business models (CBMs) through a literature review. Extant literature reveals that AI can act as efficiency catalyst, empowering firms to implement CBM. However, the journey to harness AI for CBM is fraught with challenges as firms grapple with the lack of sophisticated processes and routines to tap into AI's potential. The fragmented literature leaves a void in understanding the barriers and development pathways for AI capabilities in CBM contexts. Bridging this gap, adopting a capabilities perspective, this review intricately brings together four pivotal capabilities: integrated intelligence capability, process automation and augmentation capability, AI infrastructure and platform capability, and ecosystem orchestration capability as drivers of AI-enabled CBM. These capabilities are vital to navigating the multi-level barriers to utilizing AI for CBM. The key contribution of the study is the synthesis of an AI-enabled CBM framework, which not only summarizes the results but also sets the stage for future explorations in this dynamic field. © 2024 The Authors KW - AI future research agenda KW - Artificial intelligence KW - Business model innovation KW - Circular business models KW - AI future research agenda KW - Business model innovation KW - Business models KW - Circular business model KW - Literature reviews KW - Model contexts KW - Process automation KW - Research agenda KW - Research synthesis KW - ]+ catalyst KW - artificial intelligence KW - business KW - innovation KW - literature review KW - modeling KW - research work KW - Artificial intelligence CY - Sweden, Norway, Finland ER - TY - JOUR TI - Government-guided funds and the rise of corporate AI: Evidence from China AU - Lu J. AU - Gao H. AU - Yang L. AU - Liu Z. PY - 2026 JO - Pacific Basin Finance Journal VL - 95 SP - 103004 DO - 10.1016/j.pacfin.2025.103004 AB - We investigate how Government-Guided Funds (GGFs) influence corporate artificial intelligence (AI) development in China. Using panel data for Chinese A-share listed firms from 2012 to 2023, we find that GGFs significantly promote AI advancement through three complementary mechanisms: resource empowerment, signalling certification, and facilitation of firms' integration into platform ecosystems. This effect remains robust across multiple empirical tests. The positive impact of GGFs is stronger among firms facing more intense market competition, in regions with higher levels of intellectual property protection, and in areas where governments place greater policy emphasis on AI. Further analyses show that GGFs do not crowd out non-AI innovation; instead, they generate an innovation diffusion effect. By advancing firms' AI capabilities, GGFs contribute to employment growth, workforce upgrading, and higher labour income shares, highlighting their dual role in promoting technological innovation and improving social welfare. © 2025 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license. http://creativecommons.org/licenses/by/4.0/ KW - Artificial intelligence KW - G24 KW - G28 KW - Government-guided funds KW - Innovation diffusion KW - O32 KW - O38 KW - Platform integration KW - Resource empowerment KW - Signalling certification KW - Workforce upgrading CY - China, Australia ER - TY - JOUR TI - Direct and indirect effects of supply chain plasticity and AI capability on business performance: the moderating role of network embeddedness AU - Aboelmaged M. AU - Hashem G. AU - Mady K. PY - 2025 JO - Business Process Management Journal SP - 1 EP - 29 DO - 10.1108/BPMJ-08-2025-1392 AB - Purpose – This study examines how artificial intelligence (AI) capability and supply chain (SC) plasticity jointly influence business performance, while considering the moderating role of network embeddedness. Design/methodology/approach – Drawing on network theory and the dynamic capability (DC) view, the study develops and tests a conceptual model using survey data collected from 453 managers in manufacturing and service firms, analyzed with the PLS-SEM approach. Findings – The findings show that SC plasticity is the strongest driver of business performance, enabling rapid reconfiguration of processes and networks while mediating the impact of AI capability. Relational embeddedness positively moderates the effect of AI capability on SC plasticity but weakens the link between SC plasticity and performance, reflecting the paradox of embeddedness. Structural embeddedness weakens the effect of AI capability on SC plasticity but shows no significant effect on the SC plasticity-performance relationship. Practical implications – Managers should view SC plasticity as a strategic capability that converts digital resources into performance. While network embeddedness can strengthen adaptability, over-embeddedness may limit its benefits. Managers must also balance the benefits of embedded networks with the risks of over-embeddedness, ensuring flexibility in partnerships while leveraging trust-based ties for adaptability. Originality/value – This study is among the first to link AI capability with SC plasticity and provides empirical evidence on the boundary conditions of digital transformation and adaptability in emerging economies. © 2026 Emerald Publishing Limited KW - Artificial intelligence KW - Business performance KW - Dynamic capability KW - Network embeddedness KW - Relational embeddedness KW - Structural embeddedness KW - Supply chain plasticity CY - United Arab Emirates, Egypt, Oman ER - TY - JOUR TI - AI in strategic management and organizational agility of SMEs: leadership, policy environment, and adaptive capability AU - Zhong L. AU - Ady S.U. AU - Indrasari M. PY - 2026 JO - Future Technology VL - 5 IS - 2 SP - 1 EP - 12 DO - 10.55670/fpll.futech.5.2.1 AB - This study investigates how artificial intelligence strategic capabilities, transformational leadership, and policy environments collectively influence organizational agility in small and medium-sized enterprises through dynamic capability mechanisms. Employing a mixed-methods design, the research analyzes survey data from 300 SMEs across manufacturing, service, and technology sectors, complemented by qualitative case studies. Structural equation modeling reveals that AI strategic capabilities constitute the strongest predictor of organizational agility (β=0.42, p<0.001), with digital dynamic capabilities mediating 67% of this total effect. Technology-management fit emerges as a critical boundary condition, amplifying AI effectiveness by 123% under high alignment scenarios (β=0.58 versus β=0.26 in low alignment contexts). Transformational leadership exhibits dual mechanisms through direct positive effects on agility (β=0.28, p<0.001) and moderating influences on AI-agility relationships (β=0.21, p<0.01). Notably, AI capabilities demonstrate buffering properties against policy environment uncertainty (β=0.12, p<0.05), transforming institutional constraints into manageable strategic variables. Machine learning analyses reveal nonlinear effects with diminishing returns beyond the 75th percentile of AI adoption. The structural model explains substantial variance in organizational agility (R²=0.64) and firm performance (R²=0.52). These findings extend dynamic capability theory to digital contexts, reconceptualize AI as a strategic capability rather than an operational tool, and illuminate digital leadership dimensions, offering evidence-based guidance for SME managers, technology vendors, and policymakers navigating digital transformation challenges. © 2026, Future Publishing LLC. All rights reserved. KW - Artificial intelligence capability KW - Digital transformation KW - Dynamic capabilities KW - Organizational agility KW - Small and medium-sized enterprises CY - Indonesia ER - TY - JOUR TI - Intrinsic Motivation and the Use of Artificial Intelligence (AI) in the Public Sector: Evidence from Indonesia; [Motivação Intrínseca e o Uso da Inteligência Artificial (IA) no Setor Público: Evidências da Indonésia] AU - Chaniago H. AU - Hidayat H. AU - Efawati Y. PY - 2025 JO - Revista Brasileira de Politicas Publicas VL - 15 IS - 2 SP - 412 EP - 427 DO - 10.5102/rbpp.v15i2.10066 AB - This study is motivated by the importance of integrating humans and Artificial Intelligence (AI) within the public sector, particularly in promoting the efficiency and innovation of public services. The adoption of AI not only depends on technological readiness but also on the intrinsic motivation of employees. This study aims to understand how intrinsic motivation influences the utilization of AI in government work environments. The research was conducted between February to April 2024 in West Java Province, Indonesia, using an explanatory survey method. Data were collected through questionnaires administered to 150 randomly selected respondents from various local government agencies. The study identified two main dimensions of AI utilization: AI Capabilities & Users (AICU) and Benefits of AI (BAI). The findings reveal that Intrinsic Motivation of Employees (IME) has a partial effect on both AICU and BAI. Moreover, IME and AICU simultaneously have a positive impact on BAI. These results suggest that enhancing the benefits of AI in government is highly influenced by psychological factors and individual readiness. Therefore, governments should develop strategies that focus not only on technological aspects but also on strengthening users’ motivation and competencies. The study recommends replication in other developing country contexts to test and further develop the AICU model as a framework for AI adoption in the public sector. © 2025, Centro Universitario de Brasilia. All rights reserved. KW - AI capabilities & users KW - artificial intelligence KW - benefits of AI KW - government KW - motivation of employee CY - Indonesia ER - TY - JOUR TI - The Human-GenAI Value Loop in Human-Centered Innovation: Beyond the Magical Narrative AU - Grange C. AU - Demazure T. AU - Ringeval M. AU - Bourdeau S. AU - Martineau C. PY - 2026 JO - Information Systems Journal VL - 36 IS - 1 SP - 29 EP - 51 DO - 10.1111/isj.12602 AB - Organisations across various industries are still exploring the potential of Generative Artificial Intelligence (GenAI) to automate a variety of knowledge work processes, including managing innovation. While innovation is often viewed as a product of individual creativity, it more commonly unfolds through a collaborative process where creativity intertwines with knowledge. However, the extent and effectiveness of GenAI in supporting this process remain open questions. Our study investigates this issue using a collaborative practice research approach focused on three GenAI-enabled innovation projects conducted within different organisations. We explored how, why, and when GenAI could effectively be integrated into design sprints—a highly structured, collaborative process enabling human-centred innovation. Our research identified challenges and opportunities in synchronising AI capabilities with human intelligence and creativity. To translate these insights into practical strategies, we propose four recommendations for organisations eager to leverage GenAI to both streamline and bring more value to their innovation processes: (1) establish a collaborative intelligence value loop with GenAI; (2) build trust in GenAI; (3) develop robust data collection and curation workflows; and (4) embrace a craftsman's discipline. © 2025 The Author(s). Information Systems Journal published by John Wiley & Sons Ltd. KW - design thinking KW - generative AI KW - human-centered innovation KW - Collaborative practices KW - Collaborative process KW - Design thinking KW - Generative AI KW - Human-centered innovation KW - Individual creativity KW - Knowledge work process KW - Managing innovation KW - Practice researches KW - Research approach KW - Information use CY - Canada ER - TY - JOUR TI - Artificial Intelligence and Its Influence on Dental Hygiene AU - Hurlbutt M. PY - 2025 JO - Journal of Dental Hygiene VL - 99 IS - 5 SP - 49 EP - 58 AB - Artificial intelligence (AI) including generative AI, analytical AI, predictive AI, prescriptive AI, and hybrid AI, is rapidly evolving and continues to expand its influence across dental hygiene, transforming clinical care, education, research, public health, corporate operations, administration, and entrepreneurship. In clinical practice, AI is advancing diagnostic accuracy for radiographic interpretation, periodontal assessment, and early detection of oral pathology, while enhancing decision-making and personalized care planning. In education, AI enables adaptive learning, intelligent tutoring, predictive analytics, and generative content creation, enriching both didactic and clinical training. In research and public health, Artificial intelligence supports large-scale data analysis, disease surveillance, teledentistry, and targeted prevention strategies, with a growing emphasis on equity and inclusivity. Corporate and administrative applications include AI-driven product development, market analysis, workflow optimization, and performance management. Entrepreneurial uses span idea generation, content creation, branding, and market engagement. As AI capabilities advance, dental hygienists must balance innovation with ethical oversight, digital literacy, and equitable access. Ensuring AI is integrated through evidence-based practices, transparent decision-making, and patient-centered values will be essential to realizing its benefits while preserving the integrity of the profession. © 2025, American Dental Hygienists' Association. All rights reserved. KW - AI KW - artificial intelligence KW - dental hygienists KW - digital literacy KW - evidence-based practice KW - Artificial Intelligence KW - Dental Hygienists KW - Humans KW - Oral Hygiene KW - artificial intelligence KW - dental hygienist KW - education KW - human KW - mouth hygiene CY - United States ER - TY - JOUR TI - Transforming learning experiences and assessments through AI-empowered cocreation of quality feedback AU - Coenen C. AU - Pfenninger M. PY - 2025 JO - New Directions for Teaching and Learning VL - 2025 IS - 182 SP - 59 EP - 65 DO - 10.1002/tl.20628 AB - This article examines the transformative impact of generative artificial intelligence (GenAI) in enhancing feedback quality in a Bachelor of Science course. It the challenges of providing personalized, timely feedback to students in larger educational settings, focusing on the use of GenAI to analyze and respond to student logbooks. These logbooks are key to reflective learning and capture students’ insights and progress. The study explores the integration of GenAI in feedback provision, which moves beyond traditional automated processes to tailor feedback to individual student journeys, thus fostering a growth mindset. GenAI shows potential for revolutionizing learning feedback, with positive effects on student engagement and learning outcomes. The article details the innovation, implementation, and context of this approach, reflecting on the learning coach's experience and successes. It links these findings to educational theories, discussing the broader implications for educators, future AI integration in education, and policy considerations. The conclusion outlines the benefits of GenAI in feedback processes and future directions for exploration and ethical guidelines. © 2024 Wiley Periodicals LLC. CY - Switzerland ER - TY - JOUR TI - The Role of Artificial Intelligence in Drug Discovery and Development AU - Ozaybi M.Q.B. AU - Madkhali A.N.M. AU - Alhazmi M.A.M. AU - Faqihi H.M.A. AU - Alanazi M.M. AU - Siraj W.H.Y. AU - Zalah A.H.A. AU - Abdu Khormi M.M. AU - Al Salem A.M.A. AU - Mashragi T.Q.M. AU - Alotaibi A.N. AU - Naji A.A.M. AU - Abdo Bagal R.M. AU - Maswdi A.M.M. AU - Marwee H.A.A. PY - 2024 JO - Egyptian Journal of Chemistry VL - 67 IS - 13 SP - 1541 EP - 1547 DO - 10.21608/ejchem.2024.337877.10835 AB - Background: The drug discovery and development process has traditionally been one of the most challenging and resource-intensive endeavours in the pharmaceutical industry. On average, bringing a single drug from concept to market takes over a decade and costs approximately $2.6 billion. These processes are further hindered by high attrition rates, particularly in clinical trials, which contribute to the escalating cost and time. This inefficiency is largely attributed to the complexity of biological systems and the limitations of existing empirical methodologies. Over recent years, Artificial Intelligence (AI) has emerged as a powerful tool capable of transforming the drug development landscape. AI leverages computational algorithms, machine learning models, and data-driven approaches to overcome traditional bottlenecks in drug discovery. With capabilities spanning target identification, lead optimization, drug repurposing, and clinical trial design, AI is reshaping the future of pharmaceutical innovation. Aim: This paper provides a comprehensive examination of the role of AI in drug discovery and development. It explores the methodologies and tools employed by AI, evaluates key successes achieved in real-world applications, and examines challenges associated with its adoption. By synthesizing advancements and analyzing their impact, this paper aims to illuminate the transformative potential of AI in revolutionizing the pharmaceutical industry. Methods: The study adopts a robust methodological approach, relying on a critical review of recent literature published between 2015 and 2024. It integrates findings from academic research, industrial case studies, and regulatory perspectives to provide a holistic understanding of AI's impact across the drug development pipeline. Comparative analysis highlights the efficiencies of AI-driven approaches relative to traditional methods, with an emphasis on specific applications such as deep learning, reinforcement learning, and natural language processing (NLP). Results: AI applications have demonstrated measurable success across multiple domains of drug development. Machine learning models have expedited the identification of novel drug targets by analyzing high-dimensional omics data. Deep learning algorithms have revolutionized lead optimization by accurately predicting molecular properties and their pharmacological profiles. AI-driven platforms have also advanced drug repurposing, as evidenced by rapid therapeutic identification during the COVID-19 pandemic. Furthermore, in the realm of clinical trials, AI has significantly improved patient stratification, optimized trial protocols, and enhanced predictive analytics for outcomes. These breakthroughs have collectively reduced both the time and cost of drug development while increasing the likelihood of successful outcomes. Conclusion: AI is transforming the pharmaceutical industry, offering unparalleled solutions to challenges that have long plagued drug discovery and development. By integrating large-scale datasets, enhancing chemical design, and optimizing trial processes, AI has established itself as a cornerstone of future innovation. Nevertheless, the successful integration of AI into drug development requires overcoming challenges such as data quality, regulatory compliance, ethical concerns, and the interpretability of AI algorithms. Addressing these barriers is essential to fully realize AI's potential in meeting global healthcare needs. Moving forward, the development of standardized frameworks, interdisciplinary collaborations, and ethical guidelines will be critical in fostering equitable and effective AI-driven drug discovery. ©2024 National Information and Documentation Center (NIDOC) KW - Artificial Intelligence KW - Clinical Trials KW - Computational Biology KW - Drug Development KW - Drug Discovery KW - Drug Repurposing KW - Lead Optimization KW - Machine Learning CY - Saudi Arabia ER - TY - JOUR TI - Artificial intelligence and organisational transformation: technical skills, job insecurity and adoption AU - Arranz Lahuerta L. AU - López Ramajo M.R. AU - Gandía A. PY - 2025 JO - Management Decision SP - 1 EP - 16 DO - 10.1108/MD-05-2025-1462 AB - Purpose – This study explores the impact of Artificial Intelligence (AI) on technical skills development, job insecurity, and system adoption within organisations. It examines how businesses can navigate AI-driven workplace transformations while mitigating workforce challenges and fostering a culture of trust and innovation. Design/methodology/approach – The research adopts a mixed-method approach, combining theoretical analysis with empirical insights. Data were gathered from the AI-driven transformation Scopus database, analysing the relationship between AI implementation, employee perceptions, and organisational strategies for skill development and job security. Findings – (1) AI has a dual impact: it increases demand for advanced technical skills while also heightening job insecurity, particularly in organisations lacking structured reskilling programs. (2) Organisations that integrate transparent governance and employee participation into AI adoption strategies experience lower resistance and higher acceptance. (3) A balance between technological advancement and human capital investment is critical for minimising disruptions and ensuring a smooth transition to AI-driven operations. Research limitations/implications – The study is limited by the scope of available industry data and the generalisability of case study findings. Future research should explore sector-specific AI adoption challenges and long-term workforce adaptation strategies. Practical implications – The findings offer actionable insights for organisational leaders and policymakers, emphasising the need for structured skill enhancement programs, transparent communication, and ethical AI governance frameworks. These measures reduce workforce resistance, enhance innovation, and facilitate equitable AI-driven transformation. Social implications – By addressing concerns about job security and skill obsolescence, the study contributes to a more sustainable AI integration approach that promotes workforce adaptability, inclusion, and ethical decision-making in the digital era. Originality/value – This research provides a novel perspective by integrating AI adoption, skill development, and job insecurity within the broader framework of organisational transformation. It offers a socio-technical view of AI-driven change, highlighting the importance of ethical considerations and participatory decision-making. © 2025 Emerald Publishing Limited KW - AI adoption KW - Artificial intelligence (AI) KW - Ethical AI integration KW - Job insecurity KW - Organisational learning KW - Organisational transformation KW - Socio-technical systems KW - Technical skills development KW - Technology acceptance KW - Workforce adaptability CY - Spain ER - TY - JOUR TI - Empirical analysis of the roles of dynamic sustainable capabilities and artificial intelligence in accelerating circular business model innovation: Insights from Chinese manufacturing firms AU - Renfei C. AU - Zhongwen L. PY - 2026 JO - Technology in Society VL - 85 SP - 103176 DO - 10.1016/j.techsoc.2025.103176 AB - Despite increasing managerial focus on Artificial Intelligence (AI) -enhanced environmental strategies, few studies have examined how dynamic sustainable capabilities (DSC) and AI-enabled circular business model innovation (CBMI) affect sustainability-oriented corporate performance (SOCP). Grounded in dynamic capabilities theory and dynamic managerial capabilities theory, this study develops a unified framework to investigate the mechanisms through which DSC fosters AI capabilities and CBMI, ultimately enhancing SOCP, while examining managerial cognition as a key moderator. This study empirically analyzes data from 289 questionnaires from 120 manufacturing companies through the PLS-SEM method. The findings reveal that DSC has a positive effect on AI capabilities, CBMI, and SOCP. AI capabilities and CBMI partially mediate the DSC-SOCP relationship. Managerial cognition positively contributes to the role of CBMI on SOCP, and AI capabilities have a positive effect on CBMI. This study advances the literature by elucidating the sequential pathways from DSC to AI-driven CBMI, highlighting micro-foundations for circular transitions. Moreover, this study extends managerial cognition to CBMI practices, revealing the synergies between managerial cognition and CBMI and its role in accelerating SOCP, contributing to clarifying the sources of performance differences in circular economy practices. This lays the foundation for future research agendas on AI integration in the circular economy. © 2025 Elsevier Ltd. KW - Artificial intelligence capabilities KW - Circular business model innovation KW - Digital transformation KW - Dynamic sustainable capabilities KW - Managerial cognition KW - Artificial intelligence KW - Environmental management KW - Sustainable development KW - Artificial intelligence capability KW - Business model innovation KW - Circular business model innovation KW - Circular economy KW - Corporate performance KW - Digital transformation KW - Dynamic sustainable capability KW - Empirical analysis KW - Managerial cognitions KW - Manufacturing firms KW - artificial intelligence KW - business KW - empirical analysis KW - industrial performance KW - innovation KW - manufacturing KW - sustainability KW - Circular economy CY - China ER - TY - JOUR TI - Design and Psychometric Evaluation of the Artificial Intelligence Acceptance and Usage in Research Creativity Scale Among Faculty Members: Insights From the Network Analysis Perspective AU - Al-Rousan A.H. AU - Ayasrah M.N. AU - Salih Yahya S.M. AU - Khasawneh M.A.S. PY - 2025 JO - European Journal of Education VL - 60 IS - 1 SP - e12927 DO - 10.1111/ejed.12927 AB - The acceptance of artificial intelligence (AI) in academic settings, particularly in the context of research creativity, is a growing area of interest. This study aimed to design and validate the AI Acceptance and Research Creativity Scale (AIA&RCS) among faculty members. This exploratory mixed-method was conducted among 720 faculty members. A literature review and participant interviews were conducted in the qualitative phase to generate and develop items. In the quantitative phase, face validity, content validity, construct validity, convergent validity and reliability (internal consistency and stability) were used. Exploratory factor analysis (EFA) indicated a 4-factor model of the scale with ‘perceived usefulness and effectiveness of AI in research creativity’, ‘ethical issues in research’, ‘trusted in AI capabilities’ and ‘willingness to use AI’ accounting for 51.6% of the variance. This arrangement was verified by confirmatory factor analysis (CFA), with fit indices that were at suitable levels. Then, the network analysis took into account the four-factor structure of AIA&RCS further. Similarly, the exploratory graph analysis (EGA) indicated the four-factor configuration of the AIA&RCS. The 25-item scale is well-suited for measuring AI acceptance and research innovation among faculty because of its psychometrics. © 2025 John Wiley & Sons Ltd. KW - AI adoption in academia KW - artificial intelligence KW - faculty members KW - psychometrics KW - research creativity KW - technology acceptance CY - Jordan, Saudi Arabia ER - TY - JOUR TI - Artificial Intelligence, ESG Governance, and Green Innovation Efficiency in Emerging Economies AU - Mansour M. AU - Zobi M.A. AU - Alomair M. PY - 2026 JO - Economies VL - 14 IS - 1 SP - 11 DO - 10.3390/economies14010011 AB - Emerging economies confront the dual challenge of accelerating digital transformation while simultaneously mitigating environmental degradation under conditions of institutional and governance heterogeneity. In this context, this study examines how artificial intelligence (AI) capability influences green innovation efficiency (GIE) in emerging Asian economies and investigates whether environmental, social, and governance (ESG) performance conditions this relationship. Using an unbalanced panel of 59,112 firm-year observations from 4926 publicly listed firms across 15 emerging Asian economies over the period 2011–2022, we employ a comprehensive panel-data econometric framework that accounts for unobserved heterogeneity, dynamic effects, endogeneity, and potential self-selection bias. The empirical results indicate that AI capability is positively and significantly associated with higher green innovation efficiency. More importantly, ESG performance strengthens this relationship, suggesting that robust governance frameworks enhance firms’ ability to translate digital intelligence into environmentally efficient innovation outcomes. These findings underscore that AI adoption alone is insufficient to generate sustainable value; rather, its environmental effectiveness depends critically on complementary governance structures that promote transparency, accountability, and responsible risk management. The results remain robust after correcting for endogeneity concerns, alternative model specifications, and extensive sensitivity and heterogeneity analyses. Overall, this study contributes to the literature on digital transformation and sustainability by providing large-scale, multi-country evidence that highlights the pivotal role of ESG in shaping the sustainability returns to AI adoption in emerging economies. © 2025 by the authors. KW - artificial intelligence KW - digital transformation KW - emerging economies KW - ESG governance KW - green innovation efficiency KW - SDGs KW - sustainable development CY - Jordan, Saudi Arabia ER - TY - JOUR TI - Data stewardship and curation practices in AI-based genomics and automated microscopy image analysis for high-throughput screening studies: promoting robust and ethical AI applications AU - Taddese A.A. AU - Addis A.C. AU - Tam B.T. PY - 2025 JO - Human Genomics VL - 19 IS - 1 SP - 16 DO - 10.1186/s40246-025-00716-x AB - Background: Researchers have increasingly adopted AI and next-generation sequencing (NGS), revolutionizing genomics and high-throughput screening (HTS), and transforming our understanding of cellular processes and disease mechanisms. However, these advancements generate vast datasets requiring effective data stewardship and curation practices to maintain data integrity, privacy, and accessibility. This review consolidates existing knowledge on key aspects, including data governance, quality management, privacy measures, ownership, access control, accountability, traceability, curation frameworks, and storage systems. Methods: We conducted a systematic literature search up to January 10, 2024, across PubMed, MEDLINE, EMBASE, Scopus, and additional scholarly platforms to examine recent advances and challenges in managing the vast and complex datasets generated by these technologies. Our search strategy employed structured keyword queries focused on four key thematic areas: data governance and management, curation frameworks, algorithmic bias and fairness, and data storage, all within the context of AI applications in genomics and microscopy. Using a realist synthesis methodology, we integrated insights from diverse frameworks to explore the multifaceted challenges associated with data stewardship in these domains. Three independent reviewers, who systematically categorized the information across critical themes, including data governance, quality management, security, privacy, ownership, and access control conducted data extraction and analysis. The study also examined specific AI considerations, such as algorithmic bias, model explainability, and the application of advanced cryptographic techniques. The review process included six stages, starting with an extensive search across multiple research databases, resulting in 273 documents. Screening based on broad criteria, titles, abstracts, and full texts followed this, narrowing the pool to 38 highly relevant citations. Results: Our findings indicated that significant research was conducted in 2023 by highlighting the increasing recognition of robust data governance frameworks in AI-driven genomics and microscopy. While 36 articles extensively discussed data interoperability and sharing, AI-model explain ability and data augmentation remained underexplored, indicating significant gaps. The integration of diverse data types—ranging from sequencing and clinical data to proteomic and imaging data—highlighted the complexity and expansive scope of AI applications in these fields. The current challenges identified in AI-based data stewardship and curation practices are lack of infrastructure and cost optimization, ethical and privacy considerations, access control and sharing mechanisms, large scale data handling and analysis and transparent data-sharing policies and practice. Proposed solutions to address issues related to data quality, privacy, and bias management include advanced cryptographic techniques, federated learning, and blockchain technology. Robust data governance measures, such as GA4GH standards, DUO versioning, and attribute-based access control, are essential for ensuring data integrity, security, and ethical use. The study also emphasized the critical role of Data Management Plans (DMPs), meticulous metadata curation, and advanced cryptographic techniques in mitigating risks related to data security and identifiability. Despite advancements, significant challenges persisted in balancing data ownership with research accessibility, integrating heterogeneous data sources, ensuring platform interoperability, and maintaining data quality. Ongoing risks of unauthorized access and data breaches underscored the need for continuous innovation in data management practices and stricter adherence to legal and ethical standards. Conclusions: These findings explored the current practices and challenges in data stewardship, offering a roadmap for strengthening the governance, security, and ethical use of AI in genomics and microscopy. While robust governance frameworks and ethical practices have established a foundation for data integrity and transparency, there remains an urgent need for collaborative efforts to develop interoperable platforms and transparent data-sharing policies. Additionally, evolving legal and ethical frameworks will be crucial to addressing emerging challenges posed by AI technologies. Fostering transparency, accountability, and ethical responsibility within the research community will be key to ensuring trust and driving ethically sound scientific advancements. © The Author(s) 2025. KW - Artificial intelligence KW - Data curation KW - Data stewardship KW - Genomics KW - Microscopy image analysis KW - Scoping review KW - Artificial Intelligence KW - Data Curation KW - Genomics KW - High-Throughput Nucleotide Sequencing KW - High-Throughput Screening Assays KW - Humans KW - Image Processing, Computer-Assisted KW - Microscopy KW - algorithm bias KW - Article KW - artificial intelligence KW - automation KW - blockchain KW - cryptography KW - data analysis KW - data extraction KW - data integration KW - data integrity KW - data interoperability KW - data privacy KW - data protection KW - data quality KW - data stewardship KW - federated learning KW - genomics KW - high throughput screening KW - human KW - image analysis KW - information processing KW - information security KW - microscopy KW - proteomics KW - publication KW - systematic review KW - total quality management KW - artificial intelligence KW - ethics KW - high throughput screening KW - high throughput sequencing KW - image processing KW - information processing KW - microscopy KW - procedures CY - Ethiopia ER - TY - JOUR TI - Venus: A RISC-V Domain Specific Architecture Towards Integrated AI and Wireless Baseband Processing for 6G Edge Intelligence AU - Jiang Z. AU - Shi Y. AU - Jiang L. AU - Hu H. AU - Deng Q. AU - Xu S. AU - Liu Y. AU - Yuan F. AU - Cao S. AU - Zhou S. PY - 2025 JO - IEEE Wireless Communications VL - 32 IS - 6 SP - 18 EP - 26 DO - 10.1109/MWC.2025.3600950 AB - Future 6G local area networks (LANs) are expected to inherently feature edge artificial intelligence (AI) capabilities, despite constraints on power consumption and device dimensions. Additionally, the 6G architecture has integrated various AI-based algorithms into wireless baseband signal processing. These developments suggest a move towards a unified AI and wireless baseband architecture in 6G LANs. This article presents a framework from a computing architecture viewpoint, dubbed Venus, which is an integrated AI and wireless baseband domain-specific architecture based on RISC-V instruction extensions. Venus is conceived using a multi-level dataflow-driven approach and executed on a manycore architecture that features non-uniform memory access (NUMA). When compared to prevailing architectures, such as general-purpose processors (GPPs) with specialized accelerators, digital signal processors (DSPs), graphic processing units (GPUs), and field-programmable gate arrays (FPGAs), Venus strikes the optimal balance between programmability and efficiency. This is achieved through a tailored instruction extension for both AI and wireless signal processing, alongside an advanced dataflow-driven, highly parallel manycore architecture. Moreover, it benefits from the open RISC-V ecosystem, enabling scalability for future AI and wireless innovations. © 2002-2012 IEEE. KW - Data flow analysis KW - Digital signal processing KW - Local area networks KW - Memory architecture KW - Network architecture KW - Parallel architectures KW - Program processors KW - Reduced instruction set computing KW - Signal receivers KW - Software architecture KW - Base-band processing KW - Baseband signal processing KW - Dataflow KW - Domain specific architectures KW - Edge intelligence KW - Instruction extensions KW - Local areas KW - Many-core architecture KW - Power KW - Wireless baseband KW - Digital signal processors KW - Field programmable gate arrays (FPGA) CY - China ER - TY - JOUR TI - AI and organizational leadership: bibliometric review and future trends AU - González-Reyes C. AU - Ficapal-Cusí P. AU - Torrent-Sellens J. PY - 2025 JO - Journal of Organizational Change Management SP - 1 EP - 35 DO - 10.1108/JOCM-03-2025-0291 AB - Purpose – This article analyses the evolution of scientific literature at the intersection of artificial intelligence (AI) and organizational leadership. It identifies research trends, theoretical frameworks and emerging lines of inquiry, while addressing the practical, ethical and policy implications of AI integration in leadership settings. Design/methodology/approach – A mixed-methods approach was adopted, combining bibliometric techniques with qualitative content analysis. A total of 304 peer-reviewed articles (2014–2025) were retrieved from the Web of Science Core Collection and screened using a PRISMA-inspired procedure. Data were analysed with VOSviewer and thematic synthesis to identify networks, citation patterns, thematic clusters and theoretical foundations. Findings – The study identifies three dominant thematic areas: (1) decision support, (2) evolving leadership roles, and (3) ethics/sustainability. Influential theoretical perspectives include the dynamic capabilities view, social cognitive theory and sociotechnical systems theory. The review also highlights the emergence of diverse leadership styles (transformational, ethical, empowering and digital) shaped by AI's integration into organizational processes. Overall, the field displays both consolidation and fragmentation, underscoring the need for more integrative sociotechnical frameworks. Research limitations/implications – Beyond its diagnostic contribution, this study emphasizes the strategic, ethical and digital competencies required for AI-driven leadership, providing practical insights for organizations seeking to align governance, talent management and innovation strategies with emerging technological challenges. The research offers a systematic and integrative mapping of the AI–leadership field, combining bibliometric indicators with qualitative insights to identify conceptual trends, research gaps and actionable guidance on competencies, hybrid human–AI structures, and governance for responsible and sustainable adoption. Practical implications – The findings offer practical guidance for organizational leaders navigating AI-driven transformation. They identify key leadership competencies such as ethical reasoning, digital literacy and change management, essential for integrating AI effectively. The study also informs the design of leadership development programs, talent strategies, and governance frameworks that promote responsible AI use. By mapping leadership styles suited to AI-mediated environments, it helps organizations align human and algorithmic decision-making, foster trust, and ensure sustainable performance in increasingly digital contexts. Social implications – The integration of AI into leadership practices raises critical social concerns related to fairness, transparency, and accountability. This study highlights the need for inclusive and ethical governance models to address algorithmic bias, protect employee well-being, and ensure equitable access to AI benefits. Leadership plays a key role in mediating these challenges by fostering human-AI collaboration based on trust and ethical alignment. The findings underscore the importance of preparing leaders to navigate complex sociotechnical systems, influence organizational culture, and contribute to shaping public policies that support responsible and sustainable AI adoption. Originality/value – This study offers a systematic and integrative mapping of the academic landscape on AI and leadership. By combining bibliometric indicators with qualitative insights, it identifies conceptual trends and research gaps, while providing actionable guidance for scholars, organizational leaders and policymakers navigating the complexities of digital transformation. © 2025 Emerald Publishing Limited KW - AI adoption KW - Artificial intelligence KW - Ethics KW - Leadership KW - Leadership styles KW - Organizational transformation KW - Sustainability CY - Spain ER - TY - JOUR TI - Turning Profit Into Sustainability: Evidence on Artificial Intelligence, Education, and Ecological Footprint AU - Balci N. AU - Gürel B. AU - Okur M.R. PY - 2026 JO - Sustainable Development VL - 34 IS - 2 SP - 2697 EP - 2724 DO - 10.1002/sd.70476 AB - This paper examines how profit relates to ecological footprint intensity and how the link is shaped by artificial intelligence capability and education quality. We analyze 53,081 firm year observations from 15 innovation-leading economies during 2003–2022 using system GMM. The findings reveal that (i) profitability is associated with lower footprint intensity (ii) artificial intelligence capability is associated with higher footprint intensity and weakens the footprint-reducing effect of profitability, while education quality is associated with lower intensity and strengthens that channel, (iii) the joint effect of profitability, AI capability, and education quality increases footprint intensity. The findings speak to responsible production and climate action agendas. The study findings indicate that the interactions between profitability, artificial intelligence capability, and education quality have a multi-layered structure in terms of environmental sustainability. In line with sustainable development goals, recommendations focus on subjecting artificial intelligence investments to mandatory environmental impact assessments, and aligning education systems with sustainable production and environmental responsibility awareness. © 2025 ERP Environment and John Wiley & Sons Ltd. KW - artificial intelligence KW - ecological footprint KW - education quality KW - environmental sustainability KW - firm performance KW - artificial intelligence KW - ecological footprint KW - educational development KW - environmental education KW - environmental impact assessment KW - industrial performance KW - profitability KW - sustainability KW - sustainable development CY - Turkey ER - TY - JOUR TI - Enhancing Logistical Efficiency in Public Institutions through AI: A Managerial Framework for Regulatory and Technological Integration AU - Alnajdawi M.H. AU - Raafat R. AU - Aburayya A. AU - Al Ghurabli Z. PY - 2025 JO - International Journal of Industrial Engineering and Production Research VL - 36 IS - 3 SP - 81 EP - 92 DO - 10.22068/ijiepr.36.3.2459 AB - This study investigates regulatory gaps impeding artificial intelligence (AI) integration in public sector logistics, revealing how fragmented legislative frameworks hinder operational efficiency and innovation. Through a quantitative cross-sectional survey of 182 legal professionals, public employees, and AI/legal scholars using stratified purposive sampling and validated instruments (Cronbach’s α=0.985) we identified statistically significant stakeholder divergences (*p*<0.05) via χ² tests and Cramer’s V effect sizes. Key findings demonstrate that: (1) legal experts prioritize regulatory clarity deficits (M=4.62), while public staff emphasize institutional resistance (M=4.41); (2) human capital training is systematically undervalued (M=2.57, V=0.26) despite its theoretical importance; and (3) while regulation enhances operational efficiency (M=4.36), it paradoxically inhibits logistical innovation (M=2.48), exposing a critical innovation-governance disconnect. The study’s core contribution, a Dynamic Institutional Alignment Framework, resolves this tension through three pillars: human-centered regulatory design integrating legal-technical dimensions, adaptive policy sandboxes synchronized with AI advancement cycles, and stakeholder-specific implementation pathways. By embedding institutional adaptability within global compliance standards (EU AI Act, OECD Principles), this framework advances AI governance theory and offers public institutions actionable strategies for balancing technological advancement with accountability. © Iran University of Science and Technology 2025. KW - Artificial intelligence KW - Institutions KW - Law KW - Logistics KW - Technology CY - United Arab Emirates ER - TY - JOUR TI - Transforming Higher Education for the Digital Age: Examining Emerging Technologies and Pedagogical Innovations AU - Chadha A. PY - 2024 JO - Journal of Interdisciplinary Studies in Education VL - 13 IS - S1 SP - 53 EP - 70 DO - 10.32674/em2qsn46 AB - In this study, I explore the transformative potential of artificial intelligence (AI) and emerging technologies in higher education, focusing on case studies and pedagogical innovations that are reshaping the learning experience. Through an in-depth analysis of key initiatives—such as Stanford University's AI-driven personalized learning platform, the AI chatbot implemented at the University of Murcia, Knewton's adaptive learning system, and the intelligent tutoring platform developed by Pai et al.—the study highlights how AI enhances learner engagement, customizes educational experiences, and improves academic outcomes. The research also critically examines the ethical challenges and policy considerations associated with AI integration in educational settings. It emphasizes the need for clear guidelines to ensure responsible and equitable use of AI, particularly in addressing issues of fairness, student welfare, and access. The paper concludes by calling for further research into the long-term implications of AI on educational equity and ethical standards in higher education. © 2024, STAR Scholars Network. All rights reserved. KW - adaptive learning KW - AI KW - AI ethics KW - edtech KW - higher education KW - institutional policy KW - intelligent tutoring systems KW - personalized learning CY - India ER - TY - JOUR TI - How can artificial intelligence capabilities empower sustainable business model innovation? A dynamic capability perspective AU - Wang N. AU - Pan S. AU - Wang Y. PY - 2025 JO - Business Process Management Journal VL - 31 IS - 7 SP - 3003 EP - 3025 DO - 10.1108/BPMJ-11-2024-1045 AB - [No abstract available] KW - Artificial intelligence capabilities KW - Dynamic capability theory KW - Environmental dynamism KW - Strategic agility KW - Sustainable business model innovation CY - China ER - TY - JOUR TI - AI in education through the learners’ eyes: practical experience, perceptions, and challenges AU - Yotov K. AU - Gaftandzhieva S. AU - Hadzhikolev E. AU - Hadzhikoleva S. AU - Gorgorova M. PY - 2026 JO - Frontiers in Education VL - 11 SP - 1717886 DO - 10.3389/feduc.2026.1717886 AB - Introduction: In order to remain competitive, higher education institutions strive to enhance the student experience by integrating modern technologies for both educational and administrative purposes. This paper presents the results of a study exploring students’ attitudes toward the use of artificial intelligence (AI) in higher education. Methods: The data was collected through a survey conducted among 138 students, who responded to 50 questions regarding their level of awareness, practical experience, perceived benefits of using AI tools, potential issues and challenges, as well as ideas and suggestions for more effective use of AI technologies. The analysis was conducted using both traditional statistical techniques and contemporary machine learning methods. Results: Findings show that students who understand AI capabilities are more confident and proactive in using it for learning purposes. Those who utilize AI believe it enhances their academic performance and recommend its use to their peers. Discussion: Overall, students support the innovative use of AI and believe it will improve the educational process. According to them, the main risks associated with AI use include academic misconduct and the loss of critical thinking skills. The findings can serve as a starting point and foundation for future, more extensive studies exploring the attitudes of students from various academic disciplines and institutions. Copyright © 2026 Yotov, Gaftandzhieva, Hadzhikolev, Hadzhikoleva and Gorgorova. KW - AI in higher education KW - AI literacy KW - educational innovation KW - stem education KW - student perceptions KW - survey CY - Bulgaria ER - TY - JOUR TI - AI governance after MiFID II: beyond (mere) technological neutrality? AU - Azzutti A. PY - 2026 JO - ERA Forum VL - 27 IS - 1 SP - 7 EP - 31 DO - 10.1007/s12027-026-00871-1 AB - This article examines the evolving intersections between artificial intelligence (AI) and EU financial regulation, focusing on the Markets in Financial Instruments Directive II (MiFID II). Grounded in the principle of technological neutrality, MiFID II seeks to enhance investor protection, safeguard market integrity, and ensure that innovation develops within competitive and well-regulated markets across the Union. The article argues, however, that while this neutrality renders the framework functionally enabling, it also leaves it normatively silent in the face of the distinctive and evolving risks introduced by financial AI. As AI applications become increasingly heterogeneous—both across the financial functions in which they are deployed and in their underlying lifecycles and value chains—MiFID II’s activity-based logic increasingly struggles to accommodate their diverse and evolving risk profiles. Reflecting the EU’s broader shift toward risk-based AI governance, the article outlines an initial taxonomy of financial AI applications designed to guide the proportionate alignment of regulatory obligations with AI-related risks, thereby supporting the continued adaptability, coherence, and future-proofing of EU financial services law. © The Author(s) 2026. KW - AI governance KW - Artificial intelligence KW - MiFID II KW - Risk-based regulation KW - Technological neutrality CY - United Kingdom ER - TY - JOUR TI - Resource-Poor, Risk-Rich: Why Small Businesses Struggle to Turn AI Into Strategic Advantage AU - McIlveene T. AU - Nguyen S. AU - Batchelor J. AU - Keller S. AU - Ranelli E. PY - 2026 JO - Journal of Small Business Strategy VL - 36 IS - 2 SP - 19 EP - 26 DO - 10.53703/001c.158957 AB - While many small businesses are adopting artificial intelligence (AI), many struggle to translate this adoption into a lasting and sustainable competitive advantage. This paper posits that this implementation gap is not merely a strategic failure but also introduces significant ethical risks that can harm key stakeholders. Using a dual theoretical framework approach, we employ the resource-based view to identify how “resource poverty,” specifically, gaps in financial, human, technological, and organizational resources, inhibits successful AI implementation. The paper then applies stakeholder theory to illustrate how these internal resource gaps directly lead to external harms, including risks to customer privacy, employee welfare, and broader societal well-being. To address these challenges, a practical four-stage AI capability roadmap is introduced to help small business leaders develop their internal resources and capabilities and establish the necessary ethical safeguards. This roadmap provides a guide for small businesses to navigate the complexities of AI. © 2026 Small Business Institute. All rights reserved. KW - Artificial Intelligence (AI) KW - Ethics KW - Resource-Based View Theory KW - Small Business KW - Stakeholder Theory CY - United States ER - TY - JOUR TI - Impact of artificial intelligence on innovative work behaviour of employees AU - Rajpurohit N. AU - Sharma D. AU - Sharma D.K. AU - Jain T. PY - 2025 JO - International Journal of Process Management and Benchmarking VL - 21 IS - 3 SP - 322 EP - 341 DO - 10.1504/IJPMB.2025.149395 AB - Artificial intelligence (AI) has the potential to boost the efficiency of employees by fostering employees’ creativity and serving as a multipurpose tool for innovation. However, it is uncertain how AI affects employees’ innovative work behaviour. Consequently, this study investigates the impact of artificial intelligence on the innovative work behaviour of employees. A total of 327 responses were obtained from questionnaires administered to software engineers, IT specialists, and employees with other tech-related positions in high-tech organisations in India. The responses were evaluated using the structural equation modelling (SEM) approach. The findings of the study reveal that AI work dynamics and AI capability have a considerable impact on employees’ innovative work behaviour. This study makes a significant contribution to the literature on the effects of AI technology in the workplace and has significant implications relating to the utilisation of AI technology for employees’ innovative work behaviour. Copyright © 2025 Inderscience Enterprises Ltd. KW - AI capability KW - artificial intelligence KW - information technology KW - innovation KW - innovative work behaviour CY - India ER - TY - JOUR TI - Rethinking Competitiveness in the Age of AI: A Comparative Index-Based Approach AU - Jeon G. PY - 2025 JO - Journal of International Development VL - 37 IS - 7 SP - 1525 EP - 1542 DO - 10.1002/jid.70018 AB - This study examines the influence of artificial intelligence (AI) capabilities on national competitiveness through a comparative analysis of the IMD World Competitiveness Index and three major AI indices: Oxford AI Readiness, Tortoise AI Index and Stanford AI Index. Utilizing correlation analysis, multiple regression and K-means clustering across samples of 64, 59 and 35 countries, respectively, the research identifies infrastructure and research capacity as key predictors of national competitiveness, with regression models explaining 52.4%–60.8% of IMD variance and Pearson correlations exceeding 75% for predictive validity. Clustering analysis delineates AI-advanced nations (A2 cluster) with superior AI performance relative to national competitiveness and resource-dependent laggards (C2 cluster) at risk of stagnation without AI investment. The study proposes open innovation strategies, inspired by collaborative ecosystems like shared mobility, leveraging government-industry-academia partnerships and digital public infrastructure (DPI) to address gaps in government policy, research capacity and infrastructure, with case studies of the United States and Singapore. For Least Developed Countries (LDCs), a 2 × 2 strategy matrix outlines low-cost, high-impact AI initiatives to enable a bypass strategy, leveraging open innovation ecosystems to circumvent traditional industrial pathways. Findings underscore AI's transformative role in redefining competitiveness, driven by qualitative capabilities like efficiency, innovation and governance, offering actionable pathways for advanced economies and LDCs to close competitiveness gaps through strategic AI integration and DPI investments. © 2025 John Wiley & Sons Ltd. KW - AI Index KW - clustering analysis KW - least developed countries (LDCs) KW - multiple regressions KW - national competitiveness KW - open innovation KW - Singapore [Southeast Asia] KW - United States KW - artificial intelligence KW - cluster analysis KW - competitiveness KW - correlation KW - digitization KW - infrastructure KW - innovation KW - investment KW - multiple regression KW - public-private partnership CY - South Korea ER - TY - JOUR TI - Examining the role of higher education learning, research excellence, and innovation capacity in driving AI-technological advancements in Nordic countries AU - Zamir S. AU - Mehmood M.S. AU - Abbasi B.N. AU - Li W. AU - Wang Z. PY - 2025 JO - Humanities and Social Sciences Communications VL - 12 IS - 1 SP - 1325 DO - 10.1057/s41599-025-05665-3 AB - Higher education, research, and innovation are essential for advancing a systematic understanding of the responsible deployment and application of AI-driven technologies. These mechanisms facilitate the evaluation of societal impacts, the identification and mitigation of risks associated with misuse, and the enhancement of AI capabilities for specific, practical applications. However, how effective are these mechanisms in achieving these outcomes? This study, therefore, investigates the effectiveness of higher education learning, research excellence, and innovation capacity in relation to AI-driven technology, as well as the moderation effect of good governance on these relationships, using data from Nordic countries spanning from 2009 to 2023. The analysis employs the dynamic common correlated effects (DCCE) model by Chudik and Pesaran (2015) and the panel non-causality test by Juodis et al. (2021). The findings revealed that higher education learning, research excellence, and innovation capacity actively promote the development of AI-driven technology in Nordic countries. Furthermore, good governance positively influences the connection, with the magnitude of the influence being greatest on higher education learning, followed by innovation capacity, and then research excellence. Moreover, there is bidirectional causality between all the variables and AI-driven technology; thus, the variables and AI-driven technology are the determinants of one another. In line with these findings, policy recommendations were proposed. © The Author(s) 2025. CY - China ER - TY - JOUR TI - Service Blueprinting for Better Collaboration in Human-Centric AI: The Design of a Digital Scribe for Orthopedic Consultations AU - Magyari R. AU - Secomandi F. PY - 2023 JO - International Journal of Design VL - 17 IS - 3 SP - 63 EP - 77 DO - 10.57698/v17i3.04 AB - This case study explored the application of the service blueprinting method during the conceptual design of an AI-enabled digital scribe— an intelligent documentation support system—tailored for orthopedic consultations. In this paper, we discuss how this method can be used to enhance collaboration between user experience designers and machine learning engineers. Specifically, we show how service blueprinting can help innovation teams create a common foundation for understanding design challenges, enrich data with user-related insights, and highlight the value of AI capabilities as an organizational resource. Building on recent academic research in the field of human-computer interaction, our findings provide additional insights for addressing the design challenges associated with developing human-centric AI and incorporating service design approaches. © 2023 Magyari & Secomandi. KW - Blueprinting KW - Clinical Documentation KW - Digital Scribe KW - Human-centric AI KW - Service Design KW - UX Design CY - Netherlands ER - TY - JOUR TI - Lessons From One FQHC’s Experience With Artificial Intelligence AU - Wang G. AU - Kennedy S. AU - Johnson M. AU - Avellino L. PY - 2026 JO - Journal of Ambulatory Care Management VL - 49 IS - 1 SP - E31 EP - E38 DO - 10.1097/JAC.0000000000000541 AB - Objective – The rapid evolution of artificial intelligence (AI) presents opportunities and challenges for health systems, especially safety-net providers like Federally Qualified Health Centers (FQHCs). Safety-net systems may need help with structures and processes for assessing AI applications. To address this need, this article describes Moses-Weitzman Health System’s (MWHS) initial steps toward establishing an AI program that defines intentional and informed AI use. Approach – MWHS established two AI-focused workgroups: one of senior leaders and a cross-departmental group, providing a collaborative space for exploring potential applications, creating guidelines, and discussing concerns. With limited existing templates, MWHS crafted an AI policy emphasizing transparency, privacy, and security, outlining the criteria for implementing AI tools that interact with patient data and ensuring compliance with current regulations. Current AI-related projects focus on automating routine tasks, and research interests include evidence frameworks for making decisions about adopting AI tools and evaluating ambient listening technologies. Findings – Lessons learned in building our AI program are that effective implementation requires tech-savvy leadership, cross-department collaboration, and cautious differentiation between general automation and generative AI. Challenges include the need for agile budgeting, careful vendor vetting, and safe testing environments to assess AI benefits and risks responsibly. Conclusions and Action Steps – MWHS’s AI program underscores a cautious but proactive approach to AI, aiming to balance innovation with operational and ethical considerations, and offers a model for other safety-net systems beginning their AI journeys. © 2025 KW - artificial intelligence KW - community health centers KW - organization and administration KW - primary health care KW - Artificial Intelligence KW - Humans KW - Safety-net Providers KW - artificial intelligence KW - human KW - organization and management KW - safety net health care CY - United States ER - TY - JOUR TI - Artificial intelligence and climate risk: Toward sustainable development within a Double Helix framework AU - Tong Z. AU - Tan Z. PY - 2026 JO - Technological Forecasting and Social Change VL - 226 SP - 124592 DO - 10.1016/j.techfore.2026.124592 AB - This study examines the impact of climate risk on corporate performance and investigates whether artificial intelligence (AI) capability moderates this relationship. Drawing on the Double Helix framework, we conceptualize climate resilience as emerging from the co-evolution of institutional pressures and technological capabilities. Using panel data from 10,601 firm-year observations of Chinese A-share listed companies between 2016 and 2023, we employ fixed effects regression, instrumental variable estimation, and difference-in-differences analysis surrounding the 2018 Environmental Tax Reform. Results indicate that climate risk exposure significantly reduces firm performance measured by return on assets and return on equity. Importantly, AI capability weakens this negative effect, with stronger moderating effects observed in high climate risk regions, heavy-polluting industries, and private firms. These findings remain robust across alternative specifications and measurement approaches. This study contributes to climate finance and digital strategy literature by demonstrating how technological capabilities buffer environmental shocks within institutional contexts. The results offer practical guidance for managers seeking to leverage AI for climate adaptation and for policymakers designing digital transformation initiatives that support sustainable development. © 2026 Elsevier Inc. KW - Artificial intelligence KW - Climate risk KW - Corporate performance KW - Double Helix framework KW - Emerging markets KW - China KW - Industrial management KW - Risk assessment KW - Sustainable development KW - Climate risk KW - Co-evolution KW - Corporate performance KW - Double helix KW - Double helix framework KW - Emerging markets KW - Fixed effects KW - Institutional pressures KW - Panel data KW - Technological capability KW - artificial intelligence KW - climate change KW - estimation method KW - institutional framework KW - panel data KW - performance assessment KW - sustainable development KW - Artificial intelligence CY - China ER - TY - JOUR TI - REVOLUTION OR RISK? EXPLORING AI’S ROLE IN ENHANCING MUSEUM VISITOR EXPERIENCE AU - Eduarda M. AU - Wendhausen V. PY - 2025 JO - Journal of Science and Technology of the Arts VL - 17 IS - 1 SP - 122 EP - 135 DO - 10.34632/jsta.2025.17586 AB - As digital technologies redefine cultural engagement, Artificial Intelligence (AI) emerges as a transformative force in museum experiences, balancing innovation and tradition. This paper investigates the dual impact of AI on cultural institutions, analysing its potential to enhance visitor engagement through personalisation, accessibility, and operational efficiency while addressing significant challenges including ethical concerns, data dependency, and risks to cultural authenticity. The study highlights AI’s capability to democratise cultural heritage, drawing from diverse recently implemented examples, such as AI-powered art restoration, immersive VR/AR experiences, and adaptive educational tools. However, limitations surrounding infrastructure, algorithmic bias, and privacy underscore the need for strategic, ethical governance. By navigating these complexities, museums can leverage AI to enrich public interaction with heritage while safeguarding their foundational mission of cultural stewardship. Based on recent European Parliament briefings and regulations, this research offers actionable frameworks for institutions aiming to integrate AI responsibly, to foster innovation that honors technological potential and humanistic values. © 2025, Universidade Catolica Portuguesa. All rights reserved. KW - Aesthetics KW - AI in museums KW - Audience studies KW - Media studies CY - Portugal ER - TY - JOUR TI - Does Knowledge Heterogeneity Always Foster Innovation? The Moderating Role of Artificial Intelligence Capabilities in Value Chain Relationships AU - Huang Y. AU - Yu X. AU - Li Y. AU - Chen D. PY - 2025 JO - Knowledge Management Research and Practice DO - 10.1080/14778238.2025.2572352 AB - Interorganizational knowledge heterogeneity can fuel innovation but also create cognitive burdens–tensions that remain insufficiently understood in value chain contexts amid growing adoption of artificial intelligence (AI). We theorize an inverted U-shaped relationship between value chain knowledge heterogeneity–differences in technological knowledge between a focal firm and its key suppliers or customers–and the focal firm’s technological innovation, reflecting the competing forces of knowledge complementarity and cognitive burden. Using a patent-based knowledge-distance measure and panel data on Chinese listed firms from 2010 to 2020, we find evidence for this effect. We further show that a focal firm’s AI capabilities shifts the turning point rightwards in both supplier and customer contexts, thereby extending the range over which heterogeneity is beneficial; while it does not significantly alter the curvature in supplier contexts, it unexpectedly steepens the inverted U in customer contexts. Theoretical and practical implications are discussed. © 2025 The Operational Research Society. KW - artificial intelligence capability KW - Knowledge heterogeneity KW - technological innovation performance KW - value chain CY - China, United States ER - TY - JOUR TI - AI Readiness and Sustainable Development: A Systems Perspective on Health, Education, Innovation, and Climate Action AU - Köseoglu M.A. AU - Khaki A. AU - Arici H.E. PY - 2026 JO - Business Strategy and Development VL - 9 IS - 1 SP - e70315 DO - 10.1002/bsd2.70315 AB - This study investigates how national artificial intelligence (AI) readiness influences sustainable development performance across four Sustainable Development Goals (SDGs): Good Health and Well-Being (SDG 3), Quality Education (SDG 4), Industry, Innovation and Infrastructure (SDG 9), and Climate Action (SDG 13). Using cross-country data from the Oxford Insights AI Readiness Index and the Sustainable Development Report, we apply ensemble machine learning methods and select boosting based on predictive performance. SHAP values and Partial Dependence Plots reveal nonlinear and interaction effects across institutional, digital, and economic dimensions. The findings indicate that AI readiness functions as a strategic systems-level capability with domain-specific impacts. Data availability and ecosystem maturity shape health outcomes; digital infrastructure and economic scale influence education; technology sector strength supports innovation; and climate performance depends on economic–digital complementarities. The study positions AI capability as a structural enabler of national competitiveness and coordinated development strategy. © 2026 ERP Environment and John Wiley & Sons Ltd. KW - AI readiness KW - climate action KW - digital governance KW - innovation ecosystems KW - machine learning KW - SDGs CY - United States, Kuwait, Turkey, Spain ER - TY - JOUR TI - Wearable IoT (w-IoT) artificial intelligence (AI) solution for sustainable smart-healthcare AU - Singh G. PY - 2025 JO - International Journal of Information Management Data Insights VL - 5 IS - 1 SP - 100291 DO - 10.1016/j.jjimei.2024.100291 AB - Smart technologies, specifically wearables are cutting edge innovation of design science with an emerging Artificial Intelligence (AI) capability for sustainable healthcare. Wearable IoT (w-IoT) applications, solutions and systems can promote early warning measures for physiological parameter monitoring and other vital health observation while addressing, streamlining and enhancing emergency response procedures in the provision and deliverance of healthcare services. These solutions exhibit real-time responses with underlying machine-learning (ML) methodologies alongside ubiquitous, context-aware, pervasive and advance software features. AI frameworks, for the development and implementation of solutions are well covered in this study adopting design science (DS) principles for new product development (NPD), comprising various healthcare scenarios for distributed numbers and environments. Physiological or health activity-related data produced by embedded optical smartwatch sensors can instigate sustainable and economical health-oriented solutions for continuous monitoring, semantic predictions for constrained, intractable and autonomous environments to address cardiac disorders. This paper addresses, the practical implementation of the w-IoT health technology solution prototype for real-time applicability, covering problem identification and utilizing design science guidelines, evaluation and contribution by emphasizing on the experimental stage in general and with specificity. It covers performance results rendering research science communication on machine learning models for time series analysis, regression and classification to implement defined and adaptive thresholds, adopting standard deviation and moving average, computing mean square error (MSE), root mean square error (RSME) and mean absolute error (MAE) values, utilizing exponential moving average results on multiple features, prominently targeting resting heartrate data. Machine Learning algorithms for classification with higher F-score or performance metrics adopted are Decision Trees (DT), K-Nearest Neighbours (KNN), XGboost, One-class SVM and Logistic Regression. In Binary classification, KNN achieved F-score of 91 %, followed by DT at 81 % which seems an effective algorithm with flexibility on overfitting with high accuracy result. This study will cover all stages of design science methodology, guidelines for w-IoT healthcare solution development, by presenting experimental prototype towards pipeline implementation to address healthcare needs, alleviating previously prevalent Body Area Networks (BANs) solutions precision with advancing w-IoT smart technologies or Wireless Body Sensor Networks (WBSNs). © 2024 The Author(s) KW - Artificial intelligence KW - Binary classification KW - Defined-adaptive thresholds KW - Machine learning algorithms KW - Predictive models KW - Real-time monitoring KW - Regression KW - Smart-healthcare KW - Smart-watches KW - Time series analysis KW - Wearable IoT (w-IoT) CY - Australia ER - TY - JOUR TI - AI-powered organizational transformation: the role of digital mindset, change management, and cross-cultural leadership in shaping future business strategies AU - Teng Z. AU - Sukesi H. AU - Purnomo B.R. PY - 2026 JO - Future Technology VL - 5 IS - 2 SP - 49 EP - 59 DO - 10.55670/fpll.futech.5.2.5 AB - This study explores how artificial intelligence reshapes business strategies through synergistic effects between digital thinking, change management, and cross-cultural leadership in organizational transformation processes. Based on multi-source public data from 450 global enterprises across technology, manufacturing, finance, and retail sectors, this research integrates structural equation modeling, in-depth case analysis of 20 extreme cases, and machine learning prediction methods to construct and validate an “AI-Driven Strategic Triple Helix Evolution Framework” through seven interrelated hypotheses. Empirical findings confirm that organizational transformation plays the role of a core mediating hub (R2=0.64), connecting AI capabilities to strategic reconstruction, while the interaction with the three elements of synergy adds an additional 11% of explanatory power to it (ΔR2=0.11, P<0.001). Six strategic paths are differentiated in this research: AI-native (12%), platform transformation (23%), ecosystem orchestration (18%), niche specialization (21%), hybrid innovation (17%), and conservative following (9%), with significant cultural context dependence. Cross-cultural leadership shows the greatest moderating effect on high power distance cultures (β=0.38). The framework goes beyond the traditional technology-organization-environment models in unfolding dynamic co-evolution mechanisms among technological capabilities, cognitive reconstruction, and cultural adaptation. Machine learning models further predict 70% of enterprises participating in ecosystem strategies by 2030, and a digital mindset contributes 34.2% to strategic innovation prediction. © 2026, Future Publishing LLC. All rights reserved. KW - Artificial intelligence KW - Business strategy reconstruction KW - Cross-cultural leadership KW - Digital transformation KW - Organizational transformation CY - Indonesia ER - TY - JOUR TI - AI capability and green innovation impact on sustainable performance: Moderating role of big data and knowledge management AU - Al Halbusi H. AU - Al-Sulaiti K.I. AU - Alalwan A.A. AU - Al-Busaidi A.S. PY - 2025 JO - Technological Forecasting and Social Change VL - 210 SP - 123897 DO - 10.1016/j.techfore.2024.123897 AB - This study addresses the environmental impact of industries by focusing on increased resource consumption and waste generation that lead to ecosystem degradation. It advocates sustainable practices and a circular economy (CE) as strategies to mitigate these effects. Thus, the study examines how Artificial Intelligence (AI) capabilities directly affect green innovations and their subsequent influence on sustainable performance and CE. In addition, it introduces two key moderating factors—big data analytics and knowledge management systems—in the relationship between AI capabilities and green innovation. We validate the model using multi-sectoral population data from various Qatari industries and employ structural equation modeling (SEM) and artificial neural networks (ANN) as analytical approaches. The results indicate the significant impact of AI capability on green innovation, with these innovations critically linked to sustainable performance and CE. Remarkably, interactions with big data analytics and knowledge management systems enhance the positive impact of AI capabilities. Hence, this study emphasizes AI's noteworthy implications for green innovation, shaping sustainable performance, and CE. Identifying big data analytics and knowledge management systems as vital moderators adds complexity. The findings guide industries to integrate AI, big data analytics, and knowledge management systems for practical applications, stressing a holistic approach to promoting environmentally responsible practices across sectors. © 2024 Elsevier Inc. KW - AI capability KW - Big data analytics KW - Circular economy (CE) KW - Environmental sustainability KW - Green innovation KW - Knowledge management systems KW - Qatar KW - Green development KW - Green economy KW - Artificial intelligence capability KW - Big data analytic KW - Circular economy KW - Data analytics KW - Environmental sustainability KW - Green innovations KW - Knowledge management system KW - Resource wastes KW - Sustainable performance KW - artificial intelligence KW - artificial neural network KW - complexity KW - developing world KW - environmental economics KW - green economy KW - industrial development KW - innovation KW - performance assessment KW - sustainability KW - Circular economy CY - Qatar, United Kingdom, Oman ER - TY - JOUR TI - Driving Sustainable Performance Through Green Business Strategy, Artificial Intelligence Capability, and Green Supply Chain Management: The Mediating Roles of Knowledge Integration and Green Innovation AU - Liu X. AU - Chau K.Y. AU - Chang T.-C. AU - Moslehpour M. PY - 2026 JO - Corporate Social Responsibility and Environmental Management VL - 33 IS - 3 SP - 4531 EP - 4546 DO - 10.1002/csr.70397 AB - This research paper explores the importance of green business strategy (GBS), artificial intelligence capability (AIC), and green supply chain management (GSCM) in promoting sustainable performance (SP) in Chinese manufacturing companies. The study further examines the mediating roles of knowledge integration capability and green innovation (GI), and the moderating role of environmental dynamism (ED). It used the theoretical framework based on the Knowledge-Based View (KBV), the Natural Resource-Based View (NRBV), and the Dynamic Capabilities Theory (DCT) to develop a conceptual model empirically tested against survey data of 387 managers and executives working in various manufacturing fields. To examine the measurement model, partial least squares structural equation modeling (PLS-SEM) along with direct, indirect, and moderating relationship analyses was used. The empirical findings indicate that GBS, AIC, and GSCM have a positive impact on SP, either directly or indirectly through knowledge integration and GI. The serial mediation relationship was supported, and the results showed that knowledge integration produces firm innovation, and the latter stimulates sustainability performance. Moreover, the ED moderating the relationship between performance and GI was reinforced, indicating that the benefits of GI are more advantageous to SP in unstable environments. The results highlight the importance of environmental goals, managerial integration into strategic management systems, the creation of AI-related potentials, and green supply chain practice adoption to reach set sustainability targets. GI should also be the priority of those corporations which are in volatile environments because empirical evidence shows that in turbulent conditions, the performance implications of GI are magnified. The current research contributes significantly as it suggests a moderated mediation model through which strategic, technological, and supply-chain approaches can be combined, thus contributing to the theoretical knowledge and providing the managerial implications relevant to sustainability management in emergent economies. © 2026 ERP Environment and John Wiley & Sons Ltd. KW - artificial intelligence capability KW - green business strategy KW - green innovation KW - green supply chain management KW - sustainable performance CY - China, Taiwan, United States ER - TY - JOUR TI - Explainable neural algorithms for corporate sustainability forecasting: A layered predictive model anchored in executive awareness, green finance, and digital innovation AU - Ibrahim Y. AU - Moubarak H. AU - Badawy H. PY - 2026 JO - Innovation and Green Development VL - 5 IS - 1 SP - 100335 DO - 10.1016/j.igd.2026.100335 AB - This study investigates how artificial intelligence (AI) capability drives sustainable performance through the mediating role of Digital Green Innovation (DGI). Grounded in the Resource-Based and Natural Resource-Based Views, survey data from 321 organizations are analyzed using a multi-method approach that integrates partial least squares structural equation modeling (PLS-SEM), machine learning (ML), and explainable AI (XAI). The PLS-SEM results reveal a full mediation effect AI Capability enhances sustainable performance exclusively through DGI highlighting that technological resources must be embedded within innovation processes to generate environmental and social value. To ensure convergent validation and methodological robustness, predictive ML models (random forest, support vector regression, multilayer perceptron, and one-dimensional convolutional neural networks) are applied alongside XAI techniques (SHAP and LIME). These complementary analyses independently converge on the same key drivers DGI and top management environmental awareness providing strong empirical triangulation and interpretive transparency. Theoretically, the study advances the understanding of AI-enabled sustainability by demonstrating that AI resources yield value only when channeled through green innovation capabilities. Methodologically, it contributes by showcasing a convergent SEM–ML–XAI framework that enhances both explanatory and predictive validity. Practically, organizations should strengthen digital innovation systems and employ XAI tools to dynamically monitor and refine sustainability performance drivers. © 2026 The Authors KW - AI capability KW - Digital green innovation KW - Machine learning forecasting KW - Model interpretability KW - SEM KW - Sustainable performance CY - Egypt ER - TY - JOUR TI - Artificial Intelligence (AI)-Aided Collaborative Design in Industrial Design Education for Final Year Projects (FYP) Improving Workflow and Innovation AU - Me R.C. AU - Kamil M.J.M. AU - Razali A.F. AU - Li J. AU - Abidin S.Z. AU - Ramli S.H. PY - 2025 JO - Academic Quarter VL - 31 2025 SP - 28 EP - 46 DO - 10.54337/academicquarter.i31.11269 AB - The integration of Artificial Intelligence (AI) into design education is transforming collaborative learning and creative practice, particularly in Industrial Design. A theoretical framework was developed through the literature review to guide this study, which investigates how AI-assisted tools influence creativity, collaboration, and workflow efficiency in Final Year Projects (FYPs) among 38 Industrial Design students at a Malaysian university. Employing a mixed-methods design, two classes participated in a quasi-experimental comparison: one integrated AI tools throughout the design process, while the other used traditional methods. Students applied AI tools across five project phases: research (Notion AI, Elicit), ideation (DALL·E, MidJourney), design simulation (Fusion 360 AI, Rhino AI), reporting (ChatGPT, Grammarly), and prototyping (generative design tools). Quantitative data from project rubric scores and supervisor evaluations were complemented by qualitative insights from reflective journals and focus group discussions. Results showed that the AI-assisted class achieved higher creativity and design quality, supported by enhanced efficiency and faster iteration. However, students also reported challenges related to over-reliance on AI, ethical concerns about authorship, and reduced hands-on engagement. The study concludes that AI can serve as a valuable cognitive and creative partner in design education when integrated within a reflective and human-centered pedagogical framework that maintains critical thinking, originality, and ethical responsibility. © 2025, Aalborg University. All rights reserved. KW - AI-Aided Design KW - Collaborative Design KW - Final Year Project (FYP) KW - Human-AI Collaboration KW - Human-Centered Design KW - Industrial Design education CY - Malaysia ER - TY - JOUR TI - Youth Expectations and Perceptions of Generative Artificial Intelligence in Higher Education AU - Cotino-Arbelo A.E. AU - González-González C.S. AU - Molina-Gil J. PY - 2025 JO - International Journal of Interactive Multimedia and Artificial Intelligence VL - 9 IS - 2 SP - 84 EP - 92 DO - 10.9781/ijimai.2025.02.004 AB - Artificial Intelligence (AI) is not a recent innovation, what’s new is how accessible its features have become across multiple devices, apps, and services. Sensationalistic news can distort public perception by exaggerating AI’s capabilities and risks. This leads to misconceptions and unrealistic expectations, causing misunderstandings about the true nature and limitation of these tools. Such distortions can undermine trust and hinder the effective adoption and integration of AI into society. This study aims to address this issue by exploring the expectations and perceptions of young individuals regarding Generative Artificial Intelligence (GAI) tools. It explores their understanding of GAI and related devices, such as virtual assistants, chatbots, and social robots, which can incorporate GAI. A total of N=100 university students engaged in this study by completing a digital questionnaire distributed through the virtual campus of the University of La Laguna. The quantitative analysis uncovered a significant gap in participants’ understanding of GAI terminology and its underlying mechanisms. Additionally, it shed light on a noteworthy gender-based discrepancy in the expressed concerns. Participants commonly recognized their ability to communicate effectively with GAI, asserting that such interactions enhance their emotional well-being. Notably, virtual assistants and chatbots were perceived as more valuable tools compared to social robots within the educational realm. © 2025, Universidad Internacional de la Rioja. All rights reserved. KW - Artificial Intelligence KW - Chatbots KW - Generative Artificial Intelligence KW - Higher Education KW - Perceptions KW - Social Robots KW - Virtual Assistants CY - Spain ER - TY - JOUR TI - The AI Integration Matrix: a Framework for Responsible Artificial Intelligence in Mental Health AU - Schneider E.M. AU - E. Ayearst L. PY - 2026 JO - Journal of Technology in Behavioral Science DO - 10.1007/s41347-026-00608-4 AB - Artificial intelligence (AI) is transforming mental health care by enabling new approaches to monitoring, prevention, diagnosis, intervention, and relapse prevention. Yet, digital mental health tools introduce a range of complex challenges, spanning clinical, ethical, regulatory, technical, and contextual dimensions. A person-centered, developmentally informed approach is needed to ensure AI innovation leads to improved outcomes. This paper proposes the AI Integration Matrix (AIM), a framework for the responsible development and implementation of AI in mental health care. It integrates, builds on, and extends current regulatory, implementation science, and ethical frameworks. The Matrix offers systematic, context-sensitive guidance across seven interdependent domains: (1) clinical grounding and application, (2) ethical integrity and trust, (3) regulatory and economic sustainability, (4) user experience, (5) social and cultural impact, (6) evidence and continuous learning, and (7) technical foundations. It provides a holistic foundation for evaluating and optimizing digital mental health innovation across diverse settings and populations and equips users with a mental model for navigating the complexity of digital mental health applications, supporting responsible AI integration that drives meaningful impact. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2026. KW - AI framework KW - Artificial intelligence KW - Digital mental health KW - Person-centered care KW - Responsible AI CY - United States, Canada ER - TY - JOUR TI - Regulating Artificial Intelligence in Democratic Societies: Legal Challenges and Ethical Imperatives for Peace, Development, and Integration AU - Lushka I. PY - 2025 JO - Interdisciplinary Journal of Research and Development VL - 12 IS - 1 S1 SP - 85 EP - 92 DO - 10.56345/ijrdv12n1s110 AB - Artificial Intelligence (AI) is reshaping democratic institutions, offering significant opportunities for innovation while also raising serious legal and ethical concerns. Its use in areas like surveillance, predictive policing, hiring, and healthcare challenges core democratic principles such as transparency, accountability, and the protection of fundamental rights. This paper examines how democratic societies can govern AI effectively, ensuring that its development aligns with civil liberties and human dignity. Existing legal frameworks, often outdated, struggle to address the complexities of AI, including issues of bias, discrimination, and the lack of human oversight in automated decision-making. While regulations like the EU’s General Data Protection Regulation (GDPR) provide some safeguards, they fall short in addressing the full scope of AI’s impact. The proposed EU AI Act represents progress toward a harmonized, risk-based approach but raises questions about enforcement and adaptability. Ethical governance must go beyond voluntary guidelines. Binding legal standards are needed to enforce principles such as fairness, explainability, and human-centric design. Furthermore, international cooperation is essential to prevent regulatory gaps and ensure consistent protections across borders. Participatory oversight is also vital. Public trust depends on involving a broad range of stakeholders—citizens, experts, developers, and civil society—in shaping AI policy. Legal systems must anticipate AI’s broader effects, such as job displacement and social inequality, through proactive measures like retraining programs and social protections. Ultimately, AI governance must safeguard democratic values. Transparent, accountable, and inclusive legal frameworks are essential to ensure that AI strengthens—rather than undermines—freedom, justice, and human dignity. © 2025 Ina Lushka. KW - Artificial Intelligence KW - civil liberties KW - comparative law KW - democracy KW - ethics KW - EU AI Act KW - regulation CY - Albania ER - TY - JOUR TI - Crossing The Nexus: Language, Culture, and Technology in a Globalized World AU - Ghania O. AU - Bouguebs R. PY - 2025 JO - Traduction et Langues VL - 24 IS - 1 SP - 12 EP - 15 DO - 10.52919/translang.v24i01.1019 AB - In an era defined by globalization and digital connectivity, the intersections of language, culture, and technology have become central to understanding contemporary communication, education, and identity. Crossing The Nexus: Language, Culture, and Technology in a Globalized World presents a collection of interdisciplinary studies that explore emerging trends and challenges in applied linguistics, literary analysis, translation studies, and pedagogical innovation. Drawing on both theoretical frameworks and empirical research, this volume reflects the evolving landscape of linguistic inquiry across diverse sociocultural and geopolitical contexts. The contributions examine a wide range of topics, from contrastive linguistic analysis and syntactic theory to intercultural education, AI-assisted interpreting, and postcolonial semiotics. Key themes include the complexities of cross-linguistic phenomena and cultural fidelity in translation, investigated through contrastive studies of reduplication in Vietnamese-English literary prose and syntactic exceptions in Arabic and Spanish. Educational innovation is highlighted via the implementation of Collaborative Online International Learning (COIL) for intercultural competence and the Flipped Learning model in specialized contexts, alongside analyses of structural challenges in migrant student integration in Cyprus. The volume critically engages with technology's role, examining AI's capabilities and limitations in interpreting and handling culturally specific translation challenges like verbified proper nouns, while also demonstrating data-driven approaches to public sentiment analysis in digital discourse. Further contributions explore affective factors in language learning, such as test anxiety among Yemeni students, and the influence of gender perspectives in the translation of Qur'anic verses. Multimodal literary analysis reveals the co-construction of meaning through text and image, and postcolonial semiotics examines narratives of exile and identity. Collectively, these studies underscore the necessity of nuanced, culturally sensitive, and interdisciplinary approaches to navigate the evolving landscape of global communication, pedagogical strategies, and technological integration, significantly contributing to scholarly discourse in the humanities and social sciences. © 2025, University of Oran 2 Mohamed Ben Ahmed. All rights reserved. KW - Collaborative Online International Learning (COIL) KW - Culture and Technology KW - Exceptional Constructions in Arabic and Spanish KW - Flipped Learning in an ESP Context KW - Gender Studies KW - Reduplication in Vietnamese Literary Prose KW - Translation CY - Algeria ER - TY - JOUR TI - Transforming the 5G RAN With Innovation: The Confluence of Cloud Native and Intelligence AU - Li N. AU - Xu X. AU - Sun Q. AU - Wu J. AU - Zhang Q. AU - Chi G. AU - Chih-Lin I. AU - Sprecher N. PY - 2023 JO - IEEE Access VL - 11 SP - 4443 EP - 4454 DO - 10.1109/ACCESS.2023.3234493 AB - Intelligence and cloudification are widely recognized as key driving forces in the evolution of 5G radio access network (RAN). This paper presents a promising architecture framework for the evolution of 5G radio access network, enabled by a deep integration with cloudification and artificial intelligence/machine learning (AI/ML) technologies. To accommodate the diversified scenarios and services and handle the complexity of the 5G network in a flexible and efficient manner, the architecture framework highlights three concepts: convergence of RAN and cloud, RAN empowered by hierarchical AI capabilities, and mutual awareness between RAN and services. The key design aspects and technologies that realize those concepts are discussed systematically. Two typical use cases including the RAN slice resource allocation optimization and RAN-aware video service assurance, are demonstrated along with the simulation or lab test results to validate the potential of the architecture framework. © 2013 IEEE. KW - 5G KW - AI/ML KW - cloud-native KW - RAN KW - service-awareness KW - 5G mobile communication systems KW - Computer architecture KW - Network architecture KW - Radio KW - Radio access networks KW - 5g KW - 5g mobile communication KW - Artificial intelligence/machine learning KW - Cloud-computing KW - Cloud-native KW - Machine-learning KW - Mobile communications KW - Optimisations KW - Radio access networks KW - Service-awareness KW - Cloud computing CY - China, Israel ER - TY - JOUR TI - Artificial intelligence in action: shaping the future of public sector AU - Panda M. AU - Hossain M.M. AU - Puri R. AU - Ahmad A. PY - 2025 JO - Digital Policy, Regulation and Governance VL - 27 IS - 6 SP - 668 EP - 686 DO - 10.1108/DPRG-10-2024-0272 AB - Purpose – Artificial intelligence (AI) has transformed various sectors, including automotive, finance, media, travel and retail by leveraging new-age technologies. Education, banking, health care, social policy and regulation, within the public sector have witnessed significant AI applications and substantial benefits. The importance of AI in the public sector includes enhanced efficiency, improved decision-making, cost savings, citizen-centric services, etc. Despite these advancements, a mindful discussion on the societal impact of AI in the public sector demands comprehension regarding its subjugation. Therefore, this study aims to analyze the role of AI in transforming the public sector using a bibliometric analysis of recent trends and challenges. Design/methodology/approach – This study has used bibliometric analysis to trace the intellectual patterns of previous research. It comprises 231 articles from 2000 to 2024 from Scopus through the Scientific Procedures and Rationales for Systematic Literature Reviews protocol. This protocol has adopted a three-step process for identifying articles, i.e. assembling, arranging and assessing. Findings – The publication trend shows an upward trajectory since 2017, whereas network visualization protrudes with the recent trends and thematic expressions, namely, Global AI ethics and policy challenges in public sectors, AI adoption and governance in public sector, challenges and opportunities of implementing AI in public administration and AI’s role in economic and public transformation. Research limitations/implications – The findings suggest AI adoption in the public sector enhances transparency and efficiency but demands ethical guidelines, legal frameworks and stakeholder governance to address challenges such as data privacy, algorithmic bias and public trust. Policies should promote responsible AI use, balancing innovation with accountability to improve public service delivery and uphold democratic values. Originality/value – This paper enhances the limited literature on the integration of AI in the public sector, focusing on emerging themes and trending topics with future research directions to furnish a holistic perspective. It aims to guide researchers and policymakers in exploring areas for further investigation in this domain. © 2025 Emerald Publishing Limited KW - Artificial intelligence KW - Bibliometric analysis KW - Governance KW - Public sector KW - SPAR 4 SLR CY - India ER - TY - JOUR TI - How mindfulness shapes AI competence: a structural equation modeling analysis of mindfulness, AI literacy and behavioral intention in Chinese media students AU - Lan Y. AU - Liu S. AU - Xia L. PY - 2025 JO - Frontiers in Psychology VL - 16 SP - 1652934 DO - 10.3389/fpsyg.2025.1652934 AB - Introduction: Artificial Intelligence (AI) literacy, defined as the knowledge and ability to recognize, apply, and evaluate AI, is a key driving force of digital transformation and technological innovation. In the media industry, the demand for “intelligent+” interdisciplinary talent has prompted universities to embed AI literacy training into talent development programs. While curriculum systems have been progressively refined, the challenge remains on how to activate students’ intention to embrace and effectively utilize AI. Mindfulness, a metacognitive trait that enhances cognitive flexibility, self-regulation, and creativity, may contribute to the development of AI literacy, although its specific impact in this progress remains largely unexplored. Methods: This study constructs the integrated model of “Mindfulness-AI Literacy-Technology Application“. Survey data were collected from 588 media students in China and analyzed using SPSS and SmartPLS to conduct structural equation modeling. AI literacy is comprised of four dimensions: acknowledgment of AI (AAI), AI ethics (AIE), AI collaboration (AIC), and AI self-efficacy (AIS). Results: Mindfulness significantly and positively influenced AAI, AIE, and AIC, but showed no significant relationship with AIS. It also had a significant direct positive effect on AIBI. Furthermore, AAI and AIC partially mediated the relationship between mindfulness and AIBI. Discussion: Results confirm that mindfulness is an effective internal pathway for strengthening key AI literacy dimensions and enhancing media students’ intention to apply AI technologies. Incorporating mindfulness interventions into higher media education, aligned with curriculum and practice, could provide a strategic approach to cultivating AI-ready graduates. Copyright © 2025 Lan, Liu and Xia. KW - AI behavioral intention (AIBI) KW - AI literacy KW - media education KW - media students KW - mindfulness CY - China ER - TY - JOUR TI - Digital innovation and women's entrepreneurship: Integrating fragmented literature through a stage-contingent lens AU - Latif M. AU - Tanveer A. AU - Saeedikiya M. AU - Ullah A. AU - Bilal A. PY - 2026 JO - Digital Business VL - 6 IS - 2 SP - 100174 DO - 10.1016/j.digbus.2026.100174 AB - Women's entrepreneurship is a significant driver of innovation and economic growth; however, research on digital innovation in this context remains fragmented across disciplines and lacks an integrative theoretical foundation. This systematic literature review addresses these deficiencies by analysing how the antecedents, processes, and outcomes of digital innovation vary across three business development stages: new ventures, small and medium-sized enterprises (SMEs), and corporations. A total of 163 peer-reviewed articles published between 2000 and 2024 were analysed, following PRISMA 2020 guidelines and utilizing data from Scopus, Web of Science, and EBSCOhost. The Ability-Motivation-Opportunity (AMO) framework was employed as a multiplicative analytical model, extended to incorporate stage-contingent interactions and platform ecosystem dynamics. The findings indicate that binding constraints shift systematically across business development stages. In new ventures, Opportunity constraints are predominant, as access to funding, platforms, and networks determines the innovation potential. In SMEs, Ability constraints become salient, with leadership capabilities and digital skills limiting the success of scaling. In corporations, Motivation emerges as the primary constraint, with governance structures and strategic commitment influencing innovation outcomes. Digital innovation functions as a critical mediator linking gender-specific resources to venture performance, while platform governance, supply chain integration, and emerging generative AI capabilities reshape these relationships. This review offers five primary contributions: an integrated stage-contingent theoretical model that demonstrates context-dependent gender effects; an extension of AMO theory with explicit classification rules; integration of platform ecosystem and emerging technology perspectives; a reconceptualization of regulation as an enabler rather than a constraint; and evidence-based, stage-specific policy recommendations with documented trade-offs. In addition, nine critical research priorities are identified from synthesis gaps, providing direction for future scholarship and policy development. Copyright © 2026. Published by Elsevier B.V. KW - AMO framework KW - Digital innovation KW - Platform ecosystems KW - Stage-contingent analysis KW - Systematic literature review KW - Women's entrepreneurship CY - Australia ER - TY - JOUR TI - Demand analysis of transitional care for patients undergoing minimally invasive cardiac interventions with AI-driven solutions: a mixed-methods approach AU - Liu Y. AU - Li S. AU - Yu J. AU - Cao J. AU - Ma Q. AU - Li M. AU - Zheng Y. AU - You Y. AU - Lv W. AU - Li Q. AU - Zhang C. AU - Piao M. PY - 2025 JO - BMC Nursing VL - 24 IS - 1 SP - 453 DO - 10.1186/s12912-025-03037-5 AB - Aims: Minimally invasive cardiac intervention (MICI) patients remain at high risk of readmission and mortality during their post-discharge phase, with 30-day readmission rates of up to 10%. Although technological innovations, especially AI-driven solutions, hold promise for improving outcomes, there is a pressing need to clarify the full spectrum of patient demands during the transition from hospital to home. This study aimed to systematically identify these demands to guide the development of AI-driven solutions that reduce readmission rates and improve clinical outcomes. Methods and results: A convergent parallel mixed-methods design was employed to systematically identify patient demands and inform the development of AI-driven interventions in transitional care. Quantitative and qualitative data were collected from 137 MICI patients recruited from four hospitals (June–August 2024). Quantitatively, a 23-item survey was analyzed using the Kano model, revealing no “must-be” demands—indicating that patients were accustomed to a lack of guidance post-discharge. However, health monitoring, medication guidance, symptom management, and personalized exercise plans were identified as “one-dimensional” demands that significantly impact patient satisfaction. Additionally, continuous exercise monitoring and dietary planning emerged as “attractive” features that could enhance care quality without negatively affecting satisfaction if absent. Qualitative interviews uncovered the importance of comorbidity management, psychological support and financial transparency, which were not fully captured in the survey data. The integration of these findings underscores the need for AI-driven personalized health monitoring systems and knowledge-based AI tools to revolutionize the transitional care process for MICI patients. Conclusion: This integrated analysis highlights the significant care demands of MICI patients during the transition from hospital to home. Key recommendations include: (1) deploying AI-driven health monitoring, medication guidance, and symptom management systems, (2) designing personalized exercise and dietary tools, and (3) creating accessible, knowledge-based platforms for reliable medical information. In addition, comorbidity management, psychological support and financial transparency are areas that call for our attention. By aligning with these patient-centered demands and leveraging AI’s capabilities, future transitional care interventions—particularly in China have the potential to address healthcare staffing constraints and improve patient outcomes. However, due to the limitations of our study, these insights require further validation and exploration. © The Author(s) 2025. KW - Artificial intelligence KW - Minimally invasive cardiac interventions KW - Mixed-method KW - Transitional care CY - China ER - TY - JOUR TI - Using the Theoretical-Experiential Binomial for Educating AI-Literate Students AU - Modran H.A. AU - Ursuțiu D. AU - Samoilă C. PY - 2024 JO - Sustainability (Switzerland) VL - 16 IS - 10 SP - 4068 DO - 10.3390/su16104068 AB - In the dynamic landscape of modern education, characterized by an increasingly active involvement of IT technologies in learning, the imperative to transfer to university students the skills necessary to integrate Artificial Intelligence (AI) into the process represents an important goal. This paper presents a novel framework for knowledge transfer, diverging from traditional programming language-centric approaches by integrating PSoC 6 microcontroller technology. This framework proposes a cyclical learning cycle encompassing theoretical fundamentals and practical experimentation, fostering AI literacy at the edge. Through a structured combination of theoretical instruction and hands-on experimentation, students develop proficiency in understanding and harnessing AI capabilities. Emphasizing critical thinking, problem-solving, and creativity, this approach equips students with the tools to navigate the complexities of real-world AI applications effectively. By leveraging PSoC 6 as an educational tool, a new generation of individuals is efficiently cultivated with essential AI skills. These individuals are adept at leveraging AI technologies to address societal challenges and drive innovation, thereby contributing to long-term sustainability initiatives. Specific strategies for experiential learning, curriculum recommendations, and the results of knowledge application are presented, aimed at preparing university students to excel in a future where AI will be omnipresent and indispensable. © 2024 by the authors. KW - AI literacy KW - Artificial Intelligence (AI) KW - experiential learning KW - PSoC 6 KW - sustainability KW - innovation KW - machine learning KW - student KW - sustainability KW - theoretical study KW - university sector CY - Romania ER - TY - JOUR TI - Beyond Replacement: How Large Language Models Influence Dictionary Usage Patterns Among Chinese English Learners AU - Liu R. AU - Chen X. AU - Xu Y. PY - 2025 JO - International Journal of Lexicography VL - 38 IS - 4 SP - 342 EP - 364 DO - 10.1093/ijl/ecaf017 AB - This study investigated how Large Language Models (LLMs) influence Chinese English learners’ dictionary usage patterns. Through a mixed-methods approach combining questionnaire surveys (n = 608) and semi-structured interviews (n = 17), the findings reveal that LLMs reconstruct the language learning tool ecosystem through clear functional divisions rather than simple replacement patterns. Results demonstrate that LLMs predominantly serve complex language tasks including translation, writing assistance, and grammar correction, while traditional dictionaries maintain competitive advantages in providing authoritative information, precise definitions, and structured vocabulary learning tools. Learners have developed sophisticated task-oriented selection strategies, following a ‘dictionaries for discrete knowledge acquisition, LLMs for integrated language application’ usage pattern that maximizes learning efficiency. Beyond behavioural adaptations, this study identified significant demographic stratification in tool adoption, with age, educational background, and English proficiency level significantly influencing usage patterns. The research further revealed cognitive paradigm shifts in language learning conceptualization, exposing tensions between instrumental utility and cultural acquisition perspectives. These findings suggest two critical directions for future lexicographic development: (1) intelligent integration combining authoritative content with interactive AI capabilities, and (2) specialized personalization addressing domain-specific and scenario-based learning needs through enhanced functionality and user-centred design. © The Author(s) 2025. Published by Oxford University Press. All rights reserved. KW - Chinese English learners KW - Dictionary use behaviour KW - Language learning tools KW - Large Language Models KW - Mixed-methods research CY - China ER - TY - JOUR TI - Integrating Artificial Intelligence in dairy farm management − biometric facial recognition for cows AU - Mahato S. AU - Neethirajan S. PY - 2025 JO - Information Processing in Agriculture VL - 12 IS - 3 SP - 312 EP - 325 DO - 10.1016/j.inpa.2024.10.001 AB - The integration of Artificial Intelligence (AI) into dairy farm management through biometric facial recognition of cows marks a significant milestone in livestock care. This comprehensive review explores the development, implementation, and challenges associated with AI-powered biometric facial identification in dairy agriculture. It emphasizes the pivotal role of this innovation in enabling precise monitoring of individual cows, thereby facilitating thorough tracking of their health, behaviors, and productivity levels. Derived from facial recognition technologies originally designed for humans, this approach harnesses distinctive features of cow faces for gentle and immediate observation within large-scale farming operations. The evolution of AI from basic pattern recognition to advanced Convolutional Neural Networks (CNNs) and deep learning frameworks signifies a transition toward data-driven agriculture. This analysis addresses notable challenges such as environmental variability, data collection difficulties, ethical considerations, and technological limitations. Furthermore, it compares various AI frameworks, highlighting their unique advantages and suitability in the dairy farming context. Despite these obstacles, facial recognition technology holds promise for enhancing farm efficiency, improving animal welfare, and promoting sustainable practices, underscoring the need for ongoing research and innovation. We advocate for future investigations focused on enhancing adaptability to diverse environments, ensuring ethical AI deployment, fostering compatibility across different breeds, and integrating with complementary agricultural technologies. Ultimately, this review underscores the transformative impact of AI in advancing dairy farming towards a data-centric future while prioritizing responsible agricultural practices. © 2024 The Author(s). KW - AI-driven livestock Management KW - Animal identification technology KW - Dairy cow biometrics KW - Dairy welfare KW - Digital livestock farming KW - Facial recognition technology KW - Precision dairy farming KW - Sustainable dairy practices KW - Convolutional neural networks KW - Animal identification KW - Animal identification technology KW - Artificial intelligence-driven livestock management KW - Dairy cow KW - Dairy cow biometric KW - Dairy farming KW - Dairy welfare KW - Digital livestock farming KW - Facial recognition KW - Facial recognition technology KW - Identification technology KW - Livestock farming KW - Precision dairy farming KW - Sustainable dairy practice KW - agricultural practice KW - artificial intelligence KW - dairy farming KW - environmental assessment KW - farming system KW - integrated approach CY - Canada ER - TY - JOUR TI - Shame in the machine: affective accountability and the ethics of AI AU - McNealis R. PY - 2026 JO - AI and Society VL - 41 IS - 1 SP - 403 EP - 413 DO - 10.1007/s00146-025-02472-x AB - The cultural weaponization of shame surrounding the use of artificial intelligence (AI) tools like ChatGPT often redirects ethical scrutiny away from systemic concerns and toward individual users. Drawing on Sara Ahmed’s affect theory, this paper argues that cultural narratives of "AI shaming" function as moral displacement that redirects scrutiny away from the environmental costs, exploitative labor practices, and corporate monopolization defining contemporary AI development. The analysis examines how shame operates across academic and professional settings to create "effort anxiety" that demands both visible human labor and accelerated productivity. Current discourse treats AI use as a personal virtue problem and obscures the carbon-intensive data centers, underpaid content moderators, and proprietary knowledge systems that enable these technologies. Instead of eliminating shame, the paper proposes redirecting it toward collective accountability for AI’s systemic harms. Environmental degradation, algorithmic bias, and extractive infrastructures represent the true ethical frontier of artificial intelligence. Policy frameworks, educational interventions, and governance structures offer pathways for transforming shame from individual punishment into institutional reform. The stakes extend beyond AI itself: as emerging technologies reshape society, the patterns of moral responsibility established now will determine whether innovation serves collective flourishing or perpetuates existing inequalities. Shame can become a vehicle for institutional critique and systemic accountability if we redirect its focus from individual users to the powerful corporations, governance structures, and infrastructural systems that profit from AI’s rapid expansion. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025. KW - Affect theory KW - AI Ethics KW - Artificial intelligence KW - Environmental ethics KW - Shame KW - Systemic accountability KW - Employment KW - Ethical technology KW - Public policy KW - Affect theory KW - Artificial intelligence ethic KW - Artificial intelligence tools KW - Corporates KW - Environmental costs KW - Environmental ethics KW - Governance structures KW - Shame KW - Systemic accountability KW - Weaponization KW - Knowledge based systems CY - United States ER - TY - JOUR TI - Artificial intelligence capabilities, open innovation, and business performance – Empirical insights from multinational B2B companies AU - Sahoo S. AU - Kumar S. AU - Donthu N. AU - Singh A.K. PY - 2024 JO - Industrial Marketing Management VL - 117 SP - 28 EP - 41 DO - 10.1016/j.indmarman.2023.12.008 AB - In contrast to the operational nature of business-to-consumer (B2C) enterprises, business-to-business (B2B) organizations emphasizes offering specialized products and services to their clients, which are businesses. Recognizing that B2B organizations are dealing with complex metadata in an ever-changing business context, artificial intelligence (AI) technologies possess the potential in assisting them analyse massive amounts of data, generating actionable insights, and formulating revolutionary ideas, potentially improving collaboration and innovation. As a result, this study adopts an empirical research approach to analyse the relationship among AI capabilities, open innovation, and business performance, with an emphasis on B2B companies, based on the theoretical foundations of social-technical system and contingency theories. This study investigated the relationship between AI capabilities and open innovation practices, as well as the effect they have on business performance, using survey data collected from 398 B2B multinational companies and structural equation modelling. The findings indicate that AI capabilities have a favourable effect on open innovation practices, which subsequently leads to an improvement in business performance. Notably, the impact of AI capabilities on business performance was found to be partially mediated. The examination of the moderating effect of environmental dynamism reveals that it exerts a significant influence on the relationship between AI capabilities and outbound open innovation. However, it does not have a significant moderating impact on the causal interaction of AI capabilities on both business performance and inbound open innovation. The ramifications of these findings are significant for managers and policymakers who are interested in fostering innovation and enhancing competitiveness within the B2B sector. The results underscore the crucial role of cultivating AI capabilities. © 2023 Elsevier Inc. KW - AI KW - Artificial intelligence KW - B2B companies KW - Business performance KW - Inbound open innovation KW - Outbound open innovation CY - India, United States ER - TY - JOUR TI - Pathways to sustainable competitive performance: social entrepreneurship orientation, disruptive innovation and artificial intelligence capabilities AU - Wang C. AU - Zhang Q. AU - Zhang W. PY - 2026 JO - Humanities and Social Sciences Communications VL - 13 IS - 1 SP - 481 DO - 10.1057/s41599-026-06851-7 AB - In the era of artificial intelligence (AI), achieving a sustainable competitive advantage has become a pressing challenge for firms. Social entrepreneurship orientation (SEO) has emerged as a pivotal organizational strategy for advancing long-term sustainability. While existing studies have explored the influence of SEO on various performance outcomes, the underlying mechanisms through which SEO enhances sustainable competitive performance in high-tech firms remain underexplored. Drawing on the resource-based view (RBV) and dynamic capabilities theory (DCT), this study develops a moderated mediation model to investigate the relationship between SEO and sustainable competitive performance. Based on empirical data from 229 high-tech firms, our findings demonstrate that SEO positively influences sustainable competitive performance. Moreover, disruptive innovation mediates the relationship between SEO and sustainable competitive performance, while AI capabilities moderate the impact of SEO on disruptive innovation. This study contributes to the RBV and DCT literature, deepens the understanding of SEO outcomes, and offers valuable managerial insights for high-tech firms to promote sustainable development. © The Author(s) 2026. CY - China ER - TY - JOUR TI - Responsible Artificial Intelligence and Green Innovation Impact on MSMEs’ Sustainable Performance AU - Gupta S. AU - Dhiman A. AU - Singla A. AU - Saini G. PY - 2026 JO - Global Journal of Flexible Systems Management DO - 10.1007/s40171-026-00481-3 AB - This research investigates how MSMEs may become more sustainable by combining responsible AI (RAI) with green innovation. Although AI has revolutionary potential to boost eco-innovation and operational efficiency, the leadership’s role in coordinating new technologies with sustainable goals is not well acknowledged. The study adds to the expanding debate over the sustainable performance of MSMEs by investigating how leadership influences ethical, environmental, and economic responsibility within the organisational landscape. A mixed-methods approach was employed, beginning with quantitative analysis on 321 MSME respondents, followed by qualitative analysis. A disjoint two-stage approach using PLS-SEM is followed by meta-inferences to evaluate the hypothesised model. The findings suggest the RAI significant effect on MSMEs’ sustainable performance as well as green innovation. The influence of green innovation and RAI on sustainability is favourably moderated by sustainable transformational leadership (STL), underscoring the crucial role that leadership plays in promoting environmental and digital change. The research provides implementable recommendations for MSME leaders, industry professionals, and policymakers to incorporate RAI into business strategies, thus fostering green innovation and ensuring long-term sustainability. It affirms the key contribution of STL in overcoming barriers to AI adoption, aligning technology with sustainable goals. The study integrates RAI, green innovation and STL into a single paradigm, particularly in the unexplored setting of MSMEs. It reconceptualises RAI as a strategic factor behind sustainable performance and leadership, providing a novel viewpoint on how responsible behaviour may improve organisation performance and contribute to broader sustainability goals. © The Author(s) under exclusive licence to Global Institute of Flexible Systems Management 2026. KW - Fourth bottom line KW - Green innovation KW - Mixed methods approach KW - MSMEs KW - Responsible AI KW - Strategic flexibility KW - Sustainable transformational leadership KW - TOES framework CY - India ER - TY - JOUR TI - Developing a Consumer Electronics Robotics With a Large Language Model Based on a Trustworthy AI Framework AU - Wu H.-T. AU - Wei W. AU - Li S.-H. AU - Chen M.-Y. PY - 2025 JO - IEEE Transactions on Consumer Electronics VL - 71 IS - 1 SP - 2027 EP - 2038 DO - 10.1109/TCE.2025.3538785 AB - As many countries face the challenges of an aging population and declining birth rates, the demand for labor, particularly for assisting the elderly, is increasing. Traditional robots, being standardized products, require engineers to adjust parameters to fit the unique lifestyle of each household, which is time-consuming and inconvenient. However, with the rapid development of artificial intelligence and consumer electronics, there is a growing need for home robots with smarter interfaces to achieve the goal of intelligent living. This paper proposes a home robot based on a trustworthy AI framework, integrated with large language models (LLM). These LLM can perform natural language processing and object recognition, allowing users to control the robot’s operations through natural language commands. This innovation further advances consumer electronics. The robot’s arm can remember these actions and operate according to instructions. Additionally, the robot arm is equipped with monitoring functions, capable of overseeing the operation of other robots and using cameras to detect errors. This development is significant in the field of consumer electronics. The robot also uses Long Short-Term Memory (LSTM) networks to predict the motion paths of the robotic arm, ensuring smooth and efficient operation. This integration of AI and robotics aims to enhance the adaptability and functionality of home robots, making them more suitable for the diverse needs of modern households, improving the quality of life for the elderly, and driving innovation in the consumer electronics field. © 1975-2011 IEEE. KW - consumer electronics KW - intelligent automation KW - large language model KW - long short-term memory KW - robotic arm KW - Trustworthy AI KW - Industrial robots KW - Intelligent robots KW - Aging population KW - Home robot KW - Intelligent automation KW - Language model KW - Large language model KW - Model-based OPC KW - Natural languages KW - Robot arms KW - Short term memory KW - Trustworthy AI KW - Robotic arms CY - Taiwan, China ER - TY - JOUR TI - Artificial Intelligence for Remote Sensing Data Analysis: A review of challenges and opportunities AU - Zhang L. AU - Zhang L. PY - 2022 JO - IEEE Geoscience and Remote Sensing Magazine VL - 10 IS - 2 SP - 270 EP - 294 DO - 10.1109/MGRS.2022.3145854 AB - Artificial intelligence (AI) plays a growing role in remote sensing (RS). Applications of AI, particularly machine learning algorithms, range from initial image processing to high-level data understanding and knowledge discovery. AI techniques have emerged as a powerful strategy for analyzing RS data and led to remarkable breakthroughs in all RS fields. Given this period of breathtaking evolution, this work aims to provide a comprehensive review of the recent achievements of AI algorithms and applications in RS data analysis. The review includes more than 270 research papers, covering the following major aspects of AI innovation for RS: machine learning, computational intelligence, AI explicability, data mining, natural language processing (NLP), and AI security. We conclude this review by identifying promising directions for future research. © 2013 IEEE. KW - Data handling KW - Data mining KW - Image processing KW - Learning algorithms KW - Machine learning KW - Natural language processing systems KW - Artificial intelligence algorithms KW - Artificial intelligence techniques KW - Data understanding KW - Images processing KW - Machine learning algorithms KW - Machine-learning KW - Remote sensing data KW - Remote-sensing KW - Research papers KW - Sensing fields KW - artificial intelligence KW - image processing KW - machine learning KW - remote sensing KW - Remote sensing CY - China ER - TY - JOUR TI - Organizational sustainable artificial intelligence capabilities scale development, validation, and implications AU - Qalati S.A. AU - Siddiqui F. PY - 2026 JO - Journal of Innovation and Knowledge VL - 11 SP - 100863 DO - 10.1016/j.jik.2025.100863 AB - As artificial intelligence (AI) adoption accelerates globally, its sustainability implications remain insufficiently integrated into organizational capability frameworks. This study develops and validates the organizational sustainable AI capabilities (OSAIC) construct, extending dynamic capabilities theory by embedding sustainability as a meta-capability in AI governance and innovation processes. OSAIC is conceptualized as a five-dimensional, reflective, higher-order construct, encompassing sustainable AI learning, seizing, sensing, stakeholder integration, and transformation. A multi-phase scale development procedure—including expert Q-sorting, exploratory factor analysis, and confirmatory factor analysis, using partial least squares structural equation modeling—was employed. The scale was assessed and validated using two distinct samples: a pilot study ( n = 188) and a main study ( n = 364), both comprising managers from diverse industries and regions. The findings indicated robust psychometric attributes, characterized by substantial reliability, convergent, discriminant, and predictive validity. A positive and significant relationship between OSAIC and sustainable innovation indicated nomological validity, addressing the AI sustainability paradox by illustrating that sustainability-oriented AI capabilities enhance rather than constrain innovation. By extending the research on dynamic capabilities and paradoxes and presenting a validated measurement tool, this study contributes theoretically and methodologically, respectively, to the literature. Practically, it offers managers a diagnostic framework to align AI implementation with environmental and social accountability while fostering innovation. © 2025 The Author(s). KW - organizational sustainable AI capabilities KW - stakeholder integration KW - sustainable AI learning KW - sustainable AI seizing KW - Sustainable AI sensing KW - sustainable AI transforming CY - China ER - TY - JOUR TI - Revolutionizing urogynecology: Machine learning application with patient-centric technology: Promise, challenges, and future directions AU - Rotem R. AU - Galvin D. AU - Daykan Y. AU - Mi Y. AU - Tabirca S. AU - O'Reilly B.A. PY - 2024 JO - European Journal of Obstetrics and Gynecology and Reproductive Biology VL - 300 SP - 49 EP - 53 DO - 10.1016/j.ejogrb.2024.07.009 AB - In an epoch where digital innovation is redefining the medical landscape, electronic health records (EHRs) stand out as a pivotal transformative force. Urogynecology, a discipline anchored in intricate patient histories and meticulous follow-ups, is on the brink of profound transformation due to these digital strides. While EHRs have unified patient data, challenges related to data privacy, interoperability, and access persist. In response, we present Pelvic Health Place (PHPlace) – a multilingual, patient-centric application. Purposefully designed to bolster patient engagement, PHPlace provides clinicians with essential pre-consultation insights, streamlines the consent process, vividly delineates surgical pathways, and assures comprehensive long-term monitoring. This platform also establishes a foundation for global data amalgamation, promising to invigorate research and potentially harness artificial intelligence (AI) capabilities. With AI integration, we anticipate a more tailored treatment approach and enriched patient education, signaling a pivotal shift in urogynecology and emphasizing the imperative for ongoing academic inquiry. © 2024 Elsevier B.V. KW - Artificial intelligence KW - Digital innovation KW - Electronic health records KW - Global data amalgamation KW - Urogynecology KW - Electronic Health Records KW - Female KW - Gynecology KW - Humans KW - Machine Learning KW - Patient-Centered Care KW - Urology KW - Article KW - artificial intelligence KW - data accuracy KW - data privacy KW - digital health technology KW - electronic health record KW - follow up KW - gynecology KW - human KW - information storage KW - machine learning KW - medical history KW - patient care KW - patient coding KW - patient education KW - patient engagement KW - person centered care KW - urology KW - electronic health record KW - female KW - person centered care CY - Ireland, Israel, Romania ER - TY - JOUR TI - Harnessing Artificial Intelligence and Employee Resilience for Enhanced Business Performance: A Resource-Based and Dynamic Capabilities Perspective AU - Javed M. AU - Švecová L. AU - Danko L. AU - Tučková Z. PY - 2026 JO - Human Behavior and Emerging Technologies VL - 2026 IS - 1 SP - 1701203 DO - 10.1155/hbe2/1701203 AB - A hypercompetitive business environment has put unprecedented pressure on business firms to adopt technologies such as artificial intelligence (AI). Alongside, AI is being recognized as a strategic resource to enhance business performance. However, less is known about the mechanisms of AI capabilities toward performance-enhancing outcomes. Drawing on the resource-based view (RBV), this study explores the role of AI use in the four key organizational dimensions, i.e., supply chain management (SCM), business models (BMs), inventory management (IM), and budgeting toward business performance, along with the mediating role of employee resilience. By employing a cross-sectional research design and using survey data of 307 manufacturing firms in Pakistan, the analysis was carried out through partial least squares–structural equation modeling (PLS–SEM). Results demonstrate that the use of AI across all four domains has a significant positive influence on business performance. In addition, employee resilience partially mediates the relationship between the AI-assisted capabilities and business performance, which underscores the crucial role of human adaptive capacity in harnessing technological resources. This study contributes to the theory by demonstrating that performance improvements of AI use are not only from the technological capabilities but also from the employees’ capabilities through efficient utilization of technologies. Therefore, this study advances the literature by highlighting employee resilience as a pivotal behavioral mechanism coupling AI capabilities and business performance. This study offers practical insights for managers to strategically invest in the use of AI along with human-centered strategies to maximize business performance and long-term competitiveness. Copyright © 2026 Mohsin Javed et al. Human Behavior and Emerging Technologies published by John Wiley & Sons Ltd. KW - artificial intelligence KW - budgeting KW - business models KW - business performance KW - employee resilience KW - inventory management KW - supply chain management CY - Czech Republic ER - TY - JOUR TI - Benefits and Challenges of AI-Based Digital Twin Integration in the Saudi Arabian Construction Industry: A Correspondence Analysis (CA) Approach AU - Alnaser A.A. AU - Elmousalami H. PY - 2025 JO - Applied Sciences (Switzerland) VL - 15 IS - 9 SP - 4675 DO - 10.3390/app15094675 AB - The Fourth Industrial Revolution (4IR) has accelerated the construction industry’s shift toward digital transformation. This progress is mainly driven by the emergence of innovative technologies, including artificial intelligence (AI) and digital twins (DTs). While global research has extensively explored the benefits and challenges of AI-based DTs, the rapid growth of Saudi Arabia’s construction sector—fueled by substantial local investments and international partnerships—underscores the urgent need to examine their specific impact within this context. To address this gap, this study aims to investigate the potential benefits and challenges of integrating AI-driven DTs into Saudi Arabia’s construction industry. To achieve this, a structured literature review and a survey were conducted among architecture, engineering, and construction (AEC) firms, with 106 complete responses analyzed using correspondence analysis (CA). The findings revealed that AI-driven DTs substantially benefit Saudi Arabia’s construction industry. For example, among the 17 identified benefits, the top-ranked ones include AI capabilities to improve analytics, AI’s facilitation of digital twins in modeling complex real-world systems, and the facilitation of strategic decision making. However, several challenges hinder the realization of these benefits, including a lack of standardization of integrated DT and AI in construction projects, a lack of understanding of AI’s capabilities, a lack of logistics and the limited availability of IT infrastructure, and the complexity of AI algorithms. These findings underscore the transformative potential of integrating AI-driven DTs to optimize construction performance, improve decision-making, and address real-world complexities. This study provides actionable insights for stakeholders and recommends future research exploring strategies for overcoming adoption challenges, fostering technological innovation, and capacity building in Saudi Arabia’s construction sector. © 2025 by the authors. KW - artificial intelligence (AI) KW - benefits and challenges KW - construction industry KW - correspondence analysis (CA) KW - digital twins (DTs) KW - Decision making KW - Project management KW - Analysis approach KW - Artificial intelligence KW - Benefit and challenges KW - Construction sectors KW - Correspondence analysis KW - Correspondence analyze KW - Digital transformation KW - Digital twin KW - Industrial revolutions KW - Saudi Arabia KW - Construction industry CY - Saudi Arabia, Australia ER - TY - JOUR TI - Augmenting hotel performance in Malaysia through big data analytics capability and artificial intelligence capability AU - Naz S. AU - Haider S.A. AU - Khan S. AU - Nisar Q.A. AU - Tehseen S. PY - 2024 JO - Journal of Hospitality and Tourism Insights VL - 7 IS - 4 SP - 2055 EP - 2080 DO - 10.1108/JHTI-01-2023-0017 AB - Purpose: At the forefront of current research is the investigation of how big data analytics capability (BDAC) and artificial intelligence capability (AIC) can enhance performance in concert. Therefore, current study intended to conduct more deep research into emerging phenomena and attempts to cover the gap by exploring how entrepreneurial orientations (EO) emphasize the use of two emerging capabilities under the moderating role of environmental dynamism which in turn augment co-innovation and hotel performance. Design/methodology/approach: Data were collected from four-star and five-star hotels located in Kula Lumpur and Langkawi in Malaysia. A total of 260 responses were obtained from IT staff and senior managers with the assistance of a Manpower agency for data analysis. The hypotheses were examined by analyzing the data using PLS-SEM technique through Smart PLS 3 software. Findings: The result revealed that EO has a positive and significant effect on co-innovation (CIN). Additionally, the BDAC and AIC have been tested and proven to be potential mediators between EO and CIN. Also, environmental dynamism as moderator has positive and significant effect on BDAC and co-innovation performance, however, not significant impact on AIC and co-innovation performance. Lastly, findings displayed positive and significant moderated mediation impact of environmental dynamics on BDAC and CIN with hotel performance, but not significant influence on AIC and co-innovation with hotel performance. For theoretical corroboration of the research findings, the current study integrated EO, resource-based view theory and contingent dynamic capabilities (CDC), because neither single stance can explicate an extant research framework. Practical implications: This study anticipated the several implications for the entrepreneurs of hospitality industry. Managers are recommended to invest in the entrepreneurial traits of the employees/organizations and make strategic readjustment of their capabilities for sustained business performance. Originality/value: The study goes beyond the normal inquiry by investigating moderated mediation impact of environmental dynamism between two emerging capabilities, co-innovation and hotel performance relationships. Another novelty of this study is to culminate the exploitation and adoption of emerging IT-based capabilities in cross domains of management, entrepreneurship, information systems management within the hotel industry. © 2023, Emerald Publishing Limited. KW - Artificial intelligence capability KW - Big data analytics capability KW - Co-innovation KW - Entrepreneurial orientation KW - Environmental dynamism KW - Hotel industry performance CY - Pakistan, Malaysia ER - TY - JOUR TI - Harnessing AI for value: examining the impact of AI capabilities and the mediating role of organizational agility on project value proposition AU - Mariani C. AU - Mancini M. PY - 2025 JO - International Journal of Managing Projects in Business VL - 18 IS - 8 SP - 112 EP - 143 DO - 10.1108/IJMPB-03-2025-0068 AB - Purpose – Recent advancements in artificial intelligence (AI) have transformed it from a mere technological tool to a key strategic asset, able to enhance company value propositions by enabling deeper insights, improved decision-making and innovative business models. This study empirically examines how AI capabilities influence value definition, creation and capture in project-based organizations (PBOs) and evaluates the mediating role of organizational agility. Design/methodology/approach – Drawing on Resource-Based View and Dynamic Capability View, we propose that AI capabilities constitute a unique type of organizational capability, enabling project-based organizations to utilize technological assets and other resources to boost productivity and generate economic value. The paper employs a survey instrument and a partial least squares structural equation modeling (PLS-SEM) to assess how AI capabilities impact project value processes and the mediating role of organizational agility in this relationship. Findings – The results robustly support all proposed hypotheses concerning the direct effects. Additionally, organizational agility is identified as a mediator in the relationship between AI capabilities and project value processes. Our study confirms that developing robust AI capabilities necessitates strategic investment in core AI resources. This offers implications for managers and policymakers aiming to leverage AI for fostering competitive advantage. Originality/value – This paper explores the role of AI capabilities in enhancing project value processes. It provides empirical evidence highlighting the significance of AI capabilities as essential organizational resources that enable the leveraging of AI to generate project value. The study supports the hypothesis that technology alone is insufficient for deriving value from it. This finding underscores the need for strategic investments in AI capabilities to fully capitalize on the potential of technological advancements. © 2025 Costanza Mariani and Mauro Mancini KW - AI capabilities KW - Organizational agility KW - Project value processes CY - Italy ER - TY - JOUR TI - Revolutionising Educational Management with AI and Wireless Networks: A Framework for Smart Resource Allocation and Decision-Making AU - Koukaras C. AU - Hatzikraniotis E. AU - Mitsiaki M. AU - Koukaras P. AU - Tjortjis C. AU - Stavrinides S.G. PY - 2025 JO - Applied Sciences (Switzerland) VL - 15 IS - 10 SP - 5293 DO - 10.3390/app15105293 AB - Educational institutions face growing challenges. Rising enrolment, limited budgets, and sustainability goals demand more efficient resource management and administrative decision-making. To address challenges like these, this work proposes a conceptual framework for smart campus management which integrates Artificial Intelligence (AI) and advanced wireless networks based on 5G. The framework’s design outlines layers for campus data collection (via sensors and connected devices), high-speed communication, and AI-driven analytics for decision support. By leveraging data-driven insights enabled by reliable wireless connectivity, institutions can make more informed decisions, use resources more effectively, and automate routine tasks. Envisioned AI capabilities include forecasting (for predictive maintenance and demand planning), anomaly detection (for fault or irregularity identification), and optimisation (for resource scheduling). Rather than reporting empirical results, the framework is illustrated through hypothetical scenarios (e.g., anticipating equipment maintenance, dynamically scheduling classrooms, or reallocating resources) to present potential benefits and tools for researchers. The discussion also highlights how the framework incorporates data privacy, security, and accessibility considerations to ensure inclusive adoption. Eventually, this conceptual proposal provides a roadmap for administrators and planners, guiding the adoption of AI and wireless innovations in educational management to enable more responsive, efficient governance and, ultimately, improve outcomes for students and staff. © 2025 by the authors. KW - 5G KW - artificial intelligence KW - decision support systems KW - education KW - Internet of Things KW - resource allocation KW - Curricula KW - Distance education KW - Educational robots KW - Enterprise resource planning KW - Human resource management KW - Information management KW - Personnel training KW - Resource allocation KW - Risk perception KW - Teaching KW - 5g KW - Administrative decision making KW - Conceptual frameworks KW - Decision supports KW - Decisions makings KW - Educational institutions KW - Educational management KW - Resource management KW - Resources allocation KW - Support systems KW - Students CY - Greece ER - TY - JOUR TI - Innovation Dynamics and Ethical Considerations of Agentic Artificial Intelligence in the Transition to a Net-Zero Carbon Economy AU - Mondal S. AU - Uyen N.C.T. AU - Das S. AU - Vrana V.G. PY - 2025 JO - Sustainability (Switzerland) VL - 17 IS - 19 SP - 8806 DO - 10.3390/su17198806 AB - As climate action becomes increasingly urgent, nations and institutions worldwide seek advanced technologies for practical mitigation efforts. This study examines how agentic artificial intelligence systems capable of decision-making and learning from experience drive innovation dynamics in climate change mitigation, with a particular focus on ethical considerations during the net-zero transition. The current urgency of climate action demands advanced technologies, yet organisations struggle to effectively deploy agentic AI for climate mitigation due to unclear implementation pathways and ethical consideration. This study examines the relationships among agentic AI capabilities, innovation dynamics, and net-zero transition performance, using survey data from 340 organisations across the manufacturing, energy, and technology sectors, and analysed using structural equation modelling. Based on dynamic capabilities theory, this research proposes a novel theoretical model that examines how agentic AI drives innovation dynamics in climate change mitigation within governance frameworks that encompass transparency, accountability, and environmental justice. Results reveal significant mediation effects of innovation dynamics, dynamic capabilities, and ethical considerations, while environmental context negatively moderates innovation and ethical pathways. Findings suggest that overly restrictive ethical considerations can lead to implementation delays that undermine the urgency of climate action. This study proposes three solutions: (1) adaptive ethical protocols adjusting governance intensity based on climate risk severity, (2) pre-approved ethical templates reducing approval delays by 60%, and (3) stakeholder co-design processes building consensus during development. The research advances dynamic capabilities theory for AI contexts by demonstrating how AI-enabled sensing, seizing, and reconfiguring capabilities create differentiated pathways to climate performance. This study provides empirical validation of the responsible innovation framework, identifies asymmetric environmental contingencies, and offers evidence-based guidance for organisations implementing agentic AI for climate action. © 2025 by the authors. KW - agentic AI KW - climate innovation KW - dynamic capabilities theory KW - ethical considerations KW - artificial intelligence KW - carbon KW - climate change KW - environmental impact KW - environmental justice KW - ethics KW - innovation KW - stakeholder CY - Greece ER - TY - JOUR TI - MANAGEMENT STRATEGIES FOR AI-BASED MUSIC STARTUPS AU - Malik M. AU - Bhatnagar S. AU - Patil V.V. AU - Pallavi M. AU - Khanna L. AU - Gupta V. PY - 2025 JO - ShodhKosh: Journal of Visual and Performing Arts VL - 6 IS - 2s SP - 139 EP - 148 DO - 10.29121/shodhkosh.v6.i2s.2025.6693 AB - The music industry, has been transformed by the introduction of new startups due to the advent of the Artificial Intelligence (AI) where machine learning, deep learning, and natural language processing are used to redefine the music creation, production, and distribution. The paper will discuss the management practices which may be utilized in the success of AI-based music startups such as the organizational models, innovation models and the mechanisms of sustainable growth. Applications of AI to music have touched various themes such as generative music composition, machine learning-based playlist optimization, machine mastering, machine-generated music and emotion-based playlists, and audience analytics. These startups are difficult to administer as they will require a mediating zone between the invention of technology and aesthetic arts and be necessitated by inter-disciplinary management, which will incorporate the engineering precision and aesthetic sense. The strategic aspects found to be agile development cycles, ethical data governance, intellectual property management and collaboration with artists and technologists. The paper is devoted to the dynamic business strategies, such as the so-called AI-as-a-Service (AIaaS) and subscription-based models which can be scaled and made to maintain the relationships with customers. Similarly, strategic cooperation with record labels, streaming applications as well as independent artists are crucial agents of market entry. Acquiring talent strategies should give priority to hybrid skills with data science, good engineering and music theory to be able to maintain product relevance and continuity of innovation. Issues like data bias, ambiguity in copyright and creative ownership are resolved by transparent algorithm design and management practices which are stakeholder-centric. The conclusion of the paper is that effective AI-driven music startups are built on a dynamic leadership, cross-domain partnership, and constant ethical review of AI work. With creative innovation and sustainability of business, these startups can transform the entire music ecosystem across the world, enabling a more personalized, intelligent, and inclusive future of music creation and consumption. © 2025 The Author(s). KW - AI-Based Startup Music KW - Creative Industries KW - Digital Entrepreneurship KW - Ethical AI KW - Generative Composition KW - Ip KW - Machine Learning KW - Management KW - Management of Innovation KW - Music Analytics KW - Music Technology CY - India ER - TY - JOUR TI - Seizing the opportunity window of artificial intelligence in China: Towards an innovation policy mix framework for emerging technologies from an evolution perspective AU - Liu J. AU - Wang M. AU - Kang X. AU - Zhang X. AU - Chen X. PY - 2022 JO - Systems Research and Behavioral Science VL - 39 IS - 3 SP - 397 EP - 414 DO - 10.1002/sres.2875 AB - China's innovation policies for artificial intelligence (AI) are widely considered as having made a remarkable achievement, which offers us a pertinent case to explore how to design and implement an effective innovation policy mix for an emerging technology. On the basis of literature on the characteristics of emerging technologies and the typology of innovation policy, this paper proposes a four-dimensional framework. It then conducts a categorical principal component analysis and a k-prototype cluster analysis by using data on 116 China's AI policy programmes from 2009 to 2021, which show that the characteristics of the innovation policy mix can be captured by the four dimensions. Furthermore, our analysis indicates that China's AI innovation policy mix evolves following the changing characteristics of AI technology over time. This paper has some implications for designing AI innovation policy mixes in other countries and designing innovation policy mixes for other emerging technologies. © 2022 John Wiley & Sons Ltd. KW - artificial intelligence KW - emerging technologies KW - innovation policy mix KW - Cluster analysis KW - Principal component analysis KW - Seizing KW - Artificial intelligence technologies KW - Design and implements KW - Emerging technologies KW - Four dimensions KW - Innovation policies KW - Innovation policy mix KW - K-prototype KW - Opportunity windows KW - Principal-component analysis KW - Typology of innovation KW - Artificial intelligence CY - China ER - TY - JOUR TI - Artificial intelligence transforming healthcare and nursing: A comprehensive bibliometric analysis AU - Balpande V. AU - Rewatkar P. AU - Dhole P. AU - Alwadkar I. AU - Gomase K. PY - 2025 JO - Multidisciplinary Reviews VL - 8 IS - 9 SP - e2025267 DO - 10.31893/MULTIREV.2025267 AB - Artificial intelligence (AI) is revolutionizing healthcare and nursing by enhancing decision-making, streamlining processes, and improving patient outcomes. This bibliometric analysis explores the evolving landscape of AI applications in healthcare and nursing, highlighting key research trends, influential publications, and emerging technologies. The study examines the integration of AI tools, such as machine learning, natural language processing, and predictive analytics, in areas like disease diagnosis, personalized treatment, patient monitoring, and administrative efficiency. It also investigates the role of AI in nursing practice, emphasizing its potential to support clinical decision-making, optimize care delivery, and alleviate workforce challenges. By analyzing a vast corpus of scholarly publications, this study identifies pivotal themes, prominent authors, and leading institutions driving AI innovation in healthcare. Key findings reveal an exponential growth in research output, particularly in leveraging AI for chronic disease management, telemedicine, and predictive risk modeling. Ethical considerations, including data privacy, algorithmic bias, and patient safety, emerge as critical focal points, underscoring the need for robust frameworks to ensure responsible AI adoption. Furthermore, the study highlights interdisciplinary collaboration as a cornerstone for successful AI integration, bridging the gap between technological advancements and clinical practice. Despite its transformative potential, challenges such as skill gaps, resistance to change, and resource constraints remain barriers to widespread AI adoption in healthcare and nursing. This comprehensive analysis provides valuable insights for researchers, practitioners, and policymakers, offering a roadmap for leveraging AI to address current and future healthcare challenges. The findings underscore the transformative role of AI in reshaping healthcare delivery, enhancing nursing practice, and ultimately improving patient care on a global scale. By synthesizing current knowledge and identifying future directions, this study contributes to advancing the understanding of AI’s impact on healthcare and nursing, paving the way for more efficient, equitable, and patient-centered care systems. © 2025, Malque Publishing. All rights reserved. KW - AI applications in nursing KW - AI in nursing care KW - artificial intelligence in healthcare KW - bibliometric analysis KW - digital transformation in medicine KW - healthcare technology trends CY - India ER - TY - JOUR TI - The Computational Study of Old English AU - Martín Arista J. PY - 2025 JO - Encyclopedia VL - 5 IS - 3 SP - 137 DO - 10.3390/encyclopedia5030137 AB - This entry presents a comprehensive overview of the computational study of Old English that surveys the evolution from early digital corpora to recent artificial intelligence applications. Six interconnected domains are examined: textual resources (including the Helsinki Corpus, the Dictionary of Old English Corpus, and the York-Toronto-Helsinki Parsed Corpus), lexicographical resources (analysing approaches from Bosworth–Toller to the Dictionary of Old English), corpus lemmatisation (covering both prose and poetic texts), treebanks (particularly Universal Dependencies frameworks), and artificial intelligence applications. The paper shows that computational methodologies have transformed Old English studies because they facilitate large-scale analyses of morphology, syntax, and semantics previously impossible through traditional philological methods. Recent innovations are highlighted, including the development of lexical databases like Nerthusv5, dependency parsing methods, and the application of transformer models and NLP libraries to historical language processing. In spite of these remarkable advances, problems persist, including limited corpus size, orthographic inconsistency, and methodological difficulties in applying modern computational techniques to historical languages. The conclusion is reached that the future of computational Old English studies lies in the integration of AI capabilities with traditional philological expertise, an approach that enhances traditional scholarship and opens new avenues for understanding Anglo-Saxon language and culture. © 2025 by the author. KW - artificial intelligence KW - computational linguistics KW - corpus lemmatisation KW - digital lexicography KW - historical language processing KW - natural language processing KW - Old English KW - universal dependencies CY - Spain ER - TY - JOUR TI - Generative AI in IoT: transforming cloud services with intelligent automation AU - Raiyani A. AU - Pandya S. AU - Jani K. AU - Vyas Z. PY - 2025 JO - International Journal of Grid and Utility Computing VL - 16 IS - 5-6 SP - 579 EP - 587 DO - 10.1504/IJGUC.2025.148544 AB - This paper explores how Generative AI can revolutionise cloud services, enabling smart automation over the Internet of Things (IoT). It outlines the potential use of Generative AI in smart homes, industrial automation, healthcare and transportation, proposing an architecture that integrates cloud computing, edge computing and IoT computing for Generative AI. The integration allows IoT devices to assess data, make independent decisions and manage themselves based on customised user experiences and functionality. Case studies and experimental evaluations demonstrate significant productivity, efficiency and user satisfaction improvements. However, challenges such as data heterogeneity, security issues and ethical considerations must be addressed for reliable AI-enabled IoT applications. Collaboration with academicians, business experts and governmental representatives is suggested to build trustworthy spaces for integrating AI capabilities into IoT cloud services, leveraging the joint effort between Gen AI and IoT for innovation, productivity and competitiveness. Copyright © 2025 Inderscience Enterprises Ltd. KW - AI-Driven IoT KW - cloud services KW - generative AI KW - intelligent automation KW - internet of things KW - Artificial intelligence KW - Automation KW - Distributed database systems KW - Edge computing KW - Intelligent buildings KW - Smart homes KW - User experience KW - AI-driven internet of thing KW - Case-studies KW - Cloud services KW - Cloud-computing KW - Edge computing KW - Generative AI KW - Industrial automation KW - Intelligent automation KW - Smart homes KW - Users' experiences KW - Internet of things CY - India ER - TY - JOUR TI - Artificial Intelligence-Driven Supply Chain Agility and Resilience: Pathways to Competitive Advantage in the Hotel Industry AU - Elshaer I.A. AU - Azazz A.M.S. AU - Aljoghaiman A. AU - Mansor M. AU - Salama M.A. AU - Fayyad S. PY - 2026 JO - Logistics VL - 10 IS - 1 SP - 5 DO - 10.3390/logistics10010005 AB - Background: The extraordinary disturbances faced by the hotel industry, ranging from worldwide health problems to political instability and climate change, have highlighted the insistent need for more resilient and agile supply chain (SC) systems. This study explored how artificial intelligence (AI) capabilities can generate competitive advantage (CA) through supply chain agility (SCA) and supply chain resilience (SCR) as mediators and competitive pressure (CP) as a moderator. Methods: Drawing on the resource-based view (RBV) framework, we suggested and empirically tested the study model. Using data collected from 432 hotel managers and analyzed using Partial Least Squares Structural Equation Modelling (SEM-PLS). Results: the results reveal that AI-driven SC can significantly strengthen SCA and SCR. Furthermore, SCA and SCR can act as powerful mediators, and CP can strengthen the tested relationships (the links from AI adoption and CA) as a moderator. Conclusions: The study made several theoretical and practical contributions by integrating AI capabilities into SCR and SCA frameworks in the hotel and tourism context, and by providing practical evidence for professionals aiming to leverage AI-driven SC tools to navigate uncertainty and create sustainable CA. © 2025 by the authors. KW - AI KW - competitive advantage KW - competitive pressure KW - resilience KW - supply chain agility CY - Saudi Arabia, Egypt ER - TY - JOUR TI - Responsible AI and employee service innovation behavior: A sequential mediation model of AI self-efficacy and AI crafting AU - Xu Y. AU - Xie P. AU - Naeem R.M. AU - Almugren I. AU - Hameed Z. AU - Agarwal S. PY - 2026 JO - Technological Forecasting and Social Change VL - 224 SP - 124470 DO - 10.1016/j.techfore.2025.124470 AB - While the use of artificial intelligence (AI) has become an effective tool for transforming individuals and organizations, adopting a responsible approach to AI systems is imperative. Drawing on conservation of resources theory and social learning theory, this study examines how responsible AI enhances employees' service innovation behavior via employee AI self-efficacy and employee AI crafting, with a particular focus on the moderating role of leader AI crafting. We tested the proposed relationships using structural equation modeling with data collected from 335 U.S. employees working in various service organizations. The findings demonstrate that the indirect effect of responsible AI on employee service innovation behavior is mediated serially by employee AI self-efficacy and employee AI crafting. Furthermore, leader AI crafting strengthens the positive relationship between responsible AI and employee AI self-efficacy. This study contributes to the AI and management literature by highlighting the importance of responsible AI systems in promoting service innovation behavior among employees. This study addresses both theoretical and practical dimensions, as well as proposing directions for future research. © 2025 Elsevier Inc. KW - Employee AI crafting KW - Employee AI self-efficacy KW - Leader AI crafting KW - Responsible AI KW - Service innovation behavior KW - Artificial intelligence KW - Artificial intelligence systems KW - Conservation of resources theories KW - Effective tool KW - Employee artificial intelligence crafting KW - Employee artificial intelligence self-efficacy KW - Leader artificial intelligence crafting KW - Responsible artificial intelligence KW - Self efficacy KW - Service innovation KW - Service innovation behavior KW - artificial intelligence KW - innovation KW - numerical model KW - social theory KW - theoretical study KW - Human resource management CY - India ER - TY - JOUR TI - ELSA Labs for responsible AI: a novel approach for addressing ethical, legal, social issues AU - Wang H. AU - Blok V. AU - van Hilten M. PY - 2025 JO - Journal of Responsible Innovation VL - 12 IS - 1 SP - 2563944 DO - 10.1080/23299460.2025.2563944 AB - Artificial Intelligence (AI) is rapidly transforming our society, offering remarkable opportunities but also raising significant Ethical, Legal, and Social Aspects (ELSA) that should be addressed for responsible development. Some existing approaches to responsible AI successfully translate ELSA into concrete AI design practices but risk overlooking power dynamics and structural issues, while others excel at fostering dialogue yet struggle to turn insights into real design changes. This paper develops the ELSA Lab approach as a promising way to bridge this gap. Building on research in Responsible Research and Innovation (RRI), Social Labs, and Quadruple Helix (QH) collaboration, we show how this approach combines the strengths of practical, solutionist strategies with sufficient negotiation and reflexivity. We not only outline the key features of this ELSA Lab approach theoretically but also present a hands-on work process for putting it into practice. This approach aims to drive a systemic shift toward more responsible AI. © 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. KW - ELSA KW - Quadruple Helix stakeholder KW - responsible research and innovation KW - social lab KW - trustworthy AI CY - Netherlands ER - TY - JOUR TI - Bragging About Valuable Resources? The Dual Effect of Companies’ AI and Human Self-Promotion AU - Vorobeva D. AU - Pinto D.C. AU - González-Jiménez H. AU - António N. PY - 2025 JO - Psychology and Marketing VL - 42 IS - 6 SP - 1680 EP - 1699 DO - 10.1002/mar.22198 AB - As companies actively invest in self-promotion of Artificial Intelligence (AI) empowered services to sustain their competitive advantage, there is a growing potential for such promotional activities to backfire. Bridging signaling theory with the resource-based view, this research reveals that companies’ self-promotion of AI resources can reduce customers’ willingness to engage with AI-based (vs. human-based) services. Four studies, including text mining and experiments, demonstrate that companies’ self-promotion of AI-based resources has a detrimental effect on willingness to engage, and concurrently perceived as exaggeration. In contrast, companies’ self-promotion about human-related resources yields beneficial outcomes, since such promotional signals contribute to the enhancement of human capital. The findings suggest that self-discrepancy and trust are the key underlying factors driving the effects as customers may experience a discrepancy between their expectations of human-like service interactions and actual AI capabilities. Additionally, findings reveal the moderating effect of honest (vs. self-promotional) framing on the relationship between service type (AI vs. human) and willingness to engage. Customer perceptions of AI appear less influenced by presentation style compared to perceptions of human resources. This research provides valuable insights into how customers respond to companies’ self-promotion of AI resources and emphasizes the need for promotional alignment with customers’ expectations about AI. © 2025 Wiley Periodicals LLC. KW - artificial intelligence KW - bragging KW - competitive advantage KW - self-discrepancy KW - self-promotion CY - Portugal, Spain ER - TY - JOUR TI - Generative AI changes the book publishing industry: reengineering of business processes AU - Ryzhko O. AU - Krainikova T. AU - Vodolazka S. AU - Sokolova K. PY - 2024 JO - Communication and Society VL - 37 IS - 3 Special Issue SP - 255 EP - 271 DO - 10.15581/003.37.3.255-271 AB - The research defines main direction of book publishing houses reengineering based on the analysis of successful cases of AI use in publishing business. The timeline of the research started in August 2023 and was summarised in the beginning of January 2024. The main methods were expert interview, monitoring of international and Ukrainian internet platforms, and document analysis. The study showed that the main aspects of business processes reengineering in publishing houses, based on the use of AI, are: (1) development of business strategies and plans; (2) development of digital spaces in publishing houses; (3) emerging of new professions; (4) discussions and their summaries; (5) received manuscripts check; (6) finding plagiarism; (7) preparation of creative, advertising, and presentation materials; (8) working with numbers and databases. The recommendations on the use of AI in business processes are extracted from the policies of the organisations connected with the book publishing industry. They are presented in the convenient table for further use. One of the study results showed that Ukrainian publishing houses discuss the capabilities of AI for generating different types and formats of content, and based on that, AI capabilities for reengineering are considered. One of the biggest challenges, created by AI, is that the technology develops faster than people can perceive so they struggle to describe the technology itself and its impact. It means that we should adjust to the changes, caused by exponential development of AI, finding resources to overcome unequal access to AI capabilities in the process of specialists’ preparation. © 2024 Communication & Society. KW - book publishing KW - book publishing houses KW - business processes KW - generative AI KW - Reengineering KW - technological innovations CY - Ukraine ER - TY - JOUR TI - A systematic review of generative AI: importance of industry and startup-centered perspectives, agentic AI, ethical considerations & challenges, and future directions AU - Patel K. AU - Shah M. AU - Qureshi K.M. AU - Qureshi M.R.N. PY - 2026 JO - Artificial Intelligence Review VL - 59 IS - 1 SP - 7 DO - 10.1007/s10462-025-11435-z AB - Generative Artificial Intelligence (GenAI) is rapidly redefining the landscape of work organizations and society at large. GenAI has rapidly evolved from rule-based symbolic systems ofThe 1940 s to advanced deep learning architectures capable of producing human-like content across modalities, such as text, images, audio, and video. This review focuses on current emerging trends, such as large concept models and critical comparisons of tools, including ChatGPT, Gemini, and Claude. This study synthesizes evidence of GenAI’s essential role across major industries, revealing transformative applications in the finance, cloud and IT, healthcare, education, and energy sectors. The paper also highlights the unique opportunities GenAI offers for start-ups, enabling agile projects to leverage cutting-edge technology for competitive advantage. However, the deployment of GenAI systems through edge devices also raises critical challenges related to ethics, transparency, bias, accountability, computational issues, and many more. To address these complexities, this paper examines emerging approaches such as AI agents, agentic AI, and multi-agent systems that aim to extend the functionality of GenAI through autonomy, goal-directed behavior, and collaborative intelligence. It discovers novel incorporations with agentic AI architecture, such as BabyAGI, and discusses emerging issues of coordination, hallucination, and security risks. The findings reveal persistent challenges related to scalability, interpretability, and regulatory compliance while identifying future research directions toward developing more sophisticated, ethical, and accessible GenAI systems that will continue to reshape technological landscapes and societal interactions. This systematic review informs researchers, academicians, data scientists, and developers about the latest advancements in GenAI and highlights its applications and role across various industries, as well as supporting practitioners and scholars in staying current with the rapidly evolving landscape of generative technologies. © The Author(s) 2025. KW - Agentic AI KW - AI agents KW - Artificial intelligence KW - Compliance KW - Ethical frameworks KW - GenAI evolution KW - Generative artificial intelligence KW - Industrial GenAI KW - Large concept model KW - Large language model KW - Multi agent systems KW - Autonomous agents KW - Competition KW - Ethical technology KW - Intelligent agents KW - Regulatory compliance KW - Agentic AI KW - AI agent KW - Compliance KW - Concept model KW - Ethical framework KW - Generative artificial intelligence KW - Generative artificial intelligence evolution KW - Industrial generative artificial intelligence KW - Language model KW - Large concept model KW - Large language model KW - Multiagent systems (MASs) KW - Multi agent systems CY - Australia, India, Saudi Arabia ER - TY - JOUR TI - Integrating AI Ethics and Sustainability Through Experiential and Data-Driven Curriculum Innovation at PCCOE AU - Vivekanandan V. AU - Rajeswari R. PY - 2026 JO - Journal of Engineering Education Transformations VL - 39 IS - Special Issue 2 SP - 213 EP - 222 DO - 10.16920/jeet/2026/v39is2/26026 AB - A novel undergraduate course, Professional Ethics and Sustainability in the Age of AI, bridges critical gaps in engineering education by combining experiential learning with outcome-based assessment. Developed at Pimpri Chinchwad College of Engineering (PCCOE), the curriculum employs four research-grounded activities: historical case analyses of ethical disasters, TARES Test evaluations of AI advertisements, governance quizzes on surveillance systems, and multi-stakeholder role-plays about algorithmic grading. Interim results from 46-60 participants demonstrate significant competencies development: 91.3% of students recognize AI's ethical influence, 95.7% show heightened emotional awareness, with strong performance in persuasion literacy (M=4.40/5) and governance knowledge (M=9.43/10). Structured assessments reveal 81.8% attainment in ethical reasoning and 80.5% in communication/governance skills, while qualitative analysis uncovers sophisticated engagement with fairness, transparency, and accountability principles. Built on Kohlberg's moral development theory, UNESCO's ESD framework, and IEEE's Ethically Aligned Design, the course uniquely integrates macro ethical principles with micro ethical skill-building. Final evaluations of the sustainability-focused AI mini-projects showed attainment levels of 82.4% for CO2 and 84.1% for CO4, completing the comprehensive outcomes-based assessment cycle.. This model offers engineering educators a replicable blueprint for cultivating professional judgment in AI ethics through three key innovations: (1) contextualized historical analogies, (2) measurable persuasion literacy benchmarks, and (3) stakeholder negotiation simulations that mirror real-world tech governance challenges. The demonstrated success of this active learning approach provides empirical support for transforming traditional ethics education in response to emerging technologies. © 2026, Rajarambapu Institute Of Technology. All rights reserved. KW - AI Ethics Education KW - Experiential Learning KW - Governance Competencies KW - Outcome-Based Assessment KW - Sustainability Literacy CY - India ER - TY - JOUR TI - The role of knowledge creation modes in architectural innovation AU - Azzam A. AU - He Q. AU - Sarpong D. PY - 2020 JO - Strategic Change VL - 29 IS - 1 SP - 77 EP - 87 DO - 10.1002/jsc.2312 AB - Knowledge creation modes (especially socialization and internalization) enhance architectural innovation (AI) capability of U.K. manufacturing firms. AI is the reconfiguration of product or process components and creating completely new interfaces between them. Knowledge creation modes enhance firms' AI to create new products while utilizing their architectural knowledge. Knowledge socialization and internalization are the most important modes that affect AI. Socialization helps to share tacit knowledge while internalization enables individuals to absorb and embody accumulated know-how to envision new product ideas. © 2019 John Wiley & Sons, Ltd. CY - United Kingdom ER - TY - JOUR TI - Implementing Artificial Intelligence in Critical Care Medicine: a consensus of 22 AU - Cecconi M. AU - Greco M. AU - Shickel B. AU - Angus D.C. AU - Bailey H. AU - Bignami E. AU - Calandra T. AU - Celi L.A. AU - Einav S. AU - Elbers P. AU - Ercole A. AU - Gómez H. AU - Gong M.N. AU - Komorowski M. AU - Liu V. AU - Park S. AU - Sarwal A. AU - Seymour C.W. AU - Zampieri F.G. AU - Taccone F.S. AU - Vincent J.-L. AU - Bihorac A. PY - 2025 JO - Critical Care VL - 29 IS - 1 SP - 290 DO - 10.1186/s13054-025-05532-2 AB - Artificial Intelligence (AI) is rapidly transforming the landscape of critical care, offering opportunities for enhanced diagnostic precision and personalized patient management. However, its integration into ICU clinical practice presents significant challenges related to equity, transparency, and the patient-clinician relationship. To address these concerns, a multidisciplinary team of experts was established to assess the current state and future trajectory of AI in critical care. This consensus identified key challenges and proposed actionable recommendations to guide AI implementation in this high-stakes field. Here we present a call to action for the critical care community, to bridge the gap between AI advancements and the need for humanized, patient-centred care. Our goal is to ensure a smooth transition to personalized medicine while, (1) maintaining equitable and unbiased decision-making, (2) fostering the development of a collaborative research network across ICUs, emergency departments, and operating rooms to promote data sharing and harmonization, and (3) addressing the necessary educational and regulatory shifts required for responsible AI deployment. AI integration into critical care demands coordinated efforts among clinicians, patients, industry leaders, and regulators to ensure patient safety and maximize societal benefit. The recommendations outlined here provide a foundation for the ethical and effective implementation of AI in critical care medicine. © The Author(s) 2025. KW - Artificial intelligence KW - Critical care medicine KW - Ethics KW - Healthcare innovation KW - Personalized medicine KW - Artificial Intelligence KW - Consensus KW - Critical Care KW - Humans KW - Intensive Care Units KW - Article KW - artificial intelligence KW - clinical practice KW - emergency ward KW - health care KW - human KW - intensive care KW - intensive care medicine KW - intensive care unit KW - multidisciplinary team KW - patient care KW - patient safety KW - person centered care KW - personalized medicine KW - consensus KW - intensive care KW - organization and management KW - procedures CY - Belgium ER - TY - JOUR TI - Emerging AI Regimes and Contemporary Filmmaking in Nigeria: Governance, Practice, and Creative Futures AU - Bello R.-W. AU - Ogundokun R.O. AU - Owolawi P.A. AU - Wyk E.A.V. AU - Tu C. AU - Imoru O. PY - 2025 JO - Architecture Image Studies VL - 6 IS - 3 SP - 1056 EP - 1063 DO - 10.62754/ais.v6i3.380 AB - Filmmaking is rapidly transforming by Artificial Intelligence (AI) across the globe, with Nigeria’s vibrant film industry (i.e. Nollywood) emerging as an adopter and a contested site for technological experimentation. How technological, institutional, and regulatory frameworks as emerging AI regimes are reforming creative practices and industry structures in Nigeria is examined in this study. Also, the integration of AI tools was explored in this study across pre-production, production, postproduction, and distribution by situating Nollywood within global discourses of platform capitalism and algorithmic governance. The study highlights several case studies such as AI-enabled editing, subtitling, visual effects, and distribution on digital platforms. It addresses the challenges in governance related to intellectual property, labor, and cultural representation. The study, by drawing on a multi-level framework of AI regimes, emphasizes that in time to come, Nigerian filmmaking will depend on innovation that is balanced with protections for creative labor and cultural integrity. The study concludes by recommending policies and practices for building a comprehensive, sustainable AI-enabled film ecosystem in Nigeria and Africa in general. © by AP2 on Creative Commons 4.0 KW - Artificial Intelligence KW - Filmmaking KW - Nigeria KW - Nollywood KW - Regime. CY - Nigeria, South Africa, Canada ER - TY - JOUR TI - How the First Medical Imaging Cancer Atlas EUCAIM Was Populated: The Experience of a Reference Hospital. AU - Penadés Blasco A. AU - Cerdá Alberich L. AU - de Marco García A. AU - Soler Pons C. AU - Marín Radoszynski I. AU - Martínez R. AU - Segrelles-Quilis D. AU - Blanquer I. AU - Martí-Bonmatí L. PY - 2025 JO - Open Research Europe VL - 5 SP - 310 DO - 10.12688/openreseurope.21016.1 AB - The fragmentation and decentralization of medical data, including radiological imaging, continue to challenge large-scale observational research across Europe. Artificial intelligence (AI) applied to big datasets is transforming diagnosis and treatments towards precision medicine across many diseases, yet the lack of findable, accessible, and interoperable datasets still limits model development, validation, and final clinical translation. The European Federation for Cancer Images (EUCAIM) project was launched in 2023 to address these challenges by establishing a secure centralized and federated infrastructure for the secondary use of large-scale oncological imaging and related clinical data. By consolidating fragmented datasets, EUCAIM lays the groundwork for harmonized data governance and trusted cross-border sharing. Implementing a robust documentation framework is essential to ensure regulatory compliance, safeguard data integrity, and support secure data flows across institutional and national boundaries, fully aligned with European regulations and ethical standards. EUCAIM builds on the AI for Health Imaging (AI4HI) initiative (PRIMAGE, CHAIMELEON, EuCanImage, ProCancer-I, INCISIVE) and integrates over 94 partners and more than 180 stakeholders spanning medical imaging, high performance computing, data standardization, innovation, and legal compliance. This large collaborative ecosystem reinforces EUCAIM’s role as a reference for General Data Protection Regulation (GDPR) and European Health Data Space Regulation (EHDSR) adherence. This publication presents the real-world experience of integrating imaging and clinical data from a reference university hospital into the EUCAIM infrastructure. It outlines the procedural, ethical, and legal challenges encountered, and details the strategies implemented to ensure compliance with data protection regulations, including privacy, security, and ethical standards. These insights offer a practical framework for future large-scale oncological imaging datasets harmonization and AI development, contributing to scalable, reproducible, and legally compliant research that strengthens Europe’s capacity for trustworthy AI-driven oncology solutions. Copyright: © 2025 Penadés Blasco A et al. KW - Artificial Intelligence KW - cancer research KW - data governance KW - federated infrastructures KW - innovation KW - medical imaging KW - sustainability CY - Spain ER - TY - JOUR TI - Adding External Artificial Intelligence (AI) into Internal Firm-Wide Smart Dynamic Warehousing Solutions AU - Hamilton J.R. AU - Maxwell S.J. AU - Ali S.A. AU - Tee S. PY - 2024 JO - Sustainability (Switzerland) VL - 16 IS - 10 SP - 3908 DO - 10.3390/su16103908 AB - This study advances knowledge in the AI field. It provides deep insight into current industry generative AI inclusion systems. It shows both literature and practical leading industry operations can link, overlap, and complement each other when it comes to AI and understanding its complexities. It shows how to structurally model and link AI inclusions towards delivering a suitable sustainability positioning. It shows approaches to integrate external AI contributions from one firm into another firm’s intelligences developments. It shows how to track, and maybe benchmark, the progress of such AI inclusions from either an external or an integrated internal software developer perspective. It shows how to understand and create a more sustainable, AI-integrated business positioning. This study considers firm artificial intelligence (AI) and the inclusion of additional external software developer engineering as another AI related pathway to future firm or industry advancement. Several substantive industrial warehousing throughput areas are discussed. Amazon’s ‘smart dynamic warehousing’ necessitates both digital and generative ongoing AI system prowess. Amazon and other substantive, digitally focused industry warehousing operations also likely benefit from astute ongoing external software developer firm inclusions. This study causally, and stagewise, models significant global software development firms involved in generative AI systems developments—specifically ones designed to beneficially enhance both warehouse operational productivity and its ongoing sustainability. A structural equation model (SEM) approach offers unique perspectives through which substantive firms already using AI can now model and track/benchmark the relevance of their prospective or existing external software developer firms, and so create rapid internal ‘net-AI’ competencies incorporations and AI capabilities developments through to sustainable operational and performance outcomes solutions. © 2024 by the authors. KW - acquisition KW - artificial intelligence KW - assimilation KW - autonomous robots KW - collective knowledge KW - competitiveness KW - deep machine learning KW - digital network KW - generation AI system KW - innovation KW - productive capacities KW - strategic risk KW - sustainable performance KW - transformation KW - Amazonia KW - artificial intelligence KW - business KW - data acquisition KW - digitization KW - innovation KW - knowledge KW - machine learning KW - robotics KW - software KW - strategic approach KW - sustainability CY - Australia ER - TY - JOUR TI - HitHire: The future of ethical, fair, and sustainable AI recruitment – A governance framework AU - Albaroudi E. AU - Mansouri T. AU - Hatamleh M. AU - Alameer A. PY - 2026 JO - Array VL - 29 SP - 100592 DO - 10.1016/j.array.2025.100592 AB - Artificial Intelligence (AI) is transforming recruitment but remains susceptible to algorithmic bias and environmental inefficiencies. This paper presents HitHire, a pilot fairness- and sustainability-aware AI hiring platform tailored to the Saudi Arabian context and aligned with Vision 2030 goals. HitHire integrates large language models (LLMs), adversarial debiasing, Shapley Additive Explanations (SHAP), and real-time carbon tracking to ensure transparent and equitable candidate ranking. Evaluated on 350 anonymized CVs across four job roles (web development, finance, human resources, and data science) using a 70/20/10 train/test/validation split, HitHire achieves notable improvements in fairness metrics—Statistical Parity Difference (SPD) for gender = 0.0156 and Disparate Impact (DI) for nationality = 1.2387—while maintaining strong predictive performance (F1 = 0.96 compared to a baseline of 0.80). The system achieves over a 40% reduction in operational CO2 emissions, with inference energy consumption of 0.003 kWh per query. In a three-month pilot study involving 23 HR professionals within a large Saudi organization, 87% of participants rated system trust at 4 out of 5 or higher. These findings contribute to national digital ethics strategies such as the Saudi Green Initiative, which emphasizes carbon neutrality and sustainable innovation. © 2025 The Author(s) KW - Adversarial Debiasing KW - AI governance KW - Algorithmic Bias KW - Ethical recruitment KW - Explainable AI KW - Fairness in AI KW - Human-in-the-Loop systems KW - Saudi Vision 2030 KW - SHAP explainability KW - Sustainable AI KW - Artificial intelligence KW - Employment KW - Ethical technology KW - Personnel KW - Sustainable development KW - Adversarial debiasing KW - Algorithmic bias KW - Algorithmics KW - Artificial intelligence governance KW - De-biasing KW - Ethical recruitment KW - Explainable artificial intelligence KW - Fairness in artificial intelligence KW - Human-in-the-loop KW - Human-in-the-loop system KW - Loop systems KW - Saudi vision 2030 KW - Shapley KW - Shapley additive explanation explainability KW - Sustainable artificial intelligence KW - Carbon CY - United Kingdom ER - TY - JOUR TI - Exploring perspectives on artificial intelligence: Awareness, attitudes, and knowledge among health majors students at Saudi universities AU - Albaik M. AU - Al-Qahtani S.A. AU - Mantargi M.J.S. AU - Alghamdi A. AU - Sindi I.A. AU - Sheikh R.A. AU - Kamel M. AU - Kurdi L.A.F. PY - 2025 JO - PeerJ Computer Science VL - 11 SP - e3255 DO - 10.7717/peerj-cs.3255 AB - Background: The world is witnessing tremendous development in the field of new digital tools, including artificial intelligence (AI), in all sectors, including the health and medical sectors. However, educational and training opportunities in the field of artificial intelligence remain nascent and limited. Hence, this study aims to assess the awareness, attitudes, and knowledge of artificial intelligence among students of health specialties in Saudi universities and to assess whether artificial intelligence is viewed as a beneficial innovation or a potential threat to healthcare roles. Methods: This cross-sectional study included 498 male and female students from various health colleges at different Saudi universities. The participants completed an online questionnaire adapted from previous studies to assess their awareness, attitudes, and knowledge of artificial intelligence. Descriptive statistics and chi-square analyses were conducted to explore the associations between variables related to artificial intelligence and other factors. Results: Most students showed a high level of awareness of artificial intelligence, with social media being identified as their main source of information about artificial intelligence. While students’ attitudes towards AI were generally positive, for example, 89.2% of the students believed that AI would be crucial to the future of healthcare, 76.7% supported AI education, and 78.3% were keen to increase their knowledge of AI. In terms of assessing students’ knowledge of AI, the study revealed that the participating students had moderate knowledge of AI principles and skills, with significant gaps in understanding specific AI capabilities and functions. Conclusions: While healthcare students in Saudi Arabia demonstrate strong awareness and positive attitudes towards AI, there are significant gaps in practical knowledge. These findings underscore the need for tailored educational strategies to better integrate AI into curricula, thus preparing future healthcare professionals to effectively leverage AI. © Copyright 2025 Albaik et al. Distributed under Creative Commons CC-BY 4.0 KW - Artificial Intelligence KW - Artificial intelligence (AI) KW - Computer Education KW - Education KW - Emerging Technologies KW - Health sciences KW - Human-Computer Interaction KW - Saudi universities KW - Student perceptions KW - Artificial intelligence KW - Education computing KW - Engineering education KW - Health care KW - Health risks KW - Personnel training KW - Artificial intelligence KW - Computer education KW - Computer interaction KW - Cross-sectional study KW - Digital tools KW - Emerging technologies KW - Health science KW - Potential threats KW - Saudi university KW - Student perceptions KW - Students CY - Saudi Arabia, Egypt ER - TY - JOUR TI - How AI innovation shapes supplier concentration under the triple helix framework: Evidence from emerging markets AU - Wu W. AU - Xu J. AU - Li Y. AU - Fan Y. AU - Tang S. PY - 2026 JO - Technological Forecasting and Social Change VL - 226 SP - 124579 DO - 10.1016/j.techfore.2026.124579 AB - This study investigates how artificial intelligence innovation influences supplier concentration and management efficiency among firms in emerging markets. Using patent data from Chinese A share listed companies between 2014 and 2023, we construct firm level measures of AI innovation and examine their association with upstream supply chain outcomes. The empirical analysis employs fixed effects regression models to control for unobserved firm heterogeneity and common temporal shocks. Results indicate that AI related patents are positively and significantly associated with supplier concentration, suggesting that firms with stronger AI capabilities tend to rely on fewer upstream partners. We also find that AI innovation is positively associated with supplier management efficiency, as reflected in higher inventory turnover and faster accounts payable cycles. Further analysis reveals that these relationships are contingent on firm characteristics: digital maturity and operational risk exposure amplify the effects of AI innovation on both supplier concentration and efficiency outcomes. We interpret these findings through the lens of the Triple Helix framework, which emphasizes the institutional context of government policy, academic knowledge production, and industrial application that characterizes innovation ecosystems in emerging markets. The study contributes to the literature by shifting attention from operational performance to structural supply chain outcomes, identifying boundary conditions that shape technology driven supply chain restructuring, and demonstrating the relevance of institutional perspectives for understanding digital transformation in interfirm relationships. Practical implications are discussed for managers seeking to leverage AI for supply chain optimization and for policymakers aiming to balance innovation promotion with supply chain resilience. © 2026 KW - Supplier concentration KW - Supplier management efficiency KW - Triple Helix perspective KW - China KW - Commerce KW - Inventory control KW - Marketing KW - Patents and inventions KW - Public policy KW - Regression analysis KW - Supply chains KW - Technology transfer KW - Emerging markets KW - Empirical analysis KW - Firm heterogeneity KW - Fixed effects regression models KW - Management efficiency KW - Supplier concentration KW - Supplier management KW - Supplier management efficiency KW - Triple helix perspective KW - Triple helixes KW - artificial intelligence KW - boundary condition KW - innovation KW - supply chain management KW - Efficiency CY - China, Australia, United Kingdom ER - TY - JOUR TI - How Does Artificial Intelligence Capability Affect Product Innovation in Manufacturing Enterprises? Evidence from China AU - Gao Y. AU - Liu Y. AU - Wu W. PY - 2025 JO - Systems VL - 13 IS - 6 SP - 480 DO - 10.3390/systems13060480 AB - In today’s fast-changing business environment, artificial intelligence (AI) capability plays a critical role in fostering product innovation (PI). Resource-based theory (RBT) posits that resources and capabilities characterized as valuable, rare, inimitable, and non-substitutable can generate a sustained competitive advantage, providing an appropriate theoretical framework for this study. Using RBT this study examines how business intelligence transforming capability (BITC) mediates the relationship between AI capability and PI and how formal and informal knowledge governance mechanisms (FKGMs and IKGMs, respectively) moderate the effect of AI capability on BITC. Using partial least squares structural equation modeling on 516 Chinese manufacturing enterprises, we empirically test a mediated moderation model. The findings reveal that BITC significantly mediates the relationship between AI capability and PI. Both FKGMs and IKGMs strengthen the effect of AI capability on BITC (with IKGMs showing a stronger influence). This study theoretically contributes by identifying BITC’s mediating role, defining AI capability and BITC boundary conditions, revealing FKGMs’ and IKGMs’ asymmetries, and extending RBT. In terms of practical contributions, the findings emphasize the necessity of developing BITC and strategically applying both FKGMs and IKGMs to maximize AI capability-driven PI benefits. © 2025 by the authors. KW - artificial intelligence capability KW - business intelligence transforming capability KW - formal knowledge governance mechanisms KW - informal knowledge governance mechanisms KW - product innovation KW - Artificial intelligence KW - Competitive intelligence KW - Information analysis KW - Knowledge management KW - Artificial intelligence capability KW - Business intelligence transforming capability KW - Business-intelligence KW - Formal knowledge KW - Formal knowledge governance mechanism KW - Governance mechanisms KW - Informal knowledge governance mechanism KW - Knowledge governance KW - Product innovation KW - Competition CY - China ER - TY - JOUR TI - Artificial Intelligence Fueling Endogenous Innovation: Evidence on Global Value Chain Upgrading in Chinese Manufacturing Firms AU - Yu R. AU - Cheng T.C.E. AU - Xu X. PY - 2026 JO - IEEE Transactions on Engineering Management VL - 73 SP - 2163 EP - 2179 DO - 10.1109/TEM.2026.3658087 AB - This study investigates how artificial intelligence (AI)-driven endogenous innovation enables Chinese manufacturing firms to upgrade their positions in global value chains (GVCs). Based on survey data from 287 firms, we identify a core mechanism through which AI alleviates resource constraints by improving technical efficiency, supporting data-driven decision-making, and facilitating knowledge recombination. This mechanism helps firms overcome low-end lock-in and move toward higher value activities. Our analysis reveals two key findings that contrast with established views. First, the primary internal driver of innovation is organizational innovation culture rather than individual entrepreneurship, refining the traditional Schumpeterian paradigm's emphasis on the entrepreneur. Second, while absorptive capacity strengthens process and product upgrading, it does not support functional upgrading, revealing a disconnect between technological capability and governance power. The study contributes theoretically by clarifying the linkages among AI capabilities, endogenous innovation, and GVC upgrading. For managers, it underscores the importance of cultivating an innovation-oriented culture within the organization, while leveraging external market pressures and policy support to build a robust foundation in data, algorithms, and computing power. All findings are validated through structural equation modeling and robustness checks, providing reliable insights for both research and practice. © 2026 IEEE. KW - Artificial intelligence (AI) KW - endogenous innovation KW - global value chain KW - Artificial intelligence KW - Chains KW - Engineering research KW - Knowledge management KW - Artificial intelligence KW - Core mechanisms KW - Data driven decision KW - Decisions makings KW - Endogenous innovation KW - Global value chain KW - Manufacturing firms KW - Resource Constraint KW - Survey data KW - Technical efficiency KW - Decision making CY - China, United Kingdom ER - TY - JOUR TI - Human–AI collaboration in knowledge ecosystems: a multidisciplinary review, integrative framework and future directions AU - Ali I. AU - Nguyen K. AU - Ali A.M. AU - Cui T. PY - 2025 JO - Journal of Knowledge Management SP - 1 EP - 22 DO - 10.1108/JKM-03-2025-0431 AB - Purpose – The advancement of artificial intelligence (AI) is transforming knowledge ecosystems, reshaping the creation, dissemination and application of knowledge. This study aims to delve into the powerful synergy between human expertise and AI, illustrating how computational intelligence amplifies decision-making and sparks groundbreaking innovation in complex and data-rich business environments. Design/methodology/approach – Through a systematic review of 101 scholarly articles, this study synthesizes key insights and presents a comprehensive framework integrating socio-technical, ethical and policy dimensions of AI adoption. Findings – Human–AI collaboration in knowledge ecosystems is shaped by antecedents (trust, AI capabilities, organizational context, user expertise); mediators (cognitive alignment, explanation quality, emotional engagement); and moderators (user attitudes, task complexity, transparency, ethics). Positive configurations enhance decision quality, innovation and user satisfaction, while risks such as power imbalances, deskilling and algorithmic opacity can undermine collaboration and productivity. The authors devise an integrative antecedent–mediator–moderator–outcome framework, emphasizing human-centered design, contextual integration and equity. They also highlight the need for more empirical and theory-driven research in the domain. Originality/value – By bridging fragmented perspectives, this study advances theoretical understanding and illuminates practical pathways for leveraging AI to augment human ingenuity, uphold ethical imperatives and catalyze innovation in rapidly shifting knowledge landscapes. © 2025 Emerald Publishing Limited KW - Antecedent–moderator–mediator–outcome framework KW - Human–AI collaboration KW - Knowledge ecosystems KW - Multidisciplinary review CY - Australia, Saudi Arabia ER - TY - JOUR TI - Human strategic innovation against AI systems - analyzing how humans develop and implement novel strategies that exploit AI limitations AU - Dattijo A. AU - Jo S. PY - 2025 JO - Discover Artificial Intelligence VL - 5 IS - 1 SP - 321 DO - 10.1007/s44163-025-00439-x AB - This paper systematically analyzes documented cases and examines human strategic innovation against artificial intelligence systems. Drawing from peer-reviewed research and verified instances in strategic domains including complex games such as Go (Wang et al. in: Proceedings of the 40th international conference on machine learning, 2023), chess (McIlroy-Young et al. in Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining, 2020), Dota 2 (Berner et al. Dota 2 with large-scale deep reinforcement learning. arXiv preprint arXiv:19106680, 2019), and poker (Brown and Sandholm in Science 359:418–424, 2017), as well as real-world applications including cybersecurity (Comiter Attacking artificial intelligence: AI's security vulnerability and what policymakers can do about it. Belfer Center for Science and International Affairs, Harvard Kennedy School, 2019) and finance (Zhang et al., 2024), we identify patterns in human innovation when confronting AI opponents. Our analysis reveals that humans can achieve notable successes by developing novel strategies operating outside AI training distributions, exploiting specific AI limitations (Gleave et al. in International Conference on Machine Learning, 2020). Key findings demonstrate several critical mechanisms. First, pattern-breaking innovations enable humans to force AI systems into unfamiliar decision spaces where their training becomes insufficient (Comiter Attacking artificial intelligence: AI's security vulnerability and what policymakers can do about it. Belfer Center for Science and International Affairs, Harvard Kennedy School, 2019). Second, exploiting AI's bounded rationality allows strategic actors to leverage artificial systems' inherent computational and representational limitations (Simon, 1972). Third, adaptive resource distribution strategies permit dynamic capabilities reallocation based on real-time AI behavioral pattern assessment (Fatima and Wooldridge. in Proceedings of the Fifth International Conference on Autonomous Agents, 2001). In Go, adversarial policies have achieved win rates exceeding 97% against superhuman AI by forcing the system into unfamiliar game states it cannot correctly evaluate (Wang et al. in Proceedings of the 40th International Conference on Machine Learning, 2023). These attacks succeed not through superior Go play but by exploiting fundamental vulnerabilities in how AI systems process information outside their training distributions. Chess analysis indicates that human strategic choices often diverge from AI preferences, with models like Maia specifically designed to predict human moves achieving accuracies of 46–52% for targeted skill levels, highlighting fundamental differences in strategic evaluation between human and artificial intelligence (McIlroy-Young et al. in Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining, 2020). While AI systems like OpenAI Five have demonstrated overwhelming dominance in Dota 2, achieving a 99.4% win rate in public games under restricted rule sets (Berner et al. Dota 2 with large-scale deep reinforcement learning. arXiv preprint arXiv:19106680, 2019), and Libratus significantly outperformed top poker professionals in heads-up no-limit Texas Hold'em (Brown and Sandholm in Science 359:418–424, 2017), human approaches in these contexts reveal ongoing attempts to identify and exploit AI behavioral patterns. These efforts demonstrate the persistent potential for strategic innovation even against seemingly dominant artificial systems. The implications of these findings extend beyond gaming applications to broader strategic contexts. They suggest fundamental considerations for AI system design, particularly regarding the need for enhanced strategic flexibility and adaptation capabilities when facing novel adversarial approaches (Wang et al. in Proceedings of the 40th international conference on machine learning, 2023). We propose that these insights should inform next-generation AI system development, emphasizing robust strategic frameworks that can better anticipate and respond to human innovations that operate outside conventional training paradigms. Our research contributes to the theoretical understanding of human-AI strategic interaction and provides practical frameworks for developing more resilient AI systems. The broader implications span multiple domains, including AI safety research (Russell in Human compatible: Artificial intelligence and the problem of control, Viking Press, 2019), human-AI collaboration frameworks (Vaccaro et al. in Nat Hum Behav 8:1869–1886, 2024), and strategic decision-making system design (Chen and Kumar in J Artif Intel Res 79:245–278, 2024). © The Author(s) 2025. KW - Adversarial machine learning KW - Behavioral research KW - Complex networks KW - Computation theory KW - Cybersecurity KW - Data mining KW - Deep learning KW - Game theory KW - Learning systems KW - Network security KW - Personnel training KW - AI systems KW - International affairs KW - Large-scales KW - Machine-learning KW - Novel strategies KW - On-machines KW - Policy makers KW - Reinforcement learnings KW - Security vulnerabilities KW - Strategic innovations KW - Autonomous agents CY - South Korea ER - TY - JOUR TI - The twin transition in emerging economies: Synergizing artificial intelligence and sustainable business model innovation in Thailand’s BCG economy AU - Sangnak D. PY - 2026 JO - Sustainable Futures VL - 11 SP - 101789 DO - 10.1016/j.sftr.2026.101789 AB - This study investigates the convergence of digitalization and sustainability—termed the "Twin Transition"—within the context of Thailand’s Bio-Circular-Green (BCG) Economy. While Artificial Intelligence (AI) offers significant potential to accelerate sustainable development, research in emerging economies remains fragmented, often lacking empirical grounding in the specific institutional realities of the Global South. Integrating Institutional Theory and Dynamic Capabilities Theory (DCT), this research employs a rigorous mixed-methods sequential exploratory design (Qual → QUAN) to unpack the mechanisms driving this convergence. Phase 1 involved a reflexive thematic analysis of 30 in-depth interviews with executives. This phase revealed a "Compliance Plus" mindset—where firms evolve from regulatory adherence to competitive innovation—alongside critical structural barriers, such as the "Data-Sustainability Paradox." Subsequently, Phase 2 analyzed survey data from 425 firms using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings confirm that Institutional pressures (INP) (coercive, normative, and mimetic) are strong antecedents of AI Capability (AIC), which in turn significantly drives Sustainable Business Model Innovation (SBMI). Crucially, Environmental Turbulence (ENT) positively moderates this relationship, highlighting AI as a vital mechanism for organizational resilience in volatile markets rather than merely for operational efficiency. The study also addresses the "Dark Side" of AI, proposing a "Net-Positive" framework to mitigate energy consumption and algorithmic bias. These results challenge techno-centric views, offering policymakers actionable insights to bridge the "Data Gap" and "Talent Gap," positioning the Twin Transition as a critical lever for emerging economies to escape the Middle-Income Trap. © 2026 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license. http://creativecommons.org/licenses/by/4.0/ KW - Artificial intelligence capability KW - BCG Economy KW - Institutional theory KW - Sustainable business model innovation (SBMI) KW - Twin transition CY - Thailand ER - TY - JOUR TI - AI-Powered Digital Transformation of Government Human Resource Management: A Bibliometric and Systematic Literature Review AU - Bian X. AU - Panyagometh A. AU - Wang B. AU - Szabó R.Z. PY - 2025 JO - Journal of Innovation Management VL - 13 IS - 3 SP - 66 EP - 95 DO - 10.24840/2183-0606_013.003_0003 AB - Recent developments in modern artificial intelligence (AI) have driven profound changes in public sector human resource management systems, offering remarkable opportunities alongside intricate challenges. Governments across the globe are progressively integrating AI tools to modernize HR operations, enhance workforce planning, and respond to evolving socio-economic demands. This research utilizes the PRISMA framework for systematic literature review to explore the role of AI in transforming government HR practices. By analyzing 47 peer-reviewed articles published from 2019 to 2023, the study identifies five central themes: ethical and governance models for AI in public administration; AI’s influence on HR functions and organizational behavior; implementation barriers and potential benefits; AI applications in digital governance and policy formulation; and innovations in HR technologies driven by big data. The findings highlight critical success factors such as strong data infrastructure, structured employee training initiatives, and well-defined ethical standards. Key challenges identified include concerns around data privacy, biased algorithms, workforce adaptation, and wider societal implications like employment shifts and changing competency needs. The study underscores the importance of: (1) adaptive regulatory frameworks that support innovation while safeguarding public interest; (2) robust data governance strategies to manage confidentiality and cybersecurity risks; (3) tailored training programs aimed at improving AI understanding among government staff; and (4) collaborative efforts across sectors to promote ethical AI adoption and mitigate socio-economic disruptions. © 2025 Universidade do Porto - Faculdade de Engenharia. All rights reserved. KW - artificial intelligence KW - digital transformation KW - ethics KW - government KW - human resource management KW - morality CY - China, Thailand, Hungary ER - TY - JOUR TI - AI on drugs: can artificial intelligence accelerate drug development? evidence from a large-scale examination of bio-pharma firms AU - Lou B. AU - Wu L. PY - 2021 JO - MIS Quarterly: Management Information Systems VL - 45 IS - 3 SP - 1451 EP - 1482 DO - 10.25300/MISQ/2021/16565 AB - Advances in artificial intelligence (AI) could potentially reduce the complexities and costs in drug discovery. We conceptualize an AI innovation capability that gauges a firm’s ability to develop, manage, and utilize AI resources for innovation. Using patents and job postings to measure AI innovation capability, we find that it can affect a firm’s discovery of new drug-target pairs for preclinical studies. The effect is particularly pronounced for developing new drugs whose mechanism of impact on a disease is known and for drugs at the medium level of chemical novelty. However, AI is less helpful in developing drugs when there is no existing therapy. AI is also less helpful for drugs that are either entirely novel or those that are incremental “follow-on” drugs. Examining AI skills, a key component of AI innovation capability, we find that the main effect of AI innovation capability comes from employees possessing the combination of AI skills and domain expertise in drug discovery as opposed to employees possessing AI skills only. Having the combination is key because developing and improving AI tools is an iterative process requiring synthesizing inputs from both AI and domain experts during both the development and the operational stages of the tool. Taken together, our study sheds light on both the advantages and the limitations of using AI in drug discovery and how to effectively manage AI resources for drug development. © 2021 University of Minnesota. All rights reserved. KW - AI capability KW - Artificial intelligence KW - Biotech and pharmaceutical industries KW - Drug discovery KW - IT Innovation KW - Drug products KW - Patents and inventions KW - Personnel KW - Domain expertise KW - Domain experts KW - Drug development KW - Drug discovery KW - Innovation capability KW - Iterative process KW - Operational stages KW - Preclinical studies KW - Artificial intelligence CY - United States ER - TY - JOUR TI - Towards experimental standardization for AI governance in the EU AU - Prifti K. AU - Fosch-Villaronga E. PY - 2024 JO - Computer Law and Security Review VL - 52 SP - 105959 DO - 10.1016/j.clsr.2024.105959 AB - The EU has adopted a hybrid governance approach to address the challenges posed by Artificial Intelligence (AI), emphasizing the role of harmonized European standards (HES). Despite advantages in expertise and flexibility, HES processes face legitimacy problems and struggle with epistemic gaps in the context of AI. This article addresses the problems that characterize HES processes by outlining the conceptual need, theoretical basis, and practical application of experimental standardization, which is defined as an ex-ante evaluation method that can be used to test standards for their effects and effectiveness. Experimental standardization is based on theoretical and practical developments in experimental governance, legislation, and innovation. Aligned with ideas and frameworks like Science for Policy and evidence-based policymaking, it enables co-creation between science and policymaking. We apply the proposed concept in the context of HES processes, where we submit that experimental standardization contributes to increasing throughput and output legitimacy, addressing epistemic gaps, and generating new regulatory knowledge. © 2024 The Author(s) KW - Artificial Intelligence KW - Harmonized European standards KW - Legitimacy KW - Policy experimentation KW - Standardization KW - Artificial intelligence KW - Public policy KW - European Standards KW - Evaluation methods KW - Ex ante evaluation KW - Harmonized european standard KW - Legitimacy KW - Policy experimentation KW - Policy making KW - Policy-based KW - Standards process KW - Test standards KW - Standardization CY - Netherlands ER - TY - JOUR TI - Human-Centric AI Governance: An Adaptive Public International Law Framework for Ethical and Inclusive AI Regulation in Public Health AU - Sedeeq F.S. AU - Arman P. PY - 2025 JO - Journal of Law, Medicine and Ethics VL - 53 IS - 4 SP - 563 EP - 574 DO - 10.1017/jme.2025.10175 AB - Artificial Intelligence (AI) is transforming public health, presenting both opportunities and ethical and legal challenges. This study adopts an interdisciplinary approach, integrating legal doctrinal analysis, public health ethics, AI governance scholarship and a scoping review of international legal instruments to evaluate and operationalize three core pillars: ethical accountability, regulatory adaptability and transparency. Through a scoping review of treaties, regional regulations and policy frameworks, the study maps jurisdictional gaps and proposes an adaptive public law framework that addresses critical shortcomings in existing AI governance models, such as the WHO’s limited enforceability and the GDPR’s rigid data-sharing rules. The framework introduces scalable, region-specific regulations to enhance interoperability while respecting local governance needs. Its human-centric design, modular regulation and accountability mechanisms ensure adaptability across diverse legal, cultural and health system contexts. Informed by case studies and a thematic synthesis of global best practices, this framework offers policymakers and practitioners a structured yet flexible approach to balancing AI-driven innovation with ethical imperatives, promoting equitable public health outcomes. © The Author(s), 2025. Published by Cambridge University Press on behalf of American Society of Law, Medicine & Ethics. KW - Adaptive governance KW - Ethical accountability KW - Public health KW - Regulatory adaptability KW - Transparency KW - Artificial Intelligence KW - Humans KW - Public Health KW - Social Responsibility KW - artificial intelligence KW - ethics KW - human KW - public health KW - social responsibility CY - Cyprus ER - TY - JOUR TI - Unpacking Boundary-Spanning Search and Green Innovation for Sustainability: The Role of AI Capabilities in the Chinese Manufacturing Industry AU - Sun Y. AU - Zhang M. AU - Chang J. AU - Wang C. PY - 2025 JO - Sustainability (Switzerland) VL - 17 IS - 14 SP - 6439 DO - 10.3390/su17146439 AB - Achieving the dual carbon goal and addressing escalating environmental challenges requires that manufacturing enterprises in China must pursue sustainability via green innovation strategies. A key rationale for green innovation is to overcome boundaries and acquire knowledge through boundary-spanning search. Additionally, leveraging artificial intelligence (AI) capabilities provides technical support throughout the innovation process. Thus, both boundary-spanning search and AI capabilities are crucial for achieving sustainability objectives. Drawing on organizational search and knowledge management theories, this paper aims to analyze how dual boundary-spanning search affects sustainability performance and green innovation. It also examines the moderating role of AI capabilities and constructs a moderated mediation model. We analyzed questionnaire data collected from 171 Chinese manufacturing companies over a 13-month period, employing hierarchical regression and bootstrap sampling methods using SPSS 27.0. Our findings reveal that both prospective and responsive boundary-spanning searches significantly enhance corporate sustainability performance. Furthermore, green innovation acts as a positive partial mediator between dual boundary-spanning search and corporate sustainability performance. Notably, AI capabilities positively moderate the relationship between dual boundary-spanning search and green innovation. They also strengthen the mediating effect of green innovation on the link between dual boundary-spanning search and corporate sustainability performance. Based on these findings, more resources should be allocated to boundary-spanning search while encouraging enterprises to pursue green innovation and develop AI capabilities. These efforts will provide robust support for sustainability performance in the manufacturing sector. © 2025 by the authors. KW - AI capabilities KW - green innovation KW - knowledge management KW - prospective boundary-spanning search KW - responsive boundary-spanning search KW - sustainability performance KW - China KW - artificial intelligence KW - innovation KW - knowledge KW - manufacturing KW - performance assessment KW - sampling KW - sustainability CY - China ER - TY - JOUR TI - Artificial intelligence in the informal economy: game changer for microentrepreneurs? AU - Kolade O. AU - Egbetokun A. AU - Owoseni A. AU - Woldesenbet Beta K. PY - 2026 JO - International Journal of Entrepreneurial Behaviour and Research SP - 1 EP - 27 DO - 10.1108/IJEBR-12-2024-1457 AB - Purpose – This article integrates insights from bricolage theory and the dynamic capability (DC) framework to explore the potentialities and dangers of artificial intelligence (AI) in the informal sector, where microenterprises could harness its powers to transform their business models and scale, or risk falling further behind in the wake of AI-enabled disruption. Design/methodology/approach – This article takes a conceptual approach complemented with case illustrations. In the first part, it draws on bricolage and DCs theories to introduce nine new propositions that explicate the dynamic, sometimes bidirectional, relationships, between AI, digital bricolage, DCs and enterprise growth and competitiveness. In the second part, it highlights three illustrative cases of microenterprises to further elucidate these relationships. Findings – This study proposes a novel framework integrating AI, digital bricolage and DCs to enhance the performance of informal microenterprises. It highlights the role of digital bricolage as a mechanism for adapting existing resources to develop AI capabilities, and the complementary role of DC in deploying AI for growth, scaling and competitiveness. The study demonstrates AI's role in strengthening opportunity sensing, seizing and transformative capacities that differentiate struggling enterprises from thriving ones, while also addressing critical limitations such as infrastructural inequities and fragmented skills. Practical implications – The study offers valuable practical implications for fostering inclusive digital transformation in informal microenterprises. It highlights the role of digital bricolage in enabling resource-constrained entrepreneurs to creatively adapt and deploy AI for value creation, operational efficiency and agility. Policymakers and practitioners can leverage these insights to address barriers such as infrastructural inequities and skill gaps, fostering AI adoption. This approach supports sustainable competitiveness and market integration for marginalised enterprises. Originality/value – This study proposes a novel framework integrating AI, digital bricolage and DCs to explicate the mechanisms and processes through which informal microenterprises achieve differential outcomes that propel some microenterprises to growth and scaling, on the one hand, while leaving others to fall further behind. To the best of our knowledge, this is the first article that aims to unpack the double-edged sword of AI as both a potential leveller and stratifier in the informal sector. © Oluwaseun Kolade, Abiodun Egbetokun, Adebowale Owoseni and Kassa Woldesenbet Beta KW - Dynamic capabilities KW - Institutional theory KW - Institutions KW - Resource-based theory KW - Technology CY - United Kingdom ER - TY - JOUR TI - Governance of Artificial Intelligence Technologies and Systems in the EU and Ukraine: Legal Foundations and Institutional Mechanisms AU - Kwilinski A. AU - Reznik O. PY - 2025 JO - Forum Scientiae Oeconomia VL - 13 IS - 3 SP - 8 EP - 52 DO - 10.23762/FSO_VOL13_NO3_1 AB - The rapid advancement of artificial intelligence (AI) is transforming economic, managerial, and legal systems, creating new opportunities and risks for sustainable development and social equity. Within global digitalisation and Ukraine’s integration into the European legal and technological framework, the study of AI development management becomes a key element of national digital transformation. The topic holds significant scholarly and practical importance as it bridges law, economics, management, and sustainability. The aim of this study is to identify and systematise the legal and institutional mechanisms governing AI development in the European Union and Ukraine, and to assess their alignment with the global sustainable development agenda, based on three hypotheses: (1) the interdisciplinary nature of AI and law; (2) the integration of the UN Sustainable Development Goals into the EU AI Act (Regulation (EU) 2024/1689); and (3) the normative and institutional convergence between Ukraine and the European Union. The methodology applies a triangulated approach combining bibliometric analysis (PRISMA protocol), formal content analysis, and SWOT analysis, ensuring a comprehensive evaluation of legal and managerial aspects of AI governance. The findings show that the EU model balances innovation and accountability through ESG principles, digital ethics, and human-centred governance. Ukraine demonstrates growing alignment with EU law via strategic documents, enhanced digital governance, and participation in international harmonisation programmes. The theoretical contribution lies in conceptualising AI development management as part of a sustainable digital regulation model, reflecting the evolving roles of the state, academia, and business in responsible technology governance. The practical implications include recommendations for developing a national strategy for AI regulation and management, drafting AI legislation harmonised with the EU AI Act, and improving digital governance in the public sector. © 2025 by the authors. KW - AI Act KW - AI development management KW - artificial intelligence KW - digital governance KW - ESG principles KW - European Union KW - institutional convergence KW - legal regulation KW - sustainable development KW - Ukraine CY - United Kingdom, Poland, Ukraine ER - TY - JOUR TI - Balancing regulation and innovation: the need for agile AI governance in higher education–a cross-country study AU - Şen E. AU - Vaněček D. AU - Adnan M. PY - 2026 JO - Studies in Higher Education DO - 10.1080/03075079.2026.2614986 AB - The rapid integration of artificial intelligence (AI) necessitates governance strategies that effectively balance regulation and innovation. This mixed-methods comparative study explores academic staff perceptions of AI governance and policy readiness across two distinct universities: MSKU in Türkiye, characterized by non-binding regulations, and CTU in Czechia, which follows the European Union’s rights-based regulatory model, a structured and binding framework. The study revealed significant discrepancies between current AI adoption and institutional policy. A thematic analysis identified key areas, including ‘Perceived Value and Ethical Concerns', ‘Institutional Readiness and Training Needs', and ‘Governance and Policy Gaps'. Quantitative findings, assessing agile governance dimensions, indicated that while CTU's structured environment supports practical AI integration, it may limit perceived adaptability. Conversely, MSKU's flexible context, despite perceived autonomy, showed lower scores in user-centeredness and transparency, suggesting a risk of fragmented practices without clearer institutional guidance. This study advances the concept of agile AI governance as a practical framework for universities navigating regulatory and innovation imperatives. Such a governance framework can enable institutions to proactively navigate AI's evolving landscape, ensuring both ethical responsibility and institutional legitimacy. This study contends that agility is crucial for public policy makers to integrate AI innovatively, ethically, and sustainably. © 2026 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. KW - agile governance KW - AI regulations KW - Artificial intelligence (AI) KW - higher education institutions KW - higher education policies CY - Czech Republic, Turkey ER - TY - JOUR TI - Integrating artificial intelligence into risk management frameworks: a mixed-methods analysis of the Palestinian banking sector AU - Tanbour K.M. AU - Ben Saada M. AU - Nour A.I. AU - Elnaas N.K. PY - 2025 JO - Journal of Financial Reporting and Accounting SP - 1 EP - 42 DO - 10.1108/JFRA-06-2025-0458 AB - Purpose – This study aims to investigate the impact of artificial intelligence (AI) on risk management practices within Palestinian banks, specifically examining its application in credit, market and operational risk domains. The research assesses the extent to which AI enhances risk mitigation effectiveness within the unique economic and regulatory context of Palestine. Design/methodology/approach – The study used an explanatory sequential mixed-methods design. The initial quantitative phase involved surveying 80 internal auditors, selected via simple random sampling from a population of 95. This was followed by a qualitative phase comprising in-depth interviews with 23 purposively selected participants to contextualize and elaborate on the quantitative findings. Data were analyzed using statistical methods and deductive thematic analysis, guided theoretically by the DeLone and McLean (D&M) IS Success Model (2003). Findings – Findings demonstrate AI’s effectiveness in enhancing credit and operational risk management through improved decision-making accuracy, process automation and real-time anomaly detection. However, its potential contribution to market risk management is significantly constrained by infrastructural limitations, shortages in specialized expertise and competing strategic priorities, thereby underscoring the critical influence of contextual factors on successful AI adoption. Research limitations/implications – The study acknowledges certain limitations. Primary reliance on internal auditors, while offering crucial oversight, may not capture the full experiential range; future work could benefit from including risk managers, IT specialists and senior management. The unique Palestinian politico-economic context necessarily limits direct generalizability, though identified themes regarding infrastructure, skills and strategy likely resonate with other emerging economies. Building on this study, future research should explore the longitudinal evolution of AI’s impact as infrastructure and skills develop. Comparative cross-country studies within diverse emerging markets would further elucidate national context influences. Integrating deeper analysis of organizational culture, change management and specific ethical considerations related to AI decision-making in risk management represents another fruitful avenue. Exploring the specific impact of different AI techniques (e.g. machine learning vs deep learning) across risk domains would also yield valuable insights. Such research will deepen the understanding of how AI can be effectively and responsibly leveraged to foster resilient global financial systems. Practical implications – The findings yield significant practical implications for stakeholders within the Palestinian banking sector and, by extension, for other emerging economies confronting similar challenges. First, AI’s differential impact underscores the imperative for banks to adopt a nuanced, risk-specific integration strategy. For credit and operational risks, where AI is effective, institutions should optimize existing systems and ensure robust governance frameworks upholding transparency, accountability and regulatory compliance. Second, identified infrastructural and human capital deficiencies, pivotal impediments in market risk management, necessitate strategic investment in data infrastructure (especially real-time capabilities) and specialized expertise through training, recruitment and partnerships. Third, regulatory bodies should consider developing adaptive governance frameworks, balancing innovation with financial stability and ethics. Incorporating standards like ISO/IEC 42001:2023, with flexibility for local contexts, can guide responsible AI adoption. Finally, a phased, context-sensitive implementation, aligned with continuous evaluation of system performance and organizational readiness, is advocated over wholesale adoption to enhance long-term success and resilience, empowering leaders to maximize AI’s potential within resource-constrained and volatile environments. Originality/value – This study advances understanding of AI in finance by providing empirical evidence on its differentiated impact across credit, market and operational risks within the Palestinian banking sector, a context marked by institutional and regulatory challenges. Theoretically, it extends the DeLone and McLean IS Success Model to AI-driven risk management. Practically, it offers actionable guidance on human capital, technological infrastructure and governance, fostering sustainable, context-sensitive AI-enabled risk management in emerging economies. © 2025 Emerald Publishing Limited KW - Artificial intelligence (AI) KW - Banking KW - Credit risk KW - DeLone and McLean IS success model KW - Market risk KW - Mixed-methods research KW - Operational risk KW - Palestine KW - Risk management CY - Tunisia, Palestine ER - TY - JOUR TI - From Efficiency to Deliberation: Rethinking AI’s Role in Institutionalizing Democratic Innovations AU - Ohren A. AU - Calderón Lüning E. AU - Markov Č. PY - 2026 JO - Politics and Governance VL - 14 SP - 10632 DO - 10.17645/pag.10632 AB - As AI becomes increasingly embedded in democratic innovation (DI), critical questions arise about how these technologies shape deliberative quality, civic agency, and institutional design. While AI promises to expand and scale deliberative mini-publics, it also risks undermining the democratic goods that make such processes meaningful, particularly inclusiveness, popular control, considered judgment, and transparency. This article introduces the democracy-in-the-loop (DITL) framework as both a normative and practical approach to integrating AI into democratic settings. Building upon and expanding models like human-in-the-loop and society-in-the-loop, DITL embeds contestation, reflexivity, and participant agency into the operation and governance of AI systems. A key feature of the DITL approach is the intentional use of “meaningful frictions” (disruptions designed to slow down interaction, surface assumptions, and invite critical engagement). We explore the DITL model through the Digital Democracy Lab, a series of four experimental workshops held in 2024 in Brussels, Madrid, Kraków, and Dublin as part of the EU-funded Knowledge Technologies for Democracy project. Each workshop combined a purpose-built AI Demonstrator platform with facilitated deliberation to explore how AI can support, rather than supplant, democratic reasoning. Findings suggest that AI-enabled DIs should focus on flexibility, contestability, and democratic oversight, not merely technical efficiency. Institutionalizing DIs in the age of AI requires more than simply scaling tools; it calls for embedding democratic values into the design, deployment, and evolution of socio-technical systems. © 2026 by the author(s). KW - AI KW - algorithmic accountability KW - deliberative democracy KW - deliberative mini-publics KW - democracy-in-the-loop KW - democratic innovations KW - digital deliberation KW - human–AI interaction CY - Norway, Belgium, Serbia ER - TY - JOUR TI - AI Orientation, Capabilities, and Business Value: Case Study AU - Lee M.C.M. AU - Scheepers H. AU - Lui A.K.H. AU - Ngai E.W.T. PY - 2024 JO - Journal of Computer Information Systems DO - 10.1080/08874417.2024.2423190 AB - In this study, artificial intelligence (AI) orientation, AI capabilities, as well as process-oriented dynamic capabilities (PDCs) within the realm of AI business value creation, are unpacked through multiple case studies in Hong Kong. We propose a conceptual framework suggesting that AI resources enable organizations to develop PDCs, manifesting in several abilities, thereby contributing to business value. In addition, the case study’s findings indicate that AI capabilities developed by organizations correlate with their AI orientation, which is their overall strategic direction and aspiration of employing AI technology. Apart from basic AI capabilities, AI-oriented organizations would develop advanced AI capabilities. The proposed conceptual framework and findings can guide and assist practitioners in utilizing AI resources and building AI capabilities. This study also enriches the growing body of research on AI and contributes to the limited understanding of AI capabilities in the extant literature. © 2024 International Association for Computer Information Systems. KW - artificial intelligence capabilities KW - artificial intelligence orientation KW - Dynamic capabilities KW - process-oriented dynamic capabilities KW - Artificial intelligence capability KW - Artificial intelligence orientation KW - Business value KW - Case-studies KW - Conceptual frameworks KW - Dynamics capability KW - Orientation-capability KW - Process-oriented KW - Process-oriented dynamic capability CY - Australia ER - TY - JOUR TI - Research on the impact of generative artificial intelligence (GenAI) on enterprise innovation performance: a knowledge management perspective AU - Zhang Q. AU - Zuo J. AU - Yang S. PY - 2025 JO - Journal of Knowledge Management VL - 29 IS - 7 SP - 2238 EP - 2257 DO - 10.1108/JKM-10-2024-1198 AB - Purpose This study aims to investigate the impact of generative artificial intelligence (GenAI) on enterprise innovation performance, particularly from the perspective of knowledge management. It addresses key challenges in GenAI adoption – such as data biases, information overload and technological dependence – and proposes strategies to overcome these obstacles to enhance innovation. Design/methodology/approach Adopting a theoretical approach, this research analyzes the role of knowledge management in bridging the gap between GenAI and enterprise innovation. A structured framework based on four essential knowledge management processes – knowledge creation, retrieval and storage, transfer and sharing and application – is developed to tackle these challenges effectively. Findings The study reveals that while GenAI presents both opportunities and challenges for enterprise innovation, leveraging a structured knowledge management framework is key to unlocking its potential. It underscores the critical role of human–AI collaboration in mitigating issues such as data biases and integration challenges, ultimately improving innovation performance. The findings highlight the importance of complementing AI capabilities with human judgment to ensure successful outcomes in GenAI-driven innovation. Research limitations/implications This conceptual study calls for further empirical research to validate the findings and expand their generalizability. Future studies should explore contextual factors such as organizational characteristics, business environments and policy frameworks to refine the proposed framework. Originality/value This research offers novel insights into the intersection of GenAI, knowledge management and enterprise innovation. It stresses the importance of human involvement alongside GenAI, providing actionable recommendations for organizations navigating the complexities of AI adoption. In addition, it contributes to the evolving discourse on AI and innovation management, offering pathways for businesses to harness GenAI’s full potential and drive performance. © 2025 Emerald Publishing Limited KW - Enterprise innovation performance KW - Generative artificial intelligence KW - Human–AI collaboration KW - Knowledge management CY - China ER - TY - JOUR TI - Reframing Digital Literacy in ELT: Integrating SAMR, AI-TPACK, and Connectivism in the Global South AU - Nualprasert B. AU - Punkhoom W. AU - Jehma H. PY - 2025 JO - International Journal of Interactive Mobile Technologies VL - 19 IS - 20 SP - 55 EP - 68 DO - 10.3991/ijim.v19i20.56333 AB - This study investigates digital literacy integration within English Language Teaching (ELT) curricula across Thai local government universities through documentary analysis of 108 courses. Employing frameworks including Technological Pedagogical Content Knowledge (TPACK), Substitution, Augmentation, Modification, Redefinition (SAMR), European Digital Competence Framework (DIGCOMP), and Connectivism, we identify a hierarchical readiness gap: near-universal adoption of foundational skills (DIGCOMP: 93% strong alignment) and technological-pedagogical integration (TPACK: 84%) contrasts sharply with lagging transformative (SAMR: 64%) and networked practices (Connectivism: 50%). Crucially, socio-cultural barriers, teacher-centered traditions, rigid assessment systems, and Western-centric assumptions of learner autonomy explain persistent Connectivism underperformance, particularly in humanities disciplines. Regional disparities (e.g., 20% vs. 68% connectivism alignment across provinces) further reflect infrastructural inequities and pedagogical conservatism. Mirroring Global South trajectories, Thailand’s foundations-first approach prioritizes technical literacy over pedagogical reimagination, leaving graduates ill-equipped for AI-disrupted classrooms. This study proposes three imperatives, including an expanded AI-TPACK model integrating ethical AI governance, hybrid frameworks (e.g., SAMR + HeDiCom) for low-resource contexts, and decolonized digital integration centering cultural responsiveness. These innovations offer replicable pathways for teacher education in resource-constrained ecosystems globally. © 2025 by the authors. KW - Augmentation KW - digital literacy KW - English Language Teaching (ELT) KW - Modification KW - Redefinition (SAMR) model KW - Substitution KW - Technological Pedagogical Content Knowledge (TPACK) KW - Thailand higher education KW - Artificial intelligence KW - Curricula KW - Digital integrated circuits KW - E-learning KW - Engineering education KW - Ethical aspects KW - Learning systems KW - Teaching KW - Augmentation KW - Digital literacies KW - English language teaching KW - High educations KW - Modification KW - Redefinition (substitution, augmentation, modification, redefinition) model KW - Technological pedagogical content knowledge KW - Thailand KW - Thailand high education KW - Integration CY - Thailand ER - TY - JOUR TI - Balancing the efficiency of and ethical concerns surrounding artificial intelligence for responsible management education: a scoping review AU - Jimoh I. AU - ElAlfy A. AU - Al-Kwifi O.S. AU - Sakka G. PY - 2026 JO - Journal of Knowledge Management SP - 1 EP - 20 DO - 10.1108/JKM-11-2025-1684 AB - Purpose – This study aims to examine how artificial intelligence (AI) transforms knowledge processes within responsible management education (RME). By mapping existing research, the paper explores the adoption and application of AI within management education and its consequences for teaching, ethical concerns and knowledge management. Design/methodology/approach – A scoping review was conducted following Arksey and O’Malley’s framework and PRISMA guidelines to synthesize evidence from 40 peer-reviewed studies published between 2015 and 2025. The review systematically analyzed literature across Scopus and Web of Science using thematic mapping aligned with knowledge management clusters. Findings – AI is transforming how knowledge is created and shared in higher education. It improves efficiency by automating knowledge retrieval and connecting human insight with machine learning to support innovation in teaching and research. Yet, these advantages come with serious ethical concerns, including plagiarism, bias, data privacy and a lack of transparency that can undermine academic integrity. The review also reveals a strong concentration of research in developed countries. Practical implications – The findings highlight the need for HEIs to adopt comprehensive frameworks that integrate knowledge management systems with ethical governance mechanisms. Universities can leverage AI to strengthen absorptive capacity and organizational learning while instituting clear accountability, transparency and data ethics protocols to ensure responsible AI adoption in education and research. Originality/value – This study advances knowledge management research by linking AI-driven knowledge processes with the ethical and sustainability principles of RME. It broadens existing theory by showing how the transformation of knowledge, from individual insight to collective learning, and the view of knowledge as a strategic organizational resource can be aligned with responsible and transparent innovation. © 2026 Emerald Publishing Limited KW - Artificial intelligence KW - Ethics KW - Higher education KW - Knowledge management KW - Knowledge-based view KW - Responsible management education CY - Nigeria, Canada, Qatar, Cyprus ER - TY - JOUR TI - Navigating ethical challenges in digital transformation: insights on climate adaptation, microbiology, healthcare, robotics, and AI under the EU AI act: an experts panel discussion AU - Alghamdi S.M. AU - Chikwendu O.C. AU - Chukwuma O.U. AU - Okech D.O. AU - Okwu M.O. AU - Khalid S. AU - Vlachostergiou A. PY - 2025 JO - Global Bioethics VL - 36 IS - 1 SP - 2550823 DO - 10.1080/11287462.2025.2550823 AB - The ethical complexities of technological advancement are growing as fields such as climate adaptation, microbiology, healthcare, robotics, and artificial intelligence (AI) evolve rapidly. While these technologies offer innovative solutions to global challenges, they raise significant ethical concerns. In climate adaptation, AI-driven models and remote sensing technologies prompt questions about data privacy, environmental justice, and equitable access, especially for vulnerable populations. Similarly, advancements in microbiology and healthcare, such as genetic research and digital health tools, present ethical dilemmas related to informed consent, data security, and the exploitation of marginalized communities. In robotics and AI, ethical concerns are heightened due to their potential to automate decision-making, affect employment, and infringe on personal freedoms. The influence of AI in healthcare, law enforcement, and public services highlights the urgent need for ethical oversight to prevent bias and protect human rights. The EU AI Act addresses these challenges by categorizing AI systems by risk and setting stringent guidelines for high-risk applications, especially in sensitive sectors like healthcare. This article emphasizes the importance of balancing innovation with ethical responsibility, advocating for comprehensive regulatory frameworks, interdisciplinary collaboration, and global cooperation to ensure that technological advancements align with ethical standards and societal values. © 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. KW - AI regulation KW - climate KW - EU AI act KW - healthcare KW - robotics KW - Technological ethics CY - Greece ER - TY - JOUR TI - Challenges of Automating Fact-Checking: A Technographic Case Study AU - Kavtaradze L. PY - 2024 JO - Emerging Media VL - 2 IS - 2 Theme: Governing, Misinformation, and Discrimination SP - 236 EP - 258 DO - 10.1177/27523543241280195 AB - The prevalence of disinformation in media ecosystems has spurred efforts by researchers from various disciplines and media professionals to find effective methods for verifying information at scale. Automated fact-checking has emerged as a promising solution to combat disinformation. However, fully automated tools have not yet materialized. This technographic case study of a start-up company, “X,” investigated the challenges associated with this process. By conceptualizing automated fact-checking as a technological innovation within journalistic knowledge production, the article uncovered the reasons behind the gap between “X's” initial enthusiasm about AI's capabilities in verifying information and the actual performance of such tools. These reasons cross the disciplinary boundaries relating to the technological aspects of automated fact-checking and a requirement for such tools to be epistemically authoritative. The study revealed significant hurdles faced by the start-up, including issues with the accuracy of the AI editor and its adoption by the industry. Key obstacles included the elusive nature of truth claims, the rigidity of so-called binary epistemology (ascribing true/false values to information claims), data scarcity, algorithmic deficiencies, issues with the transparency of results, and industry-tool compatibility. While focused on a single company's experience, the study offers valuable insights for researchers and practitioners navigating the evolving landscape of automated fact-checking. © The Author(s) 2024. This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage). KW - AI KW - Automated fact-checking KW - disinformation KW - emerging technologies KW - epistemic authority KW - technography CY - Norway ER - TY - JOUR TI - National strategic artificial intelligence plans: A multi-dimensional analysis AU - Fatima S. AU - Desouza K.C. AU - Dawson G.S. PY - 2020 JO - Economic Analysis and Policy VL - 67 SP - 178 EP - 194 DO - 10.1016/j.eap.2020.07.008 AB - Nations have recognized the transformational potential of artificial intelligence (AI). Advances in AI will impact all facets of society. A spate of recently released national strategic AI plans provides valuable insights into how nations are considering their future trajectories. These strategic plans offer a rich source of evidence to understand national-level strategic actions, both proactive and reactive, in the face of rapid technological innovation. Based on a comprehensive content analysis of thirty-four national strategic plans, this article reports on (1) opportunities for AI to modernize the public sector and enhance industry competitiveness, (2) the role of the public sector in ensuring that the two most critical elements of AI systems, data and algorithms, are managed responsibly, (3) the role of the public sector in the governance of AI systems, and (4) how nations plan to invest in capacity development initiatives to strengthen their AI capabilities. © 2020 Economic Society of Australia, Queensland KW - Artificial intelligence KW - Autonomous systems KW - Intelligent systems KW - Public agencies KW - Science and technology policy KW - Strategic plans KW - Technological innovation CY - Australia, United States ER - TY - JOUR TI - The co-evolution of AI technology and information environment: Diagnosing social impacts and exploring governance strategies AU - Cha S. AU - Seo B.-G. AU - Kim T. AU - Kim J. PY - 2024 JO - Journal of Infrastructure, Policy and Development VL - 8 IS - 8 SP - 6605 DO - 10.24294/jipd.v8i8.6605 AB - The rapid advancement of artificial intelligence (AI) technology is profoundly transforming the information ecosystem, reshaping the ways in which information is produced, distributed, and consumed. This study explores the impact of AI on the information environment, examining the challenges and opportunities for sustainable development in the age of AI. The research is motivated by the need to address the growing concerns about the reliability and sustainability of the information ecosystem in the face of AI-driven changes. Through a comprehensive analysis of the current AI landscape, including a review of existing literature and case studies, the study diagnoses the social implications of AI-driven changes in information ecosystems. The findings reveal a complex interplay between technological innovation and social responsibility, highlighting the need for collaborative governance strategies to navigate the tensions between the benefits and risks of AI. The study contributes to the growing discourse on AI governance by proposing a multi-stakeholder framework that emphasizes the importance of inclusive participation, transparency, and accountability in shaping the future of information. The research offers actionable insights for policymakers, industry leaders, and civil society organizations seeking to foster a trustworthy and inclusive information environment in the era of AI, while harnessing the potential of AI-driven innovations for sustainable development. © 2024 by author(s). KW - artificial intelligence KW - governance strategies KW - information ecosystem KW - social impact KW - sustainability CY - South Korea ER - TY - JOUR TI - Transforming healthcare management: The impact of artificial intelligence on leadership and operations AU - Chu W.W. AU - Lam J.L.C. PY - 2026 JO - Management in Healthcare VL - 10 IS - 3 SP - 253 EP - 265 DO - 10.69554/AQMU2524 AB - Artificial intelligence (AI) is reshaping healthcare leadership by combining technological innovation with a focus on human values. This paper examines how AI helps tackle workforce shortages, cost pressures and inequalities. The integration of AI into health care is transforming how services are delivered and managed. As healthcare systems worldwide face significant challenges, such as an ageing population, increasing rates of chronic diseases and rising patient expectations. AI offers innovative solutions to improve efficiency and patient care. In many regions, healthcare providers are overwhelmed, struggling to meet the growing demand for quality services. AI technologies, such as machine learning and natural language processing, are being used to enhance patient engagement, streamline administrative tasks and improve clinical decision making. These tools can analyse large amounts of data to provide personalised care and support, helping healthcare professionals make better-informed decisions. For instance, AI can assist in developing personalised treatment plans, enabling providers to address individual patient needs more effectively. Moreover, AI can automate routine tasks, allowing healthcare staff to focus on more complex and value-added activities, ultimately enhancing the quality of care. Nevertheless, the adoption of AI in healthcare is not without its challenges. Concerns about data privacy, algorithmic bias, and the need for ethical guidelines must be carefully managed to ensure that AI solutions are fair and safe for all patients. By 2030, the vision for AI in health care is to create a more efficient, accessible and patient-centred system. This paper explores the impact of AI on healthcare management, highlighting the opportunities it presents while addressing the necessary considerations for successful implementation. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/. Delivered by Ingenta © Henry Stewart Publications 2397-1061 (2026). KW - artificial intelligence KW - ethical leadership KW - healthcare management KW - operational efficiency KW - predictive analytics ER - TY - JOUR TI - A Strategic Roadmap for Corporate Excellence in AI AU - Mazzeo J. PY - 2024 JO - Research Technology Management VL - 67 IS - 6 SP - 27 EP - 32 DO - 10.1080/08956308.2024.2400001 AB - Companies can adopt this four-stage AI Proficiency Framework to develop organization-wide expertise in using AI tools. There are four stages of AI proficiency—Beginner, Intermediate, Proficient, and Expert—and each stage represents a significant leap in capability, organizational integration, and strategic impact. Transforming a company through AI requires sustained commitment, significant investment, and a willingness to reimagine the business through the lens of AI capabilities. © Copyright © 2024, The National Association of Manufacturers. KW - AI proficiency KW - AI proficiency framework KW - Artificial intelligence KW - Innovation KW - Innovation culture KW - AI proficiency KW - AI proficiency framework KW - Corporates KW - Innovation KW - Innovation Culture KW - Organisational KW - Roadmap KW - Strategic impacts KW - Through the lens ER - TY - JOUR TI - An Analysis of Artificial Intelligence (AI) Capability in Libraries and Archives AU - Pinar A. AU - Cox A. PY - 2025 JO - Cataloging and Classification Quarterly VL - 63 IS - 6-7 SP - 566 EP - 599 DO - 10.1080/01639374.2025.2539790 AB - This paper seeks to evaluate the AI capability of libraries and archives using a qualitative content analysis of 54 case studies of AI uses published between 2018 and 2024. It is framed by the model of AI capability proposed by Mikalef and Gupta (Patrick Mikalef and Manjul Gupta, ‘Artificial Intelligence Capability: Conceptualization, Measurement Calibration, and Empirical Study on Its Impact on Organizational Creativity and Firm Performance’, Information & Management 58, no. 3 (2021): 103434.). The findings of the analysis largely confirm the model, but suggest that there are many gaps in library and archive AI capability, especially in areas such as infrastructure and technical resources, data issues arising from metadata inconsistencies, and financial resources. © 2025 The Author(s). Published with license by Taylor & Francis Group, LLC. KW - AI Capability KW - Archives KW - Artificial Intelligence KW - Libraries KW - Organizational Change CY - Turkey, United Kingdom ER - TY - JOUR TI - Educational leadership in the digital era: Bridging global disparities with inclusive management strategies AU - Mariyono D. AU - Yunus M. AU - Junaidi J. AU - Syam N. AU - Mazhabi Z. PY - 2026 JO - Educational Management Administration and Leadership DO - 10.1177/17411432261419475 AB - This study examines educational leadership in the digital era with a particular focus on how strategic management and artificial intelligence (AI) can be mobilized to reduce global disparities and foster inclusive learning. Drawing on a systematic literature review of 75 peer-reviewed studies identified through Scopus, Web of Science, JSTOR, and IEEE Xplore, the research employs a hybrid thematic content—Strengths, Weaknesses, Opportunities, Threats (SWOT) approach that combines inductive thematic coding with strategic analysis. The findings reveal that digital inequalities remain persistent, disproportionately affecting marginalized learners and institutions with limited resources. Transformative and AI-supported leadership models demonstrate potential to bridge technological and social gaps, yet they also raise ethical, cultural, and contextual challenges that require human oversight. Effective leaders in this environment must integrate technical expertise, cognitive agility, and socioemotional intelligence to ensure that digital transformation supports educational justice rather than deepening divides. The paper contributes theoretically by advancing a hybrid methodological framework for analyzing digital leadership and practically by offering policy recommendations, including the development of global funding mechanisms, public–private partnerships, and ethical governance of AI. While the study is limited by potential regional bias and the static nature of SWOT analysis, it provides a replicable framework for examining how inclusive leadership can navigate the tensions between technological innovation and equity in education. © The Author(s) 2026 KW - artificial intelligence KW - digital divide KW - digital transformation KW - Educational leadership KW - inclusive education ER - TY - JOUR TI - AI Platforms as Cooperation Enablers Favoring the Development of Strategic Situating Capabilities Within Solution Delivery Ecosystems AU - Vaillant Y. AU - Lafuente E. AU - Vendrell-Herrero F. PY - 2025 JO - Journal of Product Innovation Management DO - 10.1111/jpim.12807 AB - Academic Summary: By integrating artificial intelligence (AI) platforms into their processes, firms aim to enhance their strategic capabilities and gain a competitive advantage. This study investigates the impact of such platforms on value generation within solution-based strategies, proposing two connected mechanisms. First, AI platforms foster collaborative value systems between firms and value-chain agents across the stages of the solution delivery process (i.e., problem identification, solution development, and solution implementation). Second, such cooperation could foster the development of situating capabilities (i.e., grounding, bounding, and recasting), which are conceptually linked to the mitigation of situated agency constraints that stifle value creation within productive systems. These relationships underscore the value generation potential of AI platforms for solution providers, extending the premise of situated AI capabilities to the organizational and inter-organizational level. Data collected from 570 Spanish manufacturing firms in 2023 reveals that firms utilizing AI platforms exhibit greater cooperative and situating capability-building behavior during the problem identification and solution implementation stages. However, no significant association is found between AI platforms and the more creative stage of solution development. The study provides novel insights into the interplay between AI platforms, user cooperation, situated agency, and strategic capabilities as drivers of value generation and advancement of the AI-dominated paradigm. Theoretical and practical implications are discussed. Managerial Summary: This study highlights the strategic role of AI platforms in enhancing collaboration between manufacturers and solution seekers throughout the solution delivery process. AI technologies facilitate collective learning, adaptation, and knowledge sharing, particularly during the diagnostic and implementation stages, where real-time data processing and predictive analytics help tailor solutions to user-specific challenges. This more effective coordination is essential for mitigating agency problems that arise due to asymmetric information or misaligned objectives within complex solution systems. However, the findings reveal that AI's influence is limited in the co-creation of solution design and development, which relies heavily on human insight, creativity, and contextual judgment. Managers should therefore not view AI as a substitute for human input, but rather as a complementary tool that enhances efficacy and integration. For firms seeking to strengthen their solution-oriented strategies, the key takeaway is that maintaining a balanced approach—combining AI-enabled collaboration with human ingenuity—will improve solution outcomes and sustain competitive advantage in markets increasingly shaped by personalization and customer-specific problem solving. © 2025 The Author(s). Journal of Product Innovation Management published by Wiley Periodicals LLC on behalf of Product Development & Management Association. KW - AI platforms KW - inter-organizational cooperation KW - situated agency KW - situated AI theory KW - solution business model KW - solution delivery process KW - strategic capabilities KW - value ecosystems KW - Behavioral research KW - Competition KW - Coordination reactions KW - Knowledge management KW - Solution mining KW - Artificial intelligence platform KW - Business models KW - Delivery process KW - Interorganizational cooperation KW - Situated agency KW - Situated artificial intelligence theory KW - Solution business model KW - Solution delivery process KW - Solutions deliveries KW - Strategic capability KW - Value ecosystem KW - Predictive analytics CY - France, United Kingdom, Finland ER - TY - JOUR TI - Incumbent strategic renewal drivers to AI disruption AU - Bughin J. PY - 2025 JO - Technology Analysis and Strategic Management DO - 10.1080/09537325.2025.2509233 AB - In light of destructive technology theories, AI technologies disrupt markets while simultaneously offering opportunities for strategic renewal (SR). We assess how major corporations worldwide are modifying their strategies as well as organisational and AI capabilities to counter AI-induced market pressures. AI-driven stress fosters innovation, yet the primary force behind change remains internal organisational dynamics. Companies must strike a balance of exploration/exploitation when using AI for both automation and radical innovation and prioritise AI capabilities/resources, such as data and AI dynamic capability. © 2025 Informa UK Limited, trading as Taylor & Francis Group. KW - AI technologies KW - AI transformation KW - endogenous task production models KW - Strategic renewal CY - Belgium ER - TY - JOUR TI - Generative Artificial Intelligence (GenAI) in entrepreneurial education and practice: emerging insights, the GAIN Framework, and research agenda AU - Dwivedi Y.K. PY - 2025 JO - International Entrepreneurship and Management Journal VL - 21 IS - 1 SP - 82 DO - 10.1007/s11365-025-01089-2 AB - Generative AI (GenAI) is reshaping entrepreneurship by transforming the landscape of innovation, education, and ethical practices within the entrepreneurial domain. This paper provides a comprehensive review of the nascent yet impactful scholarly discourse on the role of GenAI (e.g., ChatGPT, DeepSeek, and Google Gemini) in entrepreneurship. Drawing from 39 studies, it examines four emerging themes: technology adoption, education and skill development, innovation, and performance enhancement. The analysis highlights GenAI's transformative potential to democratize entrepreneurial resources, foster creativity, and improve decision-making. However, it also identifies pressing challenges, including ethical data practices and equitable access. The paper proposes and presents the GAIN Framework along with five associated research propositions to guide future research in this domain. It concludes by emphasizing the need for a balanced integration of human ingenuity and AI capabilities, while raising critical questions about fostering ethical inclusivity and driving innovation. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025. KW - ChatGPT KW - DeepSeek KW - Entrepreneurs KW - Entrepreneurship KW - Gemini KW - Innovation KW - LLMs KW - The GAIN Framework CY - Saudi Arabia ER - TY - JOUR TI - Shopper AI: Integrating Capabilities and Parasocial Skills AU - Kennedy K.J. AU - He H. AU - Sarantopoulos P. PY - 2026 JO - Journal of Service Research DO - 10.1177/10946705261433842 AB - Shopper artificial intelligence (AI) presents a striking paradox: while massive investments drive rapid expansion and increasingly sophisticated AI solutions, two-thirds of consumers express dissatisfaction with AI shopping assistants, citing frustrations with pushy upselling, poor understanding, and inaccurate recommendations. This disconnect motivates our development of the Shopper AI taxonomy. To develop our taxonomy, we synthesized insights from multiple disciplines through a design science research process with empirical validation. Grounded in customer experience management (CEM) theory, our taxonomy identifies 14 dimensions within two meta-characteristics: AI capabilities (knowledge, intelligence, autonomy, breadth of use, quality of work, data privacy) and AI parasocial skills (personalization, anthropomorphism, communications mode, emotion recognition, emotion expression, empathy, influence, engagement). The taxonomy advances service research theory in three ways. First, we extend CEM theory by revealing how AI creates value through interrelated but discrete capabilities and parasocial dimensions. Second, we identify how AI capabilities enable autonomous value creation without active customer participation, representing a new form of value pre-creation. Third, we reveal complex dimensional interactions, where improvements in one dimension can enhance or diminish others. This multidimensional taxonomy provides managers with actionable guidance for navigating dimensional trade-offs, designing efficient, balanced AI systems, identifying context-specific investment priorities, and avoiding common pitfalls. © The Author(s) 2026 KW - artificial intelligence KW - customer experience management KW - parasocial relationships KW - shopper AI KW - taxonomy CY - United States, United Kingdom, Greece ER - TY - JOUR TI - AI capability and environmental sustainability performance: Moderating role of green knowledge management AU - Kumar S. AU - Kumar V. AU - Chaudhuri R. AU - Chatterjee S. AU - Vrontis D. PY - 2025 JO - Technology in Society VL - 81 SP - 102870 DO - 10.1016/j.techsoc.2025.102870 AB - The capabilities of AI may not only foster green technology innovations but also enhance the environmental sustainability performance of organizations. Despite this, the interplay between AI capabilities, green technology innovations, and environmental sustainability performance largely remain unexplored in view of green knowledge management. The present study aims to fill this gap by examining the impact of AI capabilities on green technology innovations and ultimately on environmental sustainability performance. This study also examines how green technology innovations mediates between AI capabilities and environmental sustainability performance, and how green knowledge management moderates the relationships between AI capabilities and green technology innovations and between AI capabilities and environmental sustainability performance. To validate the proposed conceptual relationships, data were collected from IT companies of Pune, India from 237 respondents, and they were validated with PLS-SEM 3.0 software. The results of the present study contribute theoretically to the resources-based view, knowledge-based view, dynamic capability, and absorptive capacity theories. Moreover, this study has also contributed practically by revealing the potential of AI capabilities and green technology innovations to enhance environmental sustainability performance through green knowledge management. The insights of the study could also help managers to leverage the AI capabilities to enhance green technology innovations and to improve environmental sustainability performance of organizations. © 2025 Elsevier Ltd KW - Artificial intelligence KW - Environmental sustainability performance KW - Green innovation KW - Green knowledge management KW - India KW - Maharashtra KW - Pune KW - Green economy KW - Sustainable development goals KW - Environmental sustainability KW - Environmental sustainability performance KW - Green innovations KW - Green knowledge management KW - Green technology KW - IT companies KW - Relationship data KW - Resource-based view KW - Sustainability performance KW - Technology innovation KW - artificial intelligence KW - conceptual framework KW - high technology industry KW - information technology KW - innovation KW - knowledge KW - sustainability KW - sustainable development KW - Green development CY - India, France, Cyprus, Singapore ER - TY - JOUR TI - From Man vs. Machine to Man + Machine: The art and AI of stock analyses AU - Cao S. AU - Jiang W. AU - Wang J. AU - Yang B. PY - 2024 JO - Journal of Financial Economics VL - 160 SP - 103910 DO - 10.1016/j.jfineco.2024.103910 AB - An AI analyst trained to digest corporate disclosures, industry trends, and macroeconomic indicators surpasses most analysts in stock return predictions. Nevertheless, humans win “Man vs. Machine” when institutional knowledge is crucial, e.g., involving intangible assets and financial distress. AI wins when information is transparent but voluminous. Humans provide significant incremental value in “Man + Machine”, which also substantially reduces extreme errors. Analysts catch up with machines after “alternative data” become available if their employers build AI capabilities. Documented synergies between humans and machines inform how humans can leverage their advantage for better adaptation to the growing AI prowess. © 2024 Elsevier B.V. KW - Alternative data KW - Artificial intelligence KW - Disruptive innovation KW - FinTech KW - Machine learning KW - Stock analyst CY - United States ER - TY - JOUR TI - Artificial intelligence, innovation and the new architecture of exploitation: Towards reconfiguring humanness in the age of algorithmic labour AU - Pepple D. AU - Muthuthantrige N. PY - 2026 JO - Journal of Innovation and Knowledge VL - 11 SP - 100878 DO - 10.1016/j.jik.2025.100878 AB - Purpose This conceptual study explores how artificial intelligence (AI) is transforming the nature of work and reconfiguring the experience of humanness, particularly among low-skilled and informal workers. Method Using an integrative literature review methodology, the study synthesises interdisciplinary research from organisational studies, sociology, and AI ethics to examine the mechanisms through which AI-driven labour displacement, algorithmic management, and structural precarity contribute to new forms of exploitation. Findings The study develops a novel conceptual framework that links technological transformation to the erosion of the relational, moral, and emotional dimensions of work conditions, resulting in conditions increasingly resembling modern slavery. Originality the study’s novelty lies in its reframing of AI as a socio-technical actor with ontological consequences for worker identity, autonomy, and dignity. The findings underscore the need for ethical AI design, inclusive policy frameworks, and human-centred organisational practices. Practical implications This paper offers practical implications for policymakers, technologists, and business leaders seeking to align innovation with social justice and sustainable labour futures. Plain summary Artificial intelligence (AI) is reshaping the nature of work and disrupting the human experience, especially for low-skilled and informal workers, highlighting the urgency and complexity of this research. AI-driven labour displacement and algorithmic management contribute to new forms of exploitation that echo modern slavery. The erosion of humanness at work is linked to reduced autonomy, empathy, and moral agency under opaque algorithmic systems. A socio-technical framework is needed to address AI’s impact on dignity and agency, with ethical design and inclusive governance at its core. JEL Code O330, O31, O32 © 2025 The Author(s). KW - Artificial intelligence KW - Digital labour KW - Ethical innovation KW - Humanness KW - Labour displacement KW - lgorithmic management KW - Modern slavery CY - United Kingdom ER - TY - JOUR TI - Construction of an Artificial Intelligence Literacy Ability Framework and Training System for College Students; [高校学生AI 素养能力框架及培训体系建设] AU - Hu A. PY - 2026 JO - Journal of Library and Information Science in Agriculture VL - 38 IS - 2 SP - 42 EP - 55 DO - 10.13998/j.cnki.issn1002-1248.25-0448 AB - [Purpose/Significance] The rapid proliferation of generative artificial intelligence (AI), exemplified by models like DeepSeek-R1, has precipitated a paradigm shift across various sectors, positioning AI literacy as an indispensable competency for the future workforce. University students, as digital natives and pivotal agents of technological adoption and innovation, stand at the forefront of this transformation. Their proficiency in understanding, utilizing, and critically evaluating AI technologies directly influences their academic performance, research capabilities, and long-term career adaptability. Although existing literature has begun to explore the conceptual landscape of AI literacy, a significant gap remains. There is an absence of a robust, empirically validated competency framework specifically tailored to the unique learning contexts, developmental needs, and future roles of university students within China’s higher education system. This study aims to address this critical gap by constructing and validating a comprehensive AI literacy competency framework for college students. Its primary significance lies in its ability to move beyond theoretical discourse and provide an evidence-based model that can guide the systematical development of targeted training programs. This enriches the theoretical underpinnings of AI literacy education and offers practical guidance for cultivating high-quality talent equipped for the intelligent era. [Method/Process] This research employed a mixed-methods approach, integrating qualitative and quantitative methods to provide both theoretical grounding and empirical robustness. The study commenced with a qualitative phase utilizing the grounded theory methodology. A systematic analysis of 112 core academic publications (2019-2024) from databases such as CNKI and Web of Science was conducted. Through a rigorous process of open coding, axial coding, and selective coding, facilitated by NVivo11 software, we extracted 300 initial concepts, which were subsequently synthesized into 26 sub-categories and ultimately 4 main categories. This process resulted in the preliminary construction of a four-dimensional AI literacy competency framework. Following this, a quantitative phase was implemented to test and refine the framework. A detailed questionnaire was developed based on the identified dimensions and indicators. Utilizing a five-point Likert scale, the questionnaire measured 26 variables corresponding to the framework’s sub-components. A total of 586 valid responses were collected from undergraduate students across universities in Jiangsu Province, China. The dataset was randomly split into two halves. The first subset (N=293) underwent exploratory factor analysis (EFA) using SPSS to uncover the underlying factor structure and assess the internal consistency reliability via Cronbach’s alpha. The second subset (N=293) was subjected to confirmatory factor analysis (CFA) using AMOS to verify the hypothesized factor structure, evaluate model fit indices (e.g., CMIN/DF, CFI, TLI, RMSEA), and establish convergent and discriminant validity by examining average variance extracted (AVE) and composite reliability (CR). [Results/Conclusions] The empirical analyses strongly support the validity and reliability of the proposed competency framework. The EFA clearly identified four distinct factors that aligned perfectly with the predefined dimensions, with a total variance explained of 69.916% and all factor loadings exceeding 0.6. The CFA results demonstrated excellent model fit (CMIN/DF=1.921, CFI=0.950, TLI=0.943, RMSEA= 0.056), confirming the structural integrity of the framework. Furthermore, all constructs exhibited high internal consistency (Cronbach’s α>0.90) and satisfactory convergent (AVE>0.5, CR>0.7) and discriminant validity. The finalized framework, therefore, comprises four interconnected core dimensions: AI Cognition (encompassing knowledge of basic concepts, applications, value, and risks), AI Skills (covering practical abilities from tool usage and programming to critical evaluation and innovation), AI Ethics (emphasizing social responsibility, privacy, intellectual property, and legal compliance), and AI Thinking (fostering higher-order cognitive abilities like computational, critical, and systemic thinking). Based on this validated framework, the study proposes a systematic and multi-faceted training system. This system outlines clear training objectives, identifies key stakeholders (e. g., university libraries, teaching centers, schools, and external enterprises), designs layered training content and pathways corresponding to each dimension, and suggests implementation strategies focusing on faculty development, a comprehensive assessment and feedback mechanism, and the strategic integration of AI-related resources. The main limitation of this study is that the respondents of the questionnaire were primarily college students during the empirical test stage. Future research can include teachers, business employers, and AI experts to modify and improve the index weight and content of the competency framework from multiple perspectives. This can be done through the Delphi method, expert interviews, and other methods, so as to enhance the framework’s authority and universality. © 2026, Agricultural Information Institute, Chinese Academy of Agricultural Sciences. All rights reserved. KW - ability framework KW - artificial intelligence literacy KW - factor analysis KW - grounded theory KW - training system CY - China ER - TY - JOUR TI - AI Adoption in the Public Sector: Organizational Readiness and the Pursuit of Public Value AU - Jonathan G.M. AU - Yalew S.D. AU - Gebremeskel B.K. AU - Watat J.K. PY - 2025 JO - Complex Systems Informatics and Modeling Quarterly IS - 45 DO - 10.7250/csimq.2025-45.03 AB - Artificial Intelligence (AI) has attracted significant attention among researchers and practitioners as it emerges as a strategic asset for organizations across sectors and industries. Within the public sector, the deployment of AI is anticipated to enhance the responsiveness of public organizations in delivering appropriate services and addressing complex societal challenges. This study examines the readiness of public organizations for AI adoption within Kenya’s public sector and explores its implications for public value creation. Anchored in the Technology-Organization-Environment framework and informed by Dynamic Capabilities theory, the article analyzes how structural conditions within organizations interact with adaptive capabilities to shape trajectories of AI readiness. Drawing on qualitative interviews with seventeen public sector experts, the study identifies a set and dynamic interdependence of critical readiness factors, including technological infrastructure, data quality, leadership commitment, staff competencies, organizational culture, regulatory frameworks, public trust, and external partnerships. By offering an empirically grounded and comparative perspective, the study aims to enhance our understanding of the relationship between AI readiness and public value creation, drawing on Kenya’s example. The results may also provide valuable inputs for policymakers in formulating actionable plans concerning differentiated implementation pathways, capacity development, and the ethical governance of AI in the public sector. © (2025), (Riga Technical University). All rights reserved. KW - AI Readiness KW - Artificial Intelligence (AI) KW - Dynamic Capability Theory KW - Public Value Creation KW - Technology–Organization–Environment (TOE) Framework. CY - Sweden, Ethiopia, Norway ER - TY - JOUR TI - AI Agents in Payments: Applications, Risks and Regulations AU - Amariles D.R. AU - Charlotin D. AU - He-Guelton L. PY - 2026 JO - European Journal of Risk Regulation DO - 10.1017/err.2026.10103 AB - The integration of artificial intelligence (AI) agents into payment systems signals a profound shift in the architecture of financial transactions. Building on advances in large language models and autonomous systems, “agentic payments” refer to transactions initiated and completed by AI agents without direct human intervention. This article provides a conceptual and technical analysis of agent-enabled payment systems, examining their operational logic, defining features and emerging use cases across retail, e-commerce and decentralised finance. It distinguishes agentic payments from traditional automated systems by emphasising autonomy, contextual reasoning and adaptability. The article further identifies and categorises a range of technical, legal and societal risks, including cybersecurity vulnerabilities, liability gaps, regulatory non-compliance, and potential economic disruption. Through case studies and architectural illustrations, it highlights both the innovation potential and governance challenges posed by agentic systems. It argues that current regulatory frameworks-designed for human-intermediated payments-are ill-equipped to address the dynamic and decentralised nature of agent-led transactions. The article concludes by proposing a multi-layered governance framework combining core regulatory requirements with supporting ecosystem measures to ensure accountability, security, and transparency in the age of autonomous financial agency. © The Author(s), 2026. Published by Cambridge University Press. KW - agents KW - AI regulation KW - payments CY - France ER - TY - JOUR TI - ALGORITHMIC AUTHORITY AND THE POSTHUMAN TURN IN MANAGEMENT CULTURAL, ETHICAL, AND TECHNOLOGICAL RECONFIGURATIONS IN THE AGE OF SCIENTIFIC INNOVATION AU - Save V. AU - S N.P.K. AU - Gujar S.V. AU - Sangeetha P. AU - Chandratreya A. AU - G P.K.K. AU - Jauhari R. PY - 2025 JO - Scientific Culture VL - 11 IS - 3-2 SP - 1564 EP - 1577 DO - 10.5281/zenodo.113225119 AB - Automation, machinic agency, algorithmic decision-making, predictive infrastructures, and computational governance—these overlapping formations now constitute the dominant lexicon of power in contemporary organizational life. This article interrogates the dissolution of human-centric managerial authority in favor of ambient algorithmic control across six globally significant platforms: Uber, Amazon, HireVue, Deliveroo, TikTok Hiring, and Zoom Workforce. Drawing on a mixed-methods design that combines secondary data synthesis, comparative platform analysis, and posthumanist critique, the study reveals that managerial decision-making is increasingly embedded in non-human systems of control that operate through surveillance, scoring, nudging, and behavioral prediction. These systems enact governance without deliberation, rendering workers and users legible as datafied subjects within infrastructures that lack contestability, transparency, or ethical accountability. Findings show not only a high degree of task automation and operational opacity but also the emergence of psychological and affective consequences—evidenced by elevated stress levels, low perceived fairness, and algorithm-induced burnout. The discussion engages with theoretical frameworks from Zuboff’s surveillance capitalism, Braidotti’s posthumanism, Barad’s entanglement, and Rouvroy’s algorithmic governmentality to argue that these platforms are not merely optimizing productivity, but actively transforming the ontological foundations of labor, agency, and ethical governance. The article concludes with a call for a re-politicization of algorithmic systems through epistemic justice, participatory oversight, and post-anthropocentric ethical frameworks that can re-inscribe accountability and equity into digital labor ecologies. This research contributes a theoretically grounded, empirically rich examination of how algorithmic governance displaces human authority, challenging dominant models of platform regulation and AI ethics. © 2025, University of AEGEAN. All rights reserved. KW - AI Ethics KW - Algorithmic Governance KW - Digital Labor KW - Platform Capitalism KW - Posthumanism CY - India ER - TY - JOUR TI - A novel legal analysis of Jordanian corporate governance legislation in the age of artificial intelligence AU - Albalawee N. AU - Fahoum A.A. PY - 2024 JO - Cogent Business and Management VL - 11 IS - 1 SP - 2297465 DO - 10.1080/23311975.2023.2297465 AB - This scholarly investigation explores the complex correlation between corporate governance and artificial intelligence (AI), recognizing the dynamic nature of the digital revolution. The research consists of two primary interactions: the initial interaction explicates the importance of corporate governance, and the subsequent one examines the incorporation of artificial intelligence applications into governance frameworks. By utilizing a descriptive and analytical approach, this study examines the extent to which current legal frameworks are congruent with the opportunities and challenges presented by digital transformation. The function of AI in reducing the risks associated with unethical financial and managerial practices is a primary concern, as it contributes to the ethical fortification of nations and businesses. The results emphasize the criticality for organizations to strictly comply with governance regulations, highlighting compliance as a fundamental element in demonstrating financial well-being, promoting expansion, and fortifying competitiveness in the corporate sphere. The findings have significant ramifications for both policymakers and organizations. Policymakers and organizations should adopt a proactive strategy to utilize AI’s capabilities, improving corporate governance practices and effectively navigating the intricacies of the digital age. © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. KW - artificial intelligence KW - Collins Ntim, University of Southampton, United Kingdom of Great Britain and Northern Ireland KW - companies leverage KW - competitive advantage KW - digital transformation KW - Governance KW - Law and Economics KW - Regulation and Business Law CY - Jordan ER - TY - JOUR TI - The ethical dimensions of big data in refugee contexts: A scoping review of empirical studies in the social sciences AU - Neiva L. AU - Borges G.M. PY - 2026 JO - Social Sciences and Humanities Open VL - 13 SP - 102522 DO - 10.1016/j.ssaho.2026.102522 AB - This scoping review examines the ethical dimensions of Big Data use as framed in empirical social science research on refugee protection and humanitarian response. It addresses the question: How are ethical issues conceptualized and addressed in empirical research on Big Data in refugee contexts? Following established scoping review frameworks, we systematically searched Web of Science, Scopus, Annual Reviews, and Google Scholar for peer-reviewed studies published between 2017 and 2025. Twenty-four studies met the inclusion criteria. Descriptive analysis shows that most research originates from Europe and North America, with limited contributions from refugee-hosting regions in the Global South. The reviewed studies used diverse data technologies—including social media analytics, remote sensing, machine learning, and mobile network data—to predict displacement, monitor mobility, and inform humanitarian decision-making. Thematic synthesis identified three recurring ethical tensions: (1) data-driven surveillance and refugee visibility, (2) predictive systems and algorithmic governance, and (3) humanitarian innovation and techno-solutionism. These findings reveal that while Big Data can enhance humanitarian action, it also reproduces structural inequalities and raises concerns around privacy, accountability, and refugee agency. The review concludes that ethical data practices in refugee governance must be participatory, transparent, and justice-oriented, balancing technological innovation with human rights and dignity. Copyright © 2026. Published by Elsevier Ltd. KW - Big data KW - Datafication KW - Ethics KW - Humanitarian response KW - Refugee protection KW - Surveillance CY - Portugal ER - TY - JOUR TI - Exponential Threats, Linear Responses U.S. Homeland Security Governance in the Age of Generative AI AU - Sofi J.I. PY - 2026 JO - Democracy and Security DO - 10.1080/17419166.2026.2617651 AB - While adversaries operationalize generative AI for financial fraud, infrastructure attacks, and influence operations, the U.S. homeland security apparatus responds with voluntary frameworks and process-oriented measures. This study examines this critical governance gap through qualitative content analysis of 23 documents including federal strategies, audit reports, congressional testimony, and industry frameworks (January 2023–June 2025). Drawing on Beck’s risk society theory, the analysis reveals a fundamental mismatch between exponentially evolving AI threats and linear bureaucratic adaptation. Federal agencies default to managing calculable social harms–bias, fairness, transparency–while ignoring incalculable security threats. Government audits confirm systematic failures: DHS lacks implementation plans for its AI strategy three years post-drafting; financial regulators cannot legally examine AI vendors processing trillions in daily transactions. This constitutes what Beck termed “organized irresponsibility”—conscientious proceduralism that masks collective security failure. Misaligned institutional incentives and incompatible stakeholder risk definitions reinforce this pattern.The temporal dimension proves particularly damaging: AI capabilities double every six months while policy cycles require 18–24 months. The evidence shows systematic inability to match adversarial innovation with defensive adaptation, not isolated failures. Without fundamental reconceptualization from voluntary to mandatory security controls, including proposed red-teaming requirements and procurement mandates, current trajectories point toward systematic defensive failure by 2027, when AI-enabled attacks on critical infrastructure will be limited only by adversary imagination rather than technical constraints. © 2026 Taylor & Francis Group, LLC. KW - Artificial intelligence governance KW - critical infrastructure protection KW - homeland security policy KW - policy subsystems CY - United States ER - TY - JOUR TI - The Role of Innovation Intermediaries in Bridging the AI Talent Gap AU - Hann C. AU - Chung-Wei K. AU - Chung-Han Y. AU - Yun-Chieh C. AU - Yu-Cheng C. PY - 2024 JO - Nanotechnology Perceptions VL - 20 IS - S3 SP - 82 EP - 94 DO - 10.62441/nano-ntp.v20iS3.6 AB - Artificial intelligence (AI) adoption is growing rapidly, yet talent shortages threaten implementation. This study explores how innovation intermediaries facilitated an AI talent development program in Taiwan. AIGO Team for Mid-Senior Level Talent, the three-stage talent development program leveraged course training, AI program prototype, and performance validation to build practical AI capabilities and ready organizations for adoption through partnerships with industry experts and practitioners. The study employed a qualitative research approach and adopted a semi-structured interview with a focus group format as an instrument to better understand how innovation intermediaries are operating and evolving in the context of AI talent. Coding identified the workflow of program process and five valuable innovation intermediary roles in AI talent program and AI project success: trustworthy databases, consultation and observation, stakeholder management, flexibility toward innovation, and logistic arrangement. Intermediaries empowered exploratory, customized learning aligned with the program’s knowledge-sharing mission. By leveraging connections and adaptability, they catalyzed talent growth and organizational change. The paper provides an overlook of innovation intermediary best practices for bridging talent gaps critical to emerging technology deployment. As rapid AI evolution necessitates lifelong learning, intermediaries can play a vital role in revitalizing a sustainable talent ecosystem. © 2024, Collegium Basilea. All rights reserved. KW - AI Talent KW - Innovation Intermediaries KW - Talent Ecosystem CY - Taiwan ER - TY - JOUR TI - Artificial intelligence regulation in the United Kingdom: a path to good governance and global leadership? AU - Roberts H. AU - Babuta A. AU - Morley J. AU - Thomas C. AU - Taddeo M. AU - Floridi L. PY - 2023 JO - Internet Policy Review VL - 12 IS - 2 DO - 10.14763/2023.2.1709 AB - On 29 March 2023 the United Kingdom (UK) government published its AI Regulation White Paper, a “proportionate and pro-innovation regulatory framework” for AI designed to support innovation, identify and address risks, and establish the UK as an “AI superpower”. In this article, we assess whether the approach outlined in this policy document is appropriate for meeting the country’s stated ambitions. We argue that the proposed continuation of a sector-led approach, which relies on existing regulators addressing risks that fall within their remits, could support contextually appropriate and novel AI governance initiatives. However, a growing emphasis from the central government on promoting innovation through weakening checks, combined with domestic tensions between Westminster and the UK’s devolved nations, will undermine the effectiveness and ethical permissibility of UK AI governance initiatives. At the same time, the likelihood of the UK’s initiatives proving successful is contingent on relationships with, and decisions from, other jurisdictions, particularly the European Union. If left unaddressed in subsequent policy, these factors risk transforming the UK into a reluctant follower, rather than a global leader, in AI governance. We conclude this paper by outlining a set of recommendations for UK policymakers to mitigate the domestic and international risks associated with the country’s current trajectory. © 2023, Alexander von Humboldt Institute for Internet and Society. All rights reserved. KW - AI governance KW - Artificial intelligence KW - Brussels Effect KW - Ethics KW - United Kingdom CY - United Kingdom, Italy ER - TY - JOUR TI - Understanding GenAI Teammates in the Workplace: A Sensemaking and Sensegiving Analysis of User Reviews AU - Agarwal A. AU - Sebastian M.P. AU - Krishnan S. PY - 2026 JO - Information Systems Frontiers VL - 28 IS - 2 SP - 803 EP - 825 DO - 10.1007/s10796-025-10672-5 AB - Generative AI (GenAI) applications, such as ChatGPT, are increasingly shaping work practices and employee engagement in organizations. Understanding how employees interact with these tools is critical for designing effective and responsible AI-enabled workplaces. This study analyzes 443,338 user reviews from the Google Play Store to examine how GenAI tools influence user satisfaction, continued use and their behaviors, which in turn impact productivity and well-being. Drawing on Sensemaking and Sensegiving theories, we develop a four-stage framework integrated into a 3E model (Envision-Evolve-Engage) comprising seven propositions. Findings highlight GenAI’s potential to enhance workplace effectiveness, decision-making and employee well-being, and to advance Sustainable Development Goal 8 (SDG 8) by promoting productive, inclusive, and meaningful work. The study also identifies challenges related to trust, privacy, adaptability, and ethical use. These insights offer practical guidance for designing user-centric GenAI systems and provide a theory-driven perspective for supporting responsible adoption and engagement in workplace contexts. © The Author(s) 2026. KW - AI teammates KW - Big data analytics KW - Conversational AI KW - Employee productivity KW - Employee well-being KW - Future of work KW - Generative AI KW - Text mining KW - User review KW - Workplace innovation KW - Advanced Analytics KW - Artificial intelligence KW - Behavioral research KW - Data mining KW - Decision making KW - Personnel KW - Sustainable development KW - AI teammate KW - Big data analytic KW - Conversational AI KW - Data analytics KW - Employee productivity KW - Employee well-being KW - Future of works KW - Generative AI KW - Text-mining KW - User reviews KW - Well being KW - Workplace innovation KW - Big data CY - India, Finland ER - TY - JOUR TI - The interplay of intelligent manufacturing, innovation equilibrium and cost stickiness in the artificial intelligence era AU - Wang F. AU - Li Q. AU - Chen H. PY - 2025 JO - Systems Research and Behavioral Science VL - 42 IS - 4 SP - 1232 EP - 1244 DO - 10.1002/sres.3046 AB - This study investigates the impact of intelligent manufacturing methods driven by artificial intelligence (AI) on cost stickiness in Chinese manufacturing enterprises. Leveraging the ABJ model， a regression analysis explores how different AI-enabled intelligent manufacturing approaches influence cost stickiness through the lens of innovation equilibrium. The sample comprises manufacturing companies listed on China's A-share market from 2013 to 2021. The findings reveal a negative correlation between intelligent manufacturing adoption and cost stickiness among these firms. Specifically, production-based intelligent manufacturing exhibits a more significant effect on reducing cost stickiness compared with collaborative intelligent manufacturing methods. Moreover, intelligent manufacturing positively impacts both joint equilibrium innovation and matching equilibrium innovation. While joint equilibrium innovation is negatively associated with cost stickiness, matching equilibrium innovation shows no significant relationship with cost stickiness. The results indicate that innovation equilibrium plays a mediating role in the relationship between AI-driven intelligent manufacturing and cost stickiness. Overall, this research sheds light on how AI capabilities enabling intelligent manufacturing processes and innovation equilibrium dynamics can help alleviate cost stickiness issues faced by manufacturing enterprises. It highlights the strategic value of adopting AI systems to enhance operational efficiency and cost management flexibility within manufacturing contexts. © 2024 John Wiley & Sons Ltd. KW - artificial intelligence KW - cost stickiness KW - innovation equilibrium KW - intelligent manufacturing KW - Cost benefit analysis KW - Industrial research KW - Regression analysis KW - Chinese manufacturing enterprise KW - Cost stickiness KW - Innovation equilibrium KW - Intelligent Manufacturing KW - Manufacturing companies KW - Manufacturing innovation KW - Manufacturing methods KW - Matchings KW - Share market KW - Through the lens KW - Artificial intelligence CY - China, United States ER - TY - JOUR TI - Unlocking the value of artificial intelligence in human resource management through AI capability framework AU - Chowdhury S. AU - Dey P. AU - Joel-Edgar S. AU - Bhattacharya S. AU - Rodriguez-Espindola O. AU - Abadie A. AU - Truong L. PY - 2023 JO - Human Resource Management Review VL - 33 IS - 1 SP - 100899 DO - 10.1016/j.hrmr.2022.100899 AB - Artificial Intelligence (AI) is increasingly adopted within Human Resource management (HRM) due to its potential to create value for consumers, employees, and organisations. However, recent studies have found that organisations are yet to experience the anticipated benefits from AI adoption, despite investing time, effort, and resources. The existing studies in HRM have examined the applications of AI, anticipated benefits, and its impact on human workforce and organisations. The aim of this paper is to systematically review the multi-disciplinary literature stemming from International Business, Information Management, Operations Management, General Management and HRM to provide a comprehensive and objective understanding of the organisational resources required to develop AI capability in HRM. Our findings show that organisations need to look beyond technical resources, and put their emphasis on developing non-technical ones such as human skills and competencies, leadership, team co-ordination, organisational culture and innovation mindset, governance strategy, and AI-employee integration strategies, to benefit from AI adoption. Based on these findings, we contribute five research propositions to advance AI scholarship in HRM. Theoretically, we identify the organisational resources necessary to achieve business benefits by proposing the AI capability framework, integrating resource-based view and knowledge-based view theories. From a practitioner's standpoint, our framework offers a systematic way for the managers to objectively self-assess organisational readiness and develop strategies to adopt and implement AI-enabled practices and processes in HRM. © 2022 Elsevier Inc. KW - AI capability KW - AI-employee collaboration KW - Artificial intelligence KW - Human resource management KW - Organisational resources KW - Systematic review CY - France, United Kingdom, Morocco ER - TY - JOUR TI - Generative AI for cyber threat intelligence: applications, challenges, and analysis of real-world case studies AU - Balasubramanian P. AU - Liyana S. AU - Sankaran H. AU - Sivaramakrishnan S. AU - Pusuluri S. AU - Pirttikangas S. AU - Peltonen E. PY - 2025 JO - Artificial Intelligence Review VL - 58 IS - 11 SP - 336 DO - 10.1007/s10462-025-11338-z AB - This paper presents a comprehensive survey of the applications, challenges, and limitations of Generative AI (GenAI) in enhancing threat intelligence within cybersecurity, supported by real-world case studies. We examine a wide range of data sources in Cyber Threat Intelligence (CTI), including security reports, blogs, social media, network traffic, malware samples, dark web data, and threat intelligence platforms (TIPs). This survey provides a full reference for integrating GenAI into CTI. We discuss various GenAI models such as Large Language Models (LLMs) and Deep Generative Models (DGMs) like Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Diffusion Models, explaining their roles in detecting and addressing complex cyber threats. The survey highlights key applications in areas such as malware detection, network traffic analysis, phishing detection, threat actor attribution, and social engineering defense. We also explore critical challenges in deploying GenAI, including data privacy, security concerns, and the need for interpretable and transparent models. As regulations like the European Commission’s AI Act emerge, ensuring trustworthy AI solutions is becoming more crucial. Real-world case studies, such as the impact of the WannaCry ransomware, the rise of deepfakes, and AI-driven social engineering, demonstrate both the potential and current limitations of GenAI in CTI. Our goal is to provide foundational insights and strategic direction for advancing GenAI’s role in future cybersecurity frameworks, emphasizing the importance of innovation, adaptability, and ongoing learning to enhance resilience against evolving cyber threats. Ultimately, this survey offers critical insights into how GenAI can shape the future of cybersecurity by addressing key challenges and providing actionable guidance for effective implementation. © The Author(s) 2025. KW - artificial intelligence KW - Cyber threat intelligence KW - Cybersecurity KW - GAN KW - Generative artificial intelligence KW - Large language models KW - Cybersecurity KW - Generative adversarial networks KW - Malware KW - Network security KW - Social networking (online) KW - Adversarial networks KW - Case-studies KW - Cybe threat intelligence KW - Cyber security KW - Cyber threats KW - Generative artificial intelligence KW - Language model KW - Large language model KW - Real-world KW - Social engineering KW - Data privacy CY - Finland, United States ER - TY - JOUR TI - Orchestrating artificial intelligence for urban sustainability AU - Zhang D. AU - Pee L.G. AU - Pan S.L. AU - Liu W. PY - 2022 JO - Government Information Quarterly VL - 39 IS - 4 SP - 101720 DO - 10.1016/j.giq.2022.101720 AB - Artificial intelligence (AI) is regarded as the next digital frontier in government, with many potential applications for economic development as well as sustainable urbanization. Governments have started experimenting with AI, but empirical research on how to leverage and implement AI remains limited. This study analyzed two cases of AI implementation in a large city and identified various AI capabilities useful for government. More importantly, purposeful orchestration of AI-related resources such as data, knowledge, algorithms, and information systems is necessary for developing strong AI capabilities. The findings indicate two different types of orchestration: policy-driven orchestration focuses on the integration of resources, while innovation-driven orchestration focuses on triangulation. This study contributes to the growing body of knowledge on AI in government by revealing and conceptualizing different paths and approaches to AI implementation. They also serve to inform practitioners' planning of AI implementation. © 2022 KW - Artificial intelligence KW - Big data KW - Resilient urbanization KW - Resource orchestration KW - SDGs KW - Sustainability CY - China, Singapore, Australia ER - TY - JOUR TI - Nurturing human intelligence in the age of AI: rethinking education for the future AU - Luckin R. PY - 2025 JO - Development and Learning in Organizations VL - 39 IS - 1 SP - 1 EP - 4 DO - 10.1108/DLO-04-2024-0108 AB - Purpose: The purpose of the article “Nurturing Human Intelligence in the Age of AI: Rethinking Education for the Future” is to explore the profound impact of Artificial Intelligence (AI) on education and to emphasize the need for a fundamental shift in current education systems. The article aims to provide practitioners with actionable insights on how to navigate the rapidly evolving landscape of AI in education while preparing young people for their crucial role as the workforce of tomorrow. It seeks to highlight the potential of AI to revolutionize education while also acknowledging the importance of preserving the unique human touch in the learning process. Design/methodology/approach: This article explores the disruptive impact of Artificial Intelligence (AI) on education and emphasizes the need for a fundamental shift in current education systems to prepare young people for an AI-driven future. It highlights the potential of AI to revolutionize education through personalized learning experiences, enhanced teacher professional development and automation of administrative tasks while acknowledging the importance of approaching AI implementation with caution and preserving the unique human touch in education. The article argues for a shift in focus from rote learning to fostering critical thinking, creativity and problem-solving skills, emphasizing the development of Learning Mastery and Knowledge Mastery. It underscores the vital role of educators in leveraging AI technologies and preparing young people for the future, along with the need for responsive educational policies and curriculum frameworks that integrate AI literacy and ethical considerations. The article concludes by calling for reimagining the schooling system, prioritizing high-level thinking and nurturing the unique capabilities of human intelligence. The future of education lies in harnessing the power of AI while celebrating and cultivating distinctively human qualities. Educational practitioners play a crucial role in shaping this future by bridging the gap between research and practice, ensuring a positive and prosperous future for society in an AI-driven world. Findings: (1) AI can revolutionize education through personalized learning, enhanced teacher development and task automation. (2) Balance is needed between AI and human touch in education. Current education systems fail to cultivate critical thinking and creativity. (3) Learning Mastery and Knowledge Mastery should be emphasized to foster independent thinking and problem-solving. (4) Educators play a vital role in integrating AI into the learning process. (5). AI can redefine success in education and cultivate future-proof skills. (6). Responsive and adaptable educational policies are necessary. (7) The future of education lies in harnessing AI while nurturing human intelligence. Research limitations/implications: Not appropriate for style of text. Practical implications: (1) Educators should actively engage with AI technologies and explore ways to integrate them into the learning process to enhance personalized learning experiences. (2) Professional development programs should be designed to equip teachers with the necessary skills to effectively utilize AI tools and leverage them to improve instructional practices. (3) Curriculum frameworks need to be revised to integrate AI literacy, digital citizenship and ethical considerations into the educational journey of young learners. (4) Educational institutions should invest in AI-powered assessment tools that provide a holistic understanding of a student’s abilities, capturing their strengths and areas for improvement beyond test scores. (5) Educators should focus on teaching metacognitive strategies, encouraging self-reflection and self-assessment and providing opportunities for students to develop problem-solving and critical-thinking skills. (6) Active learning strategies, such as project-based learning, problem-based learning and inquiry-based learning, should be employed to foster deep learning and knowledge mastery. (7) Educational policies should encourage innovation and collaboration between educational institutions, government bodies and industry stakeholders to ensure responsiveness to the rapidly evolving landscape of AI in education. (8) Educators should strive to create a learning environment that nurtures and celebrates the unique capabilities of human intelligence while harnessing the power of AI to enhance the learning experience. Social implications: (1) Workforce preparedness for an AI-driven future. (2) Potential exacerbation of societal inequalities. (3) Fostering human–AI collaboration skills. (4) Addressing ethical concerns regarding data privacy and security. (5) Emphasizing lifelong learning to adapt to changing demands. (6) Redefining success through a holistic view of student abilities. (7) Shaping societal values that balance human intelligence and AI capabilities. The education system must address these implications to ensure equitable access to AI-enhanced learning, maintain public trust and prepare individuals for a society where human–AI collaboration is essential, while promoting a balanced and harmonious coexistence between human intelligence and AI. Originality/value: The article “Nurturing Human Intelligence in the Age of AI: Rethinking Education for the Future” offers a fresh perspective on the impact of AI on education. While the topic of AI in education is not novel, the article’s emphasis on nurturing human intelligence alongside AI integration sets it apart. The author’s call for a fundamental shift in education systems to prioritise critical thinking, creativity and problem-solving skills is a unique approach. The article’s exploration of Learning Mastery and Knowledge Mastery as key concepts in preparing students for an AI-driven future adds originality to the discussion. Overall, the article presents a thought-provoking and original viewpoint on the future of education in the age of AI. © 2024, Emerald Publishing Limited. KW - Artificial Intelligence KW - Education KW - Ethics KW - Future-proof skills KW - Personalized learning KW - Teacher professional development CY - United Kingdom ER - TY - JOUR TI - Responsible AI and career sustainability: the intersectional role of knowledge, emotion, and capability in Vietnam AU - Tran Le Tuyet T. AU - Nguyen K.M. PY - 2026 JO - Cognition, Technology and Work DO - 10.1007/s10111-025-00851-4 AB - This study investigates how responsible AI signals (RAS) such as autonomy, justice, beneficence, explainability, and nonmaleficence enhance employees’ career sustainability, with particular focus on dynamic capability, AI emotional response, and AI knowledge management (knowledge sharing, acquisition, and application). Grounded in the Cognition–Affect–Conation (CAC) framework, the study extends its scope from psychology to human resource management by explaining how cognitive, emotional, and behavioral mechanisms jointly shape employees’ responses to responsible AI practices. A quantitative research design was employed using an online questionnaire, gathering responses from a sample of 717 employees in Vietnam and analyzed using PLS-SEM. The findings reveal that RAS strongly promotes AI emotional response, dynamic capability, and knowledge management processes, including knowledge sharing, acquisition, and application. In turn, AI emotional response, dynamic capability, and the application and sharing of knowledge exert significant positive effects on employee well-being and innovation performance, whereas knowledge acquisition shows no meaningful impact. The study advances theory by integrating the CAC framework with Responsible AI principles to explain how employees adapt and collaborate with AI in organizations. Practically, the findings indicate that adopting responsible AI principles can enhance employee creativity, emotional engagement, and adaptability by promoting knowledge sharing, supportive policies, and transparent AI practices. Managers are encouraged to design learning-oriented environments, continuous AI ethics training, and participatory mechanisms that allow employees to engage with AI fairly and autonomously, thereby fostering well-being, innovation, and long-term career sustainability. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2026. KW - AI knowledge management KW - Career sustainability KW - Dynamic capability KW - Emotional response KW - Responsible AI KW - Behavioral research KW - Employment KW - Human resource management KW - Knowledge acquisition KW - Knowledge management KW - Knowledge transfer KW - Mergers and acquisitions KW - Professional aspects KW - Sustainable development KW - AI knowledge management KW - Career sustainability KW - Dynamics capability KW - Emotional response KW - Knowledge application KW - Knowledge-sharing KW - ON dynamics KW - Responsible AI KW - Viet Nam KW - Well being KW - Personnel training ER - TY - JOUR TI - The Role of Lecturers’ AI Leadership in Enhancing Postgraduate Student Teachers’ Integration of Mobile AI Tools: A Mixed-Methods Study in Malaysian Education Faculties AU - Tang S.S. AU - Beh W.F. AU - Cheah K.S.L. PY - 2025 JO - International Journal of Interactive Mobile Technologies VL - 19 IS - 7 SP - 136 EP - 158 DO - 10.3991/ijim.v19i07.51971 AB - This study examined the influence of lecturers’ artificial intelligence (AI) leadership on postgraduate student teachers’ motivation to integrate AI into their curricula in Malaysian higher education. Using a sample of 62 participants, the study employed a mixed-methods approach to explore ethical implications and the alignment of AI with traditional teaching practices. By means of open-ended questions and online surveys, the study generated both quantitative and qualitative understanding of how leadership influences acceptance of AI in educational settings. Key findings showed that transformative and visionary AI leadership approaches not only improve feedback systems and tailored learning opportunities but also inspire teachers by means of interactive, game-like learning activities. AI leadership enables early identification of learning gaps by means of real-time analytics, enabling targeted interventions and a more inclusive learning environment. However, over-reliance on AI highlights the need for strategic planning to ensure that AI complements rather than replaces traditional teaching methods. The research emphasized the need for strategic leadership and professional development in embedding AI ethically and successfully inside curricula, offering a framework for both curriculum design and educator training programs. These results support current debates on educational innovation and place leadership as key in promoting a balanced, ethical AI integration matched with present educational aims. © 2025 by the authors. KW - AI integration in education KW - artificial intelligence (AI) transforming leadership KW - challenges of AI integration KW - educational technology KW - postgraduate student teachers KW - transformational leadership in AI KW - Adversarial machine learning KW - Curricula KW - Federated learning KW - Strategic planning KW - Students KW - Teaching KW - Artificial intelligence transforming leadership KW - Artificial intelligence integration in education KW - Challenge of artificial intelligence integration KW - Intelligence integration KW - Postgraduate student teacher KW - Postgraduate students KW - Student teachers KW - Transformational leadership KW - Transformational leadership in artificial intelligence KW - Contrastive Learning CY - Malaysia ER - TY - JOUR TI - How green knowledge-oriented leadership drives green innovation in SMEs: the mediating role of environmental strategy and the moderating role of green AI capability AU - Al Koliby I.S. AU - Al-Swidi A.K. AU - Al-Hakimi M.A. AU - Farhan S.A.G. PY - 2025 JO - Cogent Business and Management VL - 12 IS - 1 SP - 2520914 DO - 10.1080/23311975.2025.2520914 AB - This study examines how green knowledge-oriented leadership (GKOL) drives green innovation (GI) in manufacturing SMEs, with a focus on the mediating role of environmental strategy (ES) and the moderating effect of green artificial intelligence capability (GAIC). Drawing on the Natural Resource-Based View (NRBV) and Dynamic Capability Theory (DCT), the study developed and empirically tested an integrated framework that captures the interplay between leadership, strategy, and digital capability in promoting sustainable innovation. Data were collected from 219 Malaysian manufacturing SMEs using a structured questionnaire, and structural equation modeling was employed via SmartPLS to evaluate the proposed relationships, with the reliability and validity of the constructs verified through composite reliability, average variance extracted, and discriminant validity. The findings reveal that GKOL significantly enhances ES, which in turn enhances GI, with ES partially mediating this relationship. Additionally, GAIC strengthens the effect of GKOL on GI, underscoring the role of AI-enabled capabilities in amplifying green leadership outcomes. This study contributes to the literature by offering a unified leadership–strategy–technology framework for understanding sustainability transformation in resource-constrained SME settings and provides actionable insights for managers and policymakers on leveraging GKOL and digital transformation for sustainable development. © 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. KW - Business, Management and Accounting KW - Environment & Business KW - Environmental Management KW - environmental strategy KW - green artificial intelligence capability KW - green innovation KW - Green knowledge-oriented leadership KW - Manufacturing SMEs CY - China ER - TY - JOUR TI - Effectiveness in the furniture industry: artificial intelligence, big data and sustainable design AU - Adiguzel Z. AU - Sonmez Cakir F. AU - Altay Morgul U. PY - 2026 JO - Management Decision VL - 64 IS - 3 SP - 1063 EP - 1086 DO - 10.1108/MD-05-2024-1022 AB - Purpose – This research aims to investigate the interaction between artificial intelligence (AI) capability, big data capabilities, sustainability design and organizational effectiveness in the context of the furniture industry. It aims to explore how investments in AI and big data technologies can spur sustainability-focused innovation and ultimately increase corporate performance. Design/methodology/approach – Based on data collected from businesses operating in the furniture industry, this research uses a quantitative approach to analyze the relationships between independent variables (AI capability and big data features), mediating variable (sustainability design) and dependent variable (organizational effectiveness). The structural equation modeling (SEM) technique was used to test the proposed theoretical model and hypotheses. The SmartPLS program was used for analysis. Findings – Analysis results show a significant positive relationship between AI capability, big data capabilities, sustainability design and organizational effectiveness in the furniture industry. Moreover, sustainability design demonstrates its important role in translating technological advances into tangible performance results by mediating the relationship between AI capability, big data capabilities and organizational effectiveness. Research limitations/implications – Although this research contributes valuable insights, it also has limitations. It would not be appropriate to make a general assessment of the generalizability of the findings due to the focus on the furniture industry and the fact that the data of the research were collected from furniture-producing companies in Istanbul. Future research could explore additional industries and incorporate qualitative methods to provide a deeper understanding of the underlying mechanisms driving the observed relationships. Practical implications – The findings offer valuable insights to industry practitioners seeking to leverage the potential of AI and big data technologies to increase sustainable organizational effectiveness. Practical implications include strategic recommendations for integrating sustainability principles into organizational strategies, leveraging data-driven decision-making processes and encouraging innovation through technological investments. Originality/value – The originality of this research lies in its comprehensive examination of the intertwined dynamics between AI capability, big data capabilities, sustainability design and organizational effectiveness, especially in the context of the furniture industry. By combining knowledge from multiple disciplines, this research offers a new perspective on the strategic implications of technological innovation for sustainable business practices. © 2025 Emerald Publishing Limited KW - AI capability KW - Big data characteristics KW - Furniture industry KW - Organizational effective performance KW - Sustainability design CY - Turkey ER - TY - JOUR TI - Navigating artificial general intelligence development: societal, technological, ethical, and brain-inspired pathways AU - Raman R. AU - Kowalski R. AU - Achuthan K. AU - Iyer A. AU - Nedungadi P. PY - 2025 JO - Scientific Reports VL - 15 IS - 1 SP - 8443 DO - 10.1038/s41598-025-92190-7 AB - This study examines the imperative to align artificial general intelligence (AGI) development with societal, technological, ethical, and brain-inspired pathways to ensure its responsible integration into human systems. Using the PRISMA framework and BERTopic modeling, it identifies five key pathways shaping AGI’s trajectory: (1) societal integration, addressing AGI’s broader societal impacts, public adoption, and policy considerations; (2) technological advancement, exploring real-world applications, implementation challenges, and scalability; (3) explainability, enhancing transparency, trust, and interpretability in AGI decision-making; (4) cognitive and ethical considerations, linking AGI’s evolving architectures to ethical frameworks, accountability, and societal consequences; and (5) brain-inspired systems, leveraging human neural models to improve AGI’s learning efficiency, adaptability, and reasoning capabilities. This study makes a unique contribution by systematically uncovering underexplored AGI themes, proposing a conceptual framework that connects AI advancements to practical applications, and addressing the multifaceted technical, ethical, and societal challenges of AGI development. The findings call for interdisciplinary collaboration to bridge critical gaps in transparency, governance, and societal alignment while proposing strategies for equitable access, workforce adaptation, and sustainable integration. Additionally, the study highlights emerging research frontiers, such as AGI-consciousness interfaces and collective intelligence systems, offering new pathways to integrate AGI into human-centered applications. By synthesizing insights across disciplines, this study provides a comprehensive roadmap for guiding AGI development in ways that balance technological innovation with ethical and societal responsibilities, advancing societal progress and well-being. © The Author(s) 2025. KW - Artificial general intelligence KW - Brain inspired KW - Ethical AI KW - Ethics KW - Human-like AI KW - Responsible AI KW - Strong AI KW - Superintelligence KW - Topic modeling KW - Weak AI KW - Artificial Intelligence KW - Brain KW - Decision Making KW - Humans KW - article KW - artificial general intelligence KW - biological model KW - conceptual framework KW - consciousness KW - human KW - intelligence KW - Preferred Reporting Items for Systematic Reviews and Meta-Analyses KW - reasoning KW - responsible artificial intelligence KW - workforce KW - artificial intelligence KW - brain KW - decision making KW - ethics KW - physiology CY - India, United States ER - TY - JOUR TI - Artificial Intelligence (AI) Capabilities and the R&D Performance of Organizations: The Moderating Role of Environmental Dynamism AU - Kumar V. AU - Kumar S. AU - Chatterjee S. AU - Mariani M. PY - 2024 JO - IEEE Transactions on Engineering Management VL - 71 SP - 11522 EP - 11532 DO - 10.1109/TEM.2024.3423669 AB - The potential of artificial intelligence capabilities (AICs) extends beyond fostering both explorative and exploitative innovations (EXO and EXI); it may also enhance the overall performance of organizations. Despite this, the interplay between AIC and research and development performance (RDP) remains unexplored. In this article, we aim to fill this gap by investigating the influence of AIC on RDP, considering both EXO and EXI. Additionally, the study examines the potential moderating role of environmental dynamism in shaping the relationship between AIC and the two types of innovations, ultimately impacting the enhancement of RDP in organizations. To achieve this, a conceptual model was developed based on the existing literature and subsequently validated using the partial least square structural equation modeling. The research gathered 289 responses from a diverse group of industry professionals. The findings of this study contribute both theoretically and practically by shedding light on the pivotal role played by artificial intelligence (AI) capabilities, exploration, and EXI in improving the research and development (R&D) performance of organizations. Understanding these dynamics will provide valuable insights for organizations seeking to leverage AI for strategic advancement in their R&D endeavors. © 1988-2012 IEEE. KW - Artificial intelligence (AI) capability (AIC) KW - environmental dynamism (ED) KW - exploitative innovation (EXI) KW - exploration innovation (EXO) KW - research and development (R&D) performance KW - Artificial intelligence capability KW - Dynamic scheduling KW - Environmental dynamisms KW - Exploitative innovation KW - Exploration innovation KW - Performance KW - R&D performance KW - Research and development KW - Research performance KW - Technological innovation KW - Artificial intelligence CY - India, United Kingdom, Italy ER - TY - JOUR TI - Artificial intelligence capability, CEO-TMT interface and corporate innovation failure AU - Shang J. AU - Zhang K. PY - 2026 JO - Humanities and Social Sciences Communications VL - 13 IS - 1 SP - 515 DO - 10.1057/s41599-026-06856-2 AB - Artificial intelligence (AI) capability has demonstrated significant potential in driving knowledge recombination, fostering knowledge discovery and enhancing organisational innovation performance. However, the interplay between AI capability and corporate innovation failure remains underexplored. To address this research gap, this study investigated the impact of AI capability on corporate innovation failure while further examining the moderating role of top management team (TMT) digital knowledge and its variation under different levels of integrative leadership shown by chief executive officers (CEOs). Using panel data from 3,829 firm-year observations in China from 2017 to 2022, the empirical analysis reveals that AI capability significantly reduces the likelihood of corporate innovation failure. Moreover, TMT digital knowledge exerts a significant positive moderating effect on this relationship. Further analysis reveals that this positive moderating effect is more pronounced when the CEO exhibits a high level of integrative leadership. © The Author(s) 2026. CY - China ER - TY - JOUR TI - DATA GOVERNANCE FOR ARTIFICIAL INTELLIGENCE IMPLEMENTATION IN THE FINANCIAL SECTOR: AN INDONESIAN PERSPECTIVE AU - Damaris R. AU - Rosadi S.D. AU - Bratadana I.M.D. PY - 2025 JO - Journal of Central Banking Law and Institutions VL - 4 IS - 3 SP - 445 EP - 472 DO - 10.21098/jcli.v4i3.430 AB - The fast-evolving landscape of Artificial Intelligence (AI) is transforming industries worldwide, including Indonesia’s financial sector. While AI presents immense opportunities for innovation and efficiency, it also poses complex challenges in data governance. This paper explores the need for Indonesia to establish a comprehensive and forward-thinking data governance framework tailored to AI implementation in the financial sector. Using a literature review method and drawing on global and local regulatory developments, the paper outlines key principles for AI-related data governance, including transparency, accountability, specificity, enforceability, and adaptability. By reimagining its approach to data governance, Indonesia can mitigate the risks of data misuse, enhance personal data protection, and foster an environment conducive to responsible AI innovation. The research addresses the foregoing issues by offering a conceptual foundation for policymakers, regulators, and financial institutions in Indonesia to develop better rules and practices for managing AI-related data to strengthen Indonesia’s technological sovereignty, particularly in the financial sector. The study finds that Indonesia’s current data governance framework in the financial sector is not yet optimal for supporting AI implementation. Indonesia’s data governance framework requires adjustments in key areas, namely specificity, enforceability, and adaptability, while also promoting stronger cooperation among stakeholders. © 2025, Bank Indonesia Institute. All rights reserved. KW - AI governance KW - artificial intelligence KW - data governance KW - financial sector KW - technology regulation CY - Indonesia ER - TY - JOUR TI - The nexus of managerial and technical AI knowledge, disruptive innovation and the circular economy: The role of organizational change capability and financial resilience AU - Al Halbusi H. AU - Popa S. AU - Soto-Acosta P. AU - Alshallaqi M. PY - 2025 JO - Technology in Society VL - 82 SP - 102937 DO - 10.1016/j.techsoc.2025.102937 AB - Drawing on the dynamic capabilities theory, this study investigates how managerial and technical artificial intelligence (AI) knowledge influence disruptive innovation and its impact on the circular economy. It also examines the moderating effects of organizational change capability and financial resilience on the relationship between disruptive innovation and the circular economy. The proposed model and its associated hypotheses were tested using Partial Least Squares (PLS) structural equation modeling (SEM). This study is based on two-wave data collected from 242 general, IT, and operations managers in firms located within a central hub for the technology and manufacturing industries in Baghdad. The findings indicate that managerial and technical AI knowledge significantly boost disruptive innovation, which, in turn, enhances the circular economy. Moreover, organizational change capability and financial resilience strengthen the relationship between disruptive innovation and the circular economy. This research contributes to the field of AI capabilities by highlighting their role in fostering disruptive innovation and promoting sustainability through the circular economy. Additionally, it provides insights into how organizational and financial resilience, alongside AI skills and knowledge, can support sustainable business models. Practically, the study underscores the necessity for technology and manufacturing companies to invest in both managerial and technical AI skills while prioritizing robust organizational change capabilities and financial resilience to maximize the benefits of disruptive innovation and support a circular economy. © 2025 Elsevier Ltd KW - Artificial intelligence KW - Disruptive innovation KW - Managerial and technical skills KW - Organizational change KW - Resilience KW - Sustainability KW - Baghdad [Iraq] KW - Iraq KW - Circular economy KW - Disruptive innovations KW - Dynamics capability KW - Managerial skills KW - Moderating effect KW - Organizational change KW - Partial least-squares KW - Resilience KW - Structural equation models KW - Technical skills KW - artificial intelligence KW - circular economy KW - environmental economics KW - finance KW - industrial technology KW - innovation KW - knowledge KW - least squares method KW - manufacturing KW - organizational change KW - sustainability KW - Investments CY - Qatar, Spain, Saudi Arabia ER - TY - JOUR TI - Artificial Intelligence Applications in High-Frequency Magnetic Components Design for Power Electronics Systems: An Overview AU - Shen X. AU - Zuo Y. AU - Kong J. AU - Martinez W. PY - 2024 JO - IEEE Transactions on Power Electronics VL - 39 IS - 7 SP - 8478 EP - 8496 DO - 10.1109/TPEL.2024.3381431 AB - This article provides an overview of how artificial intelligence (AI) is applied in designing high-frequency magnetic components, primarily high-frequency inductors and transformers, for power electronics systems. Four categories of AI, including expert systems, fuzzy logic, metaheuristic methods, and machine learning techniques, are addressed. First, AI models for estimating losses in high-frequency magnetic components are discussed. Subsequently, AI-based design methods in high-frequency inductors and transformers are observed. Then, AI tools applied to the automatic design of high-frequency magnetic components are introduced and compared. Drawing insights from an analysis of over 200 publications, this article highlights significant advancements: the development of AI-driven models for precise loss estimation in high-frequency magnetic components, the application of AI in optimizing design configurations for the components, and the automation of design processes. These achievements demonstrate AI's capability to enhance the efficiency, performance, and innovation in high-frequency magnetic component design, offering a roadmap for future research in power electronics systems. © 1986-2012 IEEE. KW - Artificial intelligence (AI) KW - HF magnetic components KW - HF transformer design KW - high-frequency (HF) inductor design KW - loss models KW - Expert systems KW - Fuzzy logic KW - Learning systems KW - Magnetic resonance KW - Power electronics KW - Soft magnetic materials KW - Amorphous magnetic materials KW - High-frequency inductor design KW - High-frequency inductors KW - High-frequency magnetic component KW - High-frequency magnetics KW - High-frequency transformer desig KW - High-frequency transformers KW - Inductor design KW - Loss model KW - Magnetic components KW - Saturation magnetization CY - Belgium ER - TY - JOUR TI - Domain Knowledge-Based Human Capital Strategy in Manufacturing AI AU - Chung E. PY - 2023 JO - IEEE Engineering Management Review VL - 51 IS - 1 SP - 108 EP - 122 DO - 10.1109/EMR.2022.3215074 AB - As the Industry 4.0 paradigm accelerates, the importance of artificial intelligence (AI) in manufacturing industry is increasing. Manufacturing AI requires balanced capabilities between industry-specific domain knowledge and AI capability. Nevertheless, many manufacturing AI startups are mainly focusing on human capital based on AI or data science capability. This article focused on the importance of human capital based on domain knowledge for the success of manufacturing AI. In this article, the relationship between domain knowledge and corporate performance was analyzed for 127 global manufacturing AI startups. Furthermore, the moderating effects of educational level and cofounder size for domain knowledge were analyzed. In addition, an expanded analysis was conducted on effect between domain knowledge, educational level, cofounder size, and corporate performance by business model. This article has new implications for and provides practical contributions to the human capital strategy for manufacturing AI corporates. © 1973-2011 IEEE. KW - Artificial intelligence (AI) KW - domain knowledge KW - human capital KW - industry 4.0 KW - manufacturing KW - smart factory KW - Cost engineering KW - Domain Knowledge KW - Foundries KW - Knowledge engineering KW - Artificial intelligence KW - Corporate performance KW - Domain knowledge KW - Fourth industrial revolution KW - Human capitals KW - Industrial revolutions KW - Manufacturing KW - Smart factory KW - Technological innovation KW - Industry 4.0 CY - South Korea ER - TY - JOUR TI - Can Artificial Intelligence Technologies Advance Environmental Sustainability? The Role of Institutional Adaptability and Skill-Biased Technological Transformation AU - Bergougui B. PY - 2026 JO - Sustainable Development VL - 34 IS - S2 SP - 222 EP - 244 DO - 10.1002/sd.70296 AB - The ubiquitous proliferation of artificial intelligence (AI) technologies across contemporary global economic systems necessitates a comprehensive empirical examination of their environmental ramifications, particularly with respect to environmental sustainability paradigms. This study leverages a longitudinal panel of 29 advanced and developing economies over the period 2005–2024, employing AI patent filing frequencies as a quantitative proxy for national AI capability. Our econometric analysis reveals a statistically robust and economically meaningful relationship: higher AI activity is consistently associated with increases in the load capacity factor (LCF), a composite indicator of environmental sustainability. This association endures across multiple model specifications, remains significant under instrumental-variable estimation to address endogeneity, and passes a battery of robustness and sensitivity checks. Mechanism analysis uncovers two principal transmission channels. First, AI drives technological transformation in labor markets—favoring non-routine and high-skilled occupations—which in turn enhances resource efficiency and elevates LCF. Second, institutional flexibility—proxied by regulatory quality and innovation-friendly governance—magnifies AI's positive environmental effects by lowering transaction costs and facilitating diffusion. Heterogeneity tests further demonstrate that countries geographically proximate to global AI leaders experience stronger LCF gains, underscoring the importance of knowledge spillovers. Moreover, lower-income and fossil-fuel–dependent economies exhibit more pronounced benefits, indicating AI's potential as a transitional “leapfrog” technology. Among AI subfields, patents in energy-management applications deliver the largest LCF improvements. Overall, our evidence underscores the pivotal role of AI-driven patented technologies in strengthening environmental sustainability. Policies that incentivize AI innovation, support institutional adaptability, and foster international technology transfer are therefore essential to accelerate global progress toward sustainable development targets. © 2025 The Author(s). Sustainable Development published by ERP Environment and John Wiley & Sons Ltd. KW - AI KW - environmental sustainability KW - institutional framework KW - load capacity factor KW - technological transformation KW - artificial intelligence KW - econometrics KW - institutional framework KW - panel data KW - sustainability KW - sustainable development KW - technology transfer CY - Algeria, Netherlands, Qatar ER - TY - JOUR TI - Artificial intelligence and the five laws: a new vision for library science AU - Kalbande D. AU - Hemke D. AU - Motewar N. PY - 2025 JO - Library Hi Tech News VL - 42 IS - 4 SP - 1 EP - 3 DO - 10.1108/LHTN-01-2025-0005 AB - Purpose: This conceptual paper reinterprets S.R. Ranganathan’s five laws of library science in the context of artificial intelligence (AI), examining their continued relevance and adaptability in the digital age. By aligning AI capabilities with these foundational principles, this paper aims to explore how AI can enhance information access, optimize resource management and personalize library services while maintaining the ethical and philosophical core of Library and Information Science (LIS). Design/methodology/approach: This study uses a conceptual analysis approach to critically examine AI applications in LIS, including automated cataloging, AI-driven search systems, personalized recommendations and intelligent chatbots. It also addresses ethical considerations such as algorithmic bias, data privacy and equitable access. This paper proposes an AI-enhanced reinterpretation of Ranganathan’s laws, offering a guiding framework for responsible AI adoption in libraries. Findings: This study highlights the transformative potential of AI in libraries, demonstrating its ability to improve operational efficiency, user engagement and accessibility. However, it also emphasizes the necessity of aligning AI implementation with ethical principles to prevent biases and ensure inclusivity. By conceptualizing an AI-driven adaptation of Ranganathan’s laws, this paper provides a roadmap for integrating AI into library services without compromising their core values. Originality/value: This research offers a novel perspective by reconceptualizing Ranganathan’s five laws in the era of AI, providing LIS professionals with a theoretical framework to guide AI integration. It contributes to the discourse on ethical and sustainable AI adoption in libraries, ensuring that technological advancements support rather than undermine traditional LIS principles. © 2025, Emerald Publishing Limited. KW - Artificial intelligence in libraries KW - Digital transformation KW - Ethical AI KW - Library science KW - LIS innovation KW - Ranganathan’s five laws CY - India ER - TY - JOUR TI - Will the technological singularity come soon? Modeling the dynamics of artificial intelligence development via multi-logistic growth process AU - Jin G. AU - Ni X. AU - Wei K. AU - Zhao J. AU - Zhang H. AU - Jia L. PY - 2025 JO - Physica A: Statistical Mechanics and its Applications VL - 664 SP - 130450 DO - 10.1016/j.physa.2025.130450 AB - We are currently in an era of escalating technological complexity and profound societal transformations, where artificial intelligence (AI) technologies exemplified by large language models (LLMs) have reignited discussions on the ‘Technological Singularity’. ‘Technological Singularity’ is a philosophical concept referring to an irreversible and profound transformation that occurs when AI capabilities surpass those of humans comprehensively. However, quantitative modeling and analysis of the historical evolution and future trends of AI technologies remain scarce, failing to substantiate the singularity hypothesis adequately. This paper hypothesizes that the development of AI technologies could be characterized by the superposition of multiple logistic growth processes. To explore this hypothesis, we propose a multi-logistic growth process model and validate it using two real-world datasets: AI Historical Statistics and Arxiv AI Papers. Our analysis of the AI Historical Statistics dataset assesses the effectiveness of the multi-logistic model and evaluates the current and future trends in AI technology development. Additionally, cross-validation experiments on the Arxiv AI Paper, GPU Transistor and Internet User dataset enhance the robustness of our conclusions derived from the AI Historical Statistics dataset. The experimental results reveal that around 2024 marks the fastest point of the current AI wave, and the deep learning-based AI technologies are projected to decline around 2035–2040 if no fundamental technological innovation emerges. Consequently, the technological singularity appears unlikely to arrive in the foreseeable future. © 2025 Elsevier B.V. KW - Artificial intelligence KW - Development dynamics KW - Multi-logistic growth KW - Technological singularity KW - 'current KW - Artificial intelligence technologies KW - Development dynamics KW - Future trends KW - Growth process KW - Language model KW - Logistic growth KW - Multi-logistic growth KW - Technological complexity KW - Technological singularity CY - China ER - TY - JOUR TI - Enhancing Problem-Solving Skills with AI: A Case Study on Innovation and Creativity in a Business Setting AU - Hajj C. AU - Schmitt C. AU - Azoury N. PY - 2025 JO - Administrative Sciences VL - 15 IS - 10 SP - 388 DO - 10.3390/admsci15100388 AB - The adoption of artificial intelligence has risen, yet research on its impact on innovation processes between actual businesses remains sparse. This research fills the present gap by investigating ten workers from a tech startup who utilize artificial intelligence tools in operational and creative activities. The paper analyzes business-related AI functionality through a qualitative analysis of ten tech start-up employees. The examination reveals that AI produces significant enhancements in problem resolution by executing mundane actions while analyzing large datasets to deliver data-driven suggestions to users. The interview respondents mentioned that AI’s role in diminishing supply chains is 15%, while allowing AI to manage customer service without employee engagement in 80% of interactions. The implementation costs, along with data dependency and occasional contextual blindness in AI systems, represented some of the problems in this system. Analysis demonstrated that AI tools enable the development of innovative concepts and challenge established viewpoints, prompting participants to create a gamified loyalty system and dynamic content planning. Participants in the study emphasized the need for human involvement to refine AI-based insights, recognizing how human imagination complements AI capabilities effectively. The work enhances academic discussions about AI-related problem-solving and creativity while offering specific business-related recommendations for implementation. The recommendations begin with establishing initial experimental programs, while providing support for employee’s skills development, and fostering strong alliances between technical AI personnel and professional subject matter experts. Research topics focused on AI application fields and the anticipated impacts on company decision-making, as well as the ethical ramifications, need further exploration. This research confirms the revolutionary potential of artificial intelligence systems for problem-solving methods, but requires proper execution, along with human supervision, to fully realize their advantages. © 2025 by the authors. KW - AI tools KW - Artificial Intelligence (AI) KW - business innovation KW - case study KW - data-driven insights KW - human–AI collaboration KW - problem-solving skill KW - qualitative research CY - Lebanon, France ER - TY - JOUR TI - AI policies in school education: a comparative study on China, Singapore, Finland, and the US AU - Kundu A. AU - Bej T. PY - 2025 JO - Journal of Science and Technology Policy Management DO - 10.1108/JSTPM-06-2024-0218 AB - Purpose – The purpose of this study is to compare artificial intelligence (AI)-integration strategies in school education across China, Singapore, Finland and the USA, aiming to uncover shared patterns and localized innovations that could inform a globally responsive AI education framework. Design/methodology/approach – The qualitative desktop study draws on secondary data from five recent government policy documents: China’s New Generation Artificial Intelligence Development Plan, Singapore’s EdTech Masterplan 2030, Finland’s Age of Artificial Intelligence, California’s Computer Science Strategic Implementation Plan and Massachusetts’ Digital Literacy and Computer Science Standards. These were analyzed using the SMART criteria and a researcher-constructed “Nine-point framework of operational components in AI policy for schools.” Findings – Despite varying governance models and socio-cultural contexts, all four countries share a common intent to integrate AI into school education. Nine thematic propositions emerged: “SMART” policy design, balanced vision, curriculum and ethics integration, dynamic teacher training, equitable funding, multi-stakeholder partnerships, adaptive monitoring, localized implementation and contextual alignment. Finland and Singapore demonstrate strong ethical and human-centered policies, while China and the USA lean toward innovation and workforce development. Implementation remains challenged by equity gaps, teacher readiness and contextual mismatches. Research limitations/implications – These diverse models offer critical lessons: future global frameworks must prioritize ethical safeguards, localized adaptability, inclusive training and dynamic monitoring systems to ensure AI supports equity and relevance across school contexts. Originality/value – This study offers original insights derived from systematic, comparative analysis of the national AIEd policies using a robust evaluative framework. © 2025 Emerald Publishing Limited KW - AI Policies KW - AIEd KW - China KW - Finland KW - School Education KW - Singapore KW - SMART KW - The US CY - India ER - TY - JOUR TI - Artificial Intelligence in Financial Security Markets: Catalyzing Sustainable Development Through Innovation, Risk Mitigation, and Adaptive Governance AU - Bawa S. AU - Benin I.W. AU - Kancham H. AU - Munoz-Ramirez P. PY - 2026 JO - Sustainable Development DO - 10.1002/sd.71228 AB - The rapid integration of artificial intelligence (AI) into financial security markets presents both significant opportunities and emerging governance challenges for sustainable development. This study employs a comparative mixed-methods approach to examine how AI-driven innovation in trading, risk management, regulatory compliance, and sustainability analytics interacts with institutional governance structures to shape sustainability outcomes and systemic risk exposure in financial markets. Through a comparative institutional analysis of leading financial systems in China, the United States, and the United Kingdom (2022–2025), we integrate secondary quantitative indicators with qualitative documentary evidence to explore how AI adoption is governed and operationalized across contrasting regulatory environments. The analysis indicates that AI-enabled financial innovation is associated with improvements in market efficiency, ESG integration, and risk assessment capabilities, while also introducing governance challenges related to model opacity, algorithmic bias, and the potential amplification of systemic vulnerabilities. The findings highlight the conditioning role of adaptive governance in shaping how AI-driven capabilities translate into sustainability and risk outcomes. Building on these insights, the study advances an integrative framework of sustainable AI governance that emphasizes regulatory adaptability, institutional coordination, and ethical oversight as critical mechanisms for aligning AI innovation with long-term financial stability and sustainability objectives. The framework offers policy-relevant guidance for regulators and financial institutions seeking to harness AI's transformative potential while managing its systemic implications. © 2026 The Author(s). Sustainable Development published by ERP Environment and John Wiley & Sons Ltd. KW - adaptive governance KW - artificial intelligence KW - financial innovation KW - financial security markets KW - sustainable development KW - systemic risk CY - China, United Kingdom ER - TY - JOUR TI - Artificial intelligence in school mathematics education: awareness, readiness, and usage among mathematics teachers; [Искусственный интеллект в школьном математическом образовании: осведомленность, готовность и использование учителями математики] AU - Kuzmenko M.V. PY - 2025 JO - Psychological Science and Education VL - 30 IS - 3 SP - 125 EP - 139 DO - 10.17759/pse.2025300310 AB - Context and relevance. This article presents the results of the study conducted among mathematics teachers - the category of teachers particularly inclined toward critical thinking and evidence-based application of innovations in education. Objective. The objective of this study is to identify the awareness of math teachers about the AI capabilities and potential in teaching as well as the practice of their application in the educational process. Methods and materials. To achieve this objective, a questionnaire was developed, comprising three main sections: awareness, readiness, and practical application. The survey was conducted online using Yandex Forms. A total of 122 mathematics teachers from 44 regions of the Russian Federation, varying in age and teaching experience, participated in the study. Results. The results showed that approximately 70% of the respondents express a willingness to use AI in their teaching process. The directions in which math teachers are most and least inclined to trust AI have been identified. The proportion of teachers currently using AI technologies and specific software products based on AI ranges from 13% to 40%. Conclusions. A significant part of teachers is generally aware of AI’s potential. However, their knowledge is fragmentary, covering only certain aspects and lacking systematic understanding. Promising directions for further research include examining the issues surrounding the use of AI technologies in the educational process while taking into account their specific characteristics. Special attention is recommended to improving teaching methodologies based on AI technologies and identifying effective ways to apply them for the development of students’ cognitive abilities. © 2025 Irmansyah J, Mujriah, Syarifoeddin EW, Syah H KW - AI in education KW - artificial intelligence KW - digitalization of education KW - mathematics teachers KW - neural networks KW - neural networks in education KW - teacher readiness ER - TY - JOUR TI - Exploring the role of open innovation and artificial intelligence in green innovation: A dynamic capabilities approach AU - Cassânego V.M. AU - Moralles H.F. AU - Nascimento D.L.D.M. AU - Tortorella G.L. PY - 2025 JO - Journal of Innovation and Knowledge VL - 10 IS - 5 SP - 100774 DO - 10.1016/j.jik.2025.100774 AB - Addressing current environmental challenges is not solely a matter of governmental policies. Organizations are key stakeholders who play a vital role through strategic partnerships and adoption of innovative technologies. Based on the dynamic capabilities framework, this research investigates the influence of open innovation partnerships in incorporating corporate green innovation (CGI), specifically green product and process innovation. It also elucidates the potential role of artificial intelligence (AI) capabilities in developing green innovation. Our study, based on a sample of approximately 1780 firms from 93 countries distributed across five continents, show that firms actively searching for and consolidating partnerships in open innovation can enhance green innovation in products and processes. Similarly, firms that develop or incorporate AI capabilities can catalyze the output of green product and process innovation because they incentivize open innovation partnerships, indicating that adopting both simultaneously is preferable. The results also show that the impact on green process innovation is greater than the impact on green product innovation. We recommend that policymakers and firms invest in AI capabilities and open green partnerships, leveraging these synergies to enhance innovation efficiency, and adapt green strategies to varying technological, institutional, and regional contexts. © 2025 The Author(s) KW - Artificial intelligence KW - Corporate green innovation KW - Dynamic capabilities KW - Open innovation CY - Brazil, Spain, Australia, Argentina ER - TY - JOUR TI - Ethical AI in medical text generation: balancing innovation with privacy in public health AU - Liang M. PY - 2025 JO - Frontiers in Public Health VL - 13 SP - 1583507 DO - 10.3389/fpubh.2025.1583507 AB - Introduction: The integration of artificial intelligence (AI) into medical text generation is transforming public health by enhancing clinical documentation, patient education, and decision support. However, the widespread deployment of AI in this domain introduces significant ethical challenges, including fairness, privacy protection, and accountability. Traditional AI-driven medical text generation models often inherit biases from training data, resulting in disparities in healthcare communication across different demographic groups. Moreover, ensuring patient data confidentiality while maintaining transparency in AI-generated content remains a critical concern. Existing approaches either lack robust bias mitigation mechanisms or fail to provide interpretable and privacy-preserving outputs, compromising ethical compliance and regulatory adherence. Methods: To address these challenges, this paper proposes an innovative framework that combines privacy-preserving AI techniques with interpretable model architectures to achieve ethical compliance in medical text generation. The method employs a hybrid approach that integrates knowledge-based reasoning with deep learning, ensuring both accuracy and transparency. Privacy-enhancing technologies, such as homomorphic encryption and secure multi-party computation, are incorporated to safeguard sensitive medical data throughout the text generation process. Fairness-aware training protocols are introduced to mitigate biases in generated content and enhance trustworthiness. Results and discussion: The proposed approach effectively addresses critical challenges of bias, privacy, and interpretability in medical text generation. By combining symbolic reasoning with data-driven learning and embedding ethical principles at the system design level, the framework ensures regulatory alignment and improves public trust. This methodology lays the groundwork for broader deployment of ethically sound AI systems in healthcare communication. Copyright © 2025 Liang. KW - AI ethics KW - bias mitigation KW - ethical challenges KW - healthcare regulation KW - legal compliance KW - medical AI KW - privacy protection KW - text generation KW - Artificial Intelligence KW - Confidentiality KW - Electronic Health Records KW - Humans KW - Privacy KW - Public Health KW - artificial intelligence KW - confidentiality KW - electronic health record KW - ethics KW - human KW - privacy KW - public health CY - China ER - TY - JOUR TI - Banana republic: Copyright law and the extractive logic of generative AI AU - Lim D. PY - 2025 JO - Journal of Intellectual Property Law and Practice VL - 20 IS - 9 SP - 573 EP - 583 DO - 10.1093/jiplp/jpaf047 AB - This article uses Maurizio Cattelan's Comedian, a banana duct-taped to a gallery wall, as a metaphor to examine the extractive dynamics of generative artificial intelligence (AI). It argues that the AI-driven creative economy replicates colonial patterns of appropriation, transforming human expression into commodified outputs while marginalizing the creators whose work makes these systems possible. Through the figures of the fruit seller, the buyer and the artist, the article interrogates who is valued, who is erased and who reaps the rewards in this evolving landscape. The analysis turns next to the banana itself as an object of constructed value, exploring how copyright's doctrines of authorship, originality and fair use struggle to accommodate the layered and distributed nature of AI-mediated creation. These doctrinal limitations, the article contends, leave creators vulnerable while enabling dominant platforms to entrench extractive practices under the guise of innovation. Finally, the article examines the 'wall', the metaphorical and institutional surfaces against which generative AI is made legible and legitimate. It begins by situating current AI governance within broader global trends of legal fragmentation and jurisdictional arbitrage, highlighting how regulatory divergence reflects deeper normative commitments - some prioritizing innovation, others dignity and distributive justice. It then critiques reactive proposals that rely on private licensing regimes or piecemeal litigation, arguing that such approaches risk entrenching opacity and extractive control. In their place, the article advocates for structural reforms grounded in transparency, attribution and participatory design, legal scaffolding that can recognize distributed authorship and protect against enclosure. Without these interventions, the generative AI economy may replicate the very conditions that Comedian satirizes: spectacle without substance, progress without equity. © 2025 The Author(s). Published by Oxford University Press. All rights reserved. CY - United States ER - TY - JOUR TI - AI Ecosystem and Value Chain: A Multi-Layered Framework for Analyzing Supply, Value Creation, and Delivery Mechanisms AU - Billones R.K.C. AU - Lauresta D.A.S. AU - Dellosa J.T. AU - Bong Y. AU - Stergioulas L.K. AU - Yunus S. PY - 2025 JO - Technologies VL - 13 IS - 9 SP - 421 DO - 10.3390/technologies13090421 AB - Despite the rapid adoption of artificial intelligence (AI) on a global scale, a comprehensive framework that maps its end-to-end value chain is missing. The presented study employed a multi-layered framework to analyze the value creation and delivery mechanism of the five core layers of an AI value chain, including (1) hardware, (2) data management, (3) foundational AI, (4) advanced AI capabilities, and (5) AI delivery. Using a qualitative–descriptive approach with a multi-faceted thematic analysis and a SWOT-based bottleneck analysis of each core layer, the study maps a sequential value flow from a globally dependent hardware foundation to the deployment of AI services. The analysis reveals that international knowledge flows shape the ecosystem, while the “last-mile” integration challenge is not merely a technical issue; instead, it highlights a significant socio-technical disconnect between technological advancements and the preparedness of the workforce. This study provides a holistic framework that frames the AI value chain as a socio-technical system, offering critical insights for stakeholders. The findings emphasize that unlocking AI’s full potential requires strategic investment in the managerial competencies and digital skills that constitute human–capital readiness. © 2025 by the authors. KW - AI as a service (AIaaS) KW - AI governance KW - bottleneck analysis KW - data management KW - digital transformation KW - global value chain KW - socio-technical system KW - statist triple helix KW - supply chain management KW - SWOT KW - Artificial intelligence KW - Chains KW - Information management KW - Investments KW - Metadata KW - Supply chains KW - Artificial intelligence as a service KW - Artificial intelligence governance KW - Bottleneck analysis KW - Chain management KW - Digital transformation KW - Global value chain KW - Sociotechnical systems KW - Statist triple helix KW - SWOT KW - Triple helixes KW - Supply chain management CY - Netherlands ER - TY - JOUR TI - Artificial intelligence capability: Conceptualization, measurement calibration, and empirical study on its impact on organizational creativity and firm performance AU - Mikalef P. AU - Gupta M. PY - 2021 JO - Information and Management VL - 58 IS - 3 SP - 103434 DO - 10.1016/j.im.2021.103434 AB - Artificial intelligence (AI) has been heralded by many as the next source of business value. Grounded on the resource-based theory of the firm and on recent work on AI at the organizational context, this study (1) identifies the AI-specific resources that jointly create an AI capability and provides a definition, (2) develops an instrument to capture the AI capability of the firms, and (3) examines the relationship between an AI capability and organizational creativity and performance. Findings empirically support the suggested theoretical framework and corresponding instrument and provide evidence that an AI capability results in increased organizational creativity and performance. © 2021 The Author(s) KW - Artificial intelligence KW - Capability KW - Firm performance KW - Instrument development KW - Organizational creativity KW - Resource-based theory KW - Information science KW - Information systems KW - Business value KW - Empirical studies KW - Firm Performance KW - Measurement calibration KW - Organizational context KW - Resource-based theory KW - Theoretical framework KW - Artificial intelligence CY - Norway, United States ER - TY - JOUR TI - Fostering Athletes’ Mental Resilience: Artistic Innovation and AI in Sports AU - Xia Q. PY - 2023 JO - Revista de Psicologia del Deporte VL - 32 IS - 4 SP - 213 EP - 224 AB - As artificial intelligence (AI) technology rapidly advances, the world of artistic innovation in sports paintings encounters both unprecedented challenges and exciting opportunities. AI’s capacity to learn pushes the boundaries of traditional creative thinking within the realm of sports art, introducing a more diverse and intelligent approach to the creative process. However, the actual ecosystem for creativity and its application within this context lacks a robust management model, necessitating fundamental theoretical innovation and standardized discipline.This exploration embarks on a journey to elucidate the practical applications of AI technology in the creation of sports art. It delves into the pivotal roles played by AI professionals, creative artists, and viewers in shaping the future of sports art innovation. It paves the way for an innovative approach to sports painting and decorative art, grounded in AI intelligence.Emphasizing the symbiotic relationship between human and AI capabilities, intelligent product development, artistic creation, and the constraints on creative behavior, this inquiry dives into emerging application areas. Through this lens, it seeks to gain a deeper understanding of the essential cycles of innovation in the field of sports plastic art, driven by the transformative power of artificial intelligence © 2023 Sociedad Revista de Psicologia del Deporte. All rights reserved. KW - Artificial intelligence era KW - Athletes’ KW - painting applications KW - sports KW - sports painting creation CY - South Korea ER - TY - JOUR TI - THE AI PARADOX IN CENTRAL BANKING: NEW POWERS, NEW VULNERABILITIES AU - Koroye T. AU - Alaekwe S. PY - 2025 JO - Journal of Central Banking Law and Institutions VL - 4 IS - 3 SP - 533 EP - 566 DO - 10.21098/jcli.v4i3.441 AB - The integration of artificial intelligence into central banking disrupts the traditional bank-regulator relationship, creating asymmetries that private institutions exploit. This paper examines how AI-driven market surveillance and predictive risk modelling erode private banks’ informational advantages, compelling them into a Schumpeterian race for survival in which innovation becomes imperative. Using a qualitative analysis of regulatory developments and financial market adaptations, this study argues that enhanced central bank AI capabilities paradoxically accelerate the emergence of opaque financial segments designed to evade oversight. The findings indicate that this shift transforms regulatory dynamics, positioning central banks as real-time market participants while private institutions develop increasingly sophisticated methods of regulatory evasion. This evolution generates systemic risks that existing regulatory frameworks struggle to address, necessitating adaptive oversight mechanisms. The study concludes that the imperative progressively drives financial innovation to maintain opacity in response to algorithmic supervision, underscoring the need for regulatory models that balance AI’s benefits with emerging vulnerabilities. © 2025, Bank Indonesia Institute. All rights reserved. KW - ai-resistant markets KW - algorithmic supervision KW - financial innovation and opacity KW - information asymmetry KW - regulatory evasion CY - United Kingdom ER - TY - JOUR TI - Revisiting artificial intelligence in start-ups: A theoretical perspective on integration, opportunities, challenges, and strategic advancement AU - Abbas A.F. AU - Al-Lawati E.H. PY - 2025 JO - Journal of the International Council for Small Business DO - 10.1080/26437015.2025.2549059 AB - This study adopts a theory elaboration approach to systematically review 148 peer-reviewed articles published between 2016 and 2025 on the integration of artificial intelligence (AI) in start-ups. Drawing on foundational theories such as the technology acceptance model, resource-based view (RBV), dynamic capabilities, and institutional theory, it develops a conceptual framework that highlights both opportunities and challenges associated with AI adoption. Opportunities include enhanced operational efficiency, innovation enablement, and improved decision making. Challenges involve regulatory complexity, ethical concerns, scalability issues, and limited resources. The study contributes theoretically by identifying emerging constructs and refining interconstruct relationships within AI-driven start-up ecosystems. It proposes a future research agenda calling for empirical validation of the framework across sectors and geographies. Practical implications are discussed for start-up founders, investors, and policy makers, emphasizing the need for strategic alignment, AI governance, and talent development to ensure responsible and sustainable integration of AI within entrepreneurial environments in start-ups. © 2025 International Council for Small Business. KW - Artificial intelligence KW - innovation KW - start-ups KW - strategic integration KW - theory elaboration CY - Malaysia, Oman ER - TY - JOUR TI - AI washing: A conceptual exploration AU - Elhajjar S. AU - Itani O.S. PY - 2025 JO - AMS Review VL - 15 IS - 3-4 SP - 519 EP - 538 DO - 10.1007/s13162-025-00323-y AB - This paper introduces and explores the concept of AI washing, a phenomenon where companies misrepresent or exaggerate their artificial intelligence (AI) capabilities to enhance marketing appeal and gain a competitive advantage. Despite its increasing prevalence, AI washing has received limited theoretical attention. Drawing on literature from similar practices, this paper develops a conceptual framework and typology to categorize various forms of AI washing. Theoretical implications include extending marketing ethics frameworks and existing theories to the domain of AI. The paper also highlights avenues for future empirical research, particularly in validating the proposed typology and exploring the consequences of AI washing on trust and brand reputation. From a practical standpoint, the paper offers recommendations for businesses to adopt more transparent AI marketing strategies and calls for regulatory interventions to mitigate the risks of AI washing. Finally, it discusses limitations and directions for further study. © Academy of Marketing Science 2025. KW - AI KW - AI washing KW - Artificial intelligence KW - Bluewashing KW - Business ethics KW - Greenwashing KW - Technological ethics CY - Singapore, Lebanon ER - TY - JOUR TI - University Student Attitudes Towards Artificial Intelligence Integration into their Academic Performance AU - Le T.T.Q. AU - Doan C.T. AU - Vu T.V. PY - 2025 JO - Indian Journal of Information Sources and Services VL - 15 IS - 4 SP - 21 EP - 30 DO - 10.51983/ijiss-2025.IJISS.15.4.03 AB - The integration of artificial intelligence (AI) becomes more common in education, so it is crucial to be aware of students' perspectives towards the effective use of implementing AI in education. This study employed a mixed-methods approach to incorporate a researcher-made questionnaire with 385 participants and semi-structured interviews with 69 students from three institutions in Vietnam. Descriptive statistics and correlation analysis were used to examine the quantitative data, and thematic analysis was employed to address the qualitative data. The results reveal that although the participants have neutral stances on the acceptability of AI in education, they also express positive and negative opinions on the acceptance and uncertainty about AI's capabilities to enhance their academic performance. Besides, digital skills, previous experience with AI, institutional support, and ethical concerns are the main factors of acceptance and use. The participants feel concerned about whether the AI application may improve their academic integrity, privacy, and critical thinking. It is, therefore, necessary for students to receive institutional support for AI training by providing more explicit principles and resources for AI adoption. Additionally, the study reveals that AI has great potential, but it should be integrated into higher education with considerable care to solve ethical issues and allow the students to be trained and supported. Universities must give importance to AI literacy programs and set principles of ethics to foster a more positive and productive relationship between students and AI technologies. © The Research Publication,. KW - AI Integration KW - Educational Policy KW - Ethical Considerations KW - Higher Education KW - Pedagogical Innovation ER - TY - JOUR TI - Adoption of artificial intelligence in property management transactions: a systematic review and trend analysis AU - Adediran A.O. AU - Mohd Aini A. AU - Ajibade S.M. PY - 2026 JO - Property Management VL - 44 IS - 2 SP - 199 EP - 231 DO - 10.1108/PM-02-2025-0007 AB - Purpose – The integration of Artificial Intelligence (AI) in property management transactions is transforming the real estate sector via improved automation, predictive analytics, intelligent property management and enhanced decision-making. This study investigates how AI enhances property management transactions as well as the significant barriers to its implementation. Design/methodology/approach – This research employs a systematic literature review (SLR) and NVivo-based qualitative analysis to discern significant trends, innovations and obstacles in the adoption of AI. The study analyzes existing literature and industry reports to identify patterns, challenges and emerging solutions in AI-driven property management. Findings – The results indicate that AI markedly enhances efficiency (automation and predictive analytics), tenant engagement (behavior analysis and intelligent communication), property value (AI-driven assessments) and sustainability (energy optimization and waste minimization). Nevertheless, obstacles to widespread adoption persist, including data privacy issues, legal and ethical challenges, budgetary limitations and opposition from stakeholders. Smaller real estate enterprises have heightened hurdles stemming from the digital divide, security vulnerabilities and algorithmic prejudice. Research limitations/implications – The study is mostly based on secondary data from literature and industry sources, which may limit the findings' applicability to real-world scenarios. Future research could use empirical data, such as case studies or surveys, to confirm AI’s practical influence in a variety of property markets. Practical implications – The findings offer valuable insights for real estate professionals, investors and AI developers on how to effectively integrate AI into property management. Key areas for practical implication include predictive maintenance relating to IoT usage; property valuation automation; AI-powered tenant screening; Site selection and market forecasting; Chabot and NLP for leasing; and blockchain integration and fraud detection. To achieve effective integration, industry stakeholders must emphasize ethical AI governance, stringent data security and cooperation between AI and humans. Additionally, AI’s synergy with cloud computing, blockchain and the Internet of Things (IoTs) may enhance transparency, security and efficiency in real estate transactions. Social implications – The adoption of AI in property management has broader societal consequences, including the possibility of job displacement and the necessity for reskilling initiatives to assist real estate workers. An equitable strategy that encourages innovation, reduces risks and increases worker flexibility is required to realize AI’s full potential in property management. This study emphasizes the importance of collaboration among researchers, real estate companies, legislators and AI technologies developers. Originality/value – This study contributes to the expanding body of knowledge on AI in real estate by providing a structured qualitative synthesis of AI uses, barriers and future potential. Unlike prior studies that have focused only on AI benefits, this study offers a balanced evaluation of both the promise and constraints of AI-driven property management transactions. © Emerald Publishing Limited KW - AI adoption KW - Artificial intelligence KW - Property management KW - PropTech KW - Real estate transactions CY - Malaysia ER - TY - JOUR TI - Harnessing inclusive innovation to build socially impactful AI: Embracing social impact, cultural diversity, and equity AU - Vega R.P. AU - Rivero C.S. AU - Castro A.O. AU - Bosques C.V. PY - 2024 JO - Issues in Information Systems VL - 25 IS - 4 SP - 442 EP - 454 DO - 10.48009/4_iis_2024_134 AB - Society’s interest in AI/Big Data: that governments, companies, and NGOs can invest in the development of the world and obtain a successful realization of the policy of CSR & SDG. discussed in this paper is to show how governments, businesses, and NGOs can prove that they are ready to make a difference and join the global commitment to building an integrated and sustainable community that will leverage this unique opportunity to promote the common social interest by utilizing such a promising and complex technology as artificial intelligence. Consequently, the current paper integrates a range of cases. It utilizes a mixed-methods design, establishing that restricted access to resources, primarily relating to education, health, and the economic status that can be traced to marginalization dimensions, determines increased suffering because of the pandemic. These recommendations amalgamate to perform the roles of the diverse sectors to assure that AI benefits all the stages of society by promoting the equity steps of welfare. The study applies a contingency framework, and from the framework, the study develops a plan for how best to approach inclusive innovation to mitigate inequalities, which in turn suggests that societal and environmental objectives should be integrated into the observed innovation to take sustainable development principles. The research contributes to how society can employ the prospects in AI to solve societal problems, notating that such employment of the advantage in AI must incite professionalism and incorporation of society in the design of the measures. On this basis, this study contributes to the theoretical development and the practical recommendations related to using potential AI capabilities to build long-term opportunities for firms and governments to support the sustainable development of the world economy based on achieving the goals set in the UN 2030 agenda. This piece seeks to analyze the events, achievements, and barriers that culminated in the formation of this new week and lays down directions on how the strategy that focuses on inclusive innovation can be implemented. © 2024 International Association for Computer Information Systems. All rights reserved. KW - Artificial Intelligence (AI) KW - Corporate Social Responsibility (CSR KW - Inclusive Innovation KW - Strategic Implementation KW - Sustainable Development ER - TY - JOUR TI - An Engineering Framework for Artificial Intelligence-Based Marketing Systems and Digital Consumer Engagement Models AU - Kumar P. AU - Aruna V. AU - Pathamuthu P. AU - Rajamani K. PY - 2025 JO - International Academic Journal of Science and Engineering VL - 12 IS - 3 SP - 318 EP - 327 DO - 10.71086/IAJSE/V12I3/IAJSE1268 AB - This study presents the method of incorporating AI technology in digital marketing systems to establish a high degree of customer-business relationship based on the AI capabilities in digital media platforms like Twitter, Facebook, Google, etc., and AI technology like ML, NLP, and Predictive analytics. The model designed within the framework of the current work enables creation of individual marketing plans that will contribute to better customer interaction, company profitability, and business performance in general. Based on the data collected for this study, the researchers found that the use of AI technology to develop marketing systems has greatly improved consumer behavior due to the delivery of custom content that matches their individual tastes. The case study of Amazon's AI-driven recommendation system illustrates the effectiveness of using AI to increase sales and enhance the way Amazon interacts with its customers. Since 2018, Amazon has utilized AI technologies to create a more personalized shopping experience, increasing the level of engagement and sales through the use of AI as a key component of its e-commerce strategy. Amazon's recommendation system is responsible for generating a significant portion of its sales through the analysis of individual consumer purchase history, browsing activity, and preferences, with nearly 35% of total sales being accounted for by Amazon's AI-driven recommendation system (2020). To fully take advantage of AI's capabilities in marketing, more refinement needs to be done on the AI algorithm to allow for real time content personalization and also to adapt to changing consumer buying behaviour This study concluded that although AI has great potential for helping organizations grow their revenue, in order for an organization to realize the true benefits of AI they will also have to consider the ethical implications of using customer data and how much data should be available through AI technologies. © 2025, International Academic Institute for Science and Technology. All rights reserved. KW - AI-Driven Marketing KW - Consumer Engagement KW - Conversion Rate KW - Customer Satisfaction KW - Machine Learning KW - Personalization KW - Predictive Analytics CY - India ER - TY - JOUR TI - Smart insights, stronger performance: Leveraging business intelligence and dynamic capabilities in tourism and hospitality AU - Tajeddini O. AU - Tajeddini K. AU - Gamage T.C. AU - Hameed W.U. PY - 2026 JO - International Journal of Hospitality Management VL - 133 SP - 104410 DO - 10.1016/j.ijhm.2025.104410 AB - The rapid advancement of artificial intelligence (AI) and business intelligence (BI) compels tourism and hospitality firms to redefine their capabilities. This need stems from the growing imperative to fully leverage these technologies for performance enhancement—an area still underexplored in the tourism and hospitality literature. Drawing on the dynamic capabilities view, this paper investigates the interrelationships among resource orchestration capabilities (ROCs), digital marketing capabilities (DMCs), AI capabilities, and firm performance, with a specific focus on the mediating role of BI adoption and the moderating effect of technology orientation (TO). Using data from 297 tourism and hospitality firms across four major Japanese cities, the findings reveal that BI adoption mediates the relationships among ROCs, DMCs, AI capabilities, and firm performance. As anticipated, TO does not moderate the DMC–BI adoption link, potentially due to firm-specific factors warranting further exploration in different contexts. The study contributes to theory by proposing an integrative framework that conceptualizes ROCs, DMCs, and AI capabilities as distinct yet interrelated dynamic capabilities driving performance in tourism and hospitality firms. Practically, the findings encourage tourism and hospitality managers to refine their strategies to better leverage these capabilities, particularly in pursuing digital transformation. © 2025 KW - Artificial intelligence capabilities KW - Business intelligence adoption KW - Digital marketing capabilities KW - Resource orchestration capabilities KW - Technology orientation KW - Tourism and hospitality firms CY - Switzerland ER - TY - JOUR TI - Digital marketing innovation and industrial marketing: evidence from restaurants' service robots AU - Ku E.C.S. PY - 2024 JO - Asia Pacific Journal of Marketing and Logistics VL - 36 IS - 11 SP - 3099 EP - 3117 DO - 10.1108/APJML-02-2024-0185 AB - Purpose: This study aims to explore how perceived anthropomorphism, perceived warmth, and customer–artificial intelligence (AI) assisted exchange (CAIX) of service robots affect customers’ satisfaction via digital marketing innovation. Design/methodology/approach: A customer satisfaction model was formulated based on the perspective of parasocial relationships and hybrid intelligence; 236 completed questionnaires were returned by partial least squares structural equation modeling analysis. Findings: This study demonstrates that perceived anthropomorphism, perceived warmth and CAIX's impact on digital marketing innovation were supported, and customer satisfaction impacted the continued intention to use service robots. Originality/value: Restaurants that leverage service robots differentiate themselves from competitors by offering innovative and technologically advanced dining experiences. Integrating AI capabilities sets these restaurants apart and attracts tech-savvy customers who value convenience and efficiency. © 2024, Emerald Publishing Limited. KW - Customer satisfaction KW - Customer–AI assisted exchange KW - Digital marketing innovation KW - Restaurant KW - Service robots CY - Taiwan ER - TY - JOUR TI - Designing Artificial Intelligence: Exploring Inclusion, Diversity, Equity, Accessibility, and Safety in Human-Centric Emerging Technologies AU - Zallio M. AU - Ike C.B. AU - Chivăran C. PY - 2025 JO - AI (Switzerland) VL - 6 IS - 7 SP - 143 DO - 10.3390/ai6070143 AB - Background: The implementation of artificial intelligence (AI) has become a pivotal interdisciplinary challenge, creating new opportunities for sharing information, driving innovation, and transforming societal interactions with technology. While AI offers numerous benefits, its rapid evolution raises critical concerns about its impact on inclusion, diversity, equity, accessibility, and safety (IDEAS). Method: This pilot study aimed to explore these issues and identify ways to embed the IDEAS principles into AI design. A qualitative study was conducted with industrial and academic experts in the field. Semi-structured interviews gathered insights into the opportunities, challenges, and future implications of AI from diverse professional and cultural perspectives. Result: Findings highlight uncertainties in AI’s trajectory and its profound cross-sector influence. Key issues emerged, including bias, data privacy, transparency, and accessibility. Participants stressed the need for greater awareness and structured dialogue to integrate the IDEAS principles throughout the AI lifecycle. Conclusion: This study underscores the urgency of addressing AI’s ethical and societal impacts. Embedding the IDEAS principles into its development can help mitigate risks and foster more inclusive, equitable, and accessible technologies. © 2025 by the authors. KW - accessibility KW - artificial intelligence KW - diversity and equity KW - emerging technology KW - generative artificial intelligence KW - inclusion KW - inclusive design KW - safety by design CY - United Kingdom, Italy ER - TY - JOUR TI - Adopting artificial intelligence and big data tools across industry sectors in Morocco: an integrative literature review AU - Ejjami R. PY - 2024 JO - International Journal of Environment, Workplace and Employment VL - 8 IS - 2 SP - 171 EP - 198 DO - 10.1504/IJEWE.2024.141574 AB - Morocco’s strategy to integrate its industries into the Europe-Mediterranean-Africa network faces challenges in digital transformation, primarily due to a knowledge gap among leaders regarding AI and big data benefits. This literature review addresses this gap, focusing on adopting these technologies as agile innovations across Moroccan sectors. It aims to enlighten organisational leaders and IT policymakers on leveraging AI and big data to enhance operational efficiency, decision-making, and competitiveness. The study emphasises data protection, training, government support, and global partnerships. Key recommendations include fostering agile innovation, ensuring data privacy, upskilling the workforce, building collaborative ecosystems, supporting SMEs, incorporating ethical AI, and promoting interdisciplinary research. Addressing this knowledge gap is crucial for Morocco’s economic growth and digital transformation, highlighting the need for strategic technology adoption to enhance national progress and sustain competitive advantage. Copyright © 2024 Inderscience Enterprises Ltd. KW - agile innovations KW - AI KW - big data KW - digitalisation KW - industry sectors KW - Morocco KW - organisational sustainability KW - regional competitiveness CY - Morocco ER - TY - JOUR TI - Steering back to the real: does artificial intelligence promote corporate de-financialization? AU - Wang D. AU - Wang Y. PY - 2026 JO - Applied Economics DO - 10.1080/00036846.2026.2653208 AB - While the productivity effects of Artificial Intelligence (AI) have been widely studied, limited attention has been paid to its role in ‘steering back to the real economy’. Based on the investment motivation framework and data from Chinese A-share listed companies, this study finds that enhancing AI capabilities significantly diminishes corporate financialization. This effect operates via resource reallocation channels: AI influences financial distress, alleviates financing constraints, and reduces reliance on government subsidies. Specifically, a one-unit increase in AI intensity reduces financialization by 3.4%. The inhibitory effect is more pronounced among large-scale enterprises, high-innovation regions, and high-tech industries. Moreover, quantile regression reveals the effect is significant only for highly financialized firms, suggesting that operational efficiency motives outweigh speculative motives. The study provides empirical evidence on the technological drivers of de-financialization in emerging economies and offers implications for revitalizing the real economy. © 2026 Informa UK Limited, trading as Taylor & Francis Group. KW - AI KW - financial distress KW - financialization KW - financing constraints KW - government subsidies CY - China ER - TY - JOUR TI - Artificial Intelligence-based Psychotherapy: A Qualitative Exploration of Usability, Personalization, and the Perception of Therapeutic Progress AU - Beg M.J. AU - Verma M.K. PY - 2025 JO - Indian Journal of Psychological Medicine SP - 02537176251357477 DO - 10.1177/02537176251357477 AB - Background: AI-based psychotherapy apps offer accessibility and structured interventions but face challenges regarding emotional depth, personalization, engagement, and ethical concerns. This study critically examines user experiences, identifying key advantages, limitations, and areas for refinement. Methods: A qualitative approach was employed, using thematic analysis of semi-structured interviews with 17 participants (aged 18–45) who had used AI-based psychotherapy apps for at least four weeks. Ten participants had prior clinical diagnoses (e.g., anxiety, depression, adjustment disorder), while others reported subclinical psychological distress. Engagement duration ranged from 2 to 11 months, with most using the apps two to five times per week. Results: Ten core themes emerged, revealing a paradox of accessibility versus therapeutic depth. While users valued immediacy and anonymity, they struggled with fragmented therapeutic narratives, scripted empathy, and algorithmic stagnation in personalization. The over-reliance on CBT frameworks limited adaptability to diverse emotional needs, while linguistic and cultural microaggressions led to disengagement. Privacy concerns stemmed from a mismatch between perceived and actual risks, and AI-induced dependence raised ethical questions about user autonomy. Conclusions: The AI psychotherapy must evolve beyond static, standardized interventions by integrating emotionally responsive, culturally adaptive, and ethically responsible AI models. Enhancing therapeutic continuity, adaptive learning, and human-AI hybrid models can bridge the gap between accessibility and authentic engagement. These findings inform future AI-driven mental health innovations, ensuring they align with psychological, ethical, and cultural expectations. © 2025 The Author(s). This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage). KW - AI psychotherapy KW - cultural adaptation KW - emotional depth KW - ethical AI therapy KW - personalization KW - user engagement CY - India ER - TY - JOUR TI - Differentiating artificial intelligence activity clusters in Australia AU - Bratanova A. AU - Pham H. AU - Mason C. AU - Hajkowicz S. AU - Naughtin C. AU - Schleiger E. AU - Sanderson C. AU - Chen C. AU - Karimi S. PY - 2022 JO - Technology in Society VL - 71 SP - 102104 DO - 10.1016/j.techsoc.2022.102104 AB - We demonstrate how cluster analysis underpinned by analysis of revealed technology advantage can be used to differentiate geographic regions by activity in artificial intelligence (AI). Our analysis uses novel datasets on Australian AI businesses, intellectual property patents and labour markets to explore location, concentration and intensity of AI activities across 333 geographical regions. We find that Australia's AI business and innovation activity is clustered in geographic locations with higher investment in research and development. Through cluster analysis we identify three tiers of AI capability regions that are developing across the economy: ‘AI hotspots’ (10 regions), ‘Emerging AI regions’ (85 regions) and ‘Nascent AI regions’ (238 regions). While the AI hotspots are mainly concentrated in central business district (CBD) locations, there are examples when they also appear outside CBD in areas where there has been significant investment in innovation and technology hubs. Policy makers and investors can use these results to learn about the current landscape of AI business and innovation activities in Australia, identify potential growth opportunities in AI capabilities and to guide future policy and business decisions. © 2022 The Authors KW - Artificial intelligence KW - Australia KW - Cluster KW - Regional innovation KW - Revealed technology advantage KW - Australia KW - Cluster analysis KW - Employment KW - Geographical regions KW - Investments KW - Patents and inventions KW - Regional planning KW - Australia KW - Business activities KW - Central business districts KW - Cluster KW - Geographics KW - Hotspots KW - Innovation activity KW - Intelligence activities KW - Regional innovation KW - Revealed technology advantage KW - artificial intelligence KW - central business district KW - cluster analysis KW - geographical variation KW - industrial investment KW - innovation KW - Location CY - Australia ER - TY - JOUR TI - Orchestrating scalability: how patents render cloud imaginaries in CAV innovation AU - Gekker A. AU - Hind S. AU - Pereira G. AU - van der Vlist F.N. PY - 2026 JO - Information Communication and Society DO - 10.1080/1369118X.2026.2631709 AB - This article explores how cloud computing enables and shapes the scaling of connected and autonomous vehicles (CAVs), positioning cloud infrastructure as a technology, strategy, and imaginary central to the scaling of AI. Using a dataset of 69,421 global patent families, we analyse how diverse actors–including automotive manufacturers, chipmakers, electronics companies, autonomous vehicle firms, and telecom/mapping providers–mobilise cloud technologies to expand AI capabilities, manage resources, and coordinate complex socio-technical systems. Approaching patents through ‘sociotechnical imaginaries’, we show how they simultaneously codify technical innovations while projecting visions of scalable, cloud-enabled CAV futures. Our analysis identifies four thematic clusters–vehicle communication, machine vision, network architectures, and edge computing–through which cloud technologies are operationalised and imagined. We argue that the cloud functions as a technology of orchestration, with cloudification exemplifying AI’s industrialisation as it moves from laboratory research to globally scalable systems. The article contributes to debates on scale by highlighting the interplay between technical, organisational, and imaginative dimensions in shaping AI-enabled mobility. © 2026 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. KW - AI KW - Cloud computing KW - connected and autonomous vehicles KW - imaginaries KW - patents KW - scale CY - Netherlands, United Kingdom ER - TY - JOUR TI - AI capabilities for sustainable entrepreneurial innovation: the role of orientation, resilience, and turbulence effects in SMEs AU - Shahzad M.F. AU - Xu S. AU - Zahid H. AU - Khan S.A. AU - Sandhu M.A. PY - 2026 JO - Journal of Innovation and Knowledge VL - 17 SP - 101051 DO - 10.1016/j.jik.2026.101051 AB - In the evolving landscape of sustainable development, artificial intelligence (AI) has become a critical enabler for small and medium-sized enterprises (SME) striving to achieve entrepreneurial sustainable development goals (ESDG). This study investigates how AI capabilities, encompassing human skills, technology, data-driven culture (DDC), and organizational learning intensity, contribute to ESDGs through both internal organizational mechanisms and external market dynamics. Data are collected from 460 employees across various SMEs in China and analyzed via partial least squares-structural equation modeling. The results demonstrate that green ambidexterity innovation (GAI) significantly mediates the relationship between AI capabilities and ESDGs. Market resilience capacity (MRC) also serves as a significant mediator across most AI dimensions, except for DDC. Meanwhile, green entrepreneurial orientation (GEO) shows significant but negative mediating effects across AI dimensions. Market turbulence does not significantly moderate the effects of GAI and MRC on ESDGs; meanwhile, it positively moderates the relationship between GEO and ESDGs. These findings contribute to the sustainability and entrepreneurship literature by revealing how AI readiness and innovation orientation enable SMEs to circumnavigate complex market environments. They also underscore the need for SMEs and policymakers to prioritize AI-driven strategies and innovation, and build resilience for sustainable entrepreneurial performance. © 2026 The Authors. KW - Artificial intelligence KW - Green ambidexterity innovation KW - Green entrepreneurial orientation KW - Market resilience KW - Market turbulence KW - Sustainable development goals CY - China, Pakistan, United Arab Emirates ER - TY - JOUR TI - The frame problem: The ai “arms race" isn’t one AU - Roff H.M. PY - 2019 JO - Bulletin of the Atomic Scientists VL - 75 IS - 3 SP - 95 EP - 98 DO - 10.1080/00963402.2019.1604836 AB - There needs to be a change in thinking about AI. Those dealing with AI must insist on greater clarity about its definition. If policy makers and other leaders are not clear about what the term means and entails, they cannot possibly formulate best practices and governance mechanisms. It would help matters if artificial intelligence discussions were framed in an “AI +" framework, because in many cases, AI is merely a tool included in a system involving other functions or capabilities. The news media should stop framing the global artificial intelligence competition as an “arms race." This misrepresents the competition going on among countries. The policy community needs a clear-eyed appraisal of AI’s capabilities and limitations. Without that orientation, those who hope to steer research and development in positive directions will create more problems than they solve. © 2019 Bulletin of the Atomic Scientists. KW - Arms race KW - Artificial intelligence KW - Definitions KW - Responsible innovation KW - Risk CY - United Kingdom ER - TY - JOUR TI - Integrating artificial intelligence with market research: A dual approach to boosting brand value AU - Skare M. AU - Sinkovic D. AU - Kowalska M. AU - Szwajlik A. PY - 2026 JO - Journal of Innovation and Knowledge VL - 11 SP - 100853 DO - 10.1016/j.jik.2025.100853 AB - This study investigates the impact of artificial intelligence (AI) capabilities on brand management and value creation, proposing a comprehensive competitiveness framework for firms. Utilizing a panel dataset spanning 26 years across 30 countries, Arellano-Bond and Blundell-Bond System GMM estimations examine the relationship between AI stock, AI impact, and brand value. The findings reveal that AI stock and impact significantly contribute to brand value, with their interaction yielding synergistic effects. The study highlights the mediating role of gross value-added (GVA) in the relationship between AI stock and brand value, suggesting that AI-driven productivity gains enhance brand equity. Investments in computing and communication equipment and efforts to strengthen brand relationship strength (BRS) also influence brand value. Integrating AI capabilities with market research amplifies brand value through data-driven insights and consumer engagement strategies. Furthermore, novel AI-enhanced measurement methodologies capture brand value creation more accurately than traditional metrics. These findings offer valuable insights for firms seeking to leverage AI capabilities to enhance brand equity and gain a competitive advantage in a rapidly evolving marketplace. Copyright © 2025. Published by Elsevier España, S.L.U. KW - Artificial intelligence KW - Brand management KW - Customer engagement KW - Firms’ competitiveness KW - Marketing strategies KW - Value creation CY - Croatia, Poland ER - TY - JOUR TI - Navigating the dark side of AI in service ecosystems: an ethical leadership framework for risk mitigation; [穿越服务生态系统中人工智能的阴暗面：基于伦理领导力的风险缓解框架] AU - Sposato M. AU - Dittmar E.C. AU - Portillo J.P.V. PY - 2026 JO - Service Industries Journal DO - 10.1080/02642069.2026.2643384 AB - The rapid integration of artificial intelligence (AI) into service ecosystems is transforming value cocreation while generating significant ethical risks that threaten customer trust, organisational legitimacy, and social sustainability. This paper develops the Ethical AI Risk Mitigation (EAIRM) model to examine how different configurations of human-AI collaboration create distinct ethical challenges across fairness, autonomy, transparency, and accountability dimensions. Drawing on a structured literature synthesis, we identify four leadership approaches (compliance-oriented, values-based, stakeholder-engaged, and anticipatory) that systematically mitigate ethical risks while enabling service innovation. Through integrative theory building, the model contributes to service research and practice by: (1) revealing how identical ethical risks operate through different causal mechanisms depending on human-AI resource configuration; (2) specifying multi-actor governance structures for service ecosystems where no single actor controls ethical outcomes; (3) theorizing leadership mechanisms and organisational mediators that convert ethical principles into operational practices; and (4) generating testable propositions with boundary conditions, moderators, and feedback dynamics. This framework advances service ecosystem theory by demonstrating that resource relations carry ethical risk implications requiring polycentric governance, not merely value creation potential. © 2026 Informa UK Limited, trading as Taylor & Francis Group. KW - AI governance KW - Artificial intelligence KW - ethical leadership KW - human-AI relations KW - responsible AI KW - risk mitigation KW - service ecosystems CY - United Arab Emirates, Spain ER - TY - JOUR TI - AI-Enabled Circular Business Model Transition for Mitigating Climate Change: A Natural Resource-Based View Perspective on Business Strategies AU - Wang J. AU - Chaudhary S. AU - Kamal M. AU - Almasabi S. AU - Remsei S. PY - 2026 JO - Business Strategy and the Environment DO - 10.1002/bse.70649 AB - The role of artificial intelligence (AI) in achieving sustainability goals has garnered attention in academic literature. While AI has been argued to be crucial in addressing circularity challenges, organizations face challenges in configuring a business model. Designing new business calls for insights on how AI can be integrated into value creation and capture mechanisms. There is a lack of clarity on how organizations deploy AI as they transition to circular business model innovation. The purpose of the study is to explore how AI is integrated into organizational processes while adopting circular business models. We conducted online open-ended interviews with 55 participants to explore the potential role of AI in enabling the adoption of circular business models. Our findings have implications for theory building relating to AI business model innovation and provide a novel avenue for further research on business model innovation literature. Building on a natural resource-based view, the findings indicate that while implementing a circular business model is challenging, AI enables organizations to create, transfer, and capture value through resource efficiency and the reuse of resources. As AI technologies continue to evolve, organizations must develop adaptive capabilities to continually explore opportunities. AI enables organizations to reduce costs, develop novel value-creation strategies, and capture opportunities, resulting in improved efficiency. Transitioning to a circular business model requires developing routines, and organizations must adapt existing systems to ensure these systems result in pollution prevention, product stewardship, and sustainable development. It is important for managers to develop organizational resources and capabilities that enable the development of AI capabilities. © 2026 The Author(s). Business Strategy and the Environment published by ERP Environment and John Wiley & Sons Ltd. KW - artificial intelligence KW - circular business models KW - climate change KW - pollution control KW - stewardship KW - sustainable development CY - Hungary ER - TY - JOUR TI - A2C: A modular multi-stage collaborative decision framework for human–AI teams AU - Tariq S. AU - Baruwal Chhetri M. AU - Nepal S. AU - Paris C. PY - 2025 JO - Expert Systems with Applications VL - 282 SP - 127318 DO - 10.1016/j.eswa.2025.127318 AB - The increasing complexity of decision-making in dynamic environments, particularly in high-stakes domains like cybersecurity, demands more than automated solutions—it requires effective integration of human expertise with advanced AI capabilities. While approaches like ensemble learning and Mixture of Experts (MoE) enhance automated decision-making, they struggle with handling uncertainty and novel scenarios. Techniques such as learning to defer and learning to complement mitigate this by incorporating human input, but assume that a definitive expert is always available—an assumption that often fails in real-world settings. To bridge this gap, we introduce A2C, a modular, multi-stage collaborative decision-making framework that enhances adaptability and decision robustness under uncertainty by seamlessly transitioning between three decision-making modes: Automated, Augmented Deferral, and Collaborative Exploration (CoEx). A key innovation of CoEX is its ability to handle cases where both AI and human experts face uncertainty, overcoming a critical limitation of traditional deferral systems. We validate A2C through experiments on benchmark datasets, Large Language Model (LLM) simulations of human–AI collaboration, and real-world human–AI interaction studies with cybersecurity researchers. Results show that A2C consistently outperforms conventional approaches that rely solely on full automation or selective human intervention, demonstrating its potential as a practical and scalable solution for expert decision-making in complex domains. For image detection on CIFAR-10, detection rates improved from 37.8% with automation alone to 64.75% with augmented deferral, and further to 92.95% with collaborative exploration. Similarly, for intrusion detection on KDDCup, rates rose from 33.43% with automation to 35.18% with augmented deferral, and finally reached 87.04% with CoEx, highlighting its effectiveness in handling uncertainty. © 2025 The Authors KW - AI-augmented decision support KW - Collaborative decision-making KW - Decision-making under uncertainty KW - Human-centric AI KW - Human–AI systems KW - Intelligent systems KW - Adversarial machine learning KW - Chatbots KW - Contrastive Learning KW - Federated learning KW - Intelligent systems KW - Intrusion detection KW - AI systems KW - AI-augmented decision support KW - Collaborative decision making KW - Decision making under uncertainty KW - Decision supports KW - Decisions makings KW - Human-centric KW - Human-centric AI KW - Human–AI system KW - Uncertainty KW - Cybersecurity CY - Australia ER - TY - JOUR TI - DEVELOPMENT OF EXPORT POTENTIAL OF BUSINESS IN THE AI ECONOMY THROUGH PRODUCT QUALITY MANAGEMENT WITH THE HELP OF CORPORATE INFORMATION SYSTEMS AU - Dzhailova A.D. AU - Mannapova R.A. AU - Ivanova I.G. AU - Akopov S.E. AU - Tikhomirov K.Y. PY - 2025 JO - Proceedings on Engineering Sciences VL - 7 IS - 1 SP - 137 EP - 146 DO - 10.24874/PES07.01A.005 AB - This paper dwells on the influence of corporate information systems and artificial intelligence (AI) on quality management and the development of the export potential of business in the conditions of the AI economy. The role of information and information systems in product quality is considered, specifics of the AI economy and its features in international trade are disclosed, different types of corporate information systems from the position of formation of competitive advantages and ensuring better results in product quality management are characterised, the key directions of the influence of the integration of AI and corporate information systems on product quality and export potential of companies, which are achieved due to predictive servicing, visual inspection, personalisation of products to consumer demands, and optimal management of supply chains, are determined. The methodological basis of this paper is comprised of the system and process approaches which allow combining theoretical and practical aspects of studying the problem from the position of different scientific spheres and views, including international trade, quality management, the digital economy, the concepts of development of the digital economy, and Industry 4.0. The research is based on the concepts of Total Quality Management (TQM), the theory of international trade, and modern views of the development of the digital economy and AI. To substantiate the conclusions, the methods of analysis, synthesis, comparison, and generalisation, as well as table and graphical methods are used. The main value of this paper lies in progressive consideration of the role and impact of information, corporate information systems, and quality management systems on the development of the export potential of business in the conditions of the AI economy. Attention is paid to identifying interrelations between the integration of AI and corporate information systems, an increase in product quality due to better management of information flows and the use of AI capabilities, as well as features of the development of the export potential of business in the conditions of the AI economy. By the examples of the leading companies, the practical results of this integration and its influence on competitiveness in international markets are demonstrated. © 2025 Published by Faculty of Engineering. KW - AI Economy KW - Artificial Intelligence KW - Competitiveness KW - Corporate Information Systems KW - Export Potential KW - International Trade KW - Product Quality Management CY - Kyrgyzstan, Uzbekistan ER - TY - JOUR TI - Deepfake-Style AI Tutors in Higher Education: A Mixed-Methods Review and Governance Framework for Sustainable Digital Education AU - Sharif H. AU - Atif A. AU - Nagra A.A. PY - 2025 JO - Sustainability (Switzerland) VL - 17 IS - 21 SP - 9793 DO - 10.3390/su17219793 AB - Deepfake-style AI tutors are emerging in online education, offering personalized and multilingual instruction while introducing risks to integrity, privacy, and trust. This study aims to understand their pedagogical potential and governance needs for responsible integration. A PRISMA-guided, systematic review of 42 peer-reviewed studies (2015–early 2025) was conducted from 362 screened records, complemented by semi-structured questionnaires with 12 assistant professors (mean experience = 7 years). Thematic analysis using deductive codes achieved strong inter-coder reliability (κ = 0.81). Four major themes were identified: personalization and engagement, detection challenges and integrity risks, governance and policy gaps, and ethical and societal implications. The results indicate that while deepfake AI tutors enhance engagement, adaptability, and scalability, they also pose risks of impersonation, assessment fraud, and algorithmic bias. Current detection approaches based on pixel-level artifacts, frequency features, and physiological signals remain imperfect. To mitigate these challenges, a four-pillar governance framework is proposed, encompassing Transparency and Disclosure, Data Governance and Privacy, Integrity and Detection, and Ethical Oversight and Accountability, supported by a policy checklist, responsibility matrix, and risk-tier model. Deepfake AI tutors hold promise for expanding access to education, but fairness-aware detection, robust safeguards, and AI literacy initiatives are essential to sustain trust and ensure equitable adoption. These findings not only strengthen the ethical and governance foundations for generative AI in higher education but also contribute to the broader agenda of sustainable digital education. By promoting transparency, fairness, and equitable access, the proposed framework advances the long-term sustainability of learning ecosystems and aligns with the United Nations Sustainable Development Goal 4 (Quality Education) through responsible innovation and institutional resilience. © 2025 by the authors. KW - academic integrity KW - AI ethics in education KW - AI literacy KW - deepfake AI tutors KW - detection of deepfakes KW - digital sustainability KW - online education governance KW - privacy and fairness in AI KW - SDG 4 quality education KW - sustainable education KW - synthetic media in education KW - artificial intelligence KW - educational development KW - governance approach KW - higher education KW - sustainability KW - Sustainable Development Goal KW - technology adoption KW - transparency CY - Pakistan, Australia ER - TY - JOUR TI - Artificial-Intelligence-Enabled Innovation Ecosystems: A Novel Triple-Layer Framework for Micro, Small, and Medium-Sized Enterprises in the Chinese Apparel-Manufacturing Industry AU - Qu C. AU - Kim E. PY - 2025 JO - Sustainability (Switzerland) VL - 17 IS - 11 SP - 5019 DO - 10.3390/su17115019 AB - The rapid advancement of artificial intelligence (AI) in the traditional-apparel-manufacturing sector is accelerating innovation and transformation, as cutting-edge AI applications have been increasingly integrated into the industry in recent years. While China has made outstanding achievements in applying AI in the apparel-manufacturing sector, the adoption of AI by traditional apparel manufacturers has progressed slowly. This study aims to develop a sustainable triple-layer framework of an AI-enabled innovation ecosystem from grounded required AI capabilities and barriers to AI adoption, thereby generating the conceptual propositions for micro, small, and medium-sized Chinese apparel manufacturing. Through semi-structured interviews conducted with 20 organizations, this study qualitatively analyzes interviews with representatives from enterprises, universities, and apparel associations to determine the required AI capabilities and barriers to adopting AI. It proposes 13 propositions within a theoretical framework that addresses barriers and aligns multi-actor collaborations, ultimately forming a sustainable AI-enabled Triple-Layer Innovation Ecosystem Framework. This novel framework reflects the dynamic interplay between external knowledge absorption capacity and a firm’s internal innovation capacity, providing a theoretical foundation for understanding and advancing AI-driven innovation in the apparel-manufacturing sector. © 2025 by the authors. KW - artificial intelligence (AI) capabilities KW - Chinese apparel manufacturing KW - innovation ecosystems KW - micro, small, and medium-sized enterprises KW - sustainable triple-layer framework KW - China KW - artificial intelligence KW - ecosystem approach KW - innovation KW - manufacturing KW - small and medium-sized enterprise KW - sustainability KW - technology adoption CY - China, Japan ER - TY - JOUR TI - The impact of AI capability on responsible innovation in high-tech SMEs from the perspective of the knowledge-based view AU - Teng X. AU - Zhang X.-E. AU - Li Y. AU - Dong Y. PY - 2026 JO - Journal of Innovation and Knowledge VL - 11 SP - 100875 DO - 10.1016/j.jik.2025.100875 AB - The emergence of artificial intelligence (AI) technologies brings numerous opportunities andchallenges to business innovation. Understanding how firms can leverage AI technologies to create business value, propose responsible solutions to social problems, and achieve sustainable development has become important. Based on the knowledge-based view (KBV), a theoretical model is proposed to examine the impact of AI capability on responsible innovation. The study focuses on the mediating role of boundary-spanning search and the moderating roles of knowledge field activity and tech-for-good culture. Hierarchical regression analysis and bootstrapping are applied to data from 520 Chinese high-tech small and medium-sized enterprises (SMEs). The results indicate that AI capability positively influences responsible innovation. This relationship is fully mediated by boundary-spanning search. Knowledge field activity and tech-for-good culture moderate the relationships between AI capability and boundary-spanning search and between boundary-spanning search and responsibleinnovation. They also moderate the indirect effect of boundary-spanning search on the relationship between AI capability and responsible innovation. This study contributes to the literature on AI capabilities and innovation outcomes. It also provides practical insights for managers of high-tech SMEs and policymakers to foster responsible innovation. © 2025 The Authors. KW - AI capability KW - Boundary-spanning search KW - High-tech SMEs KW - Knowledge field activity KW - Responsible innovation KW - Tech-for-good culture CY - China ER - TY - JOUR TI - Unpacking the Black Box: How AI Capability Enhances Human Resource Functions in China’s Healthcare Sector AU - Chen X. AU - Martínez-Ruiz M.P. AU - Bulmer E. AU - Yáñez-Araque B. PY - 2025 JO - Information (Switzerland) VL - 16 IS - 8 SP - 705 DO - 10.3390/info16080705 AB - Artificial intelligence (AI) is transforming organizational functions across sectors; however, its application to human resource management (HRM) within healthcare remains underexplored. This study aims to unpack the black-box nature of AI capability’s impact on HR functions within China’s healthcare sector, a domain undergoing rapid digital transformation, driven by national innovation policies. Grounded in resource-based theory, the study conceptualizes AI capability as a multidimensional construct encompassing tangible resources, human resources, and organizational intangibles. Using a structural equation modeling approach (PLS-SEM), the analysis draws on survey data from 331 professionals across five hospitals in three Chinese cities. The results demonstrate a strong, positive, and statistically significant relationship between AI capability and HR functions, accounting for 75.2% of the explained variance. These findings indicate that AI capability enhances HR performance through smarter recruitment, personalized training, and data-driven talent management. By empirically illuminating the mechanisms linking AI capability to HR outcomes, the study contributes to theoretical development and offers actionable insights for healthcare administrators and policymakers. It positions AI not merely as a technological tool but as a strategic resource to address talent shortages and improve equity in workforce distribution. This work helps to clarify a previously opaque area of AI application in healthcare HRM. © 2025 by the authors. KW - artificial intelligence KW - capability KW - human resource functions KW - PLS-SEM KW - resource-based theory KW - Health care KW - Human resource management KW - Information management KW - Metadata KW - Black boxes KW - Capability KW - Digital transformation KW - Healthcare sectors KW - Human resource functions KW - Human resources management KW - ITS applications KW - Organizational functions KW - PLS-SEM KW - Resource-based theory KW - Artificial intelligence CY - Spain ER - TY - JOUR TI - Smarter, not harder: the AI capability paradox in emerging-market SMEs AU - Lambert J.M. AU - Laskovaia A. AU - Garanina O. AU - Bogatyreva K. PY - 2026 JO - Journal of Entrepreneurship in Emerging Economies VL - 18 IS - 3 SP - 813 EP - 836 DO - 10.1108/JEEE-10-2025-0632 AB - Purpose – This study aims to identify configurations of artificial intelligence (AI)-related organisational capabilities that lead to superior performance in small and medium-sized enterprises (SMEs) operating in an emerging market, moving beyond the assumption that “more AI usage is better”. Drawing on resource orchestration theory, the authors conceptualise how governance, skills, ethics, leadership and data infrastructure jointly enable value creation from AI. Design/methodology/approach – This study relies on a cross-sectional survey of Russian SMEs (October 2024 to January 2025). Of 384 firms, 47 that reported AI use were analysed. Using fuzzy-set qualitative comparative analysis (fsQCA), the authors examined how AI usage intensity combines with internal enablers, AI governance, data infrastructure, employee AI/digital skills, top management team (TMT) involvement and AI ethics preparedness, to explain four outcomes: operational efficiency, strategic decision quality, product/service innovation and customer responsiveness. The authors calibrated the conditions using the direct method and explored the robustness of configurations across alternative consistency and frequency thresholds. Findings – Across all outcomes, high AI usage intensity was not a core condition. Instead, multiple high-performance pathways featured AI governance as a central ingredient, frequently complemented by ethics preparedness and either employee training or active TMT involvement. Where governance was weaker, strong employee capabilities could serve as a substitute. These results show that SMEs can achieve strong performance with moderate AI intensity when organisational capabilities are well-aligned. In emerging-market SMEs, this points to an “AI capability paradox”: under the dual constraints of limited resources and weaker institutional environments, more intensive AI use does not necessarily yield better outcomes unless complemented by appropriate capability bundles. Originality/value – The authors shift the debate from “how much AI” to “how AI is governed and supported”. By applying a configurational lens in an emerging-market SME context, the authors reveal equifinal capability bundles, highlighting governance and ethics, paired with skills and leadership, as more decisive than sheer adoption intensity. The authors extend AI-related capabilities research to the focus on SMEs in emerging markets. Methodologically, the authors use fsQCA to identify multiple, empirically grounded resource and capability configurations associated with superior performance. © 2026 Emerald Publishing Limited KW - Artificial intelligence KW - Digital technology KW - fsQCA KW - Management of innovation KW - SMEs ER - TY - JOUR TI - Do artificial intelligence capabilities impact sustainability-oriented innovation performance: exploring the role of green intellectual capital and learning orientation AU - Zhang Y. AU - Shi J. AU - Huang Y. PY - 2025 JO - Journal of Intellectual Capital DO - 10.1108/JIC-10-2024-0315 AB - Purpose: This study examines the impact of artificial intelligence (AI) capabilities on sustainability-oriented innovation performance. Furthermore, it explores the mediating role of green intellectual capital and the moderating role of learning orientation. Design/methodology/approach: To verify the hypothesised relationships, we conducted a hierarchical regression analysis and bootstrapping method with survey data collected from 355 Chinese firms. Findings: Grounded in organisational learning theory, the study found that AI capabilities have a positive influence on green intellectual capital (i.e. green human capital, green structural capital and green relational capital), and this connection is further reinforced by learning orientation. The analysis also reveals that green intellectual capital serves as a mediator in the relationship between AI capabilities and sustainability-oriented innovation performance. Originality/value: This research explores the relationships among AI capabilities, green intellectual capital, learning orientation and sustainability-oriented innovation performance in a comprehensive model. This is the first known study to highlight that AI capabilities can improve sustainability-oriented innovation performance and gives managers implications on how to align AI capabilities while pursuing sustainability-oriented innovation performance. © 2025, Emerald Publishing Limited. KW - Artificial intelligence capabilities KW - Green intellectual capital KW - Learning orientation KW - Sustainability-oriented innovation performance CY - China ER - TY - JOUR TI - Algorithmic Decision-Making and Human Autonomy in Finance: A Systematic Review of AI Ethics Research AU - Bhatti T. PY - 2026 JO - Journal of Statistical Theory and Applications VL - 25 IS - 1 SP - 22 DO - 10.1007/s44199-026-00175-w AB - Artificial intelligence is rapidly transforming financial decision-making across banking, lending, insurance, auditing, fraud detection, and customer-facing financial services. At the same time, its growing use has intensified ethical concerns related to fairness, accountability, transparency, privacy, trust, and human oversight. Although research on artificial intelligence (AI) in finance has expanded considerably, the literature remains fragmented across disciplinary and application-specific streams, limiting a consolidated understanding of its intellectual foundations and thematic development. This study provides a bibliometric and thematic review of research at the intersection of artificial intelligence, finance, and ethics. Drawing on Scopus-indexed journal articles and a PRISMA-guided screening process, a final sample of 338 articles published between 2000 and 2025 was analyzed using the bibliometrix package in R. The findings show that the field is young but rapidly expanding, particularly after 2019, with strong momentum in recent years. Intellectual structure analysis identifies foundational contributions centered on algorithmic fairness, accountability, explainability, and governance, while historiographic patterns reveal major developmental pathways in credit scoring, financial services, accounting and auditing, and generative AI. Conceptual and thematic analyses further show that the literature is organized into six interconnected clusters covering AI ethics and governance, algorithmic fairness, explainable AI, fraud detection, trustworthy AI, and human-in-the-loop financial decision-making. The study contributes a structured map of this emerging field and shows that AI in finance is increasingly understood not merely as a technical innovation, but as a socio-technical governance challenge requiring responsible design, institutional accountability, and sustained human oversight. © The Author(s) 2026. KW - Algorithmic decision-making KW - Artificial intelligence KW - Bibliometric review KW - Ethical finances KW - Explainable AI ER - TY - JOUR TI - Governance by satellite: Remote sensing, bureaucrats and agency in the Common Agricultural Policy of the European Union AU - van der Velden D. AU - Klerkx L. AU - Dessein J. AU - Debruyne L. PY - 2025 JO - Journal of Rural Studies VL - 114 SP - 103558 DO - 10.1016/j.jrurstud.2024.103558 AB - Increasingly, European member states are using remote sensing technologies to determine if farmers comply with measures of the Common Agricultural Policy (CAP). Member states use satellite images, aerial photographs and geotagged pictures, and combine this with advanced algorithms and machine learning to determine if farmers comply with requirements that they have set out in their CAP strategic plans. Our research analyses the use of satellite images and the software used to process this data at paying agencies to understand how these technologies are enabling new forms of governance and what these technologies mean for how farmers are seen (literally and figuratively) by government agencies. This research is based on 12 semi-structured interviews with the developers of the technologies used to monitor compliance, which includes people working for paying agencies as well as people working at research institutes and companies where they provide technical support to the development and use of remote sensing at paying agencies. This research reveals that the governance of the Common Agricultural Policy (CAP), facilitated by remote sensing, fosters an audit culture characterized by strict control and compliance. The emphasis on mapping, quantifying, and representing agriculture underpins this governance model and drives further technological advancements. However, participants highlight the shortcomings of remote sensing technologies in effectively controlling farming practices. By integrating theories of bureaucracy and governance with a critical perspective on techno-utopianism, we examine these dynamics. Our findings indicate that the current application of remote sensing within the CAP is constrained not only by technical limitations but also by the existing governance framework. The push for quantification leads respondents to advocate for the further adoption of technical innovations to enhance control over agriculture. In conclusion, we suggest that a policy shift is necessary to break free from this technology trap. © 2025 The Authors KW - Algorithmic governance KW - Digitalization KW - Environmental monitoring KW - Farming subsidies KW - Street level bureaucrats KW - Common Agricultural Policy KW - digitization KW - environmental monitoring KW - European Union KW - remote sensing KW - subsidy system CY - Belgium, Chile, Netherlands ER - TY - JOUR TI - A Dynamic AI Maturity Model for Agile Audit: A Roadmap for Enhanced Effectiveness and Innovation AU - Amraoui S. AU - Elmaallam M. AU - Nassar M. PY - 2026 JO - Journal of Computer Science VL - 22 IS - 1 SP - 87 EP - 99 DO - 10.3844/jcssp.2026.87.99 AB - The intersection of Artificial Intelligence (AI) and agile methodologies is transforming information systems audit by enabling real-time risk assessment, anomaly detection, and automated control testing. These capabilities enhance the security, efficiency, and reliability of IT environments. This article introduces a dynamic AI maturity model for agile audit, structured into five levels of AI integration. Each level reflects increasing AI capabilities and outlines key transition points. The model supports strategic AI adoption across various audit domains, including data analysis, cybersecurity, compliance monitoring and fraud detection. We validate this model using interviews and a case study in a public-sector audit institution. Ethical concerns such as transparency, fairness, and accountability are integrated, recognizing the potential impact of AI on privacy, compliance, and governance. By applying this maturity model, organizations can systematically strengthen their agile audit practices while maintaining control over their information systems. © 2026 Soumaya Amraoui, Mina Elmaallam and Mahmoud Nassar. KW - Agile Audit KW - Artificial Intelligence (AI) KW - Information Systems Audit KW - Maturity Model CY - Morocco ER - TY - JOUR TI - Transparency in large language model (LLM)-powered digital human twins: the AI ethics perspective AU - Pigac T. PY - 2026 JO - AI and Society VL - 41 IS - 3 SP - 2109 EP - 2118 DO - 10.1007/s00146-025-02617-y AB - Digital human twins (DHTs), powered by large language models (LLMs), are transforming industries such as healthcare and finance by mimicking human behaviors, preferences, and decision-making processes. While their adoption offers unprecedented personalization and engagement, it also raises significant ethical concerns, particularly regarding transparency. Ensuring users understand how these systems function is critical to fostering trust and accountability. This study explores transparency in LLM-powered DHTs through qualitative analysis of 30 semi-structured interviews with users across diverse sectors. The findings reveal critical challenges, including algorithmic opacity, data privacy vulnerabilities, and threats to user autonomy. Participants consistently expressed a need for clear disclosures about data practices and emphasized the importance of robust ethical safeguards to prevent misuse. The research highlights the tension between achieving transparency and maintaining the seamless functionality of DHT systems. It underscores the risks of oversimplifying algorithmic processes while pointing out the erosion of trust caused by opaque operations. To address these challenges, the study proposes actionable strategies, including tiered transparency models, enhanced regulatory oversight, and user-centric design principles. By bridging ethical principles with practical applications, this research provides a roadmap for fostering responsible AI innovation. It advances the discourse on ethical AI by addressing transparency challenges in LLM-powered DHTs, emphasizing the need for systems that uphold trust, accountability, and user autonomy. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025. KW - AI ethics KW - Data privacy KW - Digital human twins KW - LLM KW - Personalization KW - Transparency KW - Behavioral research KW - Decision making KW - Ethical technology KW - AI ethic KW - Decision-making process KW - Digital human twin KW - Digital humans KW - Human behaviors KW - Human decisions KW - Language model KW - Large language model KW - Personalizations KW - User autonomy KW - Data privacy KW - Transparency ER - TY - JOUR TI - How can firms leverage responsible artificial intelligence to build competitive advantages? The role of supply chain justice and complexity AU - Li L. AU - Shi R. AU - Zhang R. AU - Chen L. PY - 2026 JO - International Journal of Production Economics VL - 297 SP - 110010 DO - 10.1016/j.ijpe.2026.110010 AB - In highly interconnected supply chains, firms are increasingly adopting artificial intelligence (AI) to improve their forecasting, resource allocation, and responsiveness. However, opaque and bias-prone algorithms can obscure decision-making processes and undermine collaboration between supply chain partners. Responsible AI, in which AI is designed, developed, and deployed in such a way that ensures its ethical conduct, transparency, and alignment with human values, offers a potential remedy, but its relational effects in supply chains remain unclear. Drawing on organizational justice theory, we test whether supply chain justice, encompassing both distributive and procedural justice, mediates the responsible AI–competitive advantage link, with supply chain complexity as a boundary condition. Survey data from 218 Chinese firms show that responsible AI fosters both distributive and procedural justice, which in turn facilitates firms’ competitive advantages. Further, we identify an asymmetric moderating effect: high supply chain complexity weakens the mediating effect of distributive but not procedural justice. Our study advances the research on AI-enabled supply chains by identifying how ethical AI practices can foster firm performance via supply chain justice and when supply chain complexity weakens this effect. Practically, our findings suggest that in complex supply networks, ensuring transparent and consistent decision-making processes provides a more robust mechanism for sustaining competitive advantages compared with focusing only on outcome allocation. Copyright © 2026. Published by Elsevier B.V. KW - Competitive advantages KW - Organizational justice theory KW - Responsible AI KW - Supply chain complexity KW - Supply chain justice KW - Artificial intelligence KW - Behavioral research KW - Competitive intelligence KW - Decision making KW - Decision theory KW - Ethical aspects KW - Supply chains KW - Competitive advantage KW - Decision-making process KW - Organisational KW - Organizational justice theory KW - Procedural justice KW - Resources allocation KW - Responsible artificial intelligence KW - Supply chain complexity KW - Supply chain justice KW - Supply chain partners KW - Competition CY - China ER - TY - JOUR TI - Artificial intelligence innovation to sustainable knowledge: The dual role of enterprise resilience AU - Wang S. AU - Ma L. AU - Hao F. AU - Zhang H. PY - 2025 JO - Journal of Innovation and Knowledge VL - 10 IS - 6 SP - 100832 DO - 10.1016/j.jik.2025.100832 AB - The rapid evolution of artificial intelligence (AI) presents unprecedented opportunities for knowledge creation and sustainable innovation in global digital commerce. This study investigates how AI orchestration capability generates new forms of knowledge that drive sustainable development in cross-border e-commerce multinational enterprises, with enterprise resilience serving as a critical knowledge transformation mechanism. Drawing on resource orchestration theory and employing a mixed-methods approach, we analyze data from 444 enterprises across China and Europe using partial least squares-structural equation modeling (PLS-SEM), importance–performance map analysis, and fuzzy-set qualitative comparative analysis (fsQCA), and executive interviews. Our findings reveal that AI orchestration capability—encompassing planning, integration, and reconfiguration dimensions—creates actionable knowledge that significantly enhances sustainable development both directly and indirectly through enterprise resilience. Enterprise resilience emerges as a dual-function capability that not only mediates knowledge flows between AI systems and sustainability outcomes but also amplifies the innovation potential of AI-generated insights. Regional analysis uncovers distinct knowledge creation pathways: Chinese enterprises excel at transforming AI capabilities into resilience-based knowledge, while European firms demonstrate superior translation of resilience-derived knowledge into sustainability innovations. Configurational analysis identifies multiple equifinal combinations of AI capabilities and resilience dimensions that generate high-impact sustainable innovations. This research advances our understanding of how digital technologies create enduring knowledge for sustainability, offering novel theoretical insights into innovation–knowledge dynamics and practical guidance for leveraging AI as a catalyst for sustainable business transformation in the digital economy. © 2025 The Author(s) KW - Artificial intelligence KW - Digital transformation KW - Enterprise resilience KW - Knowledge creation KW - Resource orchestration KW - Sustainable innovation CY - China ER - TY - JOUR TI - State positioning in European military AI networks: a social network analysis of European partnerships in military AI AU - Javadi M. AU - Onderco M. PY - 2025 JO - Journal of Contemporary European Studies VL - 33 IS - 4 SP - 1312 EP - 1331 DO - 10.1080/14782804.2025.2514846 AB - This study examines the positioning of nations with established AI capabilities within European military AI collaboration networks, exploring their influence and strategic centrality. Applying Social Network Analysis (SNA) to data from a 2023 expert survey (N = 479), supplemented by qualitative insights from targeted interviews, the research uncovers the structural dynamics shaping military AI cooperation. The findings indicate that countries with dedicated national AI strategies in defence leverage institutional readiness to become key nodes in the network, driving both innovation and governance. However, the overall network remains fragmented, with low density and limited cohesion, suggesting unrealized potential for deeper cooperation. The study highlights the pivotal roles of NATO and the European Defence Agency in strengthening integration and coordination. By employing SNA, this research offers new perspectives on European strategic affairs, shedding light on the evolving landscape of military AI governance and technological advancement. © 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. KW - Artificial intelligence KW - Europe KW - expert survey KW - military AI KW - social network analysis CY - Netherlands, Belgium, Czech Republic ER - TY - JOUR TI - Special report: The AgAID AI institute for transforming workforce and decision support in agriculture AU - Kalyanaraman A. AU - Burnett M. AU - Fern A. AU - Khot L. AU - Viers J. PY - 2022 JO - Computers and Electronics in Agriculture VL - 197 SP - 106944 DO - 10.1016/j.compag.2022.106944 AB - Tackling the grand challenges of 21st century agriculture (Ag) will require a fundamental shift in the way we envision the role of artificial intelligence (AI) technologies, and in the way we build agricultural AI systems. This shift is needed especially for complex, high-value agricultural ecosystems such as those in the Western U.S., where 300+ crops are grown. Farmers and policy makers in this region face variable profitability, major crop loss and poor crop quality owing to several challenges, including increased labor costs and shortages of skilled workers, weather and management uncertainties, and water scarcity. While AI is expected to be a significant tool for addressing these challenges, AI capabilities must be expanded and will need to account for human input and human behavior – calling for a strong AI-Ag coalition that also creates new opportunities to achieve sustained innovation. Accomplishing this goal goes well beyond the scope of any specific research project or disciplinary silo and requires a more holistic transdisciplinary effort in research, development, and training. To respond to this need, we initiated the AgAID Institute, a multi-institution, transdisciplinary National AI Research Institute that will build new public-private partnerships involving a diverse range of stakeholders in both agriculture and AI. The institute focuses its efforts on providing AI solutions to specialty crop agriculture where the challenges pertaining to water availability, climate variability and extreme weather, and labor shortages, are all significantly pronounced. Our approach to all AgAID Institute activities is being guided by three cross-cutting principles: (i) adoption as a first principle in AI design; (ii) adaptability to changing environments and scales, and (iii) amplification of human skills and machine efficiency. The AgAID Institute is conducting a range of activities including: using agricultural AI applications as testbeds for developing innovative AI technologies and workflows; laying the technological foundations for climate-smart agriculture; serving as a nexus for culturally inclusive collaborative and transdisciplinary learning and knowledge co-production; preparing the next generation workforce for careers at the intersection of Ag and AI technology; and facilitating technology adoption and transfer. © 2022 KW - Agriculture KW - AI KW - Decision support KW - Artificial intelligence KW - Behavioral research KW - Crops KW - Ecosystems KW - Employment KW - Human resource management KW - Wages KW - Agricultural ecosystems KW - Artificial intelligence systems KW - Artificial intelligence technologies KW - Crop loss KW - Crop quality KW - Decision supports KW - Grand Challenge KW - Labor costs KW - Labor shortages KW - Policy makers KW - agriculture KW - amplification KW - artificial intelligence KW - human behavior KW - learning KW - technology adoption KW - Decision support systems CY - United States ER - TY - JOUR TI - Do FinTech and financial incumbents have different experiences and perspectives on the adoption of artificial intelligence? AU - Zhang B.Z. AU - Ashta A. AU - Barton M.E. PY - 2021 JO - Strategic Change VL - 30 IS - 3 SP - 223 EP - 234 DO - 10.1002/jsc.2405 AB - Although FinTechs and incumbents are applying artificial intelligence (AI) differently, they both expect that the status-quo will likely be maintained through collaboration rather than competition. Both perceive BigTechs as a strategic threat given their AI capabilities and their entrance into financial services. Incumbents are experimenting with more different kinds of AI than FinTechs: FinTechs use the technologies for new products and services while incumbents are using them for incremental innovations to existing products and services. The incumbents expect that adopting AI will lead to a loss in jobs of 9% over the next 10 years and, because these companies represent a large percentage of the workforce (median company size surveyed has more than 10,000 employees), this loss in jobs cannot be compensated by the 19% increase in jobs provided by existing FinTechs (median company size surveyed has less than 50 employees). AI can reduce and increase risk, and most incumbents and FinTechs agree that there will be no effect on risk at the organizational level but that there will be an increase in risk at the societal level. While both FinTechs and incumbents agree on the relative importance of legal and human hurdles and consider the biggest hurdle is related to data and regulations concerning data, FinTechs perceive these hurdles to be greater than do incumbents. © 2021 John Wiley & Sons Ltd. KW - artificial intelligence KW - competitive advantage KW - financial services KW - Fintech KW - machine learning CY - United Kingdom, France, United States ER - TY - JOUR TI - Ethical and Regulatory Frameworks for Artificial Intelligence in Clinical Research: A European Perspective on the Artificial Intelligence Act for Ethics Committees and Researchers AU - Barucci A. AU - Colcelli V. AU - De Masi S. AU - Falconi M. AU - Leo M.C. AU - Marzola A. AU - Romagnuolo I. AU - Sforzi C. AU - Pini R. PY - 2026 JO - European Cardiology Review VL - 21 SP - 1 EP - 8 DO - 10.15420/ecr.2025.59 AB - The rapid integration of artificial intelligence (AI) into clinical research is transforming the landscape of biomedical innovation, influencing numerous phases of research, with critical ethical and legal implications. Regulation (EU) 2024/1689, commonly referred to as the AI Act and issued in 2024, introduced a new regulatory framework that classifies AI systems used in clinical settings as ‘high risk’, requiring increased scrutiny by ethics committees and national authorities. This review addresses ethical and regulatory challenges and discusses the application of the AI Act within real-world clinical research. We propose a three-phase lifecycle (training, real-world testing and post-marketing monitoring) to align regulatory burdens with AI maturity. Our recommendations include transparent protocol design with explicit data-use declarations, complementary application of the Medical Device Regulation and the AI Act, with particular attention to the early research phases. This approach provides practical indications for researchers and operational evaluation criteria for ethics committees to ensure patient safety while fostering trustworthy AI deployment in clinical trials. © The Author(s) 2026. KW - Artificial intelligence KW - clinical research KW - ethical principles KW - ethics committees KW - European Union regulations KW - article KW - artificial intelligence KW - clinical research KW - European KW - European Union KW - human KW - medical device regulation KW - patient safety KW - postmarketing surveillance KW - professional standard KW - scientist CY - Italy ER - TY - JOUR TI - Public administration with, of, and through AI: toward a new paradigm in the era of intelligence AU - Zhu X. PY - 2026 JO - Journal of Chinese Governance VL - 11 IS - 1 SP - 30 EP - 57 DO - 10.1080/23812346.2025.2578589 AB - This paper examines the future trajectory of public administration in the era of intelligence, focusing on the transformative implications of artificial intelligence (AI). It proposes a new triadic paradigm, ‘Public Administration with, of, and through AI,’ to conceptualize how AI is reshaping the theory, practice, and research of governance. The framework outlines three interrelated dimensions: the strengthening of administrative capacity with AI, the ethical and regulatory governance of AI, and the methodological advancement of the discipline through AI. Grounded in the historical evolution of public administration paradigms, the paper identifies key challenges in talent cultivation, research methodology, and evidence-based policymaking during digital transformation. As governments around the world integrate AI into administrative systems, the paper highlights the need for interdisciplinary collaboration, methodological innovation, and ethical vigilance. It concludes by advocating three strategic integrations that can guide the discipline’s renewal: scientific rigor with practical relevance, agility with long-termism, and globalization with indigenization. These integrations aim to ensure that governance in the intelligent age remains both effective and ethically grounded. © 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. KW - artificial intelligence KW - digital governance KW - intelligence era KW - paradigm shifts KW - Public administration CY - China ER - TY - JOUR TI - A Game-Theoretic Approach to Containing Artificial General Intelligence: Insights From Highly Autonomous Aggressive Malware AU - Mcintosh T.R. AU - Susnjak T. AU - Liu T. AU - Watters P. AU - Ng A. AU - Halgamuge M.N. PY - 2024 JO - IEEE Transactions on Artificial Intelligence VL - 5 IS - 12 SP - 6290 EP - 6303 DO - 10.1109/TAI.2024.3394392 AB - Artificial general intelligence (AGI) promises transformative societal changes but poses safety and containment challenges. Large language models such as ChatGPT have intensified public expectations and apprehensions regarding AGI capabilities and risks. Existing research underestimated replicating human intelligence and lacks effective containment strategies scaled for AGI's complexity. We developed a cybersecurity-inspired framework to reconceptualize AGI containment as securing critical infrastructure indispensable for its operation. We applied game theory to model the strategic interplay between AGI and humans, drawing parallels with highly autonomous malware, emphasizing infrastructural dependencies and human countermeasures. We introduced offensive/defensive containment strategies and an AGI Kill Chain model profiling escalating AGI threats. Our game-theoretic approach examined complex AGI-human interactions revealing insights for adaptive oversight mechanisms. Game simulations demonstrated AGI carefully manages resources and autonomy balancing benefits against risks, necessitating strategic human responses. Our findings provided detailed containment tactics, emphasizing flexibility to address AGI's dynamic evolution. We proposed comprehensive, multidisciplinary containment strategies, effective governance evaluating long-term efficacy, and emphasize ongoing innovation for aligning AGI progression with utility and security. © 2024 IEEE. KW - AGI containment KW - AI Governance KW - AI safety KW - Artificial general intelligence (AGI) KW - game theory KW - risk assessment KW - Artificial intelligence KW - Computer games KW - Cybersecurity KW - Malware KW - Risk assessment KW - Safety engineering KW - AI governance KW - AI safety KW - Artificial general intelligence KW - Artificial general intelligence containment KW - Artificial general intelligences KW - Australia KW - Chatbots KW - Complexity theory KW - Risks assessments KW - Game theory CY - Australia, New Zealand ER - TY - JOUR TI - Bridging Innovation and Practice: A Literature Review on Artificial Intelligence’s (AI’s) Expanding Role in Therapy AU - Shepperson C. AU - Chen H.-M. AU - Quek K.M.-T. PY - 2026 JO - International Journal of Systemic Therapy DO - 10.1080/2692398X.2026.2629637 AB - Artificial Intelligence (AI) encompassing machine learning (ML), deep learning, and generative AI (GenAI), is increasingly shaping clinical and therapeutic landscapes. This paper conducts a review of current literature on the use of AI in various therapeutic contexts. It examines how AI technologies such as chatbots, predictive models, and AI-enabled robotics are being applied across diverse populations to support prescreening, diagnosis processes, symptom tracking, behavioral monitoring, and clinical systemic interventions. These tools have demonstrated potential in improving therapeutic outcomes, including reducing symptoms of depression and anxiety and enhancing cognitive and behavioral functioning. The review highlights specific benefits and explores the broader implications of these applications within clinical practice. However, concerns such as algorithmic bias, data privacy risks, and the lack of emotional nuance in AI responses pose significant risks to therapeutic integrity and relational attunement. Moreover, the absence of formal training and clearly defined ethical guidelines for clinicians raises questions about responsible implementation. Systemic therapists, in particular, must remain vigilant about how AI may reinforce dominant cultural narratives or marginalize underrepresented voices. Therefore, as AI continues to evolve, ongoing professional engagement and ethical scrutiny are essential to ensure its responsible and equitable integration into clinical care. Given the rapidly changing nature of these technologies, therapists are recommended to remain informed and approach their use with flexibility and caution. © 2026 Taylor & Francis Group, LLC. KW - Artificial intelligence (AI) KW - Ethical Considerations KW - Systemic Therapy KW - Therapy CY - United States ER - TY - JOUR TI - Artificial Intelligence in Reliability of Engineering Design-an Overview AU - Afolalu S.A. AU - Olawale O.C. AU - Oso F. PY - 2025 JO - NIPES - Journal of Science and Technology Research VL - 7 IS - 2 SP - 2919 EP - 2927 DO - 10.37933/nipes/7.4.2025.SI348 AB - This study explores the transformative role of artificial intelligence (AI) in enhancing reliability within engineering systems. It highlights various applications of AI, including predictive maintenance, real-time monitoring, and advanced data analytics, which significantly contribute to reducing system failures and improving overall performance. By leveraging historical data and current sensor inputs, AI enables engineers to predict equipment failures before they occur, optimizing maintenance schedules and extending the life of critical systems. The research also discusses AI's capabilities in data acquisition and pattern recognition, which facilitate better understanding of failure modes and inform design decisions that enhance reliability. Furthermore, the study examines the impact of AI-driven simulations and modeling on engineering design processes, allowing for virtual testing of designs under various conditions and minimizing costly iterations. The implications of AI in automated quality control and decision support systems are also addressed, underscoring its potential to improve product quality and operational efficiency. Ultimately, the findings suggest that the integration of AI technologies across engineering disciplines can lead to more robust, efficient, and sustainable systems, paving the way for future innovations in reliability engineering. © 2025 NIPES Pub. KW - Artificial Intelligence KW - Data Analytics KW - Engineering Design KW - Predictive Maintenance KW - Reliability Engineering CY - Nigeria, South Africa ER - TY - JOUR TI - A Functionally-Grounded Benchmark Framework for XAI Methods: Insights and Foundations from a Systematic Literature Review AU - Canha D. AU - Kubler S. AU - Främling K. AU - Fagherazzi G. PY - 2025 JO - ACM Computing Surveys VL - 57 IS - 12 SP - ART320 DO - 10.1145/3737445 AB - Artificial Intelligence (AI) is transforming industries, offering new opportunities to manage and enhance innovation. However, these advancements bring significant challenges for scientists and businesses, with one of the most critical being the ‘trustworthiness” of AI systems. A key requirement of trustworthiness is transparency, closely linked to explicability. Consequently, the exponential growth of eXplainable AI (XAI) has led to the development of numerous methods and metrics for explainability. Nevertheless, this has resulted in a lack of standardized and formal definitions for fundamental XAI properties (e.g., what do soundness, completeness, and faithfulness of an explanation entail? How is the stability of an XAI method defined?). This lack of consensus makes it difficult for XAI practitioners to establish a shared foundation, thereby impeding the effective benchmarking of XAI methods. This survey article addresses these challenges with two primary objectives. First, it systematically reviews and categorizes XAI properties, distinguishing them between human-centered (relying on empirical studies involving explainees) or functionally-grounded (quantitative metrics independent of explainees). Second, it expands this analysis by introducing a hierarchically structured, functionally grounded benchmark framework for XAI methods, providing formal definitions of XAI properties. The framework’s practicality is demonstrated by applying it to two widely used methods: LIME and SHAP. © 2025 Copyright held by the owner/author(s). KW - Artificial intelligence KW - eXplainable AI (XAI) KW - interpretability KW - machine learning KW - responsible AI KW - transparency KW - trustworthiness KW - Artificial intelligence KW - Benchmarking KW - Information systems KW - Learning systems KW - Reviews KW - Artificial intelligence systems KW - Explainable artificial intelligence (XAI) KW - Exponential growth KW - Formal definition KW - Interpretability KW - Machine-learning KW - Property KW - Responsible artificial intelligence KW - Systematic literature review KW - Trustworthiness KW - Transparency CY - Luxembourg, Sweden, Finland ER - TY - JOUR TI - A Cloud-Based Computing Framework for Artificial Intelligence Innovation in Support of Multidomain Operations AU - Robertson J. AU - Fossaceca J. AU - Bennett K. PY - 2022 JO - IEEE Transactions on Engineering Management VL - 69 IS - 6 SP - 3913 EP - 3922 DO - 10.1109/TEM.2021.3088382 AB - The DoD's artificial intelligence (AI) strategy requires the delivery of transformative and disruptive capabilities that impact the 'character of the future battlefield and the pace of threats' that US forces must be prepared to handle. Candidate frameworks must also address key mission areas while enabling partnerships with the private sector, academia, and global allies. To meet these challenges, a flexible, cost-effective, and scalable computing infrastructure that incorporates cutting edge technologies and complies with stringent information assurance requirements is necessary. The DoD AI strategy mandates the agile employment of innovative AI capabilities that 'rapidly and iteratively' execute experimentation with new operating concepts, and leverage lessons learned in subsequent experiments. Using cloud computing, we present a flexible approach to solve complex systems problems. Promoting 'rapid experimentation' and collaboration on problems such as recursive algorithm implementation, deep learning, and inference in neural networks has enabled inherent advantages over existing computing frameworks. Leveraging the cloud to implement shared responsibility security models, serverless architectures, and high-performance virtual machines, aspects of the AI lifecycle including build, deploy, and monitor have resulted in an adaptable and scalable computing framework that is not only disruptive to the current computing paradigm but also promotes enhanced and productive collaboration. © 1988-2012 IEEE. KW - Artificial intelligence (AI) KW - artificial intelligence for technology management KW - cloud computing KW - collaboration KW - design of experiments KW - IS design KW - new product development process KW - productivity in software development KW - RandD management KW - systems engineering KW - technology adoption KW - technology evaluation KW - Big data KW - Cost effectiveness KW - Deep learning KW - Design of experiments KW - Engineering education KW - Inference engines KW - Iterative methods KW - Product design KW - Software design KW - Artificial intelligence KW - Artificial intelligence for technology management KW - Cloud-computing KW - Collaboration KW - D management KW - IS design KW - New product development process KW - Productivity in software development KW - R& KW - Security KW - Technology adoption KW - Technology evaluation KW - Technology managements KW - US Department of Defence KW - Cloud computing CY - United States ER - TY - JOUR TI - A feast of knowledge: How AI-powered food councils can transform policymaking in the digital era AU - Vliet L.G.V. AU - Turk J.D. PY - 2026 JO - International Journal of Innovation Studies VL - 10 IS - 2 SP - 100169 DO - 10.1016/j.ijis.2025.11.004 AB - As global food systems face intensifying challenges—from climate change and biodiversity loss to nutritional insecurity and fractured supply chains—policymakers are under increasing pressure to formulate agile, informed, and inclusive responses. While digital tools and artificial intelligence (AI) offer transformative potential, few initiatives have explored their application within food system governance. This article presents a discussion-based case study of the Council of Foods, an experimental AI-powered policymaking platform developed within the EU Horizon-funded Hungry EcoCities project. Through playful, food-character-driven dialogue, the platform enables stakeholders to explore complex policy dilemmas, surface diverse perspectives, and receive dynamically generated recommendations. Drawing on experiences from early testing environments, including public forums and conference sessions, we reflect on the conceptual framework, technical design, and pedagogical value of the Council of Foods. While the tool is still in its developmental stages, we argue that such AI-mediated, participatory platforms can help bridge knowledge gaps, stimulate policy learning, and support just transitions in food governance. The article concludes with key implications for future research, platform development, and the ethical integration of AI in public policy. Copyright © 2026. Publishing services by Elsevier B.V. KW - AI in policymaking KW - AI-Powered agents KW - Digital transformation KW - Food policy innovation KW - Food system governance KW - Green transition KW - Just KW - Sustainable food systems KW - Transition Ethical AI CY - Netherlands, Belgium ER - TY - JOUR TI - AI Capabilities and Its Impact on Organisational Innovation in Malaysian SMEs: The Role of Transformational Leadership and Digital Organisational Culture AU - Ismail R.T.Y. AU - Karamanlıoğlu A.U. PY - 2026 JO - Sustainability (Switzerland) VL - 18 IS - 3 SP - 1473 DO - 10.3390/su18031473 AB - Artificial Intelligence capabilities make the organisational innovation process more critical and sustainable, especially in SMEs. This research explored the influence of AI capabilities on organisational innovation within Malaysian SMEs and the role of transformational leadership as a mediator for the above effects, while considering the moderating role of digital organisational culture. The questionnaire was distributed electronically via Google Forms to a study sample of (900) SMEs in Kuala Lumpur, Selangor, and Johor Bahru, targeting owners and managers. Two weeks after distribution, (565) questionnaires were received; however, (215) questionnaires were excluded because the respondents were neither managers nor owners. A total of (350) questionnaires were valid for analysis. Using SMART-PLS software v.4.1.1.6 (PLS-SEM analysis) in analysing data, the study found that AI capability has a positive impact on organisational innovation and a positive impact on transformational leadership. Moreover, transformational leadership has a positive impact on organisational innovation, and transformational leadership mediates the relationship between AI capability and organisational innovation. Furthermore, the study found that digital organisational culture does not moderate the relationship between AI capability and transformational leadership. Digital organisational culture moderates the relationship between AI capability and organisational innovation; also, digital organisational culture moderates the relationship between transformational leadership and organisational innovation. © 2026 by the authors. KW - artificial intelligence KW - digital organisational culture KW - Malaysia KW - organisational Innovation KW - SMEs KW - transformational leadership KW - Johor KW - Johor Bahru KW - Kuala Lumpur KW - Malaysia KW - Selangor KW - West Malaysia KW - artificial intelligence KW - innovation KW - leadership KW - questionnaire survey KW - small and medium-sized enterprise KW - software KW - spatiotemporal analysis CY - Turkey ER - TY - JOUR TI - Digital Transformation Leadership and AI Capabilities as Drivers of Sustainable Competitive Advantage: The Mediating Role of Organizational Agility in Spain’s New S-Curve Industries AU - Alioune A. PY - 2026 JO - Managing Global Transitions VL - 24 IS - 1 SP - 33 EP - 62 DO - 10.26493/1854-6935.24.33-62 AB - The modern trend among various industrial companies to adopt digital technologies in their operations through digital transformation and reliance on artificial intelligence has become an imperative, especially with the increasing intensity of competition in global markets. To gain a realistic understanding of the role of digital transformation leadership (DTL) and AI capabilities (AIC) in achieving sustainable competitive advantage (SCA) for organizations, this study employs organizational agility (OA) as a mediating variable, based on data collected from 441 employees in Spanish ‘New S-Curve’ industries, which are innovative sectors achieving high growth returns within the framework of the ‘Spain 5.0’ national strategy. The statistical and analytical framework of Structural Equation Modelling (SEM) revealed that DTL has a significant impact on SCA, while the effect of AIC was not significant. Furthermore, OA was found to be an important mediator, reinforcing the indirect effects of both DTL and AIC on achieving SCA for Spanish organizations. © Author. KW - artificial intelligence capabilities KW - digital leadership KW - organizational agility KW - Spain’s S-curve industries KW - sustainable competitive advantage CY - Algeria ER - TY - JOUR TI - Bridging human and machine intelligence: How design thinking and generative AI capabilities drive exploratory and exploitative innovation AU - Cai Y. AU - Xin X. AU - Li L. AU - Shang Y. AU - Chen L. PY - 2026 JO - Technological Forecasting and Social Change VL - 228 SP - 124662 DO - 10.1016/j.techfore.2026.124662 AB - Although the literature has highlighted the roles of generative artificial intelligence (GenAI) and design thinking (DT) in innovation, the interplay between them remains unclear. To address this gap, we leverage the technological appropriation perspective to examine how three GenAI capabilities (i.e., relational, analytical, and creative) interact with five DT aspects (i.e., user focus, problem framing, visualization, experimentation and iteration, and embracing diversity) to influence exploratory and exploitative innovation. We use fuzzy-set qualitative comparative analysis to analyze survey data from 303 Chinese firms. The empirical results indicate that to achieve high exploratory and exploitative innovation, firms need to rely on focusing simultaneously on GenAI capabilities and DT, rather than on either factor individually. More interestingly, to achieve high exploratory innovation, firms require to focus on the combination of analytical capability, creative capability, problem framing, and embracing diversity. In contrast, to achieve high exploitative innovation, they must focus on the combination of relational capability, user focus, and visualization. Our findings contribute to the existing innovation literature by revealing the significant interplay between GenAI capabilities and DT. Our study also provides managerial implications for firms to coordinate human intelligence and machine intelligence more effectively. © 2026 Elsevier Inc. KW - Design thinking KW - Exploitative innovation KW - Exploratory innovation KW - fsQCA KW - GenAI capabilities KW - Artificial intelligence KW - Iterative methods KW - Creatives KW - Design thinking KW - Exploitative innovation KW - Exploratory innovation KW - FsQCA KW - Generative artificial intelligence capability KW - Human intelligence KW - Machine intelligence KW - Problem framings KW - User focus KW - artificial intelligence KW - innovation KW - qualitative analysis KW - visualization KW - Visualization CY - China ER - TY - JOUR TI - Digital servitization value co-creation framework for AI services: a research agenda for digital transformation in financial service ecosystems AU - Manser Payne E.H. AU - Dahl A.J. AU - Peltier J. PY - 2021 JO - Journal of Research in Interactive Marketing VL - 15 IS - 2 SP - 200 EP - 222 DO - 10.1108/JRIM-12-2020-0252 AB - Purpose: Innovative firms have rapidly developed artificial intelligence (AI) capabilities into their service ecosystems, essentially changing perceptions of what is service quality and service delivery in their respective industries. Nonetheless, the issues surrounding AI services remain relatively unknown. The purpose for this paper is to offer a digital servitization framework for understanding how AI services impact value perceptions, consumer engagement and firm performance measures. The authors use the financial service ecosystem to explore this topic. Design/methodology/approach: The authors explore relevant literature on digital servitization, service-dominant logic and AI/disruptive innovation. Next, a conceptual framework, organized by AI-Service Exchange Antecedents, Context of AI Usage and Digital Servitization Consequences, is developed. The authors conceptualize consequences for consumers and firms. Findings: The main findings suggest that the linkages between consumers, financial institutions and fintech companies with AI usage in a service ecosystem should be identified; how value is created among multiple SD Logic-AI network actors should be analyzed; and the effects of AI-consumer interactions (lower-level and higher levels of engagement) on firm performance measures should be explored. Research limitations/implications: The conceptual framework identifies gaps in the literature and suggests research questions for future studies. Practical implications: This paper may assist practitioners with the development of AI-enabled banking activities that involve direct consumer engagement. Originality/value: To the authors’ best knowledge, this research agenda is the first comprehensive framework for understanding value co-creation in the context of AI in financial services, linking antecedents, usage and consequences. © 2021, Emerald Publishing Limited. KW - Blogs KW - Consumer behaviour internet KW - Customer value KW - Digitalizations KW - e-commerce KW - Eservice quality KW - Financial services KW - Information technology KW - Online consumer behavior KW - Service quality KW - Services marketing CY - United States ER - TY - JOUR TI - Shaping the Future of Healthcare: Ethical Clinical Challenges and Pathways to Trustworthy AI AU - Goktas P. AU - Grzybowski A. PY - 2025 JO - Journal of Clinical Medicine VL - 14 IS - 5 SP - 1605 DO - 10.3390/jcm14051605 AB - Background/Objectives: Artificial intelligence (AI) is transforming healthcare, enabling advances in diagnostics, treatment optimization, and patient care. Yet, its integration raises ethical, regulatory, and societal challenges. Key concerns include data privacy risks, algorithmic bias, and regulatory gaps that struggle to keep pace with AI advancements. This study aims to synthesize a multidisciplinary framework for trustworthy AI in healthcare, focusing on transparency, accountability, fairness, sustainability, and global collaboration. It moves beyond high-level ethical discussions to provide actionable strategies for implementing trustworthy AI in clinical contexts. Methods: A structured literature review was conducted using PubMed, Scopus, and Web of Science. Studies were selected based on relevance to AI ethics, governance, and policy in healthcare, prioritizing peer-reviewed articles, policy analyses, case studies, and ethical guidelines from authoritative sources published within the last decade. The conceptual approach integrates perspectives from clinicians, ethicists, policymakers, and technologists, offering a holistic “ecosystem” view of AI. No clinical trials or patient-level interventions were conducted. Results: The analysis identifies key gaps in current AI governance and introduces the Regulatory Genome—an adaptive AI oversight framework aligned with global policy trends and Sustainable Development Goals. It introduces quantifiable trustworthiness metrics, a comparative analysis of AI categories for clinical applications, and bias mitigation strategies. Additionally, it presents interdisciplinary policy recommendations for aligning AI deployment with ethical, regulatory, and environmental sustainability goals. This study emphasizes measurable standards, multi-stakeholder engagement strategies, and global partnerships to ensure that future AI innovations meet ethical and practical healthcare needs. Conclusions: Trustworthy AI in healthcare requires more than technical advancements—it demands robust ethical safeguards, proactive regulation, and continuous collaboration. By adopting the recommended roadmap, stakeholders can foster responsible innovation, improve patient outcomes, and maintain public trust in AI-driven healthcare. © 2025 by the authors. KW - artificial intelligence KW - bias KW - ethics KW - health policy KW - large language model KW - machine learning KW - natural language processing KW - privacy KW - regulation KW - algorithm bias KW - Article KW - artificial intelligence KW - benchmarking KW - bias mitigation KW - clinical practice guideline KW - clinician KW - data privacy KW - environmental sustainability KW - ethicist KW - fairness KW - health care KW - health care need KW - health care policy KW - human KW - large language model KW - machine learning KW - natural language processing KW - patient care KW - practice guideline KW - stakeholder engagement KW - sustainable development goal KW - treatment outcome KW - trustworthiness CY - Ireland, Poland ER - TY - JOUR TI - The Adoption of Open Source Software Among Universities in Iraq: The Moderating Role of AI Capability AU - Qasim M.M. AU - Abdulkareem A.R. AU - Sneesl R. PY - 2025 JO - Human Behavior and Emerging Technologies VL - 2025 IS - 1 SP - 9937783 DO - 10.1155/hbe2/9937783 AB - Open source software (OSS) is a trendy innovation that is being used by all organizations. However, the usage of OSS is still limited in higher education. This research examines the adoption of OSS among universities in Iraq, focusing on the moderating role of artificial intelligence (AI) capabilities. The research is aimed at exploring how factors such as perceived ease of use (PEOU), compatibility, perceived risk, security, and cost-effectiveness influence OSS adoption. Using a quantitative research methodology, data was collected from 272 university decision-makers and analysed using Smart PLS 4. The results of the study indicate that factors such as PEOU, compatibility, perceived risk, security, and cost-effectiveness have a significant positive influence on the adoption of OSS. The research findings provide valuable insights for decision-makers in university settings who are grappling with the intricate process of adopting OSS. These findings offer valuable insights for higher education institutions in Iraq and other developing regions seeking to adopt OSS. Copyright © 2025 Mustafa Moosa Qasim et al. Human Behavior and Emerging Technologies published by John Wiley & Sons Ltd. KW - adoption KW - artificial intelligence KW - higher education KW - open source software KW - software engineering KW - TAM CY - Iraq ER - TY - JOUR TI - The role of AI capabilities in environmental management: Evidence from USA firms AU - Jiao A. AU - Lu J. AU - Ren H. AU - Wei J. PY - 2024 JO - Energy Economics VL - 134 SP - 107653 DO - 10.1016/j.eneco.2024.107653 AB - This study investigates the role of Artificial Intelligence (AI) capabilities in influencing firms' greenwashing behaviors. We find a robust negative association between firms' AI capabilities and unrepresentative environmental disclosure. An instrumental variable approach is employed to establish causality. The effects are more pronounced for firms (1) with a greater exposure to regulatory climate risk, (2) managed by Republican-leaning managers, (3) with stronger governance structures, (4) possessing greater product market pricing power, (5) operating in multiple regions, and (6) with CEOs with higher pay-for-performance sensitivity. We further demonstrate that AI capabilities aid firms in transitioning to green operations through engaging in green and clean innovation. Finally, we find that AI capabilities correlate with lower greenhouse gas emissions. Overall, our findings shed light on the real impact of AI-related technologies in the energy industry. © 2024 Elsevier B.V. KW - Artificial intelligence KW - Environmental management KW - Greenwashing KW - Innovation KW - IT investment KW - United States KW - Economics KW - Environmental management KW - Gas emissions KW - Greenhouse gases KW - Investments KW - Governance structures KW - Greenwashing KW - Innovation KW - Instrumental variables KW - IT investments KW - Market pricing KW - Multiple regions KW - Performance sensitivity KW - Power KW - Product markets KW - artificial intelligence KW - environmental economics KW - environmental management KW - information technology KW - innovation KW - investment KW - Artificial intelligence CY - China, United States ER - TY - JOUR TI - The nexus of artificial intelligence literacy collaborative knowledge practices and inclusive leadership development among higher education students in Bangladesh China Finland and Turkey AU - Asghar M.Z. AU - Iqbal J. AU - Özbilen F.M. AU - Abedin J. AU - Järvenoja H. AU - Widanapathirana U. PY - 2025 JO - Discover Computing VL - 28 IS - 1 SP - 172 DO - 10.1007/s10791-025-09695-y AB - This study investigates how Artificial Intelligence Literacy (AIL) fosters Inclusive Leadership (IL) development among university students through Collaborative Knowledge Practices (CKP), with cross-cultural insights from Bangladesh, China, Finland, and Turkey. Using a mixed-methods design—combining quantitative surveys of 458 students and qualitative interviews with 40 participants, this research integrates three frameworks: (1) the ABC-E model of AIL, encompassing affective, behavioral, cognitive, and ethical dimensions; (2) CKP, which involves competences such as collaboration, integration, creativity, sustainability, adaptability, engagement, and technological aspects; and (3) IL principles. Cultural interpretations are informed by Hofstede’s six-dimensional model of national culture, with a focus on the Power Distance Index (PDI) and the Individualism–Collectivism (IDV) dimensions. Quantitative analysis employed PLS-SEM and Fuzzy Set Qualitative Comparative Analysis (fsQCA) to uncover linear and non-linear relationships, while qualitative findings supported the multi-group analysis. Cross-cultural comparisons revealed that Finland emphasizes ethical AI use, China highlights innovation, Bangladesh focuses on problem-solving applications, and Turkey reflects multicultural collaboration—each of which influences students’ engagement with AI tools in distinct ways. The findings underscore CKP as a critical bridge between AIL and IL, highlighting the need for context-sensitive, collaborative pedagogies that equip students to address AI-driven challenges and lead inclusively in diverse global settings. © The Author(s) 2025. KW - Artificial intelligence literacy KW - Collaborative knowledge practices KW - Higher education KW - Inclusive leadership CY - Finland, China, Turkey ER - TY - JOUR TI - Generative artificial intelligence in supply chain and operations management: a capability-based framework for analysis and implementation AU - Jackson I. AU - Ivanov D. AU - Dolgui A. AU - Namdar J. PY - 2024 JO - International Journal of Production Research VL - 62 IS - 17 SP - 6120 EP - 6145 DO - 10.1080/00207543.2024.2309309 AB - This research examines the transformative potential of artificial intelligence (AI) in general and Generative AI (GAI) in particular in supply chain and operations management (SCOM). Through the lens of the resource-based view and based on key AI capabilities such as learning, perception, prediction, interaction, adaptation, and reasoning, we explore how AI and GAI can impact 13 distinct SCOM decision-making areas. These areas include but are not limited to demand forecasting, inventory management, supply chain design, and risk management. With its outcomes, this study provides a comprehensive understanding of AI and GAI's functionality and applications in the SCOM context, offering a practical framework for both practitioners and researchers. The proposed framework systematically identifies where and how AI and GAI can be applied in SCOM, focussing on decision-making enhancement, process optimisation, investment prioritisation, and skills development. Managers can use it as a guidance to evaluate their operational processes and identify areas where AI and GAI can deliver improved efficiency, accuracy, resilience, and overall effectiveness. The research underscores that AI and GAI, with their multifaceted capabilities and applications, open a revolutionary potential and substantial implications for future SCOM practices, innovations, and research. © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. KW - artificial intelligence KW - GAI KW - Generative artificial intelligence KW - operations management KW - supply chain KW - Decision making KW - Inventory control KW - Optimization KW - Risk management KW - Supply chain management KW - Supply chains KW - Demand forecasting KW - Generative AI KW - Generative artificial intelligence KW - Inventory management KW - Management decision-making KW - Operation management KW - Resource-based view KW - Supply-chain designs KW - Supply-chain risks KW - Through the lens KW - Artificial intelligence CY - United States, Germany, France ER - TY - JOUR TI - Policy Narratives’ Salience: A Comparative Analysis of Artificial Intelligence Policy Responsiveness to Public Attention in China and the United States AU - Xiao H. AU - Ge W. AU - Shi X. PY - 2025 JO - Journal of Comparative Policy Analysis: Research and Practice VL - 27 IS - 5-6 SP - 555 EP - 577 DO - 10.1080/13876988.2025.2551038 AB - In ever-increasing AI policies, the responsive narratives of government policies to public attention have become a crucial aspect of comparative policy analysis. This study obtained the AI policy texts and public web search indexes of China and the United States from 2017 to 2023, summarizes six major policy topics using the LDA method, and constructs a correspondence matrix. The article empirically tests the differences between China and the US in policy responsiveness to public attention on the six topics, and comparatively analyzes the reasons for the different levels of responsiveness in terms of technology development and technological philosophy. The findings indicate that, in general, both Chinese and US AI policies have responded to public attention to some extent. However, there are differences in responsive narratives on topics of application service, technology innovation, risk management, individual development, market competition, and social development. The findings contribute to understanding the correlation between policy responsiveness and public attention in Chinese and US contexts, and enrich the cutting-edge comparative AI policy research. © 2025 The Editor, Journal of Comparative Policy Analysis: Research and Practice. KW - artificial intelligence KW - comparison of Chinese and American policies KW - policy attention KW - policy responsiveness KW - public attention CY - China ER - TY - JOUR TI - Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of Artificial Intelligence on Knowledge Worker Productivity and Quality AU - Dell’acqua F. AU - McFowland E. AU - Mollick E. AU - Lifshitz H. AU - Kellogg K.C. AU - Rajendran S. AU - Krayer L. AU - Candelon F. AU - Lakhani K.R. PY - 2026 JO - Organization Science VL - 37 IS - 2 SP - 403 EP - 423 DO - 10.1287/orsc.2025.21838 AB - We introduce and study the concept of a “jagged technology frontier” to describe the uneven impact of artificial intelligence (AI) capabilities, where AI assistance improves performance for some tasks but worsens it for others, even within the same knowledge workflow and with a seemingly similar level of difficulty. In collaboration with the global management consulting firm Boston Consulting Group, we have developed realistic management consulting tasks and examined the human performance implications of using AI to perform complex and knowledge-intensive work. The preregistered experiment involved 758 knowledge workers. After establishing a performance baseline on similar tasks, subjects were randomly assigned to one of three conditions: no AI access, GPT-4 AI access, or GPT-4 AI access with a prompt engineering overview. For each one of a set of 18 realistic knowledge tasks within the frontier of AI capabilities ranging from creative to analytical tasks, subjects using AI outperformed those not using AI, completing 12.2% more tasks and completing them 25.1% more quickly on average while also delivering solutions of significantly improved quality. However, for a complex managerial task selected to be outside the frontier, subjects using AI were 19% less likely to produce correct solutions compared with those without AI, pointing to potential limitations of AI supporting knowledge workers. We discuss the positive and negative implications of AI-aided human performance in knowledge-intensive tasks. © 2026 The Author(s). KW - economics and organization KW - field experiments KW - implementation of new technology KW - organization and management theory KW - organizational economics KW - organizational processes KW - research design and methods KW - technology and innovation management CY - United States, United Kingdom ER - TY - JOUR TI - A conceptual model of employees’ behavioral intention to use generative artificial intelligence technology in mid-sized organizations AU - Wisedpanich N. AU - Wittayakom S. PY - 2026 JO - Multidisciplinary Science Journal VL - 8 IS - 8 SP - e2026531 DO - 10.31893/multiscience.2026531 AB - Rapid technological advancements have positioned Generative Artificial Intelligence (GenAI) as a strategic asset for businesses; however, its adoption in resource-constrained environments remains complex. Specifically, in the context of emerging economies where mid-sized firms drive significant employment yet face distinct digital hurdles, understanding these dynamics is crucial. This conceptual research develops a theoretical model explaining employees’ behavioral intention to use GenAI in mid-sized organizations, a sector often overlooked in favor of large corporations or small startups. The study integrates the Technology Acceptance Model (TAM), the Unified Theory of Acceptance and Use of Technology (UTAUT), Adaptive Governance Theory, and the Dynamic Capabilities Framework to capture the multidimensional interplay among organizational, governance, and psychological factors. Unlike traditional models that focus solely on utility, this research posits that organizational conditions and governance conditions act as primary antecedents influencing employees’ perceived risk and trust, which in turn determine behavioral intention toward GenAI use. Eleven research propositions (P1–P11) were formulated to describe both direct and indirect causal relationships, highlighting the mediating roles of psychological safety mechanisms. The study contributes theoretically by extending technology acceptance models beyond cognitive dimensions to include governance and ethical oversight as structural determinants of employee behavior. It also introduces perceived governance as an integrative construct linking organizational readiness to trust and risk perception. Practically, the framework provides actionable guidance for mid-sized organizations to design adaptive governance systems, strengthen transparency, and foster trust-based cultures that encourage responsible GenAI adoption. By highlighting the balance between innovation and accountability, this conceptual model establishes a robust foundation for future empirical validation and policy development aimed at promoting sustainable and ethical AI integration in organizational contexts. Copyright (c) 2026 The Authors. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. https://creativecommons.org/licenses/by-nc-nd/4.0/ KW - adaptive governance KW - organizational readiness KW - perceived risk KW - SMEs KW - technology acceptance KW - trust CY - Thailand ER - TY - JOUR TI - Quantitative evaluation of China’s artificial intelligence policies: A PMC index-based modeling approach AU - Liu X. AU - Zhuang X. AU - Zhang H. AU - Zhang H. AU - Wang Y. AU - Chen J. PY - 2026 JO - PLOS ONE VL - 21 IS - 2 February SP - e0335423 DO - 10.1371/journal.pone.0335423 AB - With the rapid development of artificial intelligence (AI), various countries have introduced policies to address the social, economic, and ethical challenges brought by technological advancements. This study systematically evaluates the effectiveness of China’s AI policies based on the Policy Model Consistency (PMC) method and conducts a comparative analysis with policies from developed countries in Europe and the United States. By constructing a multi-dimensional quantitative assessment system that encompasses indicators such as policy types, timeliness, content, fields, evaluation, tools, and effectiveness levels, this study fills a gap in the existing research on quantitative evaluation. Text mining and high-frequency word analysis revealed the core themes and focus areas of the policies, laying the groundwork for subsequent quantitative analysis. The study finds that China’s AI policies have achieved significant results in promoting technological innovation, industrial development, and social transformation; however, shortcomings remain in legal protection, ethical regulation, cross-domain collaboration, and sustainable development issues. Further cross-national comparisons indicate that there are differences between China and developed countries in Europe and the United States in terms of AI policy design and implementation, particularly regarding the application of policy tools and the driving forces behind international collaboration. Based on the empirical analysis results using the PMC index model, this study proposes targeted policy optimization suggestions aimed at enhancing policy execution and adaptability. This study not only provides an innovative framework for the quantitative evaluation of AI policies but also offers theoretical support for the collaborative development of global AI policies. © 2026 Liu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. KW - Artificial Intelligence KW - China KW - Europe KW - Humans KW - Models, Theoretical KW - Public Policy KW - United States KW - article KW - artificial intelligence KW - developed country KW - Europe KW - human KW - industrialization KW - open access publishing KW - quantitative analysis KW - sustainable development KW - timeliness KW - United States KW - China KW - public policy KW - theoretical model CY - China, South Korea ER - TY - JOUR TI - Artificial Intelligence and Strategic Decision-Making: Evidence from Entrepreneurs and Investors AU - Csaszar F.A. AU - Ketkar H. AU - Kim H. PY - 2024 JO - Strategy Science VL - 9 IS - 4 SP - 322 EP - 345 DO - 10.1287/stsc.2024.0190 AB - This paper explores how artificial intelligence (AI) may impact the strategic decision-making (SDM) process in firms. We illustrate how AI could augment existing SDM tools and provide empirical evidence from a leading accelerator program and a startup competition that current large language models can generate and evaluate strategies at a level comparable to entrepreneurs and investors. We then examine implications for the key cognitive processes underlying SDM—search, representation, and aggregation. Our analysis suggests that AI has the potential to enhance the speed, quality, and scale of strategic analysis, while also enabling new approaches, like virtual strategy simulations. However, the ultimate impact on firm performance will depend on competitive dynamics as AI capabilities progress. We propose a framework connecting AI use in SDM to firm outcomes and discuss how AI may reshape sources of competitive advantage. We conclude by considering how AI could both support and challenge core tenets of the theory-based view of strategy. Overall, our work maps out an emerging research frontier at the intersection of AI and strategy. Copyright: © 2024 The Author(s) KW - aggregation KW - artificial intelligence KW - experiments KW - representation KW - search KW - strategic decision-making KW - theory-based view CY - United States, Singapore ER - TY - JOUR TI - Impact of AI capability, digital strategy, and digital maturity on organisational performance AU - Kwiotkowska A. PY - 2025 JO - Engineering Management in Production and Services VL - 17 IS - 4 SP - 15 EP - 28 DO - 10.2478/emj-2025-0024 AB - In the face of the inevitable digitalisation of enterprises, limited research has investigated the impact of digital strategy, digital maturity, and AI capability on organisational performance. Drawing on the resource-based theory and recent work on AI in the organisational context, this research aims to uncover the configurations under which a firm’s digital strategy, digital maturity, and AI capability would jointly lead to higher performance. This study uses a unique fuzzy-set qualitative comparative analysis methodology to analyse data collected from 56 SMEs to investigate three domains of AI capability, along with digital strategy and digital maturity. The results suggest that high organisational performance does not depend on a single condition but rather on complex synergistic interactions among the studied conditions. The results indicate that three equifinal configurations lead to high performance of SMEs. The study suggests that AI technical resources are mandatory for any viable solution. This study provides pioneering insights into the empirical contributions of AI capability, digital strategy and digital maturity and their relationships to organisational performance in SMEs, by using a configurational approach. The adopted theoretical perspective addresses the need for a holistic approach to uncover the mechanisms underlying digital strategy and digital maturity in relation to AI capabilities in SMEs, and their mutual impact on organisational performance. These results have practical implications for decision-makers and owners of SMEs, providing new insights into the combination of factors that drive high performance. © 2025 Anna Kwiotkowska, published by Bialystok University of Technology. KW - artificial intelligence capability KW - digital transformation KW - firm performance KW - fsQCA KW - Artificial intelligence KW - Artificial intelligence capability KW - Condition KW - Digital strategies KW - Digital transformation KW - Firm Performance KW - FsQCA KW - Organizational context KW - Organizational performance KW - Performance KW - Resource-based theory KW - Decision making CY - Poland ER - TY - JOUR TI - Are projects ready for AI — or for the value it generates? AU - Mariani C. PY - 2026 JO - International Journal of Project Management SP - 102846 DO - 10.1016/j.ijproman.2026.102846 AB - Artificial intelligence is increasingly entering project environments. Recent research has emphasized the importance of developing organizational and project readiness for AI adoption. However, from a project management perspective, readiness to deploy AI technologies does not necessarily guarantee that these technologies will generate meaningful project value. Building on the recent debate on AI readiness, this essay argues that an important conceptual gap remains between the adoption of AI tools and the realization of value in projects. Drawing on the project value literature, this work introduces the notion of AI value generation, defined as the capability of project organizations to translate AI-enabled insights into value definition, value delivery, and value capture across multiple stakeholders. By distinguishing between AI readiness and AI value generation, this work highlights a new research frontier for understanding how artificial intelligence can effectively contribute to project success and stakeholder value creation. © 2026 Elsevier Ltd, APM and IPMA. KW - AI capabilities KW - AI value generation KW - Artificial intelligence KW - Engineering research KW - Project management KW - AI capability KW - AI Technologies KW - AI value generation KW - Organisational KW - Project environment KW - Project organization KW - Project values KW - Recent researches KW - Value captures KW - Value delivery KW - Artificial intelligence CY - Italy ER - TY - JOUR TI - Framing artificial intelligence in Chilean digital press before and after the launch of ChatGPT: From concern to optimism AU - Bucchi A. AU - Neira-Mellado C. AU - Sanchez-Sabate R. AU - Mora-Chepo M. PY - 2026 JO - PLOS ONE VL - 21 IS - 5 May SP - e0348680 DO - 10.1371/journal.pone.0348680 AB - This article examines the evolution of AI framing in Chilean national digital media before and after the launch of ChatGPT in 2022. Through topic modeling and emotion analysis of 1,466 articles published by six media outlets over periods that begin between 2000 and 2014, depending on the outlet, and extend in all cases until 2024, this study identifies significant thematic and affective changes. In the pre-ChatGPT period, coverage combined recognition of AI’s capabilities with concerns regarding labor displacement, governance, and human identity, frequently referencing global corporations and state institutions. In the post-ChatGPT period, topics became narrower in scope, with fewer actors and greater emphasis on universities and cultural organizations implementing AI. The focus shifted from the development of AI systems to their applications, predominantly framed in positive terms. At the same time, negative sentiment was largely confined to epistemological uncertainty. This transformation aligns with national surveys indicating growing public optimism, diverges from Global North debates centered on regulation and privacy, and converges with East Asian framings that are oriented toward innovation and creativity. These findings suggest that the launch of ChatGPT coincided with a reinforcement of a sociotechnical imaginary of AI as inevitable and beneficial, yet potentially limiting the diversity of public debate. © 2026 Bucchi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. KW - Artificial Intelligence KW - Chile KW - Generative Artificial Intelligence KW - Humans KW - Mass Media KW - Optimism KW - artificial intelligence KW - Chile KW - generative artificial intelligence KW - human KW - mass medium KW - optimism CY - Chile ER - TY - JOUR TI - Safeguarding Intellectual Property in the Digital Age through Artificial Intelligence AU - Nagpal C.S. AU - Raviwada A.R. AU - Kumar D.G. PY - 2025 JO - Journal of Intellectual Property Rights VL - 30 IS - 6 SP - 708 EP - 717 DO - 10.56042/jipr.v30i6.12793 AB - The landscape of Intellectual Property is facing various challenges with the onset of rapid digitalization. The current Intellectual Property (henceforth ‘IP’ in short) protection is dependent on the traditional law for enforcement and implementation of the Intellectual Property Rights (henceforth ‘IPR’ in short) Holder’s rights, but the significant technological advancements and threats from cyberspace are highlighting threats and challenges towards IP protection, leading IP experts to delve into the ramifications of Artificial Intelligence (henceforth ‘AI’ in short) assisted problemsolving for IP related issues in cyberspace. Whereas the world is looking at a cohesive effort to ideally eradicate the issues that lead to cybersecurity breaches, a comprehensive and sustained effort towards addressing IP-related cyber threats is also being explored. At present, there are no tangent solutions for this. The authors attempt to address the potential of AI-assisted solutions to target IP infringements in cyberspace. By responsibly leveraging AI capabilities, we can fortify IP protection in the digital era and foster innovation and creation in a cyber-secure environment. © 2025, National Institute of Science Communication and Policy Research. All rights reserved. KW - AI KW - Cybersecurity KW - Cyberthreat KW - Digital KW - Internet KW - IPR CY - India ER - TY - JOUR TI - Artificial Intelligence-Ready Doctor of Nursing Practice Education: A Competency-Based Approach AU - Quattrini V. AU - Taylor L. AU - Lynch-Smith D. AU - Hemphill T. AU - Gibson T. AU - Ford A. AU - Fox E. AU - Roesch A. AU - Turner T. AU - Hilliard W. AU - Anthamatten A. PY - 2026 JO - Journal of Doctoral Nursing Practice VL - 19 IS - 1 SP - 11 EP - 23 DO - 10.1891/JDNP-2025-0051 AB - Background: Artificial intelligence (AI) is rapidly transforming nursing education and clinical practice. As AI becomes increasingly embedded in health care delivery, integrating AI competencies into Doctor of Nursing Practice (DNP) education is essential to prepare advanced practice registered nurses (APRNs) to utilize these tools effectively and ethically. Objective: This manuscript examines the integration of AI into DNP education, addressing policy implications, best practices, and strategies to prepare APRNs for leadership in AI-enhanced environments. Methods: A review of institutional innovations and faculty strategies demonstrates the application of AI in nursing education through adaptive learning platforms, virtual simulations, predictive analytics, and AI-driven clinical decision support systems. Case exemplars highlight implementation approaches and educational outcomes. Results: AI-enhanced tools have demonstrated several benefits, such as improved student engagement, individualized learning, and enhanced clinical reasoning. Case-based reflections revealed enhanced decision-making, mentorship, and student competency tracking. Limitations and potential risks of AI are also identified. Key guiding principles include evaluating existing competencies within the context of AI capabilities, defining emerging AI needs, supporting faculty development through AI training, and advancing policies for responsible and ethical AI use. Conclusions: The nursing profession is well recognized for its innovative approach to adopting new technologies. Embedding AI into DNP education requires intentional curricular reform, strong leadership support, and ethical oversight to ensure sustainable adoption. Nursing faculty must champion the strategic and responsible use of AI to prepare APRNs for evidence-based, technology-driven practice. DNP-prepared nurses, with their expertise in quality improvement and ethical practice, are uniquely positioned to shape the development and implementation of AI tools. © 2026 Springer Publishing Company. KW - advanced practice registered nurses (APRNs) KW - artificial intelligence (AI) KW - Doctor of Nursing Practice (DNP) KW - nursing education KW - Advanced Practice Nursing KW - Artificial Intelligence KW - Clinical Competence KW - Competency-Based Education KW - Curriculum KW - Education, Nursing, Graduate KW - Humans KW - advanced practice nursing KW - artificial intelligence KW - clinical competence KW - competency-based education KW - curriculum KW - education KW - human KW - nursing education KW - organization and management CY - United States ER - TY - JOUR TI - Extending the about–for–through tradition for AI-enabled entrepreneurship education: the AI-enabled entrepreneurial learning progression framework AU - Bell H. AU - Bell R. PY - 2026 JO - Entrepreneurship Education DO - 10.1007/s41959-026-00184-x AB - Artificial intelligence (AI) is increasingly embedded within entrepreneurial practice, reshaping opportunity recognition, innovation processes, and venture-level decision-making. This transformation creates a clear need for entrepreneurship education (EE) to adopt theoretically coherent and developmentally structured approaches to AI integration. Although scholarship recognises the growing importance of AI-related knowledge, applied integration, and responsible judgement, integrative frameworks explaining how these capabilities can be progressively cultivated within EE remain limited. This paper addresses that gap by developing the AI-Enabled Entrepreneurial Learning Progression Framework. Building on the established about–for–through tradition, the framework reconceptualises AI integration as a staged capability-development process rather than a discrete curricular addition. It articulates how AI capability can be systematically developed from conceptual literacy and analytical understanding to applied integration and evaluative judgement, and ultimately to entrepreneurial capability, identity formation, and responsible agency within AI-enabled venture contexts. By aligning learning purpose, learning development, pedagogical orientation, AI positioning, curriculum focus, teaching activities, and assessment across stages, the framework advances a theory-building account. It explains how AI reshapes the developmental logic of EE. It provides a structured foundation for curriculum design and assessment alignment and establishes a platform for future empirical research on AI-enabled entrepreneurial capability formation. © The Author(s) 2026. KW - Artificial intelligence KW - Educational philosophy KW - Educational practice KW - Entrepreneurship education KW - Framework KW - Teaching CY - United States, United Kingdom ER - TY - JOUR TI - How will AI change intelligence and decision-making? AU - Barneaa A. PY - 2020 JO - Journal of Intelligence Studies in Business VL - 10 IS - 1 SP - 75 EP - 80 DO - 10.37380/JISIB.V1I1.564 AB - The world is facing a rapid pace of changes with a heightened sense of uncertainty, ambiguity, and complexity in both government and business landscapes. New threats and major changes in the world order are creating an external environment that demands closer monitoring and greater anticipatory and predictive skills. Deeper analysis and speed of action are becoming more important for agile organizations and governments. The needs to upgrade the capabilities of intelligence analysts, mostly in strategic intelligence, have been known for quite a long time. Scholars who are looking into intelligence failures1 and other major national security2 and business3 events when decision-makers were not warned in time, seek expert tools and methodologies to avoid these failures4. Management is constantly concerned, aspiring to receive better decisions by relying on solid analysis in order to better understand the challenges ahead5. The current direction is in the same direction, while new emerging technologies enable theory and practice to move forward. Artificial intelligence (AI) capabilities definitely are jumping two stairs up. It looks that through new AI tools, the value of humans will not become redundant but rather improve its outcomes by relying on better intelligence for their decisions. © 2020 Halmstad University. KW - Artificial intelligence (AI) KW - Competition KW - Competitive advantage KW - Decision-making KW - Intelligence failures KW - Prediction KW - Strategic surprises CY - Israel ER - TY - JOUR TI - Artificial intelligence and corporate ESG performance AU - Li J. AU - Wu T. AU - Hu B. AU - Pan D. AU - Zhou Y. PY - 2025 JO - International Review of Financial Analysis VL - 102 SP - 104036 DO - 10.1016/j.irfa.2025.104036 AB - This study examined how artificial intelligence (AI) capabilities strengthen corporate environmental, social, and governance (ESG) performance while focusing on the mediating role of green resilience and the moderating effect of organizational resilience. AI has transformative potential for ESG performance; however, its role in emerging markets remains underexplored. While AI can optimize resource use, improve workplace safety, and enhance governance through transparency, challenges such as data limitations, infrastructure gaps, and ethical issues may hinder its impact. Bridging this gap requires focused research on how AI capabilities drive sustainable outcomes in these markets, identifying practical tools, and fostering supportive policies. We employed robust statistical techniques to establish reliable findings from a comprehensive dataset of Chinese-listed companies from 2011 to 2022. The findings indicate that AI capabilities significantly strengthen ESG performance. The relationship was facilitated through green innovation initiatives. Organizational resilience enhances AI's positive impact on ESG performance, especially in technology-intensive industries. However, the influence varies significantly by context, with stronger effects observed in nonhigh-polluting sectors and state-owned enterprises, highlighting the need for tailored approaches to maximize sustainable outcomes. Our findings augment the theoretical understanding of technology-driven sustainability by elucidating how AI capabilities strengthen ESG performance through innovation pathways. We identified key organizational factors, such as resilience and innovation capacity, as well as contextual factors, including industry type, regulatory frameworks, and ownership structures, that influence the relationship between AI and ESG performance. These findings provide valuable insights for organizations in emerging markets aiming to leverage AI for enhanced sustainability. © 2024 KW - Artificial intelligence KW - Corporate sustainability KW - ESG performance KW - Green innovation KW - Organizational resilience CY - China, United States, United Kingdom ER - TY - JOUR TI - Artificial intelligence in digital marketing automation: Enhancing personalization, predictive analytics, and ethical integration AU - Islam M.A. AU - Fakir S.I. AU - Masud S.B. AU - Hossen M.D. AU - Islam M.T. AU - Siddiky M.R. PY - 2024 JO - Edelweiss Applied Science and Technology VL - 8 IS - 6 SP - 6498 EP - 6516 DO - 10.55214/25768484.v8i6.3404 AB - Artificial Intelligence (AI) is revolutionizing digital marketing automation by enhancing efficiency, personalization, and predictive capabilities. This study examines the role of AI in transforming marketing practices, focusing on its applications, benefits, ethical considerations, and future directions. By leveraging AI tools such as predictive analytics, NLP, and chatbots, businesses can achieve improved customer segmentation, content personalization, and campaign optimization in marketing strategies. Secondary data from journals, articles, and conference papers were synthesized to provide insights into AI's impact on digital marketing automation. A systematic literature review utilizing the PRISMA methodology initially identified 2,850 records from database searches. Following the removal of duplicates and non-relevant studies, 1,035 records were screened for eligibility based on defined criteria, resulting in the inclusion of 150 relevant studies and 25 high-quality reports for detailed analysis. This robust approach ensured the inclusion of high-quality research, minimizing biases. The findings reveal that AI enhances digital marketing by streamlining processes, automating repetitive tasks, and delivering hyper-personalized customer experiences. Predictive analytics helps anticipate consumer behavior, while chatbots improve real-time customer engagement. However, challenges such as data privacy, algorithmic bias, and the high costs of AI adoption persist. AI adoption allows businesses to make data-driven decisions, improve customer retention, and maximize return on investment. Ethical AI practices, such as transparency and algorithm fairness, are essential for maintaining consumer trust. The study primarily focuses on existing literature, with limited empirical validation. Future research should explore long-term effects of AI-driven marketing on consumer behavior and investigate its integration with emerging technologies like the Internet of Things (IoT) and blockchain. Additionally, tailored AI solutions for SMEs and under-researched areas, such as B2B marketing, are critical for inclusive growth. © 2024 by the authors. KW - Artificial intelligence (AI) KW - Chatbots and NLP KW - Customer personalization KW - Digital marketing automation KW - Ethical AI practices KW - Marketing innovation KW - Predictive analytics KW - PRISMA CY - United States, Bangladesh ER - TY - JOUR TI - Unveiling Market Dynamics through Machine Learning: Strategic Insights and Analysis AU - Gaikwad V.S. AU - Deore S.S. AU - Poddar G.M. AU - Patil R.V. AU - Hirolikar D.S. AU - Borawake M.P. AU - Swarnkar S.K. PY - 2024 JO - International Journal of Intelligent Systems and Applications in Engineering VL - 12 IS - 14s SP - 388 EP - 397 AB - Due to their extensive knowledge and potential to change the game, artificial intelligence (ML) and strategic analysis have become significant players in more competitive and global markets. The article "Unveiling Market Dynamics through Machine Learning: Strategic Insights and Analysis" provides the first in-depth analysis of the strong connection between machine learning and market analysis, illustrating how these two fields can collaborate to understand the complex market dynamics. Thanks to this research, businesses may now analyse complex patterns, hidden trends, and untapped opportunities in complicated market economies. He accomplishes this with the help of AI's capabilities. Another essential element of this relationship is emotion analysis, which makes use of the deep learning and natural language processing to examine public sentiment and provide vital information for improving marketing and product development strategies. The ability of ML to recognise fresh opportunities and niche markets gives it a competitive advantage. Furthermore, it excels at proactively identifying anomalies, cracks, and risks. This study highlights the integration of various data sources and the growing significance of ethical considerations in addition to providing a broad overview of ML's applications in market analysis. Thi s research expands our understanding of the potential for data-driven decision-making as we navigate the intersection of ML and strategic market analysis. It also provides a road map for organisations looking to harness ML's transformative power to make knowledgeable, quick, and strategic decisions in today's dynamic business environment. © 2024, Ismail Saritas. All rights reserved. KW - Data-driven Decision-making KW - Machine Learning KW - Market Dynamics KW - Predictive Modeling KW - Strategic Insights CY - India ER - TY - JOUR TI - Sustainable development with Artificial Intelligence: Examining the absorptive capacity pathways to green innovation AU - Zhang W. AU - Xu H. AU - Grebinevych O. AU - Chen M. PY - 2025 JO - Journal of Environmental Management VL - 381 SP - 125219 DO - 10.1016/j.jenvman.2025.125219 AB - Artificial intelligence holds a lot of promise in tackling global societal challenges. However, there is still no consensus on how companies can effectively harness AI to promote green innovation (GI). We develop a moderated mediation model grounded in absorptive capacity theory to fill this research gap. In this paper, our empirical study based on data drawn from 361 Chinese firms reveals the significant roles of two critical capacities, potential absorptive capacity (PAC) and realized absorptive capacity (RAC), in fostering a positive relationship between AI capabilities and GI. Notably, this study's results show that environmental heterogeneity (EH) amplifies the mediating effects of PAC and RAC. This implies that companies with access to Artificial intelligence (AI) capabilities will likely learn and absorb available information and knowledge outside the organizations better. This improves GI, mainly when EH levels are high. The present work advances the research by addressing how AI impacts GI through different mediating and moderating factors. It can help inform companies wanting to achieve GI amid sustainability imperatives. © 2025 Elsevier Ltd KW - Absorptive capacity KW - AI capabilities KW - Environmental heterogeneity KW - Green innovation KW - Artificial Intelligence KW - China KW - Conservation of Natural Resources KW - Sustainable Development KW - Green economy KW - Absorptive capacity KW - Artificial intelligence capability KW - Chinese firms KW - Empirical studies KW - Environmental heterogeneity KW - Green innovations KW - Learn+ KW - Mediating effect KW - Moderating factors KW - Research gaps KW - artificial intelligence KW - heterogeneity KW - innovation KW - knowledge KW - sustainable development KW - absorption KW - article KW - artificial intelligence KW - empiricism KW - human KW - sustainable development KW - China KW - environmental protection KW - Green development CY - China, France ER - TY - JOUR TI - Artificial intelligence and the impact of the EU AI Act in business organizations AU - Cors M.S. AU - Thiébaut R. PY - 2025 JO - AI Magazine VL - 46 IS - 4 SP - e70039 DO - 10.1002/aaai.70039 AB - Artificial intelligence (AI) is transforming industries worldwide, and the e-commerce sector is at the forefront of leveraging its capabilities to drive innovation and efficiency. The paper explores the integration of artificial intelligence in e-commerce, focusing on the ethical and regulatory implications introduced by the EU AI Act. This legislative framework aims to ensure the responsible deployment of AI by classifying AI systems into risk categories and imposing compliance requirements. It also underscores both the opportunities and challenges that AI presents to businesses, particularly in enhancing consumer experiences through automation and data-driven decision-making processes. The paper provides a comprehensive review of the AI landscape in Europe, analyzing the impact of the EU AI Act, particularly on small and medium-sized enterprises and startups. Through a mixed-methods approach, the study investigates how regulatory compliance may influence business innovation, market competitiveness, and consumer trust. The recommendations proposed aim to develop a trustworthy AI ecosystem that could stimulate long-term growth and enhance the global positioning of small European businesses. © 2025 The Author(s). AI Magazine published by John Wiley & Sons Ltd on behalf of Association for the Advancement of Artificial Intelligence. KW - Artificial intelligence KW - Competition KW - Decision making KW - Electronic commerce KW - Marketplaces KW - Artificial intelligence systems KW - Business innovation KW - Business organizations KW - Data driven decision KW - Decision-making process KW - E- commerces KW - Legislative frameworks KW - Mixed method KW - Risk categories KW - Small and medium-sized enterprise KW - Regulatory compliance CY - Spain, United Arab Emirates ER - TY - JOUR TI - Managing artificial intelligence across functions for enhanced retail firm performance AU - Cao L. AU - Yang J. AU - Chen Y.-T. PY - 2025 JO - European Management Review DO - 10.1111/emre.70042 AB - Firms are increasingly deploying AI across business functions, yet the core challenge lies not in adoption itself, but in integrating these applications to achieve coherent, firm-level outcomes. Despite growing interest, prior research has largely overlooked the dynamic interdependencies, unpredictable interactions, and organization-wide effects that shape how AI-enabled functions collectively drive performance. Addressing this gap, we draw on a business process perspective and dynamic capability theory to develop a multi-level framework of AI integration at the task, function, and firm levels. Using a sequential mixed-methods design, we first construct a validated measurement instrument based on grounded analysis of 6,519 AI-related documents from 37 retailers. We then apply fuzzy-set qualitative comparative analysis (fsQCA) to survey data from 140 executives to identify configurations of AI-enabled functions associated with efficiency, innovation, or both. The results show that superior performance stems not from isolated AI uses but from synergistic combinations—particularly those involving customer service and cybersecurity—interacting with other functions. These configurations appear to give rise to emergent capabilities such as adaptive learning, predictive analytics, and uncertainty mitigation, enabling firms to reconcile exploitation and exploration. This study offers a dynamic, process-based view of AI capability and provides strategic guidance for designing ambidextrous AI portfolios in retail. © 2025 European Academy of Management (EURAM). KW - AI implementation KW - artificial intelligence KW - business process management KW - innovation configuration KW - organizational efficiency KW - retail performance CY - France ER - TY - JOUR TI - The influence of individuals’ capability to use generative AI on their idea generation: the mediating role of cognitive information-processing styles AU - Held P. AU - Heubeck T. AU - Meckl R. PY - 2025 JO - European Journal of Innovation Management VL - 28 IS - 10 SP - 5376 EP - 5399 DO - 10.1108/EJIM-06-2025-0711 AB - Purpose – This study investigates how individuals’ capability to use generative artificial intelligence (GenAI) influences their idea generation and explores the cognitive mechanisms underlying this relationship. Drawing on cognitive experiential theory, which posits that individuals rely on two distinct and stable information processing styles (rational and experiential), this study examines how these styles mediate the link between GenAI usage capability and idea generation and all underlying relationships between these constructs. Design/methodology/approach – This study employs a quantitative research design based on survey data from 399 business consultants located in Germany, Austria, and Switzerland at a leading global consultancy. Partial least squares structural equation modeling (PLS-SEM) is applied to test the hypothesized structural relationships. Findings – The findings demonstrate that (1) individuals’ capability to use GenAI enhances their idea generation, (2) individuals’ capability to use GenAI influences both information processing styles, (3) rational information processing style enhances idea generation and not experiential information processing and (4) significant mediation effect of individuals’ tendency to rely on the rational system that translates GenAI usage capability into idea generation. Originality/value – This study enriches GenAI research in innovation management by identifying individuals’ capability to use GenAI as a critical antecedent of idea generation. This capability perspective complements recent studies focusing on the extent, frequency or purpose of GenAI usage and its influence on creative outputs. © 2025 Emerald Publishing Limited KW - AI capability KW - Cognitive experiential theory KW - Generative AI KW - Generative AI usage KW - Idea generation KW - Innovation management CY - Germany ER - TY - JOUR TI - Generative AI on innovation performance of construction enterprises: the role of knowledge-based dynamic capabilities and enterprise AI capabilities AU - Qiao S. AU - Zhiwei L. AU - Jie W. AU - Yuxi M. AU - Guo Z. AU - Han W. PY - 2025 JO - Engineering, Construction and Architectural Management DO - 10.1108/ECAM-01-2025-0051 AB - Purpose: The aim of this study was to investigate the associations among generative artificial intelligence (AI), knowledge-based dynamic capabilities, enterprise AI capabilities (EAIC) and innovation performance of the construction enterprises. Design/methodology/approach: The structural equation model was used in this study. First, the hypothesis of the relationship between generative AI, knowledge-based dynamic capabilities, EAIC and innovation performance was proposed based on the previous relevant literature; then, the research data were collected by 310 questionnaires; finally, these hypotheses were tested through data analysis. Findings: Generative AI positively influenced knowledge-based dynamic capabilities and innovation performance of the construction enterprises; knowledge-based dynamic capabilities had a mediating effect on the influence of generative AI on innovation performance of the construction enterprises; EAIC had a positive moderating effect on the influence of generative AI on innovation performance of the construction enterprises. Originality/value: In this study, knowledge-based dynamic capability and EAIC are introduced into the relationship model between generative AI and innovation performance of the construction enterprises, and an integrated model is proposed about the relationship between these factors. This study enriches the research content of AI application, dynamic capability and innovation management. The research results are conducive to generative AI in the innovation process and the formulation of innovation strategies. © 2025, Emerald Publishing Limited. KW - Construction enterprises KW - Enterprise AI capabilities KW - Generative AI KW - Innovation performance KW - Knowledge-based dynamic capabilities KW - Construction enterprise KW - Design/methodology/approach KW - Dynamics capability KW - Enterprise artificial intelligence capability KW - Generative artificial intelligence KW - Innovation performance KW - Knowledge based KW - Knowledge-based dynamic capability KW - Research data KW - Structural equation models KW - Knowledge based systems CY - China ER - TY - JOUR TI - Transforming weed management in sustainable agriculture with artificial intelligence: A systematic literature review towards weed identification and deep learning AU - Vasileiou M. AU - Kyrgiakos L.S. AU - Kleisiari C. AU - Kleftodimos G. AU - Vlontzos G. AU - Belhouchette H. AU - Pardalos P.M. PY - 2024 JO - Crop Protection VL - 176 SP - 106522 DO - 10.1016/j.cropro.2023.106522 AB - In the face of increasing agricultural demands and environmental concerns, the effective management of weeds presents a pressing challenge in modern agriculture. Weeds not only compete with crops for resources but also pose threats to food safety and agricultural sustainability through the indiscriminate use of herbicides, which can lead to environmental contamination and herbicide-resistant weed populations. Artificial Intelligence (AI) has ushered in a paradigm shift in agriculture, particularly in the domain of weed management. AI's utilization in this domain extends beyond mere innovation, offering precise and eco-friendly solutions for the identification and control of weeds, thereby addressing critical agricultural challenges. This article aims to examine the application of AI in weed management in the context of weed detection and the increasing impact of deep learning techniques in the agricultural sector. Through an assessment of research articles, this study identifies critical factors influencing the adoption and implementation of AI in weed management. These criteria encompass factors of AI adoption (food safety, increased effectiveness, and eco-friendliness through herbicides reduction), AI implementation factors (capture technology, training datasets, AI models, and outcomes and accuracy), ancillary technologies (IoT, UAV, field robots, and herbicides), and the related impact of AI methods adoption (economic, social, technological, and environmental). Of the 5821 documents found, 99 full-text articles were assessed, and 68 were included in this study. The review highlights AI's role in enhancing food safety by reducing herbicide residues, increasing effectiveness in weed control strategies, and promoting eco-friendliness through judicious herbicide use. It underscores the importance of capture technology, training datasets, AI models, and accuracy metrics in AI implementation, emphasizing their synergy in revolutionizing weed management practices. Ancillary technologies, such as IoT, UAVs, field robots, and AI-enhanced herbicides, complement AI's capabilities, offering holistic and data-driven approaches to weed control. Additionally, the adoption of AI methods influences economic, social, technological, and environmental dimensions of agriculture. Last but not least, digital literacy emerges as a crucial enabler, empowering stakeholders to navigate AI technologies effectively and contribute to the sustainable transformation of weed management practices in agriculture. © 2023 KW - Agroecology KW - Artificial intelligence KW - Deep learning KW - Precision agriculture KW - Sustainability KW - Weed management KW - agroecology KW - alternative agriculture KW - artificial intelligence KW - food safety KW - herbicide KW - paradigm shift KW - weed control CY - Greece, France, United States ER - TY - JOUR TI - From Satisfaction to Strategy: A Structural Model for Implementing Generative AI Chatbots in Campus Bureaucracy Toward Sustainable Service Innovation AU - Sofiyah F.R. AU - Dilham A. AU - Lubis M.A. AU - Lubis A.S. AU - Marpaung J.L. AU - Hayatunnufus PY - 2025 JO - Mathematical Modelling of Engineering Problems VL - 12 IS - 9 SP - 3013 EP - 3024 DO - 10.18280/mmep.120906 AB - The growing demand for digital transformation in higher education has highlighted the limitations of conventional bureaucratic systems. This study aims to develop and evaluate a structural model for implementing generative AI chatbots in campus administration, focusing on their ability to deliver sustainable service innovation. Integrating behavioral modeling and computational logic, the research adopts a mixed-methods approach. A questionnaire was distributed to 300 respondents, and data were analyzed using Partial Least Squares Structural Equation Modeling (SmartPLS). This study integrates 11 latent constructs — including AI capability, system usability, information quality, service availability, privacy and security, institutional support, user satisfaction, service experience, customer relationship management (CRM), administrative efficiency, and digital literacy (as a moderator)—into a validated structural model. The findings reveal that all primary structural paths are statistically significant (p < 0.001). Notably, customer relationship management (CRM) demonstrates a very strong effect on Administrative Efficiency (β = 0.833, p < 0.001; R2= 0.694), confirming its central role in translating satisfaction and service experience into organizational outcomes. In addition, the study introduces an operational AI algorithm and a multi-criteria optimization model that simulate trade-offs between CRM and efficiency. These computational insights provide university leaders with practical decision-making tools for aligning chatbot deployment with strategic goals such as cost savings, service scalability, and student retention. © 2025 The authors. KW - campus bureaucracy KW - chatbot KW - customer relationship management (CRM) KW - generative AI KW - SmartPLS KW - sustainable innovation CY - Indonesia ER - TY - JOUR TI - AI Capability, Digital Agility, and Strategic Innovation: The Moderating Role of Government Intervention and Competitive Intensity AU - Liu D.Y. AU - Zhang J.Z. AU - Sun J.J. AU - Dai B. PY - 2026 JO - Thunderbird International Business Review DO - 10.1002/tie.70136 AB - This study investigates how artificial intelligence (AI) capability and digital agility shape strategic innovation in firms, and how government intervention and competitive intensity condition these effects. Using survey data from 310 Chinese firms that have adopted AI technologies, we employ structural equation modeling to test the proposed hypotheses and estimate the relationships among the focal constructs. The results show that AI capability and digital agility are both positively associated with strategic innovation. Moreover, government intervention strengthens the positive effect of AI capability on strategic innovation, whereas competitive intensity amplifies the positive effect of digital agility on strategic innovation. These findings indicate the complementary roles of internal digital capabilities and external contextual forces in enabling strategic innovation in digitally intensive environments. By integrating AI capability and digital agility within a moderated framework, this study advances strategic innovation research by clarifying when and how digital capabilities translate into innovation outcomes. The study also offers actionable implications for managers and policymakers seeking to foster strategic innovation through AI deployment and organizational agility across varying institutional and competitive conditions. © 2026 Wiley Periodicals LLC. KW - AI capability KW - competitive intensity KW - digital agility KW - government intervention KW - strategic innovation CY - Australia, United States, New Zealand ER - TY - JOUR TI - The Uses of Belief: A Psychoanalytic-Feminist Critique of ‘AI Ethics' AU - Jeon W. PY - 2026 JO - Australian Feminist Studies DO - 10.1080/08164649.2026.2658028 AB - Postwar cybernetics transformed information from a measure of relation into a medium of regulation, reconfiguring communication as a problem of prediction and control. This article traces how that shift produced the modern ‘user’: a calculable figure whose rationality and adaptability were built into feedback systems linking psychology, computation, and governance. Through a close reading of Joseph Weizenbaum’s ELIZA (1966) and his reflections ‘On the Impact of the Computer on Society’ (Hamburg, 1971; Science, 1972), the paper shows how simulated dialogue substitutes plausibility for understanding, and how ethical critique becomes absorbed into the structures it seeks to address. Feminist and psychoanalytic perspectives clarify why users’ projections and affective investments sustain the appearance of machine intelligence. Contemporary AI ethics inherits these problems in translating the tension between responsibility and innovation into matters of governance and design. Recovering Weizenbaum’s ethical insight, the article argues that judgment cannot be automated without forfeiting the capacity for reflection that makes it ethical–and that avowing, rather than resolving, the contradiction between knowledge and control remains the task of critical thought in the age of AI. © 2026 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. KW - chatbots KW - design KW - Generative AI KW - language KW - large language models KW - use KW - user-interface CY - United States ER - TY - JOUR TI - Artificial Intelligence in Cyberspace: Between Danger and Innovation AU - Dumchikov M. AU - Maletova O. AU - Mishchenko T. AU - Lytvynenko Y. PY - 2025 JO - Revista de Direito, Estado e Telecomunicacoes VL - 17 IS - 1 SP - 117 EP - 142 DO - 10.26512/lstr.v17i1.53386 AB - [Purpose] The purpose of the article is to examine the impact of artificial intelligence on cybersecurity and to explore both the risks and the opportunities it presents. The article examines the primary forms of criminal use of AI in cyberspace, as well as develops effective methodologies for its application in the process of investigation, prevention, and analysis of these socially dangerous actions. [Methodology/approach/design] The authors employed an interdisciplinary approach combining methods from legal science, economics, and information technology in their work. Numerous scientific works on AI's characteristics, its role in digitization, and its use in criminal investigations have been noted. These studies offer suggestions, particularly for law enforcement agencies, financial institutions, and cybersecurity organizations. However, they are mainly theoretical and overlook new cyber threats and techniques used by cybercriminals with AI. In contrast, the authors analyzed illegal activity websites and forums, including cyberspace, using AI capabilities. They used cognitive methods to analyze how AI is used in cybercrimes, both as an auxiliary and primary tool. Content analysis methodology facilitated a systematic review of web content related to AI-enabled cybercrimes. The comparative-legal method compared AI-enabled cybercrimes to similar crimes without AI. Reviewing scientific articles, books, and conference proceedings helped understand AI, cybersecurity, and law enforcement. Case studies examined specific instances of AI in cybercrime, aiding in real-life prevention and investigation. The systematic method ensured a comprehensive examination of previous studies, identifying trends, challenges, and development prospects in AI and cybersecurity. By adopting this multifaceted and innovative approach, the authors were able to provide a more comprehensive and nuanced understanding of the emerging landscape of AI-assisted cybercrime. This research not only contributes to the academic discourse but also offers practical insights for law enforcement agencies, policymakers, and cybersecurity professionals working to combat these evolving threats. [Findings] The utilization of AI in the realm of cybercrimes unveils new prospects for the criminals themselves, as well as offers opportunities for effective combat and investigation of these crimes through AI. It is emphasized that the application of AI in crime investigations aids in refining the processes of detection and analysis of cybercriminal activities, allowing for quicker identification of anomalies and response to them. However, despite AI's significant potential, its use necessitates a cautious approach and the development of ethical and legal standards. This is essential to avoid possible negative consequences and ensure balanced development in cybersecurity. © 2025 Universidade de Brasilia. All rights reserved. KW - Artificial intelligence KW - Cybercrime KW - Cybersecurity KW - Information Technology KW - Innovation in Cybersecurity CY - Ukraine ER - TY - JOUR TI - Adoption of AI in human capital development: a multi-industry perspective AU - Behera M.K. AU - Behera R.K. AU - Bala P.K. PY - 2025 JO - Journal of Enterprise Information Management SP - 1 EP - 27 DO - 10.1108/JEIM-06-2025-0490 AB - Purpose – Employees are invaluable resources that are of significant value to a firm when it aims to perform human capital development (HCD). Eventually, any firm intending to preserve a competitive advantage over rivals must invest in HCD. Therefore, to gain a competitive edge in the digital age and to yield net benefits, this study endeavours to define the research problem, i.e. should a firm adopt artificial intelligence (AI) in HCD? For the investigation, it explores various disciplines of HCD, validates whether AI has capabilities to meet the net benefits of HCD, and measures the adoption intention. Design/methodology/approach – The source data were collected from 315 individuals through a survey with a five-point Likert-scale questionnaire. The empirical analysis is accomplished using covariance-based structural equation modelling. Findings – AI capability plays a positive role in HCD disciplines, including talent management, change management, performance management, human resource management and strategic planning. Subsequently, each AI-enabled HCD discipline positively influences net benefits. Eventually, the net benefits of AI-enabled HCD positively influence AI adoption intention. Moreover, organisational culture moderates the relationship between net benefits and AI adoption intention. Originality/value – This study demonstrates an empirical analysis of the adoption intention of AI in HCD by presenting the theoretical underpinnings of HCD disciplines, and subsequently, building and validating the structural relationships amongst HCD disciplines, net benefits and AI adoption intention with organisational culture as moderator. In this vein, the study offers multifaceted advantages of AI-enabled HCD. © 2025 Emerald Publishing Limited KW - Artificial intelligence KW - Human capital development KW - Net benefits KW - Organisational culture KW - Technology adoption CY - India ER - TY - JOUR TI - Revisiting the Six Human-Centered Artificial Intelligence Grand Challenges in the Age of Generative AI AU - Winslow B. AU - Ozmen Garibay O. AU - Goyal T. AU - Koon S. AU - Margetis G. AU - Salvendy G. AU - Shneiderman B. AU - Tayebi A. AU - Vardoulakis L. PY - 2026 JO - International Journal of Human-Computer Interaction VL - 42 IS - 7 SP - 4697 EP - 4738 DO - 10.1080/10447318.2026.2641703 AB - Generative AI (GenAI) has shifted AI capabilities from discriminative prediction to creative interaction, offering opportunities to augment productivity and innovation. However, realizing these benefits requires navigating risks where development outpaces governance. This article revisits the Six Human-Centered AI (HCAI) Grand Challenges to analyze their relevance in the generative era. Critical new requirements are identified: preserving human autonomy, ensuring operational safety against non-deterministic outputs, and navigating complex intellectual property landscapes. These findings are synthesized into an updated, actionable research agenda for each challenge, serving as a call to action to operationalize these principles. By shifting focus from risk mitigation to human empowerment, this agenda establishes human-centeredness as the organizing principle for a future where GenAI enhances human agency, dignity, and collective flourishing. © 2026 The Author(s). Published with license by Taylor & Francis Group, LLC. KW - evaluation KW - generative artificial intelligence (GenAI) KW - governance KW - Human-centered artificial intelligence (HCAI) KW - responsibility KW - Creatives KW - Deterministics KW - Evaluation KW - Generative artificial intelligence KW - Governance KW - Grand Challenge KW - Human-centered artificial intelligence KW - Operational safety KW - Property KW - Responsibility KW - Artificial intelligence CY - United States, Greece ER - TY - JOUR TI - Influence of AI on CE: underlying roles of network centrality and green product (process) innovations in manufacturing industry AU - Fawad Sharif S.M. AU - Wenping W. AU - Guo M. AU - Alghamdi O. AU - Huang Y. PY - 2026 JO - Journal of Strategy and Management DO - 10.1108/JSMA-05-2025-0152 AB - Purpose – Literature reviews unanimously report an affirmative influence of artificial intelligence (AI) capabilities on circular economy practices (CE), whereas empirical investigations, although scarce, do not align with these assertions and exhibit conflicting findings. This study aims to understand the mechanism behind the AI and CE relationship through mediation of green product (process) innovation and moderation of network centrality. Design/methodology/approach – This study integrates dynamic capability theory (DCT) with network theory to examine the moderated-mediation effect of AI on CE. The online survey raised 224 valid responses, which were examined through partial least squares structural equation modeling. Findings – Green product (process) innovation completely mediates between AI and CE. Network centrality positively moderates the mediation effects, such that mediation lowers as the firm becomes more central. Originality/value – This study views AI as a lower-order dynamic capability and integrates DCT with network theory to illustrate how higher-order capabilities, i.e. green innovation and CE, can be availed. Besides, we provide empirical support to prevailing literature reviews by presenting a novel explanation to the AI and CE relationship. © Emerald Publishing Limited KW - Artificial intelligence KW - Circular economy KW - Dynamic capability theory KW - Green innovation KW - Network theory CY - China, Saudi Arabia ER - TY - JOUR TI - What To Do About HAL-Market and Governmental Approaches to Regulating Artificial Intelligence AU - Myers G. PY - 2025 JO - Louisiana Law Review VL - 86 IS - 1 SP - 167 EP - 203 AB - This article explores the challenges and risks associated with extensive government regulation of artificial intelligence (AI), arguing that such an approach is both impractical and detrimental to innovation. AI's rapid evolution far outpaces legislative and regulatory processes, making broad command-and-control efforts ineffective, burdensome, and likely to stifle competition, particularly among startups and open-source developers. Overregulation could also hinder the United States' ability to compete globally, especially against foreign nations that aggressively invest in AI advancement and are likely to have few if any constraints on its development. Examples of expansive regulatory efforts include the European Union's AI Act, the now-repealed Biden Administration Executive Order on AI, and multiple state legislative proposals. Rather than relying on rigid government oversight, existing legal frameworks-including common law and statutory liability doctrines, consumer protection laws, and antidiscrimination statutes-can address Al-related risks without impeding progress. Additionally, industry-led standards-such as the National Institute of Standards and Technology's (NIST) and the International Organization for Standardization's AI risk management frameworks- offer adaptive, market-driven solutions for AI governance. Private ordering mechanisms like third-party audits, liability frameworks, and insurance-based oversight further ensure responsible AI development while allowing for flexibility as the technology advances. By leveraging these existing legal and industry-driven approaches, the United States can maintain its leadership in AI development, balancing safety and accountability without hindering technological progress. © 2025 The LSU Scholarly Repository. All rights reserved. CY - United States ER - TY - JOUR TI - Integrating AI and ESG in digital platforms: New profiles of platform-based business models AU - Nevi G. AU - Montera R. AU - Cucari N. AU - Laviola F. PY - 2025 JO - Journal of Engineering and Technology Management - JET-M VL - 78 SP - 101913 DO - 10.1016/j.jengtecman.2025.101913 AB - The integration of artificial intelligence (AI) into digital platforms is transforming the way businesses tackle environmental, social and governance (ESG) issues. This study investigates how AI can enable platform business models (Platform BMs) to create, deliver and capture ESG-related value, with a particular focus on the ESG rating industry. Using the Platform Business Model Canvas as a conceptual framework, and conducting a comparative analysis of six case studies, the research identifies three distinct configurations of AI-enabled Platform BMs: (1) ESG data wrangling and integration; (2) financial analysis and provision of ESG data to investors and companies; and (3) compliance and management of ESG issues in supply chains. Each configuration embeds specific mechanisms, such as predictive analytics, compliance automation and stakeholder coordination, through which AI can support ESG-oriented business innovation. Based on these findings, the study proposes four theoretical propositions that clarify the relationships between AI capabilities, data governance, and ESG value creation within platform ecosystems. The paper advances the academic understanding of the relationship between AI and sustainability and provides a typology to inform the strategic development of ESG-focused digital platforms. © 2025 KW - Artificial Intelligence KW - Business Model KW - Digital Platform KW - ESG KW - Compliant mechanisms KW - Information management KW - Predictive analytics KW - Research and development management KW - Supply chains KW - Sustainable development KW - Business innovation KW - Business models KW - Case-studies KW - Comparative analyzes KW - Conceptual frameworks KW - Digital platforms KW - Environmental, social and governance KW - Financial analysis KW - Platform business KW - Stakeholder coordination KW - Artificial intelligence CY - Italy ER - TY - JOUR TI - Complying with the EU AI Act: Innovations in explainable and user-centric hand gesture recognition AU - Seifi S. AU - Sukianto T. AU - Carbonelli C. AU - Servadei L. AU - Wille R. PY - 2025 JO - Machine Learning with Applications VL - 20 SP - 100655 DO - 10.1016/j.mlwa.2025.100655 AB - The EU AI Act underscores the importance of transparency, user-centricity, and robustness in AI systems, particularly for high-risk applications. In response, we present advancements in XentricAI, an explainable hand gesture recognition (HGR) system designed to meet these regulatory requirements. XentricAI addresses fundamental challenges in HGR, such as the opacity of black-box models using explainable AI methods and the handling of distributional shifts in real-world data through transfer learning techniques. We extend an existing radar-based HGR dataset by adding 28,000 new gestures, with contributions from multiple users across varied locations, including 24,000 out-of-distribution gestures. Leveraging this real-world dataset, we enhance XentricAI's capabilities by integrating a variational autoencoder module for improved gesture anomaly detection, incorporating user-specific dynamic thresholding. This integration enables the identification of 11.50% more anomalous gestures. Our extensive evaluations demonstrate a 97.5% success rate in characterizing these anomalies, significantly improving system explainability. Furthermore, the implementation of transfer learning techniques has shown a substantial increase in user adaptability, with an average performance improvement of at least 15.17%. This work contributes to the development of trustworthy AI systems by providing both technical advancements and regulatory compliance, offering a commercially viable solution that aligns with the EU AI Act requirements. © 2025 The Authors KW - Explainable AI (XAI) KW - Frequency-modulated continuous wave (FMCW) radar KW - Hand gesture recognition (HGR) KW - Machine learning (ML) KW - Gesture recognition KW - Image compression KW - Variational techniques KW - AI systems KW - Explainable AI (XAI) KW - Frequency-modulated-continuous-wave radars KW - Hand gesture recognition KW - Hand-gesture recognition KW - Learning techniques KW - Machine learning KW - Machine-learning KW - Transfer learning KW - User-centric KW - Transfer learning CY - Germany, Austria ER - TY - JOUR TI - Artificial intelligence, economic inequality, and the financial hurdles to sustainable peace: Navigating the interconnected challenges of the 21st century AU - Zandieh A. PY - 2026 JO - Social Sciences and Humanities Open VL - 13 SP - 102687 DO - 10.1016/j.ssaho.2026.102687 AB - Artificial intelligence (AI) is transforming economic and political systems at a pace that outstrips existing governance capacities. While AI offers significant potential for innovation, its rapid and uneven diffusion is amplifying structural inequalities within and between states. This paper develops an integrated conceptual framework to explain how AI-driven technological disruption interacts with political economy dynamics to deepen inequality, erode fiscal capacity, and weaken the foundations of sustainable peace. It argues that AI reshapes labor markets, concentrates wealth and data governance power, and reinforces global asymmetries, producing a systemic pattern of vertical and horizontal inequality. These inequalities narrow domestic tax bases, heighten debt dependency, and redirect scarce fiscal resources away from peacebuilding priorities toward technological adaptation and crisis management. As a result, in fragile contexts, institutions face diminishing ability to provide essential services, address grievances, and maintain legitimacy. By linking AI-induced inequality with fiscal fragility, the paper frames technological transformation as a potential economic, political and security threat. The analysis advances policy pathways for mitigating these risks through inclusive AI governance, equitable global cooperation, progressive fiscal reforms, and investment in local technological capacity. Ensuring that AI becomes a driver of resilience rather than a catalyst for exclusion is essential for building durable and just peace in the digital age. © 2026 The Author. KW - Artificial intelligence KW - Development finance KW - Economic inequality KW - Fiscal fragility KW - Global governance KW - Sustainable peace ER - TY - JOUR TI - Fostering responsible AI adoption in startups through entrepreneurial orientation: A sustainable approach AU - Alshibani S.M. AU - Korayim D. AU - Mehrotra A. AU - Agarwal V. PY - 2025 JO - Technological Forecasting and Social Change VL - 219 SP - 124272 DO - 10.1016/j.techfore.2025.124272 AB - In the dynamic and fluid business landscape, startups face the daunting task of maintaining their position and navigating the path of growth. Responsible AI (RAI) is technological support that promises to be a differentiator and propeller for startups. The study employed grounded theory method within a qualitative framework to analyze the themes from cross-cultural data. These themes aligned with the Entrepreneurial Orientation theory and the Responsible Innovation framework. The retrieved opinions were then divided into five sub-themes: innovativeness and reflexivity, proactiveness and anticipation, risk-taking and reflexivity, autonomy and inclusion, and competitive aggressiveness and responsiveness, which laid the foundation for understanding the drivers and adopters of RAI in startups. This research contributes to the literature on the emerging Responsible AI domain in businesses, particularly startups, by elaborating on the factors that would lead to the adoption of Responsible AI for business sustainability. © 2025 Elsevier Inc. KW - Entrepreneurial orientation theory KW - Gioia method KW - Grounded theory KW - Qualitative study KW - Responsible AI KW - Startups KW - Competition KW - Differentiators KW - Entrepreneurial orientation KW - Entrepreneurial orientation theory KW - Gioium method KW - Grounded theory KW - Grounded theory methods KW - Qualitative study KW - Responsible AI KW - Startup KW - Technological supports KW - artificial intelligence KW - entrepreneur KW - innovation KW - sustainability KW - technology adoption KW - Sustainable development CY - Saudi Arabia, India ER - TY - JOUR TI - Exploring the future of learning and the relationship between human intelligence and AI. An interview with Professor Rose Luckin AU - Luckin R. AU - Rudolph J. AU - Grünert M. AU - Tan S. PY - 2024 JO - Journal of Applied Learning and Teaching VL - 7 IS - 1 SP - 346 EP - 363 DO - 10.37074/jalt.2024.7.1.27 AB - Professor Rose Luckin, a pioneer in the integration of artificial intelligence with education, holds the position of Professor of Learner Centred Design at the UCL Knowledge Lab, University College London. Her trailblazing research has profoundly deepened our understanding of AI in education (AIEd). Rose Luckin has authored over 50 peer-reviewed articles and key works, including “Machine learning and human intelligence: The future of education for the 21st century.” As the Director of EDUCATE, she merges academic insights with ed-tech industry innovation. She is the co-founder of the Institute for Ethical AI in Education. In our interview, Rose Luckin shares her educational awakening and her personal journey into AIEd, addressing gender bias and the unique challenges faced by women in the AI field. She delves into the ethical dimensions of AI deployment in educational settings, underscoring the Institute for Ethical AI in Education’s pivotal role in fostering ethical standards. Professor Luckin advocates for AI’s potential to bolster learner-centred methodologies and stresses the critical importance of forging robust partnerships between educators and technology developers. She evaluates the impact of generative AI on assessment, learning and teaching within K-12 and higher education. She provides insights into AI’s evolving role in education and the imperative of lifelong learning. Emphasising a collaborative ethos among educators, researchers, and developers, Professor Luckin argues for AI’s integration into education within strategically crafted ethics and governance frameworks. Our interview sheds light on AIEd’s current landscape, highlighting the critical need for ongoing research and collaborative efforts in navigating its considerable dangers while seizing opportunities. © 2024. Rose Luckin, Jürgen Rudolph, Martin Grünert and Shannon Tan. KW - (human) intelligence KW - AIEd KW - Artificial intelligence (AI) KW - education KW - ethical AI KW - generative AI (GenAI) KW - higher education KW - machine learning CY - United Kingdom, United States ER - TY - JOUR TI - Enhancing green innovation through university–industry collaboration and artificial intelligence: insights from regional innovation systems in China AU - Xia S. AU - Zhou Y. AU - Wang Z. AU - He Q. AU - Parry G. PY - 2026 JO - Journal of Technology Transfer VL - 51 IS - 2 SP - 653 EP - 681 DO - 10.1007/s10961-025-10232-8 AB - Green innovation is essential for sustainable development worldwide. This study investigates how university engagement, coupled with the Artificial Intelligence (AI) capabilities of industrial actors, enhances regional green innovation performance within the framework of Regional Innovation Systems (RIS) theory. Using a longitudinal dataset of 31 Chinese provinces from 2008 to 2019 and employing a dynamic panel analysis with the GMM estimator, the results show that university embeddedness in regional innovation networks significantly increases green innovation performance. Contrary to previous studies, our research shows that within RIS, absorptive capacity plays a more critical role than AI in enhancing the effectiveness of knowledge transfer and exploitation, highlighting the primacy of human and organisational factors over technological tools alone. This research advances RIS theory by highlighting the critical role of university-embedded networks and systemic interactions among heterogeneous actors, demonstrating higher-order returns from knowledge exchange beyond dyadic partnerships, and enriching the understanding of the integration of AI into RIS frameworks. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025. KW - Absorptive capacity KW - Artificial intelligence KW - Green innovation performance KW - University–industry collaboration KW - Absorptive capacity KW - Dynamic panels KW - Embeddedness KW - GMM estimators KW - Green innovation performance KW - Green innovations KW - Innovation performance KW - Regional innovation KW - Regional innovation systems KW - University-industry collaboration CY - United Kingdom, China ER - TY - JOUR TI - A Qualitative Theory Building Research on Digital Law, Legal AI, and LegalTech AU - Qian Y. AU - Siau K.L. PY - 2025 JO - Journal of Global Information Management VL - 33 IS - 1 DO - 10.4018/JGIM.396821 AB - Artificial intelligence (AI) is transforming modern life by driving innovation and efficiency, with legal AI playing an increasingly significant role in supporting legal work. However, the growing use of AI introduces new challenges, including privacy breaches, deepfakes, ethical dilemmas, and legal uncertainty. Despite the ongoing initiatives to establish AI governance principles, research on regulating legal AI and broader AI applications remains limited. This study addresses this gap through a qualitative case study examining effective AI governance frameworks. Interviews with four senior legal experts, i.e., two judges, a law professor, and a legal researcher, identified emerging challenges of legal AI and beyond. The findings reveal pressing needs for adaptive, transparent, and equitable regulation to ensure responsible AI development and use. The study contributes theoretically by linking AI governance with legal scholarship and offers practical insights for policymakers, legal professionals, and organizations navigating AI's evolving regulatory landscape. © 2025 IGI Global. All rights reserved. KW - Artificial Intelligence (AI) KW - Case Study KW - Digital Law KW - Generative AI (GenAI) KW - Legal AI KW - LegalTech KW - Qualitative Research KW - RegTech KW - Artificial intelligence KW - Artificial intelligence KW - Case-studies KW - Digital law KW - Generative artificial intelligence KW - Legal artificial intelligence KW - Legaltech KW - Privacy breaches KW - Qualitative research KW - Regtech KW - Theory building KW - Laws and legislation CY - China, Singapore ER - TY - JOUR TI - The influence of AI capability on enterprise competitive advantage: the mediating effect of business model innovation AU - Shao S. AU - Shao Z. AU - Xiong Y. PY - 2025 JO - Journal of Enterprise Information Management SP - 1 EP - 25 DO - 10.1108/JEIM-08-2024-0453 AB - Purpose – This study seeks to examine the relationships among artificial intelligence capability (AIC), business model innovation (BMI), and the competitive advantage of enterprises (CAE) within the framework of dynamic capabilities theory. It specifically focuses on how small and medium-sized (SMEs) enterprises utilise artificial intelligence capability to foster business model innovation in a digital context, thereby attaining a sustainable competitive advantage. Design/methodology/approach – This study utilises a questionnaire survey to gather empirical data from 546 SMEs in China. Structural equation modelling was employed for quantitative analysis to examine the direct effect of artificial intelligence capabilities on competitive advantage, alongside the mediating role of business model innovation. Findings – Research indicates that three primary components of artificial intelligence capabilities, tangible resources, intangible resources, and skill resources, exert a significant positive influence on a company's competitive advantage. At the same time, business model innovation serves as a mediating factor within this relationship. Moreover, the findings underscore the necessity for firms to proactively adapt to technological advancements and to foster the synergistic development of artificial intelligence capabilities alongside business model innovation to enhance their competitiveness in a rapidly evolving environment. Originality/value – This study extends the dynamic capabilities theory from the perspective of artificial intelligence, proposing AI capability as a systemic and multidimensional dynamic capability, emphasising its transformative role in the ways small and medium-sized enterprises create and capture value. The research not only enriches the theoretical understanding in the field of artificial intelligence but also offers practical insights and policy recommendations for SMEs on how to achieve a competitive advantage through the development of AI capabilities. © 2025 Emerald Publishing Limited KW - Artificial intelligence capability KW - Business model KW - Business model innovation KW - Enterprise competitive advantage KW - Innovation CY - China, United Kingdom ER - TY - JOUR TI - Harnessing Competitive Intelligence and AI for Corporate Growth and Sustainability AU - Maune A. PY - 2025 JO - Journal of Intelligence Studies in Business VL - 15 IS - 2 SP - 6 EP - 24 DO - 10.37380/jisib.v15i2.3105 AB - This study investigates how artificial intelligence (AI) integration enhances competitive intelligence (CI) effectiveness and, in turn, drives corporate growth and sustainability performance in Zimbabwean firms. Employing a mixed methods design, the research combines a quantitative survey of 312 senior managers and strategy professionals from medium and large firms with qualitative data from 28 semi structured interviews across manufacturing, financial services, telecommunications, and retail sectors. Quantitative findings reveal that AI capability significantly predicts CI effectiveness (β = 0.62, p < .001), while CI effectiveness significantly predicts corporate growth (β = 0.51, p < .001) and sustainability performance (β = 0.47, p < .001). Mediation analysis indicates that CI effectiveness partially mediates the relationship between AI capability and both corporate growth and sustainability outcomes. Qualitative analysis using the Gioia methodology further identifies three aggregate dimensions: AI enabled competitive intelligence, strategic decision making and growth, and sustainable value creation, illustrating how AI enhances sensing, analytics, and reporting capabilities, and how these capabilities are embedded into strategic routines. The findings extend the resource based, knowledge based, and dynamic capabilities perspectives by conceptualising CI as a mediating dynamic capability that transforms AI driven data into actionable strategic knowledge. The study contributes to theory and practice by demonstrating that AI delivers strategic value only when integrated into CI processes and organisational routines, enabling firms to achieve sustainable competitive advantage in volatile emerging economy contexts. © 2025 Halmstad University. All rights reserved. KW - Artificial Intelligence KW - Competitive Intelligence KW - Corporate Growth KW - Dynamic Capabilities KW - Sustainability Performance KW - Zimbabwe CY - South Africa, Zimbabwe ER - TY - JOUR TI - Exploring the Themes of Chinese Artificial Intelligence Policy: An LDA Topic Modeling Approach AU - Gao Y. AU - Dai Q. AU - Wu G. PY - 2025 JO - Proceedings of the Association for Information Science and Technology VL - 62 IS - 1 SP - 199 EP - 206 DO - 10.1002/pra2.1248 AB - As a representative of next-generation artificial intelligence, generative AI is profoundly transforming contemporary societal structures. As a pivotal player, China serves as both a primary application market and a key innovator in AI technology, with its developmental trajectory significantly shaped by national policy frameworks. This study employs Latent Dirichlet Allocation (LDA) topic modeling to systematically analyze 78 valid and currently implemented AI policy documents in China. The research aims to identify core focus areas in China's current AI policy landscape and provide insights for sustainable development of AI. Analytical results highlight seven key policy themes: (1) technological innovation and industrial integration, (2) social governance and mechanism evaluation, (3) model training and disciplinary methodologies, (4) software algorithms and data security, (5) pilot zone construction and innovation development, (6) infrastructure and intelligent service systems, and (7) AI research project implementation. Based on these findings, the study concludes with targeted policy recommendations. Annual Meeting of the Association for Information Science & Technology | Nov. 14 – 18, 2025 | Washington, DC, USA. KW - AI Policies KW - Chinese Artificial Intelligence KW - Information Governance KW - LDA Topic Modeling KW - Computer software KW - Engineering research KW - Information systems KW - Intelligent systems KW - Public policy KW - Security of data KW - Sustainable development KW - AI policy KW - AI Technologies KW - Chinese artificial intelligence KW - Information governance KW - Latent Dirichlet allocation KW - Latent dirichlet allocation topic modeling KW - Modeling approach KW - National policy framework KW - Policy documents KW - Topic Modeling KW - Industrial research CY - China ER - TY - JOUR TI - Ten principles for responsible quantum innovation AU - Kop M. AU - Aboy M. AU - De Jong E. AU - Gasser U. AU - Minssen T. AU - Cohen I.G. AU - Brongersma M. AU - Quintel T. AU - Floridi L. AU - Laflamme R. PY - 2024 JO - Quantum Science and Technology VL - 9 IS - 3 SP - 035013 DO - 10.1088/2058-9565/ad3776 AB - This paper proposes a set of guiding principles for responsible quantum innovation. The principles are organized into three functional categories: safeguarding, engaging, and advancing (SEA), and are linked to central values in responsible research and innovation (RRI). Utilizing a global equity normative framework and literature-based methodology, we connect the quantum-SEA categories to promise and perils specific to quantum technology (QT). The paper operationalizes the responsible QT framework by proposing ten actionable principles to help address the risks, challenges, and opportunities associated with the entire suite of second-generation QTs, which includes the quantum computing, sensing, simulation, and networking domains. Each quantum domain has different technology readiness levels, risks, and affordances, with sensing and simulation arguably being closest to market entrance. Our proposal aims to catalyze a much-needed interdisciplinary effort within the quantum community to establish a foundation of quantum-specific and quantum-tailored principles for responsible quantum innovation. The overarching objective of this interdisciplinary effort is to steer the development and use of QT in a direction not only consistent with a values-based society but also a direction that contributes to addressing some of society’s most pressing needs and goals. © 2024 The Author(s). Published by IOP Publishing Ltd KW - engaging & advancing KW - quantum R&D KW - quantum safeguarding KW - quantum-AI ethics & governance KW - responsible quantum innovation KW - responsible quantum technology KW - responsible research and innovation (RRI) KW - Philosophical aspects KW - Quantum optics KW - Engaging & advancing KW - Global equities KW - Guiding principles KW - Quantum R&D KW - Quantum safeguarding KW - Quantum technologies KW - Quantum-AI ethic & governance KW - Responsible quantum innovation KW - Responsible quantum technology KW - Responsible research and innovation KW - Quantum computers CY - Canada ER - TY - JOUR TI - Harnessing artificial intelligence in shaping entrepreneurial success: Rethinking HR practices AU - Bajrami N. AU - Ahmeti F. PY - 2026 JO - Multidisciplinary Science Journal VL - 8 IS - 5 SP - e2026361 DO - 10.31893/multiscience.2026361 AB - This study examines the impact of artificial intelligence (AI) on human resource management (HRM) practices in Kosovo’s small and medium–sized enterprises (SMEs), focusing on outcomes such as accuracy, automation, computing power, and real-time analytics, to understand their effects on efficiency and cost reduction. The research employs a quantitative approach, utilizing data collected from 109 SMEs through structured surveys and in-depth interviews. Structural equation modeling (SEM) was used to analyze the relationships between AI capabilities and HRM efficiency outcomes. The results reveal that AI-driven accuracy and automation significantly enhance time efficiency and cost reduction, while real-time analytics has a moderate influence, and computational power has a limited impact within the SME context assessed. The findings suggest that SMEs should prioritize accuracy and automation tools to achieve immediate operational benefits and gradually integrate real-time analytics as the digital infrastructure improves. This study provides practical insights for SMEs on how to implement AI strategically in HRM processes, demonstrating that AI serves as an essential complement to human expertise rather than a replacement, offering a clear pathway for enhancing HR practices in developing economies. Copyright (c) 2026 The Authors. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. KW - artificial intelligence KW - digital transformation KW - entrepreneurial success KW - human resource practices KW - strategic innovation ER - TY - JOUR TI - Bridging the AI Ethics Gap: A Tripartite Framework for Accountability, Implementation, and Governance AU - Nakano M. PY - 2026 JO - International Journal of Software Innovation VL - 14 IS - 1 DO - 10.4018/IJSI.401497 AB - To address the largely unmitigated societal impact of artificial intelligence (AI) technologies, this study proposes a Tripartite Ethical Policy Framework for AI governance in global industries. The framework comprises three key components: AI ethics, technical implementation, and institutional governance. Drawing on current international standards, it integrates six core principles (i.e., human-centricity, fairness, accountability, transparency, privacy, and safety) and translates them into three actionable ethical domains: data, algorithms, and autonomy. An empirical analysis of corporate disclosures in Japan reveals a gap between stated commitments and actual implementation, highlighting the need for stronger governance and credible accountability. By embedding AI ethics in internal and external audits, the framework enhances transparency, strengthens oversight, and promotes responsible AI use. Its emphasis on adaptability provides a foundation for AI audit and responsible innovation amid rapid technological change. © 2026 Taru Publications. All rights reserved. KW - AI Audit KW - AI Ethics KW - AI Governance KW - Ethical Algorithms KW - Ethical Autonomy KW - Ethical Data Governance KW - Ethical Policies Framework KW - External Audit KW - Internal Audit KW - Technology Disclosure CY - Japan ER - TY - JOUR TI - A hybrid framework for creating artificial intelligence-augmented systematic literature reviews AU - Malik F.S. AU - Terzidis O. PY - 2025 JO - Management Review Quarterly DO - 10.1007/s11301-025-00522-8 AB - The integration of artificial intelligence (AI), particularly generative AI (GenAI) and large language models (LLMs), into systematic literature reviews (SLRs) represents a transformative advancement in research methodologies. This paper proposes a hybrid framework combining AI’s computational power with the epistemological rigor of human expertise, anchored in transparency, validity, reliability, comprehensiveness, and reflective agency. Through three interconnected phases—design, study collection, and interpretation—the framework employs AI model selection, knowledge base curation, and iterative prompt engineering to enhance scalability, uncover interdisciplinary connections, and ensure methodological integrity through robust human oversight. It addresses key SLR challenges, including handling vast datasets, ensuring reproducibility, and maintaining epistemic rigor while leveraging advanced AI capabilities. Key innovations include cyclical validation, inter-model comparisons, and sensitivity testing to enhance trustworthiness and mitigate biases. The framework aligns AI processes with ethical standards and research objectives by emphasizing domain-specific LLMs, reliability metrics, and standardized reporting protocols. It establishes SLRs as a foundation for advancing knowledge in complex, interdisciplinary research landscapes, harmonizing AI efficiency with human expertise. © The Author(s) 2025. KW - M00 KW - M1 CY - Germany ER - TY - JOUR TI - Harnessing AI capabilities for startup scalability: unlocking potential through AI-driven innovation ecosystems and AI-infrastructure readiness AU - Chotia V. AU - Sharma P. AU - Alshaghdali N.O. AU - Galgotia D. AU - Sahore N. PY - 2025 JO - European Journal of Innovation Management SP - 1 EP - 38 DO - 10.1108/EJIM-03-2025-0397 AB - Purpose – In today's rapidly changing digital world, the ability of startups to grow has become a major issue. This research looks at how AI-enhanced Human Decision-Making, AI-powered Entrepreneurial Agility and Ethical AI Governance help startups create an AI-driven Innovation Ecosystem that improves AI Infrastructure Readiness and supports both internal and external startup scalability potential. Design/methodology/approach – The Prolific platform was used to collect data from 274 decision-makers who worked for AI-intensive firms in the US. We used modified Likert-scale items from previous validated research to measure constructs. Partial Least Squares Structural Equation Modelling in SmartPLS4 was used to test the model. Both direct and indirect effects were analysed to examine the hypothesized relationships. Findings – All AI-enabled capabilities had a strong positive association with AI-driven Innovation Ecosystem. AI-driven Innovation Ecosystem significantly influenced AI Infrastructure Readiness, which in turn positively impacted both Startup Scalability Potential – Internal and Startup Scalability Potential – External. Mediation investigations showed that AI-driven Innovation Ecosystems and AI-Infrastructure Readiness serially mediate the impact of AI-enabled capabilities on startup scalability. Practical implications – Theoretically, this study builds on the Dynamic Capabilities Theory by adding AI-driven ecosystem and infrastructure preparedness as sequential mediators. This gives us a new way to look at how businesses use AI to develop quickly. In practice, the results give startup leaders and policymakers useful information on how to build AI adoption plans that are not only new but also follow the rules and fit with the infrastructure. Originality/value – This article presents a new serial mediation paradigm that connects AI capabilities to startup scalability and backs it up with real-world evidence from the US, a nation that is driven by innovation. © 2025 Emerald Publishing Limited KW - AI infrastructure readiness KW - AI-driven innovation ecosystem KW - AI-enhanced human decision-making KW - AI-powered entrepreneurial agility KW - Ethical AI governance KW - Startup scalability potential CY - India, Saudi Arabia ER - TY - JOUR TI - Ethical concerns in AI development: analyzing students’ perspectives on robotics and society AU - Ferhataj A. AU - Memaj F. AU - Sahatcija R. AU - Ora A. AU - Koka E. PY - 2025 JO - Journal of Information, Communication and Ethics in Society VL - 23 IS - 2 SP - 165 EP - 187 DO - 10.1108/JICES-08-2024-0111 AB - Purpose: The rapid advancement and integration of robotics and artificial intelligence (AI) are transforming various sectors, presenting profound ethical, economic, legal and societal challenges. This study aims to examine ethical concerns in AI development, with a specific focus on robotics, from the perspectives of university students in Albania. Design/methodology/approach: A structured questionnaire was used to collect data from 233 university students, focusing on their experiences with AI and robotics, ethical perceptions, preferences and recommendations for advancing these technologies. Hypotheses were tested at a 95% confidence interval, with data analyzed using JASP software version 0.18.3.0. Findings: The results reveal a high level of ethical awareness among students, particularly regarding transparency, liability and privacy in AI and robotics. Practical experience with robotics and understanding of AI’s ethical implications significantly shape students’ attitudes, fostering support for ethical governance. Students also advocate for robust regulatory measures to safeguard individual rights, ensure data security, promote transparency in AI decision-making and uphold privacy. Research limitations/implications: This study focuses on university students in Albania, which may limit the generalizability of its findings. Future research should explore diverse populations and cross-cultural contexts to validate and extend the proposed framework. Practical implications: Insights from this study can guide policymakers and technology developers in designing laws, regulations and practices that balance innovation with public interest, fostering trust and acceptance of AI systems. Social implications: The findings underscore the importance of Albania adopting and harmonizing its policies with the EU Civil Law Rules on Robotics, the EU AI Act and AI Strategy, supporting ethical AI integration aligned with the country’s EU accession objectives. Originality/value: This study introduces the Ethical Awareness-Trust Framework, a novel theoretical model integrating ethical literacy, experiential trust and regulatory advocacy to foster responsible AI adoption and governance. The findings address critical gaps in the literature by offering actionable recommendations for aligning national policies with European regulations and embedding ethics into AI research and education. © 2024, Emerald Publishing Limited. KW - Artificial intelligence KW - Ethics KW - Privacy KW - Regulation KW - Robotics KW - Students’ perspectives KW - Transparency CY - Albania, Canada ER - TY - JOUR TI - Harnessing artificial intelligence in the public sector: the critical role of strategic foresight in driving performance AU - Cao L.H.N. AU - Nguyen P.V. AU - Nguyen V.T.H. AU - Tran T.T. AU - Vrontis D. PY - 2025 JO - Business Process Management Journal SP - 1 EP - 23 DO - 10.1108/BPMJ-08-2025-1317 AB - Purpose – This study aims to examine how artificial intelligence (AI) capabilities influence organizational performance in the public sector, with strategic foresight as a mediating mechanism. It investigates how institutional enablers, including government incentives, regulatory support and perceived financial costs, contribute to AI capabilities and how these capabilities translate into performance outcomes. Design/methodology/approach – Drawing on the resource-based view, survey data were collected from 303 Vietnamese public officials and analyzed using partial least squares structural equation modeling. AI capabilities were conceptualized as a second-order construct encompassing AI basics, AI skills and AI proclivity, while strategic foresight comprised environmental scanning and strategic selection. Findings – Government incentives, regulatory support and cost awareness significantly enhance AI capabilities. These capabilities have both direct and indirect effects on performance through strategic foresight, which partially mediates the relationship. Although perceived financial cost strengthens AI capabilities, it does not directly affect performance. Organizational innovation shows no significant influence on AI capabilities or performance, emphasizing the greater importance of institutional support and foresight capacity. Originality/value – This study advances understanding of how AI capabilities contribute to public value creation by integrating strategic foresight into the capability and performance link. It highlights that technology adoption alone is insufficient without supportive institutional frameworks and future-oriented strategic processes, offering actionable insights for policymakers and public managers in emerging economies. © 2025 Emerald Publishing Limited KW - AI capabilities KW - Government incentives KW - Organizational context KW - Organizational performance KW - Regulatory support KW - Strategic foresight CY - Cyprus, Singapore ER - TY - JOUR TI - Unlocking AI capabilities: exploring strategic fit, innovation ambidexterity and digital entrepreneurial intent in driving digital transformation AU - Ahmad Z. PY - 2025 JO - Journal of Management Development VL - 44 IS - 2 SP - 194 EP - 218 DO - 10.1108/JMD-05-2024-0171 AB - Purpose: The insurgence of the COVID-19 pandemic insinuated that family-owned small hotels (F-OSH) should adopt AI capabilities and innovation activities and digitize their operations to survive. This study examines the potential of AI capabilities to digitally transform F-OSHs by leveraging innovation ambidexterity, preparing them for future disasters proactively. Additionally, it sheds light on how the impact of AI capabilities on innovation ambidexterity varies based on strategic fit. In addition, this research explores the influence of digital entrepreneurial intention on fostering innovation ambidexterity, essential for digital transformation in F-OSHs. Design/methodology/approach: The study collected primary data from 318 descendant entrepreneurs designated as chairpersons or managing directors in F-OSH and analyzed the data using the partial least structural equation modeling technique. Findings: This study found a positive association of AI capabilities, and digital entrepreneurial intention with the digital transformation of F-OSHs, while strategic fit does not have an association with innovation ambidexterity. Innovation ambidexterity mediates the relationship between AI capabilities and digital transformation in F-OSHs. Moreover, a strong strategic fit increases the effect of AI capabilities on innovation ambidexterity. Furthermore, a high intention for digital entrepreneurship reduces the impact of innovation ambidexterity on the digital transformation of F-OSHs. Practical implications: The combination of AI capabilities and innovation ambidexterity has transformed F-OSHs' digital transformation. This proactive approach to dealing with economic recessions such as COVID-19 is also influenced by digital entrepreneurial intention and strategic fit. Originality/value: Anchored on the dynamic capability theory, this study provides valuable insights and novel empirical evidence by investigating the mediating mechanism of innovation ambidexterity and boundary condition of strategic fit and digital entrepreneurial intention between AI capabilities and digital transformation in F-OSHs. © 2025, Emerald Publishing Limited. KW - AI capability KW - Digital entrepreneurial intention KW - Digital transformation KW - Family-owned small hotels KW - Innovation ambidexterity KW - Strategic fit CY - Malaysia ER - TY - JOUR TI - A GPT-Aided literature review process for total quality management and business excellence (2020-2023) AU - Hsueh J.-T. AU - Hsu S.-H. PY - 2024 JO - Total Quality Management and Business Excellence VL - 35 IS - 7-8 SP - 835 EP - 859 DO - 10.1080/14783363.2024.2345897 AB - ABSTRACTS: In an era of rapid technological evolution and competitive pressures, Total Quality Management (TQM) requires up-to-date literature reviews to reflect its evolving nature. This study addresses this need through a comprehensive analysis of TQM&BE journal articles from 2020 to 2023 and by introducing the LitRev-GPT framework. The research data was collected from the Scopus database, specifically targeting articles published in the TQM&BE journal from 2020 to 2023. This LitRev-GPT framework employs the Generative Pretrained Transformer (GPT) for efficient categorization and summarization of academic papers, setting new standards for reproducibility and methodological soundness. We identified emergent themes such as ‘Corporate Social Responsibility’, intertwining TQM with ethical practices, and ‘Industry 4.0’, showcasing TQM's adaptability to technological advancements. Additionally, trend analysis highlighted a sustained interest in foundational TQM themes, with a growing emphasis on innovation management. The LitRev-GPT framework demonstrates significant methodological advancements, enhancing the efficiency and depth of literature reviews beyond traditional AI capabilities. © 2024 Informa UK Limited, trading as Taylor & Francis Group. KW - Generative Pretrained Transformer (GPT) KW - Literature Review KW - Thematic Analysis KW - Topic analysis KW - Total Quality Management (TQM) CY - Taiwan ER - TY - JOUR TI - Web-Based Multimodal Deep Learning Platform with XRAI Explainability for Real-Time Skin Lesion Classification and Clinical Decision Support AU - Aksoy S. AU - Demircioglu P. AU - Bogrekci I. PY - 2025 JO - Cosmetics VL - 12 IS - 5 SP - 194 DO - 10.3390/cosmetics12050194 AB - Background: Skin cancer represents one of the most prevalent malignancies worldwide, with melanoma accounting for approximately 75% of skin cancer-related deaths despite comprising fewer than 5% of cases. Early detection dramatically improves survival rates from 14% to over 99%, highlighting the urgent need for accurate and accessible diagnostic tools. While deep learning has shown promise in dermatological diagnosis, existing approaches lack clinical explainability and deployable interfaces that bridge the gap between research innovation and practical healthcare applications. Methods: This study implemented a comprehensive multimodal deep learning framework using the HAM10000 dataset (10,015 dermatoscopic images across seven diagnostic categories). Three CNN architectures (DenseNet-121, EfficientNet-B3, ResNet-50) were systematically compared, integrating patient metadata, including age, sex, and anatomical location, with dermatoscopic image analysis. The first implementation of XRAI (eXplanation with Region-based Attribution for Images) explainability for skin lesion classification was developed, providing spatially coherent explanations aligned with clinical reasoning patterns. A deployable web-based clinical interface was created, featuring real-time inference, comprehensive safety protocols, risk stratification, and evidence-based cosmetic recommendations for benign conditions. Results: EfficientNet-B3 achieved superior performance with 89.09% test accuracy and 90.08% validation accuracy, significantly outperforming DenseNet-121 (82.83%) and ResNet-50 (78.78%). Test-time augmentation improved performance by 1.00 percentage point to 90.09%. The model demonstrated excellent performance for critical malignant conditions: melanoma (81.6% confidence), basal cell carcinoma (82.1% confidence), and actinic keratoses (88% confidence). XRAI analysis revealed clinically meaningful attention patterns focusing on irregular pigmentation for melanoma, ulcerated borders for basal cell carcinoma, and surface irregularities for precancerous lesions. Error analysis showed that misclassifications occurred primarily in visually ambiguous cases with high correlation (0.855–0.968) between model attention and ideal features. The web application successfully validated real-time diagnostic capabilities with appropriate emergency protocols for malignant conditions and comprehensive cosmetic guidance for benign lesions. Conclusions: This research successfully developed the first clinically deployable skin lesion classification system combining diagnostic accuracy with explainable AI and practical patient guidance. The integration of XRAI explainability provides essential transparency for clinical acceptance, while the web-based deployment democratizes access to advanced dermatological AI capabilities. Comprehensive validation establishes readiness for controlled clinical trials and potential integration into healthcare workflows, particularly benefiting underserved regions with limited specialist availability. This work bridges the critical gap between research-grade AI models and practical clinical utility, establishing a foundation for responsible AI integration in dermatological practice. © 2025 by the authors. KW - clinical deployment KW - deep learning KW - dermatoscopy KW - explainable artificial intelligence KW - melanoma detection KW - multimodal fusion KW - skin lesion classification KW - XRAI KW - actinic keratosis KW - adult KW - aged KW - Article KW - artificial intelligence KW - artificial neural network KW - basal cell carcinoma KW - clinical decision making KW - clinical decision support system KW - clinical practice KW - controlled study KW - deep learning KW - dermatofibroma KW - dermatoscopy KW - diagnostic test accuracy study KW - female KW - follow up KW - health care personnel KW - histopathology KW - human KW - human tissue KW - major clinical study KW - male KW - melanoma KW - middle aged KW - pigmented nevus KW - precancer KW - sensitivity and specificity KW - skin cancer KW - skin defect KW - vascular lesion CY - Germany, Turkey ER - TY - JOUR TI - Generative AI in Game Design: Enhancing Creativity or Constraining Innovation? AU - Alharthi S.A. PY - 2025 JO - Journal of Intelligence VL - 13 IS - 6 SP - 60 DO - 10.3390/jintelligence13060060 AB - Generative AI tools are increasingly being integrated into game design and development workflows, offering new possibilities for creativity, efficiency, and innovation. This paper explores the evolving role of these tools from the perspective of game designers and developers, focusing on the benefits and challenges they present in fostering creativity. Through a mixed-method study, we conducted an online survey (n = 42) with game design professionals, followed by in-depth online interviews (n = 9), to investigate how generative AI influences the creative process, decision-making, and artistic vision. Our findings reveal that while generative AI accelerates ideation, enhances prototyping, and automates repetitive tasks, it also raises concerns about originality, creative dependency, and undermine of human-authored content. Future work will aim to address these challenges by investigating strategies to balance leveraging AI’s capabilities with preserving the integrity of human creativity. This includes developing collaborative human-AI workflows that maintain human oversight, designing systems that support rather than replace creative decision-making, and establishing ethical guidelines to ensure transparency, accountability, and authorship in AI-assisted content creation. By doing so, we aim to contribute to a more nuanced understanding of generative AI’s role in creative practices and its implications for the game design and development lifecycle. © 2025 by the author. KW - creativity KW - game design KW - games KW - generative AI KW - user experiece CY - Saudi Arabia ER - TY - JOUR TI - Evaluating chatbot architectures for public service delivery: balancing functionality, safety, ethics, and adaptability AU - Papadopoulos T. AU - Alexopoulos C. AU - Charalabidis Y. PY - 2025 JO - Frontiers in Political Science VL - 7 SP - 1601440 DO - 10.3389/fpos.2025.1601440 AB - The increasing integration of AI-driven interfaces into public service channels has catalyzed a vibrant discourse on the interplay between technological innovation and the traditional values of public governance. This discussion invites a critical exploration of how emerging chatbot architectures can be aligned with ethical principles and resilient public sector practices. While there is research assessing the potential benefits of integrating chatbots in service delivery, existing evaluation approaches often lack specificity to the unique context of public administration, failing to adequately balance technical performance with crucial ethical considerations, safety requirements, and core public service principles like transparency, fairness, and accountability. This research addresses this critical gap by developing and applying a structured evaluation framework specifically designed for assessing diverse chatbot architectures within the public sector. The methodology offers actionable insights to guide the selection and implementation of chatbot solutions that enhance citizen engagement, streamline government services, and uphold key public service values. A key contribution is the introduction of fifteen pre-assessed evaluation criteria, encompassing areas such as input understanding, error handling, legal compliance, safety, and personalization, which are applied to four distinct chatbot architectures. Our findings indicate that while no single architecture is universally optimal, hybrid retrieval-augmented generation (RAG) systems emerge as the most balanced approach, effectively mitigating the risks of pure generative models while retaining their adaptability. Ultimately, this work provides actionable guidance for policymakers and researchers, supporting informed decisions on the responsible use of chatbots and emphasizing the critical balance between innovation and public trust. Copyright © 2025 Papadopoulos, Alexopoulos and Charalabidis. KW - AI ethics KW - chatbots KW - evaluation framework KW - LLMs KW - politics of technology KW - public service delivery CY - Greece ER - TY - JOUR TI - Harmonizing innovation and regulation: The EU Artificial Intelligence Act in the international trade context AU - REN Q. AU - DU J. PY - 2024 JO - Computer Law and Security Review VL - 54 SP - 106028 DO - 10.1016/j.clsr.2024.106028 AB - The European Union's Artificial Intelligence Act focuses on establishing harmonized rules across EU Member States so that AI systems are safe, transparent, and respectful of existing laws and fundamental rights. It introduces a risk-based regulatory approach, classifying AI applications by risk levels and imposing stringent compliance requirements on high-risk applications. The paper critically examines the Act's provisions, including its prohibitions on certain AI practices, requirements for high-risk AI systems, and mandates for transparency and human oversight. The paper examines the implications of the Act for international trade and technological regulation, particularly in the context of the World Trade Organization's Technical Barriers to Trade (TBT) Agreement. It addresses the Act's potential impact on developing countries, highlighting concerns that the Act's uniform standards could potentially exacerbate the digital divide and create barriers in global AI innovation and trade. The paper suggests incorporating flexibility and differential standards in the Act, enhancing technical assistance for developing countries, and advocating the EU's active participation in global standard-setting. © 2024 Elsevier Ltd KW - Developing countries and AI compliance KW - EU Artificial Intelligence Act KW - International trade regulation KW - Risk-based AI regulation KW - Technical barriers to trade agreement KW - Artificial intelligence KW - International trade KW - Regulatory compliance KW - AI systems KW - Developing country and AI compliance KW - EU artificial intelligence act KW - International trade regulation KW - Risk-based KW - Risk-based AI regulation KW - Technical barrier to trade agreement KW - Technical barriers to trade KW - Trade agreements KW - Trade regulations KW - Developing countries CY - China, United Kingdom ER - TY - JOUR TI - Enhancing Supply Chain Innovation via Generative AI: Mediating Effects of Knowledge Sharing and Supply Chain Learning AU - Yongsheng L. AU - Zhaoxia Z. PY - 2026 JO - Journal of Information and Knowledge Management SP - 2650007 DO - 10.1142/S0219649226500073 AB - As generative AI applications in supply chain management become increasingly thorough, systematic studies on how it could promote enterprise innovation are yet to come to light. This paper takes 298 manufacturing enterprises in Zhejiang Province as samples, uses questionnaire surveys and PLS-SEM methods to investigate how generative AI exerts its influence on supply chain innovation, and tests the role of knowledge sharing and supply chain learning as a mediator. Research has found that generative AI capabilities can significantly enhance knowledge sharing and supply chain learning levels. Knowledge sharing not only promotes supply chain learning but also has a direct driving effect on supply chain innovation, playing a key mediating role between generative AI capabilities and innovation. In contrast, the hypothesised mediation of supply chain learning did not receive statistical support. This indicates that the impact of generative AI on supply chain innovation does not depend on supply chain learning. The results reveal the transmission path of generative AI in supply chain innovation, emphasising the core position of knowledge sharing in the process of transforming technological capabilities into innovative results. This paper provides new empirical evidence to understand AI-driven innovation and provides reference practice to promote digital transformation and collaborative innovation among manufacturing enterprises. © 2026 World Scientific Publishing Co. KW - Generative AI KW - knowledge sharing KW - PLS-SEM KW - supply chain innovation KW - supply chain learning KW - Collaborative learning KW - Engineering research KW - Knowledge acquisition KW - Knowledge management KW - Knowledge transfer KW - Supply chains KW - AI applications KW - Chain management KW - Generative AI KW - Knowledge supply KW - Knowledge-sharing KW - Manufacturing enterprise KW - Mediating effect KW - PLS-SEM KW - Supply chain innovations KW - Supply chain learning KW - Supply chain management CY - China ER - TY - JOUR TI - Human-AI Intersection: Understanding the Ethical Challenges, Opportunities, and Governance Protocols for a Changing Data-Driven Digital World AU - Mujtaba B.G. PY - 2025 JO - Business Ethics and Leadership VL - 9 IS - 1 SP - 109 EP - 126 DO - 10.61093/bel.9(1).109-126.2025 AB - Artificial intelligence (AI), in this data-driven digital world, is revolutionizing modern life with far-reaching implications for individuals, teams, organizations, and society. Using comments from 126 undergraduate students in South Florida, this theoretical paper highlights concepts and concerns regarding AI challenges related to cheating, plagiarizing, and biased information. The worries about the impact of AI are analogous to what the internet was three decades ago. People were using the internet as it was being developed, fine-tuned, and improved; it felt like walking over a long and tall bridge as it was being built, and the same is true for the growth of AI. Drawing parallels with the internet’s transformative impact over the past three decades, this paper emphasizes that AI is poised to drive similar positive changes, fostering increased productivity, transparency, accountability, and ethics, but at a much faster pace. In the meantime, due to the availability of data and digital content, the virtual world increased misinformation, disinformation, bias, and prejudiced speech, which AI can easily exacerbate. While AI adoption may cause process-related disruptions, its integration into everyone’s daily life is inevitable. As a natural extension of the information superhighway, AI will usher in a new wave of innovation, ultimately and perpetually transforming the fabric of our personal and professional lives. Drawing on literature and recent trends forecasted by experts, this theoretical manuscript provides an overview of AI uses, its history, challenges, and ethical implications for us all. The conceptual paper ends with recommendations for educators, managers, entrepreneurs, and human resources professionals to create awareness regarding the benefits of this new endemic technology, to ease people’s anxiety, and to reduce or mitigate hallucinations so AI tools can be used to enhance everyone’s effectiveness and efficiency. © 2025 by the author. KW - AI and sustainability KW - AI ethics KW - AI implications KW - AI training KW - Artificial intelligence KW - biases in AI KW - generative-AI KW - hallucinations of AI CY - United States ER - TY - JOUR TI - Artificial Intelligence in Higher Education: A State-of-the-Art Overview of Pedagogical Integrity, Artificial Intelligence Literacy, and Policy Integration AU - Adamakis M. AU - Rachiotis T. PY - 2025 JO - Encyclopedia VL - 5 IS - 4 SP - 180 DO - 10.3390/encyclopedia5040180 AB - Artificial Intelligence (AI), particularly Generative AI (GenAI) and Large Language Models (LLMs), is rapidly reshaping higher education by transforming teaching, learning, assessment, research, and institutional management. This entry provides a state-of-the-art, comprehensive, evidence-based synthesis of established AI applications and their implications within the higher education landscape, emphasizing mature knowledge aimed at educators, researchers, and policymakers. AI technologies now support personalized learning pathways, enhance instructional efficiency, and improve academic productivity by facilitating tasks such as automated grading, adaptive feedback, and academic writing assistance. The widespread adoption of AI tools among students and faculty members has created a critical need for AI literacy—encompassing not only technical proficiency but also critical evaluation, ethical awareness, and metacognitive engagement with AI-generated content. Key opportunities include the deployment of adaptive tutoring and real-time feedback mechanisms that tailor instruction to individual learning trajectories; automated content generation, grading assistance, and administrative workflow optimization that reduce faculty workload; and AI-driven analytics that inform curriculum design and early intervention to improve student outcomes. At the same time, AI poses challenges related to academic integrity (e.g., plagiarism and misuse of generative content), algorithmic bias and data privacy, digital divides that exacerbate inequities, and risks of “cognitive debt” whereby over-reliance on AI tools may degrade working memory, creativity, and executive function. The lack of standardized AI policies and fragmented institutional governance highlight the urgent necessity for transparent frameworks that balance technological adoption with academic values. Anchored in several foundational pillars (such as a brief description of AI higher education, AI literacy, AI tools for educators and teaching staff, ethical use of AI, and institutional integration of AI in higher education), this entry emphasizes that AI is neither a panacea nor an intrinsic threat but a “technology of selection” whose impact depends on the deliberate choices of educators, institutions, and learners. When embraced with ethical discernment and educational accountability, AI holds the potential to foster a more inclusive, efficient, and democratic future for higher education; however, its success depends on purposeful integration, balancing innovation with academic values such as integrity, creativity, and inclusivity. © 2025 by the authors. KW - academic integrity policies KW - artificial intelligence literacy KW - cognitive debt KW - generative artificial intelligence KW - large language models KW - pedagogical innovation CY - Greece ER - TY - JOUR TI - From user needs to AI solutions: a human-centered design approach for AI-powered virtual teamwork competency training AU - Hu W. AU - Chan C.K.Y. PY - 2025 JO - International Journal of Educational Technology in Higher Education VL - 22 IS - 1 SP - 52 DO - 10.1186/s41239-025-00551-z AB - This study develops a human-centered design (HCD) approach to create a GenAI trainer that addresses critical gaps in virtual teamwork training for engineering students. While virtual teamwork competency is increasingly essential, current programs often prioritize task completion over competency development. Leveraging generative AI's capabilities for personalized interaction, scenario simulation, and tailored feedback, we employ a three-phase HCD approach: (1) identifying unmet user needs through stakeholder interviews, revealing key challenges in instructional support, training formats, feedback mechanisms, and teamwork awareness; (2) co-designing solutions with instructors and students to create an AI trainer that combines Socratic questioning and scenario-based learning; and (3) testing the system and obtaining feedback from engineering students. Results demonstrate significant improvements across multiple dimensions: transforming passive learning into active experiences, delivering real-time actionable feedback, enhancing conceptual understanding and awareness of virtual teamwork, and developing practical virtual teamwork skills through authentic scenarios. Participant feedback also identified future improvements for enhanced personalization and immersion. This study contributes both theoretically and practically by illustrating how HCD can effectively integrate AI capabilities with pedagogical needs, while providing a replicable model for developing competency-based training tools that balance technological innovation with educational effectiveness. © The Author(s) 2025. KW - Generative AI KW - Human-centered design KW - Virtual teamwork competency ER - TY - JOUR TI - Dynamic capabilities perspective on innovation ecosystem of China’s universities in the age of artificial intelligence: Policy-based analysis AU - Qu C. AU - Kim E. PY - 2022 JO - Journal of Infrastructure, Policy and Development VL - 6 IS - 2 SP - 1661 DO - 10.24294/jipd.v6i2.1661 AB - Universities play a key role in university-industry-government interactions and are important in innovation ecosystem studies. Universities are also expected to engage with industries and governments and contribute to economic development. In the age of artificial intelligence (AI), governments have introduced relevant policies regarding the AI-enabled innovation ecosystem in universities. Previous studies have not focused on the provision of a dynamic capabilities perspective on such an ecosystem based on policy analysis. This research work takes China as a case and provides a framework of AI-enabled dynamic capabilities to guide how universities should manage this based on China’s AI policy analysis. Drawing on two main concepts, which are the innovation ecosystem and dynamic capabilities, we analyzed the importance of the AI-enabled innovation ecosystem in universities with governance regulations, shedding light on the theoretical framework that is simultaneously analytical and normative, practical, and policy-relevant. We conducted a text analysis of policy instruments to illustrate the specificities of the AI innovation ecosystem in China’s universities. This allowed us to address the complexity of emerging environments of innovation and draw meaningful conclusions. The results show the broad adoption of AI in a favorable context, where talents and governance are boosting the advance of such an ecosystem in China’s universities. © by author(s). KW - artificial intelligence KW - dynamic capability KW - innovation ecosystem KW - policy instruments KW - policy stakeholders KW - text analysis KW - universities CY - Japan, China ER - TY - JOUR TI - The dynamics of AI capability and its influence on public value creation of AI within public administration AU - van Noordt C. AU - Tangi L. PY - 2023 JO - Government Information Quarterly VL - 40 IS - 4 SP - 101860 DO - 10.1016/j.giq.2023.101860 AB - Artificial Intelligence (AI) technologies in public administration are gaining increasing attention due to the potential benefits they can provide in improving governmental operations. However, translating technological opportunities into concrete public value for public administrations is still limited. One of the factors hindering this progress is the lack of AI capability within public organisations. The research found that various components of AI capability are essential for successfully developing and using AI technologies, including tangible, intangible, and human-related factors. There is a distinction between the AI capability to develop and the AI capability to implement AI technologies, with more administrations capable of the former but finding difficulties in the latter. A lack of in-house technical expertise to maintain and update the AI systems, legal challenges in deploying developed AI systems, and the capability to introduce changes in the organisation to ensure the system remains operational and used by relevant end-users are among the most critical limiting factors for long-term use of AI by public administrations. The research underlines the strong complementarity between historical eGovernment developments and the capability to deploy AI technologies. The study suggests that funding alone may not be enough to acquire AI capability, and public administrations need to focus on both the capability to develop and implement AI technologies. The research emphasizes that human skillsets, both technical and non-technical, are essential for the successful implementation of AI in public administration. © 2023 The Authors KW - AI-capability KW - Artificial intelligence KW - Digital government KW - Digital government transformation KW - Emerging technologies KW - Public sector innovation CY - Estonia, Italy ER - TY - JOUR TI - Artificial Intelligence Capabilities and R&D Leaps: An Analysis of the Key Factors of Enterprise Innovation Transformation AU - Li J. AU - Pongtornkulpanich A. PY - 2024 JO - Pakistan Journal of Life and Social Sciences VL - 22 IS - 2 SP - 5952 EP - 5965 DO - 10.57239/PJLSS-2024-22.2.00443 AB - This research seeks to assess how AI capabilities and improvements in research and developmental technology impact the generation of innovative transformations in Chinese firms. This paper evaluates the impact of AI and R &D integration on innovation performance using survey data from 500 firms cutting across different industries. In the context of the research study, PLS-SEM was used to demonstrate the impact of AI talents on operation efficiency and decision-making in improving R&D outcomes, thus promoting product innovation and process improvement. Consequently, the research and development undertakings done within commercial organizations will be bound to change the approaches to innovations, with artificial intelligence needed to increase the speed at which such exercises are accomplished. The current study assists policymakers and managers in understanding how to improve innovation performance with AI and R&D. © (2023), (Elite Scientific Publications). All Rights Reserved. KW - Artificial Intelligence KW - Chinese Enterprises KW - Enterprise KW - Innovation Transformation KW - PLS-SEM KW - R&D CY - Thailand ER - TY - JOUR TI - Integrating Innovation in Healthcare: The Evolution of 'CURA's' AI-Driven Virtual Wards for Enhanced Diabetes and Kidney Disease Monitoring AU - Aljaafari M. AU - El-Deep S.E. AU - Abohany A.A. AU - Sorour S.E. PY - 2024 JO - IEEE Access VL - 12 SP - 126389 EP - 126414 DO - 10.1109/ACCESS.2024.3451369 AB - The healthcare sector faces intricate challenges that demand innovative solutions to enhance patient outcomes and streamline operations. The advent of Artificial Intelligence (AI) has unleashed groundbreaking potential in numerous healthcare domains, including diagnostics, patient care, and disease management. This study explores the incorporation of AI-driven methodologies for the advanced monitoring of diabetes and kidney diseases. It underscores the development of predictive models that utilize six Machine Learning (ML) and four deep learning (DL) models: Our comprehensive data analysis and rigorous model evaluation showcase AI's capability to significantly enhance clinical practices, fostering a proactive healthcare environment marked by precision, personalization, and predictive care. Our results demonstrate substantial enhancements in the accuracy of disease monitoring. For diabetes prediction, the Gradient Boosting (GB) and Random Forest (RF) models achieved up to 89.61% accuracy, while the hybrid LSTM-CNN model outperformed other DL models with an accuracy of 89.7%. For kidney disease prediction, the RF model reached 97.5% accuracy, and the LSTM-CNN model demonstrated a remarkable accuracy of 98.9%. These findings underscore the transformative potential of AI in healthcare, fostering a proactive environment characterized by precision, personalization, and predictive care. Integrating AI within CURA's virtual wards facilitates earlier disease detection and timely interventions and enables more tailored treatment plans, ultimately optimizing healthcare delivery and patient management. © 2013 IEEE. KW - artificial intelligence (AI) KW - chronic kidney disease (CKD) KW - CURA KW - deep learning (DL) KW - diabetes disease KW - Healthcare KW - kidney disease KW - LSTM-CNN KW - machine learning (ML) KW - virtual wards KW - Diagnosis KW - Diseases KW - Patient treatment KW - Personalized medicine KW - Accuracy KW - Artificial intelligence KW - Chronic kidney disease KW - CURA KW - Deep learning KW - Diabetes disease KW - Healthcare KW - Kidney KW - Kidney disease KW - LSTM-CNN KW - Machine-learning KW - Medical services KW - Predictive models KW - Virtual ward KW - Deep learning CY - Egypt, Saudi Arabia ER - TY - JOUR TI - AI-driven sustainability: the interplay of generative capabilities, resource optimization, and institutional support AU - Baquero A. AU - Más-Ferrando A. AU - Navarro-Navarro J. PY - 2026 JO - Journal of Hospitality and Tourism Insights DO - 10.1108/JHTI-09-2025-1053 AB - Purpose – This study examines how generative artificial intelligence capabilities (GAIC) influence sustainable performance (SP) in the Spanish hotel industry. Drawing on the resource-based view (RBV) and the AI4GoodTourism framework, it analyzes the mediating role of resource optimization (RO) and the moderating role of institutional support (IS) in this relationship. Design/methodology/approach – A moderated mediation model was tested using partial least squares structural equation modeling. Data were collected through a survey of 367 hotel firms in Spain. Both direct and indirect effects of GAIC on SP were assessed, considering RO as a mediator and IS as a conditional moderator. Findings – GAIC exerts a strong positive influence on SP directly and indirectly via RO. IS strengthens the impact of RO on SP, amplifying the indirect effect of GAIC on SP through RO. The findings demonstrate the dual role of internal resource efficiency and external IS in driving sustainability outcomes in hotels. Practical implications – Hotel managers and policymakers should promote generative AI adoption alongside supportive institutional frameworks. Aligning technological capabilities with RO strategies and policy incentives can enhance hotels’ operational efficiency, reduce environmental impacts, and achieve SP. Originality/value – This study pioneers the integration of the RBV and AI4GoodTourism by conceptualizing GAIC as a driver of sustainability. It provides insights into how AI capabilities promote resource efficiency and sustainable outcomes, offering theoretical and practical contributions to the hospitality industry. © Emerald Publishing Limited KW - Generative AI capabilities KW - Institutional support KW - Resource optimization KW - Sustainable performance CY - Spain ER - TY - JOUR TI - The impact of AI capability on breakthrough technological innovation in China: a perspective of value co-creation within innovation ecosystems AU - Xu X. AU - Yuan H. PY - 2025 JO - Asia Pacific Business Review DO - 10.1080/13602381.2025.2496511 AB - This study aims to uncover the mechanism through which artificial intelligence (AI) capability influences breakthrough technological innovation within the context of innovation ecosystems. Based on 201 survey responses collected from Chinese enterprises, the research model was tested using partial least squares structural equation modelling (PLS-SEM). The findings demonstrate that AI capability positively influences both high-end and low-end breakthrough technological innovations, with ecosystem value co-creation serving as a mediating mechanism. These results illuminate the black box of how AI capability affects breakthrough technological innovation and provide new insights for open innovation research in the AI era. © 2025 Informa UK Limited, trading as Taylor & Francis Group. KW - AI capability KW - Artificial intelligence KW - breakthrough technological innovation KW - China KW - innovation ecosystem KW - value co-creation CY - China ER - TY - JOUR TI - A design framework for operationalizing trustworthy artificial intelligence in healthcare: Requirements, tradeoffs and challenges for its clinical adoption AU - Moreno-Sánchez P.A. AU - Del Ser J. AU - van Gils M. AU - Hernesniemi J. PY - 2026 JO - Information Fusion VL - 127 SP - 103812 DO - 10.1016/j.inffus.2025.103812 AB - Artificial Intelligence (AI) holds great promise for transforming healthcare, particularly in disease diagnosis, prognosis, and patient care. The increasing availability of digital medical data, such as images, omics data, biosignals, and electronic health records, combined with advances in computing, has enabled AI models to approach expert-level performance. However, widespread clinical adoption remains limited, primarily due to challenges beyond technical performance, including ethical concerns, regulatory barriers, and lack of trust. To address these issues, medical AI systems must align with the principles of Trustworthy AI (TAI), which emphasize human agency and oversight, algorithmic robustness, privacy and data governance, transparency, bias and discrimination avoidance, and accountability. Yet, the complexity of healthcare processes (e.g., screening, diagnosis, prognosis, and treatment) and the diversity of stakeholders (clinicians, patients, providers, regulators) complicate the integration of TAI principles. To bridge the gap between TAI theory and practical implementation, this paper proposes a design framework to support developers in embedding TAI principles into medical AI systems. Thus, for each stakeholder identified across various healthcare processes, we propose a disease-agnostic collection of requirements that medical AI systems should incorporate to adhere to the principles of TAI. Additionally, we examine the challenges and tradeoffs that may arise when applying these principles in practice. To illustrate the discussion, we focus on cardiovascular diseases, which is a field marked by both high prevalence and active AI innovation, and demonstrate how TAI principles have been applied and where key obstacles persist. © 2025 The Author(s). KW - AI fairness KW - AI safety KW - Design framework KW - Explainable AI KW - Health stakeholders KW - Healthcare KW - Human agency and oversight KW - Medical AI KW - Privacy KW - Trustworthy AI KW - Artificial intelligence KW - Diseases KW - Electronic health record KW - Ethical aspects KW - Medical computing KW - Medical imaging KW - Medical information systems KW - Patient treatment KW - Artificial intelligence fairness KW - Artificial intelligence safety KW - Design frameworks KW - Explainable artificial intelligence KW - Health stakeholder KW - Healthcare KW - Human agency KW - Human oversight KW - Medical artificial intelligence KW - Privacy KW - Trustworthy artificial intelligence KW - Diagnosis CY - Finland, Spain ER - TY - JOUR TI - Empowering AI-Driven Proactive and Reactive Green Innovation: Exploring Knowledge Management, Trust and Sustainability AU - Abdulmuhsin A.A. AU - Alkhwaldi A.F. AU - Rehman S.U. AU - Thabit S.M.M. AU - Hussein H.D.H. AU - Dbesan A.H. PY - 2026 JO - Knowledge and Process Management DO - 10.1002/kpm.70026 AB - This study examines the integration of artificial intelligence (AI) and knowledge management (KM) processes and their role in fostering proactive and reactive green innovation (GI) in the Iraqi oil industry. It also explores the moderating effects of trust in technologies and sustainability orientation. Using a cross-sectional design, data were collected from 612 middle-level managers in Iraqi oil companies through a structured questionnaire. The data were analysed using SmartPLS v3.9 and SPSS v26 to assess measurement validity, reliability and the hypothesised relationships. The findings indicate that AI has a significant positive effect on both KM processes and GI. KM processes play a crucial mediating role by transforming AI capabilities into proactive and reactive GI outcomes. While trust in technologies and sustainability orientation moderate these relationships, their effects are relatively modest. Theoretically, the study underscores the importance of integrating AI and KM to enhance environmental performance. It contributes original empirical evidence from a challenging and underexplored context, offering insights into the conditions enabling GI in traditional industries. © 2026 John Wiley & Sons Ltd. KW - artificial intelligence KW - green innovation KW - knowledge management KW - oil industry KW - sustainability KW - trust in technology CY - Iraq, Jordan, United States, Malaysia ER - TY - JOUR TI - From Synergy to Strain: Exploring the Psychological Mechanisms Linking Employee–AI Collaboration and Knowledge Hiding AU - Li Y.-B. AU - Liao T.-H. AU - Tsai C.-H. AU - Wu T.-J. PY - 2026 JO - Behavioral Sciences VL - 16 IS - 1 SP - 13 DO - 10.3390/bs16010013 AB - As artificial intelligence (AI) becomes an integral part of organizational operations, collaboration between humans and AI is transforming employees’ work experiences and behavioral patterns. This study examines the psychological challenges and coping responses associated with such collaboration. Drawing on Cognitive Appraisal Theory, we construct and test a theoretical framework that connects employee–AI collaboration to knowledge hiding via job insecurity, while considering AI trust as a moderating variable. Data were collected through a three-wave time-lagged survey of 348 employees working in knowledge-intensive enterprises in China. The empirical results demonstrate that (1) employee–AI collaboration elevates perceptions of job insecurity; (2) job insecurity fosters knowledge-hiding behavior; (3) job insecurity mediates the link between collaboration and knowledge hiding; and (4) AI trust buffers the positive effect of collaboration on job insecurity, thereby reducing its indirect impact on knowledge hiding. These findings reveal the paradoxical role of AI collaboration: although it enhances efficiency, it may also provoke defensive reactions that inhibit knowledge exchange. By highlighting the role of AI trust in shaping employees’ cognitive appraisals, this study advances understanding of how cognitive appraisals influence human adaptation to intelligent technologies. Practical insights are offered for managers aiming to cultivate trust-based and psychologically secure environments that promote effective human–AI collaboration and organizational innovation. © 2025 by the authors. KW - AI trust KW - Cognitive Appraisal Theory KW - employee–AI collaboration KW - job insecurity KW - knowledge hiding KW - adult KW - Article KW - artificial intelligence KW - cognitive appraisal KW - conceptual framework KW - descriptive research KW - employee KW - female KW - human KW - job insecurity KW - knowledge KW - male KW - psychological functioning KW - social problem KW - workplace CY - China, Taiwan ER - TY - JOUR TI - DEVELOPING EMPATHY AS A STRATEGIC AND TACTICAL SKILL IN THE CONTEXT OF INNOVATING FOR TRANSGENDER CONSUMERS AU - Braig B.M. AU - Witt H. PY - 2024 JO - Marketing Education Review VL - 34 IS - 1 SP - 60 EP - 76 DO - 10.1080/10528008.2023.2226124 AB - We begin with the premise that empathy adds value as a strategic marketing skill. Getting into the heads and hearts of consumers enables tailored offerings and tactics that meet the unique, richly contextualized needs of a given target audience segment. The advent of marketing automation and artificial intelligence (AI) is predicted to make human-centered skills even more critical to develop, as AI’s capabilities stop short of emotional connection and interpretation. As a result, we ask how empathy can be cultivated and applied, and if so, will empathy toward a given market segment result in attitude or behavior change? This paper details a three-stage project that gives students the opportunity to practice using empathy toward a specific market segment–transgender consumers. Students leverage empathy to ultimately develop concrete innovations across a broad range of marketing tactics. We also discuss the impact of the project on students. Results indicate that students completing the project embraced empathy as a strategic skill, and in the process, many also expressed intended support behaviors for the transgender community, although attitudes toward the transgender community did not change. © 2023 Society for Marketing Advances. CY - United States ER - TY - JOUR TI - The impact of artificial intelligence capabilities on servitization: The moderating role of absorptive capacity-A dynamic capabilities perspective AU - Abou-Foul M. AU - Ruiz-Alba J.L. AU - López-Tenorio P.J. PY - 2023 JO - Journal of Business Research VL - 157 SP - 113609 DO - 10.1016/j.jbusres.2022.113609 AB - The advent of artificial intelligence (AI)-based technologies has opened new opportunities for manufacturers to maintain their technological edge and address pressing societal challenges. This research investigates the nature of the relationships between AI capabilities, servitization, and the role of absorptive capacity. Building on dynamic capabilities literature, we developed and empirically tested a model using structural equation modeling (SEM) and further applied a fuzzy-set qualitative comparative analysis (fsQCA). Through the construct of AI capabilities and its four sub-dimensions, we find supportive evidence from our model estimates employing data from 185 manufacturing firms in the US and EU. The study findings highlight the positive impact of AI capabilities on servitization; this relationship is positively moderated by absorptive capacity. Furthermore, the road to servitization is through advancing AI capabilities related to internal process and resource optimization coupled with AI for social innovation services. The study's theoretical and pragmatic implications are discussed. © 2022 Elsevier Inc. KW - Artificial intelligence KW - Dynamic capabilities KW - Fuzzy set qualitative comparative analysis (fsQCA) KW - Servitization KW - Social innovation CY - Palestine, United Kingdom, Spain ER - TY - JOUR TI - KEY COMPETENCES for the ADOPTION of AI-BASED INNOVATIONS in ORGANISATIONS AU - Baumgartner M. AU - Horvat D. AU - Kinkel S. AU - Kick E. PY - 2024 JO - International Journal of Innovation Management VL - 28 IS - 10 SP - 2440002 DO - 10.1142/S1363919624400024 AB - Successfully adopting AI and realising its full innovation potential requires different competences within a company. We identified five clusters, namely, AI decision-making, AI utilisation, AI foundational, AI development and leadership & moderation competences, as the basis for our AI competence framework, combining 35 individual competences. Based on a quantitative survey of 215 companies, we determined the importance of these competences for the successful adoption of AI innovations and their current availability within companies. According to our findings, AI foundational competences play a particularly critical role compared to the other competence clusters, which are considered important but comparatively rarely available. Furthermore, our analyses show that companies with higher levels of AI foundational, AI development, and AI utilisation competences have significantly higher AI innovation capabilities. Again, in particular AI foundational competences seem to fertilize the capabilities to identify appropriate AI use cases, to make decisions about AI innovation adoption, to successfully integrate AI into internal processes, and to use the AI innovation effectively within the organisation. Our findings thus enrich the theoretical discourse on competences for organisational adoption of AI innovations and guide practitioners in taking action to develop the necessary competences. © 2024 World Scientific Publishing Europe Ltd. KW - AI capabilities KW - artificial intelligence KW - company KW - Competences KW - digital competences KW - framework KW - future skills KW - innovation adoption KW - organisation KW - survey CY - Germany ER - TY - JOUR TI - The green transition journey: How digital platforms and AI capabilities drive green ambidexterity innovation, circular economy and sustainable performance AU - Duong C.D. PY - 2026 JO - World Development Sustainability VL - 8 SP - 100296 DO - 10.1016/j.wds.2026.100296 AB - Despite growing attention to sustainability in SMEs, limited research explains the capability-building mechanism through which digital and AI capabilities enable green transition and circular economy outcomes under resource constraints. Drawing on Dynamic Capability Theory, this study examines how digital platform capability and AI capability foster green transition and ambidextrous green innovation, thereby enhancing circular economy practices and sustainable performance. Using survey data from 286 Vietnamese SMEs and applying hierarchical regression and polynomial regression with response surface analysis, the findings show that digital capability exerts a stronger effect on green transition than AI capability. Both exploratory and exploitative green innovation significantly improve sustainability outcomes, though through distinct pathways. Balanced innovation maximizes circular economy practices, whereas imbalance may weaken circularity but improve performance. The study advances dynamic capability theory by clarifying the innovation-mediated pathway linking digital transformation to sustainability in emerging-economy SMEs. © 2026 The Author(s). KW - AI capabilities KW - Circular economy practices KW - Digital platform capabilities KW - Green ambidexterity innovation KW - Green transition KW - Sustainable performance ER - TY - JOUR TI - Subthalamic nucleus or globus pallidus internus deep brain stimulation for the treatment of parkinson's disease: An artificial intelligence approach AU - Shin D. AU - Tang T. AU - Carson J. AU - Isaac R. AU - Dinh C. AU - Im D. AU - Fay A. AU - Isaac A. AU - Cho S. AU - Brandt Z. AU - Nguyen K. AU - Shaffrey I. AU - Yacoubian V. AU - Taka T.M. AU - Spellicy S. AU - Lopez-Gonzalez M.A. AU - Danisa O. PY - 2025 JO - Journal of Clinical Neuroscience VL - 138 SP - 111393 DO - 10.1016/j.jocn.2025.111393 AB - Background: Generative artificial intelligence (AI) in deep brain stimulation (DBS) is currently unvalidated in its content. This study sought to analyze AI responses to questions and recommendations from the 2018 Congress of Neurological Surgeons (CNS) guidelines on subthalamic nucleus and globus pallidus internus DBS for the treatment of patients with Parkinson's Disease. Methods: Seven questions were generated from CNS guidelines and asked to ChatGPT 4o, Perplexity, Copilot, and Gemini. Answers were “concordant” if they highlighted all points provided by the CNS guidelines; otherwise, answers were considered “non-concordant” and sub-categorized as either “insufficient” or “overconclusive.” AI responses were evaluated for readability via the Flesch-Kincaid Grade Level, Gunning Fog Index, Simple Measure of Gobbledygook (SMOG) Index, and Flesch Reading Ease tests. Results: ChatGPT 4o showcased 42.9% concordance, with non-concordant responses classified as 14.3% insufficient and 42.8% over-conclusive. Perplexity displayed a 28.6% concordance rate, with 14.3% insufficient and 57.1% over-conclusive responses. Copilot showed 28.6% concordance, with 28.6% insufficient and 42.8% over-conclusive responses. Gemini demonstrated 28.6% concordance, with 28.6% insufficient and 42.8% over-conclusive responses. The Flesch-Kincaid Grade Level scores ranged from 14.44 (Gemini) to 18.94 (Copilot), Gunning Fog Index scores varied between 17.9 (Gemini) and 22.06 (Copilot), SMOG Index scores ranged from 16.54 (Gemini) to 19.67 (Copilot), and all Flesch Reading Ease scores were low, with Gemini showing the highest score of 30.91. Conclusion: ChatGPT 4o displayed the most concordance, Perplexity displayed the highest over-conclusive rate, and Copilot and Gemini showcased the most insufficient answers. All responses showcased complex readability. Despite the possible benefits of future developments and innovation in AI capabilities, AI requires further improvement before independent clinical usage in DBS. © 2025 Elsevier Ltd KW - Artificial intelligence KW - Chatgpt KW - Deep brain stimulation KW - Neurosurgery KW - Parkinson's disease KW - Artificial Intelligence KW - Deep Brain Stimulation KW - Globus Pallidus KW - Humans KW - Parkinson Disease KW - Subthalamic Nucleus KW - Article KW - artificial intelligence KW - artificial intelligence chatbot KW - brain depth stimulation KW - ChatGPT KW - clinical outcome KW - clinical practice guideline KW - Copilot KW - Gemini KW - globus pallidus KW - globus pallidus internus KW - human KW - neurosurgery KW - Parkinson disease KW - Perplexity KW - reading KW - reliability KW - subthalamic nucleus KW - Unified Parkinson Disease Rating Scale KW - Parkinson disease KW - procedures KW - therapy CY - United States ER - TY - JOUR TI - Merging two revolutions: A human-artificial intelligence method to study how sustainability and Industry 4.0 are intertwined AU - Calabrese A. AU - Costa R. AU - Tiburzi L. AU - Brem A. PY - 2023 JO - Technological Forecasting and Social Change VL - 188 SP - 122265 DO - 10.1016/j.techfore.2022.122265 AB - Industry 4.0 is an important contributor to industrial innovation and sustainability. Nevertheless, few studies empirically analyse how it acts as a binding force of both business practices. This study examines 1501 sustainability reports using a mixed human-artificial intelligence method based on Python's text mining libraries. This method takes advantage of AI's capabilities to extract information from large samples of data and of human critical thinking to find patterns in those data. Specifically, the method is used to evaluate the adoption of Industry 4.0 technologies, analyse how they are deployed worldwide, and investigate their sustainability outcomes. In terms of overall frequency, robots and cybersecurity are the most often reported technologies. Broken down by the firm's region, Asian firms have the highest rate of adoption, while African firms are lagging. Regarding the themes, Industry 4.0 is mainly adopted to improve production processes and customer experience. A small percentage of firms, particularly in Europe and North America, utilize Industry 4.0 to reduce the environmental footprint of their operations. Furthermore, results indicate that Industry 4.0 and sustainability are following two routes. Some firms have massively adopted Industry 4.0 to increase operational efficiency and reaped environmental gains as an indirect consequence of improved operations. Others have chosen to balance the adoption of technologies aimed to increase productivity with innovations whose explicit aim is the reduction of their operations' environmental footprint, such as additive manufacturing. Eastern firms tend to follow the first route while western firms the second. African and South American firms are still at a very early stage in their Industry 4.0 and sustainability journey. At the global level, Industry 4.0 is still far from being utilized as a catalyst to develop sustainability-driven business models. © 2023 Elsevier Inc. KW - GRI KW - Industry 4.0 KW - Innovation KW - Sustainability KW - Text mining KW - Europe KW - North America KW - Artificial intelligence KW - Data mining KW - Environmental technology KW - Industry 4.0 KW - Artificial intelligence methods KW - Binding forces KW - Business practices KW - Environmental footprints KW - GRI KW - Industrial innovation KW - Industrial sustainability KW - Innovation KW - Sustainability report KW - Text-mining KW - artificial intelligence KW - empirical analysis KW - industrial development KW - innovation KW - sustainability KW - Sustainable development CY - Italy, Germany, Denmark ER - TY - JOUR TI - Experiential learning and governance in the socio-technical era: Modeling responsible AI performance via explainability and adaptability AU - Liu M. AU - Almugren I. AU - Chotia V. AU - Sahore N. AU - Kurucz A. PY - 2026 JO - Technological Forecasting and Social Change VL - 227 SP - 124624 DO - 10.1016/j.techfore.2026.124624 AB - The concept of artificial intelligence (AI) is altering the way organizations operate. AI systems will deliver more intelligent results in a shorter period of time, starting with decision-making up to innovation. However, the more it is adopted, the more issues to do with fairness, transparency, and accountability are raised. Most organizations are finding it difficult to reconcile innovation and ethical responsibility. This study discusses the role of internal capabilities in making firms govern AI responsibly. The study proposes a model linking four key organizational capabilities, i.e., explainable AI capability, contextual learning adaptability, experiential learning orientation, and organizational ethical alignment to responsible AI performance. The impact of these capabilities on user interpretability and trust, responsible AI governance maturity, and decision transparency is also examined in this study. The results show that explainable AI capability and learning adaptability enhance user trust, while experiential learning orientation and organizational ethical alignment significantly improve governance maturity. Governance maturity and decision transparency lead to stronger responsible AI performance. Interestingly, not all expected paths held as user interpretability trust and governance maturity did not directly predict decision transparency. The findings show that building technical and cultural capabilities inside firms is essential not just to deploy AI effectively, but to do it responsibly. For leaders, this means moving beyond checklists and toward meaningful governance rooted in learning, transparency, and ethical alignment. © 2026 Elsevier Inc. KW - Contextual learning adaptability KW - Decision transparency KW - Experiential learning orientation KW - Explainable AI capability KW - Organizational ethical alignment KW - Responsible AI governance maturity KW - Responsible AI performance KW - User interpretability trust KW - Alignment KW - Artificial intelligence KW - Decision making KW - Ethical technology KW - Learning systems KW - Contextual learning KW - Contextual learning adaptability KW - Decision transparency KW - Experiential learning KW - Experiential learning orientation KW - Explainable artificial intelligence capability KW - Interpretability KW - Learning orientation KW - Organisational KW - Organizational ethical alignment KW - Performance KW - Responsible artificial intelligence governance maturity KW - Responsible artificial intelligence performance KW - User interpretability trust KW - artificial intelligence KW - ethics KW - governance approach KW - learning KW - performance assessment KW - Transparency CY - China, Saudi Arabia, India, Hungary ER - TY - JOUR TI - AI-Augmented Design Thinking: Potentials, Challenges, and Mitigation Strategies of Integrating Artificial Intelligence in Human-Centered Innovation Processes AU - Polster L. AU - Bilgram V. AU - Gortz S. PY - 2025 JO - IEEE Engineering Management Review VL - 53 IS - 5 SP - 193 EP - 214 DO - 10.1109/EMR.2024.3512866 AB - The integration of artificial intelligence (AI) into innovation management has expanded into creative domains such as design thinking (DT), yet its role within complex, collaborative innovation frameworks remains underexplored. This article addresses this gap by investigating how professionals perceive and utilize AI in DT workshops. Using affordance theory as a conceptual lens, we conducted observational studies and semistructured interviews with DT experts to identify AI's action potentials, constraints, and mitigation strategies in this context. Our findings highlight four key affordances of AI in DT workshops: enhanced creativity, support for analytical tasks, facilitation of task initiation, and acceleration of processes. However, these benefits are tempered by challenges, including reduced team collaboration, diminished ownership of AI-generated outputs, and disruptions to workshop flow. The article reveals distinct human–AI interaction archetypes, underscoring the dynamic interplay between human expertise and AI capabilities. To mitigate constraints, we propose strategies such as prepreparing AI-generated content, defining clear roles for AI and human inputs, and fostering collaborative reflection on AI outputs. By illuminating AI's potential and limitations within DT, this article contributes to the innovation management literature and offers actionable insights for practitioners seeking to integrate AI into hybrid innovation processes. © 1973-2011 IEEE. KW - AI-augmented design thinking KW - artificial intelligence (AI) KW - ChatGPT KW - design thinking (DT) KW - generative AI (GenAI) KW - ideation workshop KW - innovation KW - large language models KW - Ai-augmented design thinking KW - Chatgpt KW - Design thinking KW - Generative artificial intelligence KW - Ideation workshop KW - Innovation KW - Language model KW - Large language model KW - Mitigation strategy CY - Germany ER - TY - JOUR TI - AI Legislation, Private International Law and the Protection of Human Rights in the European Union AU - Malacka M. PY - 2024 JO - European Studies: The Review of European Law, Economics and Politics VL - 11 IS - 1 SP - 122 EP - 151 DO - 10.2478/eustu-2024-0006 AB - The emergence of artificial intelligence challenges existing legal frameworks, notably in civil liability, cross-border regulation, and the protection of fundamental rights. The European Union has developed the AI Regulation and AI Liability Directive to address these issues, emphasizing transparency, accountability, and consumer protection while promoting innovation. This regulatory framework categorizes AI systems by risk levels and mandates strict compliance for high-risk applications, ensuring alignment with fundamental EU values. Additionally, the Council of Europe AI Convention complements these efforts by focusing on human rights, democracy, and the rule of law, offering a broader international perspective. Both frameworks present complementary yet distinct approaches to AI governance, with the EU focusing on market harmonization and innovation, and the Convention prioritizing ethical and social dimensions. The interplay between these instruments underscores the EU's ambition to set a global standard for AI regulation while addressing the complexities of private international law and cross-border liability. The success of this legal framework will depend on its flexibility, coherence, and ability to adapt to rapid technological developments. © 2024 Michal Malacka, published by Sciendo. KW - Accountability KW - AI Liability Directive KW - Artificial Intelligence (AI) KW - Civil Liability KW - Council of Europe's AI Convention KW - Cross-border Regulation KW - EU AI Regulation KW - Human Rights KW - Innovation KW - Private International Law (PIL) KW - Transparency CY - Czech Republic ER - TY - JOUR TI - Generative artificial intelligence in animal genomics for smart agriculture: Applications, challenges, and future prospects AU - Ghavi Hossein-Zadeh N. PY - 2026 JO - Veterinary and Animal Science VL - 33 SP - 100702 DO - 10.1016/j.vas.2026.100702 AB - Generative artificial intelligence (AI) is becoming a groundbreaking paradigm in the field of animal genomics and is providing the possibility to take a step towards intelligent agriculture, with better data integration, predictive modeling, and biological design. This review focuses on the shift from predictive to generative modelling paradigms, examining their implications for data synthesis, biological sequence design, and integrative smart livestock systems. It provides a comprehensive overview of recent developments, applications, and challenges at the intersection of generative AI and animal genomics, as well as future directions. In doing so, it sheds light on novel opportunities and constraints specific to livestock genomics that are not adequately addressed in broader AI or human genomics studies. Thus, it bridges the gap between computational innovations and biological constraints. It initially sets the conceptual background in place by looking at the development of smart agriculture, the essentiality of animal genomics, and the development of generative model architectures in life sciences, as well as fundamental methodological aspects, including livestock genomic and multi-omics data peculiarities and the representation of biological sequences. The review then comprehensively discusses a wide range of applications such as genomic data augmentation, prediction of new genetic variants, design of protein and gene sequences, augmentation of genomic selection and trait prediction, regulatory and epigenomic modeling, accurate breeding and reproductive technologies, and cross-species genomic modeling, illustrating how generative AI is transforming genomics into something generative, enhanced through simulation. It is discussed in terms of integration into systems of smart agriculture, connections with precision livestock farming, digital twin, genomics-to-management pipelines, and sustainability-focused systems of decision-making, where the adaptive, individualized, and system-level optimization can be applied. Critical analysis of major challenges and limitations, such as heterogeneity and scarcity of data, model bias and generalization, computational and resource limitations, validation and interpretability issues, and ethical, legal, and social constraints that drove the responsible deployment are also critically reviewed. Lastly, the future opportunities are discussed, which should center on generative genome engineering, multimodal and federated modeling, species preservation, real-time interaction with smart farming technologies, and the creation of responsible and ethical AI frameworks. Overall, it is possible to state that this review makes generative AI a base technology of the new generation of animal genomics and smart agriculture, but it highlights that interdisciplinary cooperation, stringent validation, and alignment with the notions of sustainability, animal welfare, or values are necessary to realize its capabilities. © 2026 The Author(s). KW - Animal genomics KW - Digital twins KW - Generative AI KW - Large biological models KW - Precision livestock farming KW - Smart agriculture KW - Sustainable breeding and management KW - agricultural worker KW - agriculture KW - animal welfare KW - article KW - biological model KW - breeding KW - data integration KW - data synthesis KW - decision making KW - digital twin KW - drug development KW - gene sequence KW - generative artificial intelligence KW - generative model KW - genetic variability KW - genomics KW - human KW - livestock KW - multiomics KW - nonhuman KW - prediction KW - predictive model KW - simulation CY - Iran ER - TY - JOUR TI - Role of AI capabilities in sustainable firm performance: mediating role of innovative work behavior and the moderating effect of environmental dynamism AU - Shahid M.K. AU - Mahmood K. AU - Altayyar R.S. AU - Abdullah H. PY - 2026 JO - International Journal of Productivity and Performance Management VL - 75 IS - 2 SP - 651 EP - 671 DO - 10.1108/IJPPM-02-2025-0114 AB - Purpose – This study assesses the role of organizational AI capabilities (AIC) in framing employees' innovative work behavior (IWB) and their subsequent effect on sustainable firm performance (SFP). In addition, the role of environmental dynamism (ED) was also assessed on the relationship between AIC and IWB pursuant to SFP. Design/methodology/approach – We collected data from managerial-level employees working in the pharmaceutical industry across Pakistan. A validated survey questionnaire was circulated using snowball sampling technique, and partial least square structural equation modeling (PLS-SEM) was applied to the data collected from 366 respondents. After confirming the reliability and validity of the model, we assessed the robustness of the results through linearity, endogeneity and unobserved heterogeneity tests. Findings – The results of the study indicate that AIC significantly influences IWB and SFP. The moderation-mediation analysis confirms the importance of both the direct and indirect effects of ED on the relationship between AIC and SFP, as well as the role of ED in enhancing IWB among employees in the pharmaceutical industry. Originality/value – This study provides significant insights in advancing sustainable development through technological innovation, considering ED among the pharmaceutical industry within the developing economies having identical technological, sectoral and environmental concerns. © 2025 Emerald Publishing Limited KW - AI capabilities KW - Environmental dynamism KW - Innovative work behavior KW - PLS-SEM KW - Sustainable firm performance CY - Pakistan, Malaysia, Saudi Arabia ER - TY - JOUR TI - Command Redefined: Neural-Adaptive Leadership in the Age of Autonomous Intelligence AU - Riti R.I. AU - Abrudan C.I. AU - Bacali L. AU - Bâlc N. PY - 2025 JO - AI (Switzerland) VL - 6 IS - 8 SP - 176 DO - 10.3390/ai6080176 AB - Artificial intelligence has taken a seat at the executive table and is threatening the fact that human beings are the only ones who should be in a position of power. This article gives conjectures on the future of leadership in which managers will collaborate with learning algorithms in the Neural Adaptive Artificial Intelligence Leadership Model, which is informed by the transformational literature on leadership and socio-technical systems, as well as the literature on algorithmic governance. We assessed the model with thirty in-depth interviews, system-level traces of behavior, and a verified survey, and we explored six hypotheses that relate to algorithmic delegation and ethical oversight, as well as human judgment versus machine insight in terms of agility and performance. We discovered that decisions are made quicker, change is more effective, and interaction is more vivid where agile practices and good digital understanding exist, and statistical tests propose that human flexibility and definite governance augment those benefits as well. It is single-industry research that contains self-reported measures, which causes research to be limited to other industries that contain more objective measures. Practitioners are provided with a practical playbook on how to make algorithmic jobs meaningful, introduce moral fail-safes, and build learning feedback to ensure people and machines are kept in line. Socially, the practice is capable of minimizing bias and establishing inclusion by visualizing accountability in the code and practice. Filling the gap between the theory of leadership and the reality of algorithms, the study provides a model of intelligent systems leading in organizations that can be reproduced. © 2025 by the authors. KW - AI–human governance KW - algorithmic decision-making KW - engineering management innovation KW - ethical leadership in AI KW - neural-adaptive leadership CY - Romania ER - TY - JOUR TI - Unveiling the transformative role of artificial intelligence in improving business process performance AU - Sharma P. AU - Dang G.P. PY - 2025 JO - Journal of Manufacturing Technology Management SP - 1 EP - 21 DO - 10.1108/JMTM-04-2025-0308 AB - Purpose – The present study intends to examine the role of AI in enhancing the process performance of manufacturing entities in India. It aims to explore factors that measure the business process performance, which are influenced by the adoption of AI within various business processes. Design/methodology/approach – The research conducted an empirical survey on Indian Manufacturing organizations using a questionnaire-based survey method on those businesses that have adopted AI within their business processes. For this, the study targeted C-level technology managers in the select manufacturing businesses in India. The structural equation modelling (SEM) technique was applied for data analysis. Findings – The findings of the study indicate that the adoption of AI has a significant positive role in improving the business process performance of manufacturing firms. It can bring manifold advantages to business firms, including accuracy, speed, operational cost reduction, improved quality, enhanced productivity and efficiency. These benefits can help organizations in India to gain a competitive advantage in the changing business world. Originality/value – The present article explores the transformative role of AI in the manufacturing sector of a rapidly developing economy like India. It provides empirical evidence on certain crucial benefits of AI, including quality enhancement, cost efficiency and productivity enhancement. This research offers valuable insights for both business leaders and policymakers on leveraging AI to drive industrial growth and competitiveness. It contributes to the limited literature on the practical implications of AI in emerging markets, particularly within the Indian context. © 2025 Emerald Publishing Limited KW - AI capabilities KW - Business process performance KW - Infrastructure flexibility KW - Management capabilities KW - Manufacturing entities KW - Personnel expertise KW - Administrative data processing KW - Competition KW - Cost reduction KW - Efficiency KW - AI capability KW - Business Process KW - Business process performance KW - Design/methodology/approach KW - Empirical surveys KW - Infrastructure flexibility KW - Management capabilities KW - Manufacturing entities KW - Personnel expertise KW - Process performance KW - Industrial research CY - India ER - TY - JOUR TI - The critical role of HRM in AI-driven digital transformation: a paradigm shift to enable firms to move from AI implementation to human-centric adoption AU - Fenwick A. AU - Molnar G. AU - Frangos P. PY - 2024 JO - Discover Artificial Intelligence VL - 4 IS - 1 SP - 34 DO - 10.1007/s44163-024-00125-4 AB - The rapid advancement of Artificial Intelligence (AI) in the business sector has led to a new era of digital transformation. AI is transforming processes, functions, and practices throughout organizations creating system and process efficiencies, performing advanced data analysis, and contributing to the value creation process of the organization. However, the implementation and adoption of AI systems in the organization is not without challenges, ranging from technical issues to human-related barriers, leading to failed AI transformation efforts or lower than expected gains. We argue that while engineers and data scientists excel in handling AI and data-related tasks, they often lack insights into the nuanced human aspects critical for organizational AI success. Thus, Human Resource Management (HRM) emerges as a crucial facilitator, ensuring AI implementation and adoption are aligned with human values and organizational goals. This paper explores the critical role of HRM in harmonizing AI's technological capabilities with human-centric needs within organizations while achieving business objectives. Our positioning paper delves into HRM's multifaceted potential to contribute toward AI organizational success, including enabling digital transformation, humanizing AI usage decisions, providing strategic foresight regarding AI, and facilitating AI adoption by addressing concerns related to fears, ethics, and employee well-being. It reviews key considerations and best practices for operationalizing human-centric AI through culture, leadership, knowledge, policies, and tools. By focusing on what HRM can realistically achieve today, we emphasize its role in reshaping roles, advancing skill sets, and curating workplace dynamics to accommodate human-centric AI implementation. This repositioning involves an active HRM role in ensuring that the aspirations, rights, and needs of individuals are integral to the economic, social, and environmental policies within the organization. This study not only fills a critical gap in existing research but also provides a roadmap for organizations seeking to improve AI implementation and adoption and humanizing their digital transformation journey. © The Author(s) 2024. KW - AI knowledge KW - AI leadership KW - AI policies KW - AI tools KW - Behavioral science KW - HRM KW - Humanizing AI KW - Organizational culture KW - Behavioral research KW - Data handling KW - Environmental protection KW - Metadata KW - Artificial intelligence knowledge KW - Artificial intelligence leadership KW - Artificial intelligence policy KW - Artificial intelligence tools KW - Behavioral science KW - Digital transformation KW - Human resources management KW - Human-centric KW - Humanizing artificial intelligence KW - Organizational cultures KW - Human resource management CY - United Kingdom ER - TY - JOUR TI - A Review of Generative AI's Impact on Workforce Transformation and Future Skill Requirements AU - Oyetade K. AU - Zuva T. PY - 2025 JO - OIDA International Journal of Sustainable Development VL - 18 IS - 12 SP - 187 EP - 196 AB - The Fourth Industrial Revolution (4IR) is transforming industries and workforce structures through rapid advancements in artificial intelligence (AI), automation, and digital technologies. Among these innovations, Generative AI (GAI) has emerged as a disruptive force capable of autonomously producing text, images, and code, thereby redefining traditional job roles and skills requirements. While GAI boosts productivity and creativity in a variety of industries, it also poses issues such as job displacement, skill mismatches, and ethical concerns. This study reviews 46 peer-reviewed journal articles, conference papers, and policy reports published between 2018 and 2025 to examine GAI’s impact on workforce transformation and the evolving demand for future skills. Using a qualitative literature review approach and thematic analysis, the study identifies recurring themes such as technological disruption, job displacement, skill mismatches, and the emergence of AI-driven professions. To ensure validity and minimize internal bias from third-party sources, the analysis applied triangulation, source credibility checks, and cross-disciplinary comparison, ensuring that findings were grounded in verified evidence. The results emphasize the growing need for continuous learning, reskilling, and integration of AI-related competencies, particularly digital literacy, critical thinking, creativity, and emotional intelligence, within education and professional development programs. Policymakers and industries must collaborate to develop inclusive strategies that promote equitable workforce adaptation, ethical AI governance, and resilience in the face of automation. This study contributes to the discussion on responsible AI adoption by providing insights into workforce evolution, skill adaptation, and policy directions in the era of 4IR. © Authour(s) OIDA International Journal of Sustainable Development, Ontario International Development Agency, Canada. KW - AI Policy and Ethical Governance KW - Fourth Industrial Revolution (4IR) KW - Future Skills Development KW - Generative Artificial Intelligence (GAI) KW - Workforce Transformation CY - South Africa ER - TY - JOUR TI - Adoption of robust business analytics for product innovation and organizational performance: the mediating role of organizational data-driven culture AU - Chaudhuri R. AU - Chatterjee S. AU - Vrontis D. AU - Thrassou A. PY - 2024 JO - Annals of Operations Research VL - 339 IS - 3 SP - 1757 EP - 1791 DO - 10.1007/s10479-021-04407-3 AB - In the present digital environment, a data-driven organizational culture has become a vital emerging driver of organizational growth. This data-driven culture has assumed an advanced shape due to adoption of artificial intelligence (AI) integrated business analytics tools in the organization. Data-driven culture in the organization could considerably impact product innovation strategy as well as organizational process alteration. In this context, the aim of this study is to investigate how an organization’s data-driven culture impacts process performance and product innovation that led to enhanced organizational overall performance and higher business value. Methodologically, supported by relevant extant literature and inputs from the resource-based view and dynamic capability theories (organizational context), a conceptual model and a set of hypotheses are initially developed. These are subsequently statistically validated through a survey involving 513 usable responses from employees of different organizations using business analytics tools embedded with AI capability. The findings demonstrate that an organizational data-driven culture has considerable moderating impact on product innovation and process improvement, which ultimately enhance business value through improved organizational overall performance. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021. KW - Business analytics KW - Business value KW - Data acquisition KW - Data-driven culture KW - Organizational performance KW - Product innovation CY - India, Cyprus ER - TY - JOUR TI - Addressing brain drain and strengthening governance for advancing government readiness in artificial intelligence (AI) AU - Socol A. AU - Iuga I.C. PY - 2024 JO - Kybernetes VL - 53 IS - 13 SP - 47 EP - 71 DO - 10.1108/K-03-2024-0629 AB - Purpose: This study aims to investigate the impact of brain drain on government AI readiness in EU member countries, considering the distinctive governance characteristics, macroeconomic conditions and varying levels of ICT specialists. Design/methodology/approach: The research employs a dynamic panel data model using the System Generalized Method of Moments (GMM) to analyze the relationship between brain drain and government AI readiness from 2018 to 2022. The study incorporates various control variables such as GDP per capita growth, government expenditure growth, employed ICT specialists and several governance indicators. Findings: The results indicate that brain drain negatively affects government AI readiness. Additionally, the presence of ICT specialists, robust governance structures and positive macroeconomic indicators such as GDP per capita growth and government expenditure growth positively influence AI readiness. Research limitations/implications: Major limitations include the focus on a specific region of countries and the relatively short period analyzed. Future research could extend the analysis with more comprehensive datasets and consider additional variables that might influence AI readiness, such as the integration of AI with emerging quantum computing technologies and the impact of governance reforms and international collaborations on AI readiness. Practical implications: The theoretical value of this study lies in providing a nuanced understanding of how brain drain impacts government AI readiness, emphasizing the critical roles of skilled human capital, effective governance and macroeconomic factors in enhancing AI capabilities, thereby filling a significant gap in the existing literature. Originality/value: This research fills a significant gap in the existing literature by providing a comprehensive analysis of the interaction between brain drain and government AI readiness. It uses control variables such as ICT specialists, governance structures and macroeconomic factors within the context of the European Union. It offers novel insights for policymakers to enhance AI readiness through targeted interventions addressing brain drain and fostering a supportive environment for AI innovation. © 2024, Adela Socol and Iulia Cristina Iuga. KW - Artificial intelligence (AI) KW - Brain drain KW - EU member countries KW - Governance KW - ICT specialists KW - Macroeconomic indicators KW - Inflation KW - Artificial intelligence KW - Control variable KW - EU member country KW - Governance KW - Governance structures KW - Government expenditure KW - ICT specialist KW - Macroeconomic indicators KW - Member countries KW - Per capita KW - Artificial intelligence CY - Romania ER - TY - JOUR TI - Did the COVID-19 pandemic propel usage of AI in pharmaceutical innovation? New evidence from patenting data AU - Rathi S. AU - Majumdar A. AU - Chatterjee C. PY - 2024 JO - Technological Forecasting and Social Change VL - 198 SP - 122940 DO - 10.1016/j.techfore.2023.122940 AB - It is now much discussed that Artificial Intelligence (AI) as a General-Purpose Technology (GPT) can resolve the efficiency problems of industries, including in pharmaceutical markets where productivity challenges continue in costs and time for new drug discovery. But did the COVID-19 pandemic inadvertently accelerate the pace of AI adoption in pharmaceutical innovation? We answer this question using novel data on pharmaceutical patents. We use two different databases to analyze abstracts of pharmaceutical patents applied in the USA. Topic modeling was used to identify patents with technical artifacts and classify them as treated group AI-adopting patents. An AI dictionary is used to match AI-related keywords in the patent abstracts. Subsequently, using a difference-in-differences research design we observe that both presence and count of AI keywords in pharmaceutical patents have increased with pandemic. An increase in AI is also related to reduced time taken from application to publication of a patent suggesting innovation efficiencies in the industry. Finally, we find that results are driven by firms that have already built AI capability in the past. Our results remain consistent with various robustness checks, and we conclude by discussing managerial and policy implications of our findings. © 2023 KW - AI KW - Innovation management KW - Pandemic KW - Patents KW - Pharmaceutical industry KW - United States KW - Abstracting KW - Patents and inventions KW - Public policy KW - Drug discovery KW - General purpose technologies KW - Innovation management KW - Pandemic KW - Patent KW - Pharmaceutical industry KW - Pharmaceutical innovations KW - Pharmaceutical market KW - Technical artifacts KW - Topic Modeling KW - artificial intelligence KW - COVID-19 KW - data KW - epidemic KW - innovation KW - pharmaceutical industry KW - Efficiency CY - India, United States ER - TY - JOUR TI - Achieving green competitive advantage through generative AI: the mediating roles of organisational creativity and green innovation ambidexterity in manufacturing AU - Ruangkanjanases A. AU - Chen S.-C. AU - Sivarak O. AU - Khan A. PY - 2025 JO - International Journal of Logistics Research and Applications DO - 10.1080/13675567.2025.2573664 AB - This study examines the implementation pathway of Generative AI Capabilities (GAIC) in manufacturing organisations, focusing on the roles of organisational creativity and green innovation ambidexterity in achieving a green competitive advantage. Drawing on the Technology-Organisation-Environment (TOE) framework, this research develops and empirically tests an integrated model using data collected from 297 senior and middle-level managers of manufacturing firms in Taiwan. The results indicate that both technological and organisational contexts have a significant influence on GAIC implementation, whereas the environmental context shows no significant impact. Furthermore, GAIC demonstrates significant positive effects on both organisational creativity and green innovation ambidexterity, which in turn enhances green competitive advantage. This study addresses critical research gaps by making several contributions by extending the TOE framework to encompass GAIC, thereby advancing the understanding of human-AI collaborative creativity. The findings provide novel insights into how manufacturing organisations can strategically implement GAIC to achieve a green competitive advantage. © 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. KW - environmental context KW - Generative AI capabilities KW - green competitive advantage KW - green innovation ambidexterity KW - organisational context KW - technological context CY - Thailand, Taiwan ER - TY - JOUR TI - From Chat To Cheat: the Disruptive Effects of ChatGPT and Academic Integrity in Hong Kong Higher Education AU - Lo N. AU - Chan S. PY - 2025 JO - SN Computer Science VL - 6 IS - 8 SP - 993 DO - 10.1007/s42979-025-04532-x AB - The rapid adoption of AI-powered conversational agents such as ChatGPT is transforming the landscape of higher education in Hong Kong, offering both unprecedented opportunities for personalszed learning and complex challenges to academic integrity. This study investigates the perceptions and experiences of students across eight Hong Kong universities, employing a quantitative design that analyses questionnaire data from 200 students. Results reveal a pronounced polarisation: while students—particularly those with lower GPAs—appreciate ChatGPT’s capacity to clarify complex concepts and facilitate research, their counterparts with higher GPAs express deep concerns about dependency, plagiarism, and the erosion of critical thinking skills. The findings underscore the urgent need for Hong Kong universities to implement comprehensive policies, advanced AI-detection tools, and targeted educational initiatives that foster a culture of integrity and responsible AI use. This research contributes to ongoing debates about the integration of generative AI in higher education, advocating for localised, policy-driven solutions that balance innovation with ethical stewardship. © The Author(s) 2025. KW - Academic integrity KW - Artificial intelligence KW - Higher education KW - Hong kong KW - Perceptions CY - United Kingdom ER - TY - JOUR TI - Curtain call for AI: Transforming theatre through technology AU - Horváth D. PY - 2025 JO - Sustainable Futures VL - 9 SP - 100747 DO - 10.1016/j.sftr.2025.100747 AB - The creative and cultural industries, including theatre, are significantly affected by the development of artificial intelligence (AI). In the theatre sector, there is a need for a deeper understanding of the impact of AI in this area, but the amount of research on this topic is extremely limited. The aim of this paper was to explore, within a complex framework, the applications of AI in the operational, support and artistic areas of theatre. The study also sought to understand the concerns of theatre practitioners regarding the application of AI and to formulate recommendations for its effective integration. To address the research questions, a series of 24 semi-structured interviews were conducted, employing grounded theory methodology with theatre practitioners who already actively utilising AI in their work were or exploring its potential impact. The findings of the study indicate that the adoption of AI-based solutions in operational and support areas is predominantly a bottom-up initiative, primarily in marketing, audience management and sales. In contrast, experimentation with AI is more prevalent in independent theatres and contemporary productions within the artistic domain. However, opinions on the utilisation of AI remain divided. The study emphasises the significance of human creativity and the necessity for a nuanced exploration of the role of AI in theatre. It advocates for transparency, collaboration, education, regulation and policy advocacy to ensure responsible AI integration. © 2025 KW - Artificial intelligence KW - Culture KW - Digital transformation KW - Grounded theory KW - Innovation KW - Theatre CY - Hungary ER - TY - JOUR TI - Artificial Intelligence capabilities in Digital Servitization: Identifying digital opportunities for different service types AU - Ayala N.F. AU - Rodrigues da Silva J. AU - Cannarozzo Tinoco M.A. AU - Saccani N. AU - Frank A.G. PY - 2025 JO - International Journal of Production Economics VL - 284 SP - 109604 DO - 10.1016/j.ijpe.2025.109604 AB - The advancement of digital technologies and the pursuit of higher-value solutions have driven companies to expand their portfolios with smart products and digital services, resulting in the innovation known as 'digital servitization.' This concept merges servitization —integrating services with products— and digitization —enhancing operations through digital technologies. While previous research has examined digital servitization and smart technologies, a gap remains in understanding how Artificial Intelligence (AI) specifically supports various types of digital servitization across both back-office and front-office activities. This study addresses this gap by investigating how AI enhances digital servitization through six case studies of companies implementing AI-driven servitized solutions. Adopting a capability theoretical perspective, we analyze the application of AI in both back-office and front-office activities for the delivery of base, intermediate, and advanced services. Our findings reveal that AI's role varies by service type, affecting customer interactions and operational tasks differently. We present a theoretical framework with five propositions that elucidate how AI capabilities enhance digital servitization. This framework gives scholars a refined understanding of AI's roles beyond the generalized black box approach and offers practitioners practical insights on leveraging AI for digital transformation. © 2025 Elsevier B.V. KW - Artificial intelligence KW - Capabilities KW - Case study KW - Digital servitization KW - Servitization KW - Back office KW - Capability KW - Case-studies KW - Different services KW - Digital opportunity KW - Digital servitization KW - Digital technologies KW - Servitization KW - Smart products CY - Brazil, Mexico, Italy ER - TY - JOUR TI - A COMPARATIVE ANALYSIS OF ARTIFICIAL INTELLIGENCE REGULATION IN ASEAN AND THE EUROPEAN UNION AU - Lu Y. AU - Tie F.H. PY - 2025 JO - Journal of Governance and Regulation VL - 14 IS - 4 special issue SP - 401 EP - 411 DO - 10.22495/jgrv14i4siart16 AB - This study conducts a comparative analysis of artificial intelligence (AI) regulation in the European Union (EU) and the Association of Southeast Asian Nations (ASEAN), examining their governance frameworks, enforcement mechanisms, and regulatory impact. The EU AI Act (EU, 2024) establishes a legally binding, centralized regulatory model that prioritizes risk-based AI classification, strict compliance obligations, and human rights protections (Huang et al., 2024). In contrast, ASEAN follows a decentralized, voluntary governance approach, promoting flexibility, innovation, and industry self-regulation (Putra, 2024). The analysis highlights the trade-offs between regulatory stringency and innovation flexibility. The EU’s strict enforcement model ensures accountability and consumer protection but poses compliance burdens for businesses, potentially slowing AI adoption. Conversely, ASEAN’s market-driven approach fosters rapid AI deployment but raises concerns about regulatory fragmentation, ethical risks, and cross-border governance inconsistencies. These findings are crucial for policymakers and businesses navigating AI governance complexities. As AI continues to evolve globally, harmonizing regulatory approaches and establishing mutual recognition mechanisms between regions could enhance AI accountability while supporting innovation, shaping a more cohesive global AI governance landscape. © 2025 The Authors. KW - AI Regulation KW - ASEAN AI Governance KW - Cross-Border AI Governance KW - EU AI Act KW - Regulatory Compliance CY - Malaysia ER - TY - JOUR TI - Building trust through Technology: AI, public service perception, and citizen satisfaction in Abu Dhabi policing AU - Alhefaity S.R.S.A. AU - Mohamad E. AU - Jamli M.R. AU - Ito T. AU - Larasati A. AU - Mohamad N.A. PY - 2026 JO - Multidisciplinary Science Journal VL - 8 IS - 4 SP - e2026277 DO - 10.31893/multiscience.2026277 AB - This study examines the impact of artificial intelligence (AI) implementation, perceptions of public services, and trust in government on citizen satisfaction within the Abu Dhabi Police, offering insights into the role of emerging technologies in public administration. Guided by Public Value Theory, Expectation-Confirmation Theory, and IT Assimilation Theory, the research develops a conceptual framework to examine both direct and mediated relationships among these constructs. A purposive sample of 500 police employees from AI-enabled, operational, and administrative units was surveyed using a structured questionnaire, from which 365 valid responses were analyzed to assess the relationships among AI implementation, perception of public services, trust in government, and citizen satisfaction. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) in SmartPLS, enabling a robust assessment of the measurement and structural models. The findings reveal that AI implementation significantly enhances citizens’ satisfaction, particularly when services are transparent, efficient, and aligned with public expectations. Perceptions of public service quality also emerged as a critical determinant of satisfaction, reflecting the importance of accessibility, responsiveness, and fairness in shaping positive citizen experiences. Trust in government was found to play a crucial mediating role, strengthening the link between AI-enabled services, public service perceptions, and satisfaction outcomes. Importantly, the results indicate partial mediation, suggesting that while AI and service quality directly influence satisfaction, their effects are amplified when mediated by trust. These findings highlight the dual importance of technological advancement and institutional credibility in fostering citizen satisfaction. The study contributes theoretically by integrating three complementary frameworks to explain how AI influences service outcomes, and practically by providing evidence-based recommendations for policymakers, AI developers, and law enforcement agencies. Emphasizing transparency, accountability, and ethical AI use can further enhance public trust and maximize satisfaction. This research aligns with the UAE’s Vision 2031 of positioning the nation as a global leader in safety, innovation, and smart governance, while also offering a model for other countries seeking to integrate AI into public service delivery. Copyright (c) 2025 The Authors This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. KW - artificial intelligence KW - citizen satisfaction KW - perception of public services KW - trust in government CY - Malaysia, Japan, Indonesia ER - TY - JOUR TI - The EU AI Act: a proactive framework for comprehensive AI regulation AU - Mahmutovic A. PY - 2025 JO - International Journal of Law and Information Technology VL - 33 SP - eaaf028 DO - 10.1093/ijlit/eaaf028 AB - The rapid advancement of artificial intelligence (AI), enabling autonomous decision-making and selflearning, is transforming legal systems and societal structures, while presenting significant governance challenges. Regulators grapple with the Collingridge Dilemma, uncertainty hinders early intervention, and entrenchment complicates later action, exacerbated by the pacing problem, where AI’s exponential growth outpaces static laws, rendering traditional command-and-control regulation inadequate. The European Union’s AI Act (Regulation (EU) 2024/1689) introduces proactive tools like regulatory sandboxes and data governance, yet struggles to balance innovation with ethical oversight. This paper explores proactive law, an anticipatory and collaborative approach emphasizing stakeholder engagement and adaptive governance, as a viable paradigm for AI regulation. Through doctrinal, comparative, and socio-legal analysis of the EU AI Act alongside USA and Chinese strategies, focusing on transparency mandates and codes of practice, the study discusses a refined proactive model to mitigate risks, foster trust, and sustain innovation, advancing global responsible AI governance. © The Author(s) 2025. Published by Oxford University Press. All rights reserved. KW - AI governance KW - China KW - EU AI Act KW - European Union KW - pacing problem KW - proactive law KW - USA CY - Saudi Arabia ER - TY - JOUR TI - Linking Psychological Safety Climate to Dual Innovation Through AI-Enabled Dynamic Capabilities AU - Tao K. AU - Tan C.C. PY - 2025 JO - Emerging Science Journal VL - 9 IS - 6 SP - 3268 EP - 3287 DO - 10.28991/ESJ-2025-09-06-022 AB - Objective: This study develops and empirically validates an integrated model that explains how the psychological safety climate influences dual innovation through AI-enabled dynamic capabilities in Chinese design organizations. Methods: A cross-sectional survey was conducted among 281 designers from industry design firms and departments. Data analysis employed partial least squares-structural equation modeling, including mediation bootstrapping analysis, importance-performance map analysis, necessary condition analysis, and quadratic effect analysis. Findings: All hypotheses received strong empirical support. The psychological safety climate has a significant influence on AI-enabled dynamic capabilities, with a path coefficient of 0.452 at p <0.001, and on dual innovation, with a coefficient of 0.383 at p < 0.001. AI-enabled dynamic capabilities have a positive impact on dual innovation, with a coefficient of 0.384 at p < 0.001, and significant mediation effects, indicating an indirect effect of 0.174 at p < 0.001. The model explains 42.7% of the variance in dual innovation. Importance-performance analysis reveals a psychological safety climate as highly important but moderately performing, indicating strategic opportunities for improvement for organizations. Necessary condition analysis confirms both constructs as essential requirements for innovation outcomes. The findings demonstrate that psychological safety climate, as a higher-order cultural resource, enables lower-order AI-enabled dynamic capabilities, supporting socio-technical systems structure for dual innovation. Organizations should prioritize investments in psychological safety while maintaining their AI capabilities. Novelty: This research introduces AI-enabled dynamic capabilities as a second-order formative construct and establishes the meta-capability role of psychological safety climate in AI-enabled dynamic capabilities and dual innovation, thereby extending the resource-based view and dynamic capabilities theories through micro-foundational perspectives. © 2025 by the authors. Licensee ESJ, Italy. KW - AI-Enabled Dynamic Capability KW - Dual Innovation KW - Industrial Design KW - Psychological Safety Climate KW - Resource-Based View CY - Thailand, China ER - TY - JOUR TI - Artificial intelligence ethics in authoritarian Vietnam: governance, trust, and societal tensions AU - Tran H.T. AU - Dang B.H. AU - Nguyen M.T.T. AU - Pham Q.T.T. AU - Nguyen P.V. PY - 2025 JO - Policy Design and Practice VL - 8 IS - 4 SP - 427 EP - 441 DO - 10.1080/25741292.2025.2529625 AB - This study investigates the ethical challenges of artificial intelligence (AI) deployment in Vietnam, driven by the need to understand how authoritarian governance and public trust shape technology’s societal impact in a rapidly modernizing state. The objective is to analyze the governance-trust interplay, identifying ethical dilemmas and their implications in Vietnam’s single-party context. Employing a qualitative approach, the research draws on policy documents, semi-structured interviews with policymakers, developers, and citizens, social media analysis, and case studies of facial recognition in Hanoi and AI in public health. Results reveal that Vietnam’s centralized governance enables swift AI adoption but falters in ethical flexibility and accountability, evident in privacy concerns and unclear responsibility for errors. Public trust varies, supported by state narratives in urban areas yet weakened by opacity and rural digital divides. The governance-trust dynamic shows transparency deficits undermining confidence, countered by societal resistance prompting modest policy adjustments. The study concludes that AI in Vietnam is a socio-political process with ethical stakes, offering a non-Western perspective to global debates and insights for aligning innovation with societal well-being. © 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. KW - AI ethics KW - authoritarian governance KW - public trust KW - societal tensions KW - Vietnam ER - TY - JOUR TI - The Barcelona principles: An agreement on the use of human donated tissue for ocular transplantation, research, and future technologies PY - 2018 JO - Cornea VL - 37 IS - 10 SP - 1213 EP - 1217 DO - 10.1097/ICO.0000000000001675 AB - Preamble The Barcelona Principles: An Agreement on the use of human donated tissue for ocular transplantation, research, and future technologies (Agreement) is a global bioethical framework (GBF), developed by the eye bank and ophthalmic communities, to inform on the management of altruistic and voluntary donations; their subsequent utility within ophthalmology and research; their retention as a public resource for the shared benefit of all; and their accessibility by waiting recipients. The Agreement is the result of global sector engagement over a 12-month period-led by the Global Alliance of Eye Bank Associations. Its aim is to provide leadership, guidance and recommendations that inform and support sound policy, and sector wide strategic planning and implementation at local, national, regional, and international levels. Inspired by the Declaration of Istanbul on Organ Trafficking and Transplant Tourism, this Agreement affirms the importance of the missions of the United Nations Sustainable Development Goals (Transforming our World: the 2030 Agenda for Sustainable Development); Universal Declaration of Human Rights; World Medical Association's Declaration of Helsinki-Ethical Principles for Medical Research Involving Human Subjects, and their Statement on Organ and Tissue Donation; The Council for International Organizations of Medical Science's International Ethical Guidelines for Health-related Research Involving Humans 2016; and accords with the World Health Organization's 2010 Guiding principles on human cell, tissue, and organ transplantation -WHA63.22. With millions waiting for a corneal transplant at any given moment1-and the majority residing in lower resource locations, a significant component of this Agreement promotes equitable allocation systems for waiting recipients, and the development of self-sufficient services. It recognises important instruments, such as the International Council of Ophthalmology 2017 Position Statement: Donation, Processing, Allocation, Advocacy, and Legislation Supporting Human Corneal Tissue for Ocular Transplant; the World Health Organization's Universal Eye Health-Global Action Plan 2014 to 2019, and the mission of the International Agency for the Prevention of Blindness. Future biological innovations/technologies are also addressed within the Agreement, promoting research and development that seek to improve donation utility, reduce burden, and improve therapeutic options for recipients, without ethical compromise. The Agreement has been developed by the Global Alliance of Eye Bank Associations in conjunction with the International Council of Ophthalmology, International Agency for the Prevention of Blindness, The Corneal Society, Asian Eye Bank Association, European Eye Bank Association, Eye Bank Association of America, Eye Bank Association of Australia and New Zealand, Eye Bank Association of India, the Pan American Association of Eye Banks, and in countries and regions without eye bank organizations, their ophthalmic societies-such as the Ophthalmological Society of the West Indies, and the Pacific Eye Care Society. Finally, we endorse the current international consensus that prohibits trafficking and transplant tourism. Copyright © 2018 The Global Alliance of Eye Bank Associations, Inc. KW - Bioethics KW - Biomedical Research KW - Eye KW - Eye Banks KW - Humans KW - International Cooperation KW - Organ Transplantation KW - Tissue and Organ Procurement KW - Article KW - bioethics KW - cornea transplantation KW - donor by donated tissue KW - eye bank KW - human KW - leadership KW - organ transplantation KW - organizational policy KW - priority journal KW - research KW - strategic planning KW - sustainable development KW - World Health Organization KW - bioethics KW - ethics KW - eye KW - international cooperation KW - medical research KW - organization and management KW - transplantation ER - TY - JOUR TI - The power of personalization: Generation Z's emotional response to AI food marketing under the EU AI act AU - Jackson K.M. AU - Kiss H. AU - Bergman M.E. PY - 2025 JO - British Food Journal SP - 1 EP - 18 DO - 10.1108/BFJ-05-2025-0727 AB - Purpose – AI-driven marketing is transforming how food brands connect with younger audiences, especially Generation Z (Gen Z). Artificial intelligence has evolved from a trend into a structural norm that influences personalization and persuasion in food marketing. This paper examines how Gen Z engages with AI versus traditional food ads, mapping psychological pathways and their relevance to the EU AI Act. Design/methodology/approach – An online survey of 982 participants (ages 18–27) from Hungary, Germany and Spain compared responses to a traditional Lidl ad and an AI-generated Heinz ad. Engagement was measured using a shortened Multimedia Ad Exposure Scale (MMAES) alongside established psychological scales for social media addiction, compulsive buying, flourishing and eating style. Analyses included t-tests, correlations and clustering. Two new measures, the Impulse Susceptibility Score (ISS) and the Flourishing Engagement Delta (FED), were introduced as scalable risk assessment tools. Findings – The AI-generated ad elicited significantly higher engagement than the traditional ad. Impulsivity-related traits correlated with elevated engagement, indicating susceptibility to persuasive content. Unexpectedly, participants with high well-being (flourishers) also showed stronger engagement, revealing a “flourishing paradox”. Clustering identified two pathways aligning with EU AI Risk categories. The ISS and FED detected that about 28% of participants fall into a high-risk group. Originality/value – The study shows how AI-generated ads activate dual psychological pathways: impulsive and reflective. By introducing ISS and FED, it offers scalable tools for assessing digital risk profiles and supports responsible innovation in AI food marketing by exposing its algorithmic influence on consumer behaviour and engagement. © 2025 Emerald Publishing Limited KW - AI food marketing KW - Digital vulnerability KW - Emotional flourishing KW - EU AI act KW - Generation Z KW - Personalized content CY - Hungary ER - TY - JOUR TI - Influence of artificial intelligence (AI) on firm performance: the business value of AI-based transformation projects AU - Wamba-Taguimdje S.-L. AU - Fosso Wamba S. AU - Kala Kamdjoug J.R. AU - Tchatchouang Wanko C.E. PY - 2020 JO - Business Process Management Journal VL - 26 IS - 7 SP - 1893 EP - 1924 DO - 10.1108/BPMJ-10-2019-0411 AB - Purpose: The main purpose of our study is to analyze the influence of Artificial Intelligence (AI) on firm performance, notably by building on the business value of AI-based transformation projects. This study was conducted using a four-step sequential approach: (1) analysis of AI and AI concepts/technologies; (2) in-depth exploration of case studies from a great number of industrial sectors; (3) data collection from the databases (websites) of AI-based solution providers; and (4) a review of AI literature to identify their impact on the performance of organizations while highlighting the business value of AI-enabled projects transformation within organizations. Design/methodology/approach: This study has called on the theory of IT capabilities to seize the influence of AI business value on firm performance (at the organizational and process levels). The research process (responding to the research question, making discussions, interpretations and comparisons, and formulating recommendations) was based on a review of 500 case studies from IBM, AWS, Cloudera, Nvidia, Conversica, Universal Robots websites, etc. Studying the influence of AI on the performance of organizations, and more specifically, of the business value of such organizations’ AI-enabled transformation projects, required us to make an archival data analysis following the three steps, namely the conceptual phase, the refinement and development phase, and the assessment phase. Findings: AI covers a wide range of technologies, including machine translation, chatbots and self-learning algorithms, all of which can allow individuals to better understand their environment and act accordingly. Organizations have been adopting AI technological innovations with a view to adapting to or disrupting their ecosystem while developing and optimizing their strategic and competitive advantages. AI fully expresses its potential through its ability to optimize existing processes and improve automation, information and transformation effects, but also to detect, predict and interact with humans. Thus, the results of our study have highlighted such AI benefits in organizations, and more specifically, its ability to improve on performance at both the organizational (financial, marketing and administrative) and process levels. By building on these AI attributes, organizations can, therefore, enhance the business value of their transformed projects. The same results also showed that organizations achieve performance through AI capabilities only when they use their features/technologies to reconfigure their processes. Research limitations/implications: AI obviously influences the way businesses are done today. Therefore, practitioners and researchers need to consider AI as a valuable support or even a pilot for a new business model. For the purpose of our study, we adopted a research framework geared toward a more inclusive and comprehensive approach so as to better account for the intangible benefits of AI within organizations. In terms of interest, this study nurtures a scientific interest, which aims at proposing a model for analyzing the influence of AI on the performance of organizations, and at the same time, filling the associated gap in the literature. As for the managerial interest, our study aims to provide managers with elements to be reconfigured or added in order to take advantage of the full benefits of AI, and therefore improve organizations’ performance, the profitability of their investments in AI transformation projects, and some competitive advantage. This study also allows managers to consider AI not as a single technology but as a set/combination of several different configurations of IT in the various company’s business areas because multiple key elements must be brought together to ensure the success of AI: data, talent mix, domain knowledge, key decisions, external partnerships and scalable infrastructure. Originality/value: This article analyses case studies on the reuse of secondary data from AI deployment reports in organizations. The transformation of projects based on the use of AI focuses mainly on business process innovations and indirectly on those occurring at the organizational level. Thus, 500 case studies are being examined to provide significant and tangible evidence about the business value of AI-based projects and the impact of AI on firm performance. More specifically, this article, through these case studies, exposes the influence of AI at both the organizational and process performance levels, while considering it not as a single technology but as a set/combination of the several different configurations of IT in various industries. © 2020, Emerald Publishing Limited. KW - Artificial intelligence KW - Business value KW - Cases studies KW - Firm performance KW - Process innovation CY - Cameroon, France ER - TY - JOUR TI - Governing the machine: leadership priorities for industry 5.0 in emerging digital economies AU - Abdallah Y.O. AU - Elnazer A.A. PY - 2026 JO - Cogent Business and Management VL - 13 IS - 1 SP - 2656023 DO - 10.1080/23311975.2026.2656023 AB - The transition to Industry 5.0 is reshaping leadership expectations as organisations seek to balance advanced technologies with human-centred and sustainable priorities. This study examines the leadership competencies most critical for guiding this transition in developing digital economies. A structured assessment was conducted using the Best–Worst Method (BWM), a multi-criteria decision-making technique that reduces cognitive burden, enhances internal consistency, and enables robust prioritisation of expert judgements. Fifteen senior experts from industry, academia, and policy across the Middle East evaluated five leadership domains: technological and AI capability; strategic foresight and innovation; adaptability and learning agility; relational and people-oriented skills; and ethical governance and sustainability stewardship. The findings indicate a clear prioritisation of governance-related competencies, particularly transparency, accountability, and the ability to interpret and communicate algorithmic decisions. Interpersonal competence and sound judgement were also rated highly, while visionary innovation and technical implementation skills received comparatively lower emphasis. Overall, the results suggest that leadership in digitally intensive contexts is becoming increasingly ethics- and governance-oriented. The study contributes to the leadership and digital transformation literature by offering an empirically grounded hierarchy of Industry 5.0 leadership competencies situated within the socio-technical realities of the Middle East. © 2026 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. KW - BWM KW - digital leadership KW - emerging economy KW - Ethical governance KW - Human-centric leadership KW - Industry 5.0 CY - United Kingdom, Egypt, Saudi Arabia ER - TY - JOUR TI - The transformative power of AI and its impact on business strategy, financial operations, and marketing decision-making: a case study method AU - Gabelaia I. AU - Hendieh J. PY - 2025 JO - International Journal of Innovation Science SP - 1 EP - 21 DO - 10.1108/IJIS-02-2025-0051 AB - Purpose – While artificial intelligence (AI) is widely integrated into modern enterprises, its concrete impacts on strategic agility, financial accuracy and marketing personalization remain under-researched. The purpose of this study is to bridge that gap by empirically assessing AI’s transformative role across business strategy, finance and marketing functions. Design/methodology/approach – Drawing on a systematic literature review, the authors developed three hypotheses and examined them through a combination of case study analyses and a survey conducted within three small and medium-sized enterprises (SMEs). This approach clarifies how AI transforms these areas and what this means for the future of business continuity. Findings – The results of this study demonstrate that integrating AI leads to more data-driven decision-making, thereby enhancing strategic planning and execution. Moreover, businesses with advanced AI capabilities display greater agility and adaptability. In financial terms, AI technologies simplify processes, reduce operational costs and improve efficiency. In addition, AI-driven models improve the accuracy of financial forecasting and risk management. Finally, AI technologies such as chatbots and recommendation systems enhance customer experience and satisfaction. Research limitations/implications – The authors acknowledge several limitations. First, the case study analysis was limited to three SMEs and may not be generalizable to all industries or regions; however, it still provided substantial experiential insights. Second, the survey, despite a substantial sample size, relies on self-reported data, which may be subject to bias. This study assumes that the selected SMEs and their employees provided accurate and truthful information about their AI adoption and perceived business outcomes. It further assumes that the observed patterns and themes reflect broader trends in AI adoption across industries. Practical implications – In financial terms, AI technologies simplify financial processes, reduce operational costs and improve efficiency. In addition, AI-driven models improve the accuracy of financial forecasting and risk management. Social implications – From a societal perspective, this research supports a future in which AI facilitates more informed and efficient business practices. Economically, this study paves the way for innovations that foster productivity and profitability across industries, driving overall market competitiveness and progress. Originality/value – This research offers valuable insights into AI’s transformative capabilities, emphasizing its significant impact on business strategy, financial operations and marketing decision-making. The findings are practical for businesses, offering recommendations for integrating AI technologies effectively to achieve a competitive advantage and pursue sustainable business growth. © 2025 Emerald Publishing Limited KW - AI adoption KW - Artificial intelligence KW - Business continuity KW - Business strategy KW - Decision-making KW - Financial management KW - G32 KW - G32 KW - G32 KW - JEL Code M15 KW - L25 KW - L25 KW - L25 KW - L81 KW - L81 KW - L81 KW - M15 KW - M15 KW - M21 KW - M21 KW - M21 KW - Marketing analytics KW - SMEs KW - Behavioral research KW - Commerce KW - Costs KW - Customer satisfaction KW - Efficiency KW - Financial data processing KW - Forecasting KW - Strategic planning KW - Artificial intelligence adoption KW - Business continuity KW - Business strategy KW - Decisions makings KW - Financial managements KW - G32 KW - JEL code m15 KW - JEL codes KW - L25 KW - L81 KW - M15 KW - M21 KW - Marketing analytic KW - Small and medium-sized enterprise KW - Decision making CY - Lithuania, France ER - TY - JOUR TI - The Evolution of Generative AI: Trends and Applications AU - Trigka M. AU - Dritsas E. PY - 2025 JO - IEEE Access VL - 13 SP - 98504 EP - 98529 DO - 10.1109/ACCESS.2025.3574660 AB - Generative artificial intelligence (AI) has revolutionized AI by enabling high-fidelity content creation across text, images, audio, and structured data. This survey explores the core methodologies, advancements, applications, and ongoing challenges of generative AI, covering key models such as Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), Diffusion Models, and Transformer-based architectures. These innovations have driven breakthroughs in healthcare, scientific computing, Natural Language Processing (NLP), computer vision, and autonomous systems. Despite its progress, generative AI faces challenges in bias mitigation, interpretability, computational efficiency, and ethical governance, necessitating research into scalable architectures, explainability, and AI safety mechanisms. Integrating Reinforcement Learning (RL), multi-modal learning, and self-supervised techniques enhances controllability and adaptability in generative models. Additionally, as AI reshapes industrial automation, digital media, and scientific discovery, its societal and economic implications demand robust policy frameworks. This survey provides a comprehensive analysis of generative AI’s current state and future directions, highlighting innovations in efficient generative modelling, AI-driven scientific reasoning, adversarial robustness, and ethical deployment. By consolidating theoretical insights and real-world applications, it offers a structured foundation for researchers, industry professionals, and policymakers to navigate the evolving landscape of generative AI. © 2013 IEEE. KW - ethical AI and governance KW - Generative AI KW - multi-modal AI KW - multi-modal learning KW - Generative adversarial networks KW - Multi-task learning KW - Natural sciences computing KW - Supervised learning KW - Text processing KW - Content creation KW - Ethical artificial intelligence and governance KW - Generative artificial intelligence KW - Generative model KW - High-fidelity KW - Multi-modal KW - Multi-modal artificial intelligence KW - Multi-modal learning KW - Text data KW - Text images KW - Reinforcement learning CY - Greece ER - TY - JOUR TI - The Role of Artificial Intelligence in Healthcare: A Critical Analysis of Its Implications for Patient Care AU - Mashabab M.F. AU - Sheniff M.S.A. AU - Alsharief M.S. AU - Yami M.A.A.A. AU - Matnah H.N.M. AU - Abbas A.M.A. AU - Shenief H.Y.M.A. AU - Abbas D.A.A.A.A. AU - Raseen F.M.S.A. AU - Kulayb A.H.A.A. PY - 2024 JO - Journal of Ecohumanism VL - 3 IS - 7 SP - 597 EP - 604 DO - 10.62754/joe.v3i7.4228 AB - Artificial Intelligence (AI) is rapidly transforming healthcare, with significant implications for patient care. This article critically analyzes AI's role in improving healthcare delivery, focusing on diagnostic accuracy, personalized treatments, and system efficiency. It highlights key benefits such as enhanced decision-making, reduced human error, and the potential for better patient outcomes through AI-driven tools like predictive analytics and robotic surgery. However, the article also addresses challenges including ethical concerns, algorithmic bias, data privacy issues, and the need for clear regulations and accountability structures. The study explores how AI affects healthcare professionals, reshaping their roles and requiring new skill sets. Through case studies, the article illustrates both the successes and limitations of AI in clinical applications. Ultimately, this critical analysis emphasizes that while AI holds promise in improving patient care, responsible implementation is necessary to address ethical, legal, and technical challenges. © 2024, Creative Publishing House. All rights reserved. KW - Ai Ethics KW - Algorithmic Bias KW - Artificial Intelligence KW - Data Privacy KW - Diagnostic Accuracy KW - Healthcare KW - Healthcare Innovation KW - Healthcare Professionals KW - Patient Care KW - Personalized Medicine KW - Predictive Analytics KW - Robotic Surgery CY - Saudi Arabia ER - TY - JOUR TI - Artificial intelligence in sport management education: A students' perspective AU - López-Carril S. AU - Alguacil M. AU - Gregori-Faus C. AU - Anagnostopoulos C. PY - 2026 JO - International Journal of Management Education VL - 24 IS - 3 SP - 101433 DO - 10.1016/j.ijme.2026.101433 AB - Artificial intelligence (AI) is transforming both higher education and the sport industry. This study qualitatively explores the perceptions of sport management students regarding AI's integration into academic contexts and the sport industry, guided by the Uses and Gratifications Theory (U&G) and the Disruptive Innovation Theory (DIT). Seventy-nine undergraduates from a Spanish university completed an open-ended questionnaire after a classroom debate on AI. Thematic analysis revealed that, consistent with U&G, students use AI to fulfil cognitive needs (e.g., acquiring and clarifying information, generating ideas) and instrumental needs (e.g., improving efficiency, solving problems quickly). Reported benefits included rapid access to information, enhanced learning support, and time savings, while concerns focused on plagiarism, reduced creativity, overreliance, and unreliable outputs. From a DIT perspective, students viewed AI as a potentially disruptive force capable of transforming sport management education and industry practices, generating opportunities for innovation but also posing risks such as job displacement and over-automation. Overall sentiment was cautiously positive, with calls for ethical guidelines, targeted training, and balanced adoption that leverages AI's advantages without eroding essential human skills. This situated perspective provides practical and theoretical insights for integrating AI into sport management curricula and informs future quantitative or mixed-method research. © 2026 The Authors KW - AI KW - Disruptive Innovation Theory KW - Higher education KW - Student perceptions KW - Technology adoption KW - Uses and Gratifications Theory CY - Spain, Qatar ER - TY - JOUR TI - Artificial intelligence-driven innovation for enhancing supply chain resilience and performance under the effect of supply chain dynamism: an empirical investigation AU - Belhadi A. AU - Mani V. AU - Kamble S.S. AU - Khan S.A.R. AU - Verma S. PY - 2024 JO - Annals of Operations Research VL - 333 IS - 2-3 SP - 627 EP - 652 DO - 10.1007/s10479-021-03956-x AB - Supply chain resilience (SCRes) and performance have become increasingly important in the wake of the recent supply chain disruptions caused by subsequent pandemics and crisis. Besides, the context of digitalization, integration, and globalization of the supply chain has raised an increasing awareness of advanced information processing techniques such as Artificial Intelligence (AI) in building SCRes and improving supply chain performance (SCP). The present study investigates the direct and indirect effects of AI, SCRes, and SCP under a context of dynamism and uncertainty of the supply chain. In doing so, we have conceptualized the use of AI in the supply chain on the organizational information processing theory (OIPT). The developed framework was evaluated using a structural equation modeling (SEM) approach. Survey data was collected from 279 firms representing different sizes, operating in various sectors, and countries. Our findings suggest that while AI has a direct impact on SCP in the short-term, it is recommended to exploit its information processing capabilities to build SCRes for long-lasting SCP. This study is among the first to provide empirical evidence on maximizing the benefits of AI capabilities to generate sustained SCP. The study could be further extended using a longitudinal investigation to explore more facets of the phenomenon. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021. KW - Artificial intelligence KW - Digital transformation KW - organizational information processing theory KW - Supply chain performance KW - Supply chain resilience CY - Morocco, France, China, Denmark ER - TY - JOUR TI - Exploring the Impact of AI Capabilities on Employee Well-Being: A Mediated Moderation Analysis AU - Bibi M. AU - Tan T.G. AU - Yao H. PY - 2025 JO - SAGE Open VL - 15 IS - 3 DO - 10.1177/21582440251361981 AB - Around the globe, technological advancements such as artificial intelligence (AI) are reshaping workplaces affecting employee wellbeing (EWB). To understand the AI-EWB link, a conceptual model is developed to explore the link between AI-driven capabilities and employee wellbeing (EWB), with cybernetic thinking (CT) as a mediator. Furthermore, organizational ambidexterity (OA) is introduced as a moderating factor between CT and EWB grounded on integrated dynamic capabilities with resource-based theory in the context of a developing country like Pakistan. Data were collected from 490 doctors working in private sector hospitals across two major cities of Pakistan—Karachi & Islamabad and data analysis was performed using PLS-SEM 4.0. Results indicate that AI-driven capabilities significantly relate to EWB. Furthermore, CT explains the relationship between tangible, human resources, intangible-driven AI capabilities, and EWB. In addition, OA moderates the link between CT and EWB. Hence, mediated moderation is established. To remain resilient, this study offers theoretical as well as practical insights into how healthcare practitioners can harness AI through integrating organizational factors like CT can help reduce stress and improve EWB through adopting a balanced approach to manage innovation. Policy implications along with directions for studies to be conducted by researchers are also provided. © The Author(s) 2025. This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage). KW - AI capabilities KW - cybernetic thinking KW - dynamic capabilities KW - employee wellbeing KW - mediated moderation KW - organizational ambidexterity KW - resource-based theory CY - Malaysia, Pakistan, China ER - TY - JOUR TI - Developing artificial intelligence (AI) capabilities for data-driven business model innovation: Roles of organizational adaptability and leadership AU - Ghosh S. PY - 2025 JO - Journal of Engineering and Technology Management - JET-M VL - 75 SP - 101851 DO - 10.1016/j.jengtecman.2024.101851 AB - More and more industrial businesses leverage AI to optimize operations and introduce new and innovative business models for competitive advantage. Businesses are collecting huge amounts of data from their business processes and plan to utilize them for better customer experience and insights. However, industrial managers are still determining the AI capabilities they need to analyze the data and develop customer-centric business models. Based on the discussions with twenty-five elite informants from five large industrial businesses, in this study, we propose a set of AI capabilities that can help managers develop data-driven business models. We offer a conceptual framework of AI capabilities and their influence on data-driven business model innovation that can guide managers in their transformation journeys. © 2024 KW - Advanced technologies KW - Artificial intelligence KW - Data-driven business model innovation KW - Digital servitization KW - Digital transformation KW - Advanced technology KW - Business model innovation KW - Business models KW - Competitive advantage KW - Data driven KW - Data-driven business model innovation KW - Digital servitization KW - Digital transformation KW - Organizational adaptabilities KW - Servitization CY - United States ER - TY - JOUR TI - A framework and exemplars for ethical and responsible use of AI Chatbot technology to support teaching and learning AU - Chauncey S.A. AU - McKenna H.P. PY - 2023 JO - Computers and Education: Artificial Intelligence VL - 5 SP - 100182 DO - 10.1016/j.caeai.2023.100182 AB - The aim of this paper is to investigate the ethical and responsible use of AI chatbots in education in support of critical thinking, cognitive flexibility and self-regulation in terms of their potential to enhance and motivate teaching and learning in contemporary education environments. AI chatbots such as ChatGPT by OpenAI appear to be improving in conversational and other capabilities and this paper explores such advances using version 4. Based on a review of the research literature, a conceptual framework is formulated for responsible use of AI chatbots in education supporting cognitive flexibility in AI-rich learning environments. The framework is then operationalized for use in this paper through the development of exemplars for math, english language arts (ELA), and studying with ChatGPT to close learning gaps in an effort to foster more ethical and responsible approaches to the design and development of AI chatbots for application and use in teaching and learning environments. This paper extends earlier foundational work on cognitive flexibility and AI chatbots as well as work on cognitive flexibility in support of creativity and innovation with AI chatbots in urban civic spaces. © 2023 The Authors KW - AI ethics KW - AI responsibility KW - AI-Rich learning environments KW - Cognitive flexibility KW - Critical thinking KW - Self-regulation KW - Computer aided instruction KW - Deregulation KW - Ethical technology KW - AI ethic KW - AI responsibility KW - AI-rich learning environment KW - Chatbots KW - Cognitive flexibility KW - Conceptual frameworks KW - Critical thinking KW - Learning environments KW - Self regulation KW - Teaching and learning KW - Teaching CY - United States, Canada ER - TY - JOUR TI - Is AI a ‘substance driver’ or a ‘fiction booster’? The impact of AI application on corporate green innovation bubbles AU - Zhao K. AU - Liu X. PY - 2026 JO - Finance Research Letters VL - 91 SP - 109100 DO - 10.1016/j.frl.2025.109100 AB - This study investigates whether artificial intelligence (AI) acts as a "substance driver" that promotes genuine green innovation or a "fiction booster" that facilitates sophisticated greenwashing. Analyzing panel data from Chinese A-share listed manufacturing companies spanning 2013 to 2023, fixed effects regression results reveal that AI application significantly suppresses green innovation bubbles, demonstrating a substantial reduction effect at the sample mean. However, this effect is highly contingent on external institutional and competitive contexts. Stringent environmental regulation amplifies AI's bubble-suppressing effect by channeling AI capabilities toward substantive compliance-oriented innovation. Additionally, intense market competition amplifies this effect, as competitive pressure disciplines firms to deploy AI toward substantive innovations that generate genuine competitive advantages rather than superficial green credentials. Heterogeneity analyses reveal AI's effectiveness is significantly stronger among non-state-owned enterprises and non-heavy-polluting firms, while SOEs and heavy-polluting firms show insignificant effects. Firms located in AI innovation pilot zones exhibit stronger bubble-suppressing effects compared to those outside. These findings contribute an integrated "institution-market" contingency framework to technology adoption literature, introduce an objective patent-based measure of innovation quality, and demonstrate that AI's role in corporate sustainability is neither technologically deterministic nor universally beneficial but critically depends on the alignment of institutional pressures and market incentives. The results remain robust to instrumental variable analysis, propensity score matching, Heckman correction, and multiple robustness checks. © 2025 Elsevier Inc. All rights are reserved. KW - Artificial intelligence KW - Environmental regulation KW - Green innovation bubbles KW - Greenwashing KW - Market competition CY - China ER - TY - JOUR TI - Navigating the Tech Turn: A Bibliometric Analysis of Decision-Making Trends in 21st Century Education AU - Prasad R.D. AU - Pek L.S. AU - Yob F.S.C. AU - Von W.Y. AU - Magulod G.C., Jr. AU - Adom D. PY - 2025 JO - International Journal of Learning, Teaching and Educational Research VL - 24 IS - 11 SP - 297 EP - 313 DO - 10.26803/ijlter.24.11.14 AB - This bibliometric analysis illustrates how, between 2020 and 2024, technology has impacted educational decisions. Using the Web of Science Core Collection, 371 English-language publications in the field of education and educational research were analysed. In VOSviewer, assessments of performance, co-citation, and keyword co-occurrence were carried out. Six thematic clusters emerged: (1) qualitative research and pedagogical frameworks; (2) technology acceptance and behavioral theories; (3) e-learning, learning analytics, and pandemic adaptation; (4) artificial intelligence, ethics, and mixed-method evaluation; (5) active learning, diffusion of innovations, and learning efficacy; and (6) social cognitive and motivational perspectives on STEM pathways. The corpus is worldwide in scope and has strong ties to analytics-informed leadership, policy responsiveness, and teacher practice. The findings demonstrate a growing interest in evidence-based education, data-driven leadership, and AI governance – all of which are consistent with Sustainable Development Goal 4 (Quality Education). This study's thorough intellectual map connects adoption, pedagogy, analytics, and governance while offering helpful recommendations for institutional and policy decision-making on curriculum, funding, and capacity building. The bibliometric analysis updates to track this rapidly evolving topic and spot a clear research gap: the requirement for multi-theoretical, equity-sensitive models that integrate analytics and artificial intelligence with institutional decision-making processes. © Authors. KW - AI in education KW - digital transformation KW - educational technology KW - inclusive education KW - technology adoption CY - Malaysia, Philippines, Ghana ER - TY - JOUR TI - AI Capability and Sustainable Performance: Unveiling the Mediating Effects of Organizational Creativity and Green Innovation with Knowledge Sharing Culture as a Moderator AU - Gazi M.A.I. AU - Rahman M.K.H. AU - Masud A.A. AU - Amin M.B. AU - Chaity N.S. AU - Senathirajah A.R.B.S. AU - Abdullah M. PY - 2024 JO - Sustainability (Switzerland) VL - 16 IS - 17 SP - 7466 DO - 10.3390/su16177466 AB - The purpose of this study is to investigate the role of AI capability (AIC) on organizational creativity (OC), green innovation (GI), and sustainable performance (SP). It also aims to investigate the mediating roles of OC and GI, as well as the moderating role of knowledge sharing culture (KNC). This study used quantitative methodology and utilized a survey to collect data from 421 employees in different organizations in Bangladesh. We used the structural equation modeling (SEM) technique to analyze the data. This study finds that AI capability significantly influences OC, GI, and SP. OC and GI work as mediators, and KNC serves as a moderator among the suggested relationships. This study is notable for its novelty in examining multiple unexplored aspects in the current body of research. This research also provides valuable insights for policymakers and practitioners regarding the effective integration of AI to enhance organizational competitiveness. © 2024 by the authors. KW - AI capability KW - green innovation KW - knowledge sharing culture KW - organizational creativity KW - sustainable development KW - sustainable performance KW - Bangladesh KW - artificial intelligence KW - competitiveness KW - cultural influence KW - industrial performance KW - innovation KW - knowledge KW - planning method KW - sustainable development CY - China, Malaysia, Bangladesh, Hungary ER - TY - JOUR TI - The AI Revolution in Higher Education: Transforming Teaching and Research AU - Flückiger Y. PY - 2025 JO - Journal of Higher Education Policy and Leadership Studies VL - 6 IS - 4 SP - 30 EP - 44 DO - 10.61882/johepal.6.4.30 AB - The rapid integration of Artificial Intelligence (AI) into higher education is profoundly transforming both teaching and research. This article explores how AI-driven technologies enable personalized and adaptive learning, empowering educators to shift from content delivery to mentorship and creativity. By tailoring instruction to individual needs, AI fosters inclusivity and enhances student engagement through adaptive platforms, virtual tutors, and chatbots. Case studies from leading universities demonstrate tangible improvements in learning outcomes, retention, and student support, while also underscoring ethical and pedagogical challenges. Beyond education, AI is revolutionizing research practices by accelerating data analysis, generating hypotheses and promoting interdisciplinary collaboration. From genomics to computational social sciences, AI expands the capacity of researchers to address complex global challenges. However, these opportunities raise pressing ethical issues, including data privacy, algorithmic bias, transparency and equity. The article concludes by emphasizing the need for responsible AI governance, institutional investment, and international collaboration. When integrated thoughtfully, AI can enhance learning experiences, broaden access, and accelerate innovation for the benefit of society. © This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International https://creativecommons.org/licenses/by-nc/4.0/ (CC BY-NC 4.0) which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format for noncommercial purposes only, and only so long as attribution is given to the creator. KW - AI in Higher Education KW - Educational Technology KW - Ethical Challenges of AI KW - Interdisciplinary Collaboration KW - Personalized & Adaptive Learning CY - Switzerland ER - TY - JOUR TI - Fostering Tech Innovation: Exploring TRIZ and ChatGPT Integration for Developer and Entrepreneur Challenges AU - Shin W.-S. AU - Lee S.-H. AU - Sue H.-J. PY - 2024 JO - Tehnicki Glasnik VL - 18 IS - 4 SP - 588 EP - 597 DO - 10.31803/tg-20231212081808 AB - This study aimed to interpret the value of integration of TRIZ (Teoriya Resheniya Izobretatelskih Zadach; Theory of Inventive Problem Solving) and ChatGPT(Chat Generative Pre-trained Transformer) to enhance technological and entrepreneurial problem-solving and decision-making. TRIZ offers a structured approach to innovation, while ChatGPT excels in generating diverse and innovative responses through advanced natural language processing. From the combination of them, we tried to discover synergies that promote innovation in highly competitive business areas. Through the analysis of case studies, including "Imperfect Waterproof Zipper" and "Drilling a Hole in a Thin-Walled Tube", we discovered that this integration not only aligns with actual problem-solving outcomes but also enhances the quality of solutions, particularly benefiting developers with limited TRIZ knowledge. We identified that leveraging ChatGPT enables developers and entrepreneurs to approach challenges with enhanced creativity, yielding practical and innovative solutions through these case studies. Our approach, focusing on real-world applications, demonstrates the study's contribution by providing a novel strategy for combining structured problem-solving with AI capabilities. The primary motivation behind this research was to ascertain whether AI can amplify the problem-solving framework of TRIZ, thereby extending its utility beyond traditional domains. The findings underline the importance of AI in creative problem-solving, suggesting that even those unfamiliar with TRIZ can apply its principles effectively with the aid of ChatGPT. This research adds to the existing knowledge by showcasing how AI can be a powerful ally in the creative process, offering new avenues for problem-solving and strategic decision-making. In conclusion, our study demonstrates that the collaboration between TRIZ and ChatGPT not only elevates creativity but also equips developers and entrepreneurs with competitive strategies, emphasizing the role of AI in driving forward human innovation and creativity. © 2024 University North. All rights reserved. KW - AI-driven insights KW - ChatGPT KW - decision-making KW - entrepreneurial strategy KW - problem-solving KW - technological innovation KW - TRIZ CY - South Korea ER - TY - JOUR TI - Geoscience in the era of generative artificial intelligence (Geo[AI]-LSM): understanding the potential benefits of Google Gemini in producing landslide susceptibility mapping AU - Sahin E.K. AU - Demir S. AU - Ozturk M. AU - Duzce M.S. PY - 2026 JO - Advances in Space Research VL - 77 IS - 3 SP - 3061 EP - 3085 DO - 10.1016/j.asr.2025.11.048 AB - In recent years, many technological innovations have marked the 21st century. One of the most rapid and unpredictable is the Artificial Intelligence (AI) revolution. The integration of AI systems, particularly generative AI, has just started manifesting itself in geoscience applications. This study investigates the potential benefits and limitations of the state-of-the-art generative AI framework, Google-Gemini, in improving the accuracy and efficiency of landslide susceptibility maps (LSMs). The research aims to shed light on the efficacy of Gemini AI and its implications for enhancing geoscience applications beyond LSM through empirical trials and comparative analysis. Furthermore, a web-based, user-friendly interface called Geo[AI]-LSM has been produced and is freely available to all users for producing LSMs. In the proposed framework, two distinct tools play critical roles: the Data Preparation tool, which prepares the landslide conditioning factor dataset, and the Geo[AI]-LSM tool, which constructs model architecture based on the provided prompt, applies the model training strategies, displays the accuracy values, and finally plots the LSM. In this study, Geo[AI]-LSM is employed to estimate the landslide susceptibility of Mudurnu district in Bolu Province, Türkiye to demonstrate the generative AI’s capabilities. The current work develops models using various machine learning (ML) pipelines, each more sophisticated than the previous one. For this purpose, five alternative prompts (i.e., Prompts [1], [2], [3], [4], [5]) ranging from relatively simple to complex, were employed to generate ML models using the well-known Random Forest (RF) algorithm. The findings are evaluated using various performance metrics, including accuracy, Kappa, precision, recall, and F1 statistics. Experiments with datasets from the study area showed that the proposed Geo[AI]-LSM approach achieved an accuracy of about 89 % for the Prompt [5] model. Ultimately, it is believed that this research’s findings will make a substantial contribution to the current conversation about using AI technology to address geoscience challenges and improve landslide risk assessment and management. © 2025 COSPAR. Published by Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies. KW - Artificial intelligence (AI) KW - Google Gemini KW - Landslide susceptibility mapping KW - Large language model (LLM) KW - Machine learning (ML) KW - Data mining KW - Learning systems KW - Machine learning KW - Mapping KW - Artificial intelligence KW - Google geminus KW - Google+ KW - Landslide susceptibility KW - Landslide susceptibility mapping KW - Language model KW - Large language model KW - Machine learning KW - Machine-learning KW - Susceptibility maps KW - Landslides CY - Turkey ER - TY - JOUR TI - Leveraging generative AI capabilities for competitive advantage: A moderated mediation analysis of environmental dynamism and service innovation AU - Li L. AU - Xu C. AU - Zhang Q. AU - Liu Y. AU - Li Q. PY - 2025 JO - Industrial Marketing Management VL - 128 SP - 10 EP - 20 DO - 10.1016/j.indmarman.2025.05.007 AB - As generative AI increasingly transforms industrial markets, B2B firms face the imperative to strategically leverage it to sustain competitive advantage. Grounded in dynamic capabilities theory, this study establishes a novel framework to examine the interconnections among generative AI capabilities, service innovation, and competitive advantage under varying levels of environmental dynamism. Analyzing data from 260 Chinese firms, this study reveals that the three dimensions of service innovation—service concept, service process, and customer experience—serve as key mediators between generative AI capabilities and competitive advantage. Furthermore, a counterintuitive discovery is that environmental dynamism negatively moderates the mediation effects of service concept innovation and customer experience innovation on the relationship between generative AI capabilities and competitive advantage. These findings contribute to the literature on AI integration in B2B contexts by elucidating the conditions under which generative AI capabilities translate into competitive advantage, offering practical insights for firms navigating dynamic industrial landscapes. © 2024 KW - Competitive advantage KW - Dynamic capabilities theory KW - Environmental dynamism KW - Generative AI capabilities KW - Service innovation CY - China, France ER - TY - JOUR TI - Startup category membership and boundary expansion in the field of artificial intelligence AU - Truong Y. PY - 2024 JO - International Journal of Entrepreneurial Behaviour and Research VL - 30 IS - 2-3 SP - 398 EP - 420 DO - 10.1108/IJEBR-08-2022-0773 AB - Purpose: An important but neglected area of investigation in digital entrepreneurship is the combined role of both core and peripheral members of an emerging technological field in shaping the symbolic and social boundaries of the field. This is a serious gap as both categories of members play a distinct role in expanding the pool of resources of the field. I address this gap by exploring how membership category is related to funding decisions in the emerging field of artificial intelligence (AI). Design/methodology/approach: The first quantitative study involved a sample of 1,315 AI-based startups which were founded in the period of 2011–2018 in the United States. In the second qualitative study, the author interviewed 32 members of the field (core members, peripheral members and investors) to define the boundaries of their respective role in shaping the social boundaries of the AI field. Findings: The author finds that core members in the newly founded field of AI were more successful at attracting funding from investors than peripheral members and that size of the founding team, number of lead investors, number of patents and CEO approval were positively related to funding. In the second qualitative study, the author interviewed 30 members of the field (core members, peripheral members and investors) to define their respective role in shaping the social boundaries of the AI field. Research limitations/implications: This study is one of the first to build on the growing literature in emerging organizational fields to bring empirical evidence that investors adapt their funding strategy to membership categories (core and peripheral members) of a new technological field in their resource allocation decisions. Furthermore, I find that core and peripheral members claim distinct roles in their participation and contribution to the field in terms of technological developments, and that although core members attract more resources than peripheral members, both actors play a significant role in expanding the field’s social boundaries. Practical implications: Core AI entrepreneurs who wish to attract funding may consider operating in fewer categories in order to be perceived as core members of the field, and thus focus their activities and limited resources to build internal AI capabilities. Entrepreneurs may invest early in filing a patent to signal their in-house AI capabilities to investors. Social implications: The social boundaries of an emerging technological field are shaped by a multitude of actors and not only the core members of the field. The author should pay attention to the role of each category of actors and build on their contributions to expand a promising field. Originality/value: This paper is among the first to build on the growing literature in emerging organizational fields to study the resource acquisition strategies of entrepreneurs in a newly establishing technological field. © 2023, Emerald Publishing Limited. KW - Financing KW - Innovation KW - Small firm/new venture strategy KW - Technology CY - France ER - TY - JOUR TI - The Impact of Artificial Intelligence on Business Performance in Saudi Arabia: The Role of Technological Readiness and Data Quality AU - Alarefi M. PY - 2024 JO - Engineering, Technology and Applied Science Research VL - 14 IS - 5 SP - 16802 EP - 16807 DO - 10.48084/etasr.7871 AB - This study aims to examine the impacts of Machine Learning (ML) and Artificial Intelligence (AI) capabilities on Business Performance (BP) of technology enterprises in the Kingdom of Saudi Arabia (KSA). Building on established theories such as the Resource-Based View (RBV) and the Technology Organization Environment (TOE) framework, the study proposes that AI and ML capabilities impact business performance. Their effects are anticipated to be mediated by Technological Readiness (TR) and moderated by Data Quality (DQ). A total of 190 executives and IT professionals in KSA participated in this study. Smart PLS 4 was used to analyze the data. The findings showed that AI and ML capabilities positively affected business performance. Technological readiness acted as a mediator in the relationship between AI and ML capabilities, and BP. Data quality significantly increased the impact of AI capabilities on BP. The business performance of enterprises in KSA will increase with the presence of efficient AI and ML capabilities as well as the development of a high level of technological readiness and data quality. © by the authors. KW - artificial intelligence capability KW - business performance KW - data quality KW - machine learning KW - technological readiness CY - Saudi Arabia ER - TY - JOUR TI - Inception, development and evolution of guidelines for AI in parliaments AU - Fitsilis F. AU - von Lucke J. AU - De Vrieze F. PY - 2025 JO - Theory and Practice of Legislation VL - 13 IS - 3 SP - 405 EP - 428 DO - 10.1080/20508840.2025.2474791 AB - The rapid rise of Artificial Intelligence (AI) is transforming numerous sectors, including governance. As AI technologies become increasingly integrated into governance frameworks, several issues arise, ranging from integration with legacy systems to ethical considerations. Parliaments worldwide face similar challenges while planning to adopt AI tools for the purpose of strengthening their institutional processes such as automated transcription, legislative support tools and AI-powered public engagement. Therefore, the development of AI guidelines for parliaments is a necessary step to manage technological evolution while upholding democratic values and principles. They outline a democratic approach to AI governance and the role of parliaments in its implementation. This article argues that parliaments must develop AI governance capacities and presents the inception, development and evolution of a comprehensive set of 40 guidelines published in July 2024 designed to facilitate the integration and utilisation of AI in the parliamentary workspace. Apart from a brief outline of the guidelines, the article offers a detailed analysis of their development process, during a course of over one and a half years, from late 2022 till mid-2024. It first highlights the methodological approach used for capturing diverse perspectives of an international expert team composed of scholars and practitioners. Furthermore, it explores the composition of the expert team, its interdisciplinarity, as well as the strategies used to address critical aspects such as how to ensure global applicability amidst international and cultural differences. The article also discusses the agile development process and the potential of these guidelines to promote innovation and adaptability within parliaments. By examining these factors, the article contributes to a deeper understanding of how parliaments around the world can strategically deploy AI while balancing ethical, practical and cultural complexities. The findings provide valuable insights for parliamentarians, administrators and civic stakeholders, thus offering a structured approach for AI governance. © 2025 Informa UK Limited, trading as Taylor & Francis Group. KW - Artificial Intelligence KW - democratic values KW - ethics KW - governance KW - guidelines KW - institutional resilience KW - international collaboration KW - parliaments KW - technological integration CY - Greece, Germany, United Kingdom ER - TY - JOUR TI - ARTIFICIAL INTELLIGENCE APPLICATION IN HUMAN RESOURCES MANAGEMENT AU - Tairov I. AU - Stefanova N. AU - Aleksandrova A. PY - 2024 JO - Business Management VL - 2024 IS - 3 SP - 72 EP - 88 DO - 10.58861/tae.bm.2024.3.05 AB - In the contemporary landscape marked by the pervasive influence of artificial intelligence (AI), technological innovations continue to reshape conventional practices across various domains. Within the realm of human resources management, the intricate process of decision-making has long posed challenges in terms of analytical elucidation. However, the advent of AI technologies has ushered in a new era, offering unprecedented opportunities to augment and refine HR administration practices. This paper delves into the transformative potential of AI applications within human resources management, shedding light on how diverse AI modalities, including narrow and general AI, are revolutionizing traditional approaches. Through a comprehensive review of literature sourced from esteemed databases such as Scopus and Google Scholar, this study identifies key advancements poised to drive future research endeavors. Beyond the realm of recruitment, AI presents a myriad of possibilities spanning talent acquisition, employee training and development, performance assessment, compensation management, engagement initiatives, and even employee well-being programs. The synergy between human capabilities and AI integration emerges as a cornerstone for achieving enhanced outcomes, often serving as a determinant for competitive advantage within organizations while also impacting broader societal dynamics. By exploring the symbiotic relationship between human ingenuity and AI capabilities, this research seeks to elucidate the pathways through which AI-driven innovations can foster organizational excellence and societal progress. © 2024, Dimitar A Tsenov Academy of Economics. All rights reserved. KW - General artificial intelligence KW - Human resources management KW - Narrow artificial intelligence CY - Bulgaria ER - TY - JOUR TI - An Analytical Framework for Evaluating the Impact of Digital Transformation Technologies on Business Performance: A Natural Language Processing Approach AU - Vanani I.R. AU - Yalpanian M.A. AU - Taghavifard M.T. AU - Tahmaseby Y. PY - 2025 JO - Journal of Information Technology Management VL - 17 IS - 3 SP - 41 EP - 88 DO - 10.22059/jitm.2025.384662.3872 AB - Extensive technological advancements have highlighted the importance of digital transformation in improving business performance. While prior research on this topic has been done in the information systems and business management domains, it has been limited to specific areas. Therefore, it is crucial to evaluate the impact of digital transformation comprehensively. This research aims to systematically identify critical themes, significant opics, main concepts, and trend priorities. The study involved the analysis of 474 research papers from 2015 to 2024 from reputable databases such as SCOPUS, Web of Science, and IEEE Xplore. First, thematic analysis identified the main themes and interpreted their relationships. Identified themes refer to technological changes at the operational and strategic levels through data analytics, digitalization, collaborative learning, and digital interaction. Realizing that digital transformation leads to value creation, improved service quality, customer experience, and long-term communication in digital ecosystems. These findings were related to dynamic capability theory concepts and compared with theory constructs like sensing, seizing, and transforming. Next, text mining techniques were used for deeper investigation, including word cloud, topic modeling (Latent Dirichlet Allocation), and text clustering (K-means). Findings were categorized into three perspectives: business, customer, and systemic, highlighting the influential role of digital technologies, particularly artificial intelligence (AI) capabilities. Moreover, trend analysis presented research priorities using VOSviewer. Finally, research innovation involved designing thematic networks and examining the relevance of significant topics as a research artifact with subtle differences compared to the conducted research. This novel approach provides five targeted propositions to audiences for future research. © 2025 University of Tehran. All rights reserved. CY - Iran ER - TY - JOUR TI - Leveraging Artificial Intelligence Capability and Open Innovation to Optimize Agility: Is Generative AI Outmatching Human Expertise? AU - Arias-Pérez J. AU - Vélez-Jaramillo J. AU - Callegaro-de-Menezes D. PY - 2026 JO - Journal of the Knowledge Economy VL - 17 IS - 2 SP - 3635 EP - 3662 DO - 10.1007/s13132-025-02799-2 AB - Artificial intelligence (AI) will be performing 30% of creative and knowledge-intensive tasks by the year 2030. The use of affordable user-friendly generative AI, such as ChatGPT, has consequently experienced a significant surge within the corporate sector. Concurrently, it is assumed that human expertise, which refers to the business and technical knowledge in AI of individuals, is gradually losing its significance. However, until recently, human expertise was regarded as the primary catalyst for the positive effect of open innovation on organizational agility. Hence, this study aims to examine the mediating effect of AI capability on the relationship between open innovation and organizational agility, particularly in the presence of human expertise. The moderated mediation was tested with survey data. The main finding reveals that 93% of the variance of agility is explained by the effect of open innovation that is transmitted by the mediator. Moreover, human expertise only moderates the pathway between AI capability and organizational agility. The study offers a realistic understanding of the role of individuals in the context of increased use of AI in firms, in contrast to prior research that predicted an abrupt substitution of personnel with AI. AI capability, particularly generative AI (Gen-AI), is essential for the efficient generation of innovation ideas and prototypes, as well as the identification of unconventional commercial exploitation routes by leveraging data from external sources. Nevertheless, human expertise is essential to extract more accurate and contextually relevant outcomes from Gen-AI. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025. KW - Agility KW - AI capability KW - Digital transformation KW - Generative AI KW - Human expertise KW - Open innovation CY - Colombia, Brazil ER - TY - JOUR TI - Model Construction and Strategies for AI-enabled University Library Services to Facilitate Scientific and Technological Achievement Transformation; [AI赋能高校图书馆服务科技成果转化的 模式构建与策略研究] AU - Guo H. AU - Zeng M. AU - Feng Y. PY - 2026 JO - Journal of Library and Information Science in Agriculture VL - 38 IS - 2 SP - 56 EP - 65 DO - 10.13998/j.cnki.issn1002-1248.25-0568 AB - [Purpose/Significance] Against the backdrop of national innovation-driven development strategies and the pressing need to enhance the efficiency with which scientific and technological achievements are transformed within universities, university libraries are undergoing a critical transition. They are shifting from being traditional, passive information providers to becoming proactive, embedded partners in the research and innovation value chain. However, this transition is often hampered by inherent limitations in traditional service models. This study, therefore, posits artificial intelligence (AI) as a pivotal enabler and investigates the specific mechanisms through which AI technologies can empower university libraries to achieve deep, systemic integration into the entire lifecycle of technology transfer. The research aims to provide a comprehensive theoretical framework for understanding this transformation and offer actionable, evidence-based practical pathways for academic libraries to redefine their functional boundaries and substantially strengthen the institutional support ecosystem for university technology transfer. [Method/Process] This research employs a qualitative multi-case study design, underpinned by an analytical framework constructed around the four critical, sequential stages of the technology transfer lifecycle: 1) research topic selection and project initiation, 2) research and development, 3) project conclusion and evaluation, and 4) marketization and industrialization of outcomes. Case selection followed purposive sampling criteria to ensure representation across diverse contexts, including domestic and international universities, as well as varied library types. The primary data comprised detailed case descriptions from published academic literature, institutional reports, and official service platforms. Within this staged framework, the analysis focuses on two intertwined dimensions at each phase: the evolution of the library’s core service functions and the transformative impact of AI empowerment. Through a comparative cross-case analysis, this study examines how specific AI technologies augment traditional services, fundamentally changing the role and value proposition of libraries. [Results/Conclusions] The results show that through intelligent information analysis, knowledge association, data mining, and precise matching, AI can promote university libraries to shift from resource supply-oriented support to collaborative services that run through the entire lifecycle of technology transfer. This transformation manifests across the four-stage lifecycle as a shift: from providing literature to forecasting opportunities at the initiation phase; from offering patent data to navigating R&D pathways and risks during development; from archiving outputs to assessing value and potential at conclusion; and from disseminating information to intelligently brokering industry partnerships at the commercialization phase. Synthesizing these stage-specific transformations, this study constructs a novel, integrated service framework. This framework explicitly links specific AI capabilities with the redefined core functions of the library at each stage, illustrating the transition from a linear support model to a dynamic, AI-augmented ecosystem wherein the library serves as a central intelligence node. Meanwhile, this study reveals practical challenges in current practices, including ambiguous organizational boundaries, insufficient professional capabilities, and imperfect evaluation mechanisms oriented toward technology transfer. Correspondingly, it proposes strategies such as clarifying collaborative positioning, strengthening the construction of AI-empowered service capabilities, and improving technology transfer-oriented evaluation mechanisms to promote the sustainable development of AI-empowered research services in university libraries. © 2026, Agricultural Information Institute, Chinese Academy of Agricultural Sciences KW - artificial intelligence KW - case study KW - full-lifecycle services KW - technology transfer KW - university libraries CY - China ER - TY - JOUR TI - Green Leadership and AI-Enabled Innovation Pathways Toward Circular Supply Chain Practices AU - Hou G. AU - Zhang J. AU - Gao X. AU - Yu J. PY - 2026 JO - Corporate Social Responsibility and Environmental Management DO - 10.1002/csr.70487 AB - The study examines how AI-enabled capabilities, innovation ambidexterity, and green leadership affect the circular supply-chain practices in the Chinese furniture industry. The current research is motivated by increasing environmental pressures, regulatory expectations, and the strategic necessity of manufacturing firms to unite digital technologies with sustainability-oriented ventures. Based on the Resource based View and the Dynamic Capability Theory, the paper develops a framework in which dynamic routines (sensing, coordinating, learning, integrating, and reconfiguring) supplement AI-mediated capabilities of firms, enhancing both exploratory and exploitative innovation. The responses of 387 managers working in production, operations, supply-chain, and sustainability departments were collected and analyzed using Partial Least Squares Structural Equation Modelling (PLS-SEM). The results show that AI-driven functions have a significant influence on both types of innovation that, in turn, stimulate the introduction of circular supply-chain practices. The mediation analysis also shows that innovation is the important component by which AI-enabled capabilities contributes to sustainable supply-chain. Further, green leadership has a positive direct impact on circular practice and increases the impact of innovation on sustainability performance. These results support the idea that technological resources, balanced innovation strategies, and strong leadership commitment all accelerate firms to shift towards the practice of a circular economy, therefore providing a more accurate understanding of how AI-enabled capabilities are translated into measurable sustainability results. The combination of AI capabilities, innovation ambidexterity, and green leadership into a single system leads to a scientific impact on sustainability and digital transformation literature, as well as providing practical solutions to industry practitioners and policymakers that aim to encourage the deployment of circular supply chains. © 2026 ERP Environment and John Wiley & Sons Ltd. KW - AI-enabled capabilities KW - circular supply chain KW - exploitative innovation KW - exploratory innovation KW - green leadership CY - China ER - TY - JOUR TI - Imagining AI at work: the impact of polarized imaginaries on AI use in human resources management AU - Ecclesia S. PY - 2025 JO - Journal of Workplace Learning SP - 1 EP - 15 DO - 10.1108/JWL-06-2025-0162 AB - Purpose – The purpose of this study is to investigate how polarized imaginaries about the future of artificial intelligence (AI) for work impact AI practices in Human Resources Management (HRM). In doing so, this study provides insights into the influence replacement narratives have on AI use, showing that AI imaginaries can be understood as social practice imaginaries emerging from the present. Design/methodology/approach – The study is based on 20 interviews with HRM practitioners in Italy and two organizational observations with companies using AI in the recruitment process. The use of interviews and observations together has allowed to gather information about both HR practitioners’ expectations and emerging practices. Findings – The results show that HR practitioners negotiate replacement narratives against the backdrop of their own experience in using AI. Learning is adopted as a strategy to protect themselves from replacement, while popular fears and hopes are delayed as the current AI capabilities do not fulfill their promises. AI becomes a useful tool allowing them to dedicate themselves to meaningful tasks contributing to their self-actualization. From these emerging practices stems an alternative imaginary of AI, positioning workers’ wellbeing at the center of automation. Originality/value – This study challenges narratives on the future of AI for work by providing empirical evidence of their negotiation by workers and intersecting them with research on social practices. By doing so, it proposes an alternative imaginary of the future of AI in the workplace that moves away from traditional expectations about replacement and invites reflection on workers’ needs. © 2025 Emerald Publishing Limited KW - Artificial intelligence KW - Human resources KW - Imaginaries KW - Replacement KW - Social practices KW - Workplace innovation CY - Norway ER - TY - JOUR TI - Configurational pathways to smart city AI Adoption: Evidence from local governments in Australia, Hong Kong, Saudi Arabia, Spain, and the United States AU - Yigitcanlar T. AU - Liu K. AU - Senadheera S. AU - Marasinghe R. AU - David A. AU - Cheong P.H. AU - Corchado J. PY - 2026 JO - Cities VL - 175 SP - 107117 DO - 10.1016/j.cities.2026.107117 AB - Despite increasing policy attention and technological progress, AI adoption in smart city governance and local governments remains uneven. While previous studies have identified individual drivers of adoption, limited research has examined how multiple factors interact to enable or constrain implementation. Drawing on the technology-community-policy framework, this study employs fuzzy-set qualitative comparative analysis to investigate configurational pathways leading to AI-enabled smart city adoption across eleven local governments in five countries, Australia, Hong Kong, Saudi Arabia, Spain, and the United States. The findings reveal three different equifinal configurations leading to high AI adoption. First, the technology-driven pathway shows that robust smart city infrastructure and data capability can offset limited regulatory preparedness. Second, the balanced pathway integrates technological readiness, policy awareness, and organisational attention to community considerations to support adoption holistically. Third, the policy-driven pathway demonstrates that strong institutional mandates can compensate for weaker technical capacity. Across all pathways, perceived implementation constraints emerge as a core enabling condition, suggesting that recognition of challenges can stimulate proactive adoption strategies. The findings highlight substitutability between technological and policy dimensions, offering strategic flexibility for municipalities with differing resource endowments. This study advances configurational thinking in smart city and public sector innovation research and provides actionable insights for context-sensitive, resource-appropriate AI governance in local governments. © 2026 The Authors. KW - AI adoption KW - AI governance KW - Local government KW - Organisational configuration analysis KW - Public sector innovation KW - Smart city KW - Australia KW - China KW - Hong Kong KW - Saudi Arabia KW - Spain KW - United States KW - artificial intelligence KW - innovation KW - local government KW - smart city KW - technology adoption CY - South Africa ER - TY - JOUR TI - How Do AI Capabilities Affect Ambidextrous Green Innovation? A Mechanistic Analysis Based on Green Knowledge Management and Human–Organization–Technology Fit AU - Zhao P. AU - Cao Y. AU - Liu W. PY - 2026 JO - Systems VL - 14 IS - 4 SP - 357 DO - 10.3390/systems14040357 AB - Although artificial intelligence (AI) capabilities have emerged as a critical driver of corporate innovation in the contemporary business landscape, how they facilitate ambidextrous green innovation (AGI) during the manufacturing sector’s green transition—and under what conditions these benefits are most pronounced—remains unclear. Drawing on the Resource-Based View (RBV) and Knowledge-Based View (KBV), this study investigates the mechanism by which AICs foster AGI through the mediating role of green knowledge management (GKM), while further examining how Human–Organization–Technology (HOT) fit moderates these pathways. An analysis of survey data from 238 Chinese manufacturing firms using PLS-SEM reveals that AICs significantly drive AGI, with GKM playing a pivotal mediating role. Furthermore, the study confirms that Human–Organization–Technology (HOT) fit acts as a boundary condition, moderating the impact of AICs on GKM. These findings clarify the underlying mechanisms and boundary conditions of AICs, offering actionable insights for manufacturers seeking to boost green innovation capabilities by optimizing HOT alignment and leveraging green knowledge management systems. © 2026 by the authors. KW - AI capabilities KW - ambidextrous green innovation KW - green knowledge management KW - human–organization–technology fit KW - Knowledge acquisition KW - Knowledge organization KW - Ambidextrous green innovation KW - Artificial intelligence capability KW - Condition KW - Corporate innovation KW - Green innovations KW - Green knowledge management KW - Human–organization–technology fit KW - Manufacturing sector KW - Mechanistic analysis KW - Mediating roles KW - Green manufacturing CY - China ER - TY - JOUR TI - The impact of artificial intelligence capabilities on the sustainability with the mediating role of green innovation in the Jordanian hotels sector AU - Rawash H.N. AU - Alkawaja M. AU - Albadarneh M. AU - Jahmani K. AU - Salah A. PY - 2025 JO - Journal of Project Management (Canada) VL - 10 IS - 3 SP - 549 EP - 562 DO - 10.5267/j.jpm.2025.3.007 AB - The hotel industry in Jordan plays a crucial role in stimulating economic expansion by attracting tourists and creating job prospects. The industry can benefit from the use of Artificial Intelligence (AI) to improve sustainability through the promotion of green innovation, efficient resource utilization, and reduction of environmental harm. Hence, this study designs a model to enhance the environmental, economic, and social sustainability in the Jordanian hotels Sector. The study aimed to examine the impact of the AI Capabilities (tangible, intangible and human) on social, economic, and environmental sustainability with the mediating effect of the green innovation. The population of this study is all employees in 19 eco-friendly hotels in Jordan, they were 18,850 distributed over four Jordanian regions (Amman, Aqaba, Dead Sea and Petra). A total of 377 questionnaires distributed to respondents using stratified sampling. The study used SEM with SMART-PLS 4 to analyze the data collected. The measurement model applied to analyze the reliability and reliability of the model, the path coefficient in the structural equation model used to test the study hypotheses. The results of this study supported most of the study’s hypotheses, as it supported the impact of tangible and human capabilities on the sustainability, while the study did not find any direct impact of the intangible capabilities on the sustainability in the hotel sector in Jordan. The results show significant direct impact of the three AI capabilities; tangible, intangible and human on the green innovation, also the study found significant impact of the green innovation on the sustainability. The study confirms the three mediation hypotheses of the green innovation on the impact of the AI capabilities on the sustainability in the Jordanian hotel sector. The study provides important implications to the managers in the Jordanian hotel sector to enhance their environmental, economic and social sustainability by improving AI capabilities and innovation. © 2025 by the authors; licensee Growing Science, Canada. KW - AI capabilities KW - Hotel Sector KW - Human Capabilities KW - Intangible Capabilities KW - Sustainability KW - Tangible Capabilities CY - Jordan ER - TY - JOUR TI - Survey and Tutorial on Hybrid Human-Artificial Intelligence AU - Shi F. AU - Zhou F. AU - Liu H. AU - Chen L. AU - Ning H. PY - 2023 JO - Tsinghua Science and Technology VL - 28 IS - 3 SP - 486 EP - 499 DO - 10.26599/TST.2022.9010022 AB - The growing computing power, easy acquisition of large-scale data, and constantly improved algorithms have led to a new wave of artificial intelligence (AI) applications, which change the ways we live, manufacture, and do business. Along with this development, a rising concern is the relationship between AI and human intelligence, namely, whether AI systems may one day overtake, manipulate, or replace humans. In this paper, we introduce a novel concept named hybrid human-Artificial intelligence (H-AI), which fuses human abilities and AI capabilities into a unified entity. It presents a challenging yet promising research direction that prompts secure and trusted AI innovations while keeping humans in the loop for effective control. We scientifically define the concept of H-AI and propose an evolution road map for the development of AI toward H-AI. We then examine the key underpinning techniques of H-AI, such as user profile modeling, cognitive computing, and human-in-The-loop machine learning. Afterward, we discuss H-Al's potential applications in the area of smart homes, intelligent medicine, smart transportation, and smart manufacturing. Finally, we conduct a critical analysis of current challenges and open gaps in H-AI, upon which we elaborate on future research issues and directions. © 1996-2012 Tsinghua University Press. KW - artificial intelligence (AI) KW - hybrid human-Artificial intelligence (H-AI) KW - Internet of Things (IoT) KW - Artificial intelligence KW - Automation KW - Intelligent buildings KW - Artificial intelligence KW - Artificial intelligence systems KW - Computing power KW - Human intelligence KW - Human-in-the-loop KW - Hybrid human-artificial intelligence KW - Improved * algorithm KW - Internet of thing KW - Large scale data KW - Novel concept KW - Internet of things CY - China, United Kingdom ER - TY - JOUR TI - Future-ready AI: A framework for ethical and sustainable adoption AU - Kittipanya-ngam P. AU - Tan K.H. AU - Cavite H.J. PY - 2025 JO - Technology in Society VL - 83 SP - 102993 DO - 10.1016/j.techsoc.2025.102993 AB - The rise of artificial intelligence (AI) is transforming industries through automation, data-driven decision-making, and innovation. However, its adoption also poses challenges, including high implementation costs, limited technical capacity, and growing concerns around ethical and sustainable practices. While research on AI adoption continues to grow, the intersection of ethics, sustainability, and small and medium-sized enterprises (SMEs) remains underexplored. This study addresses that gap by investigating the dynamics of AI adoption among SMEs in Thailand—a key Southeast Asian economy—through multiple in-depth case studies. Within-case and cross-case analyses reveal that AI adoption presents both opportunities and challenges across the technological, organizational, and environmental (TOE) framework. Key factors include job security, data protection, and cost savings, while user education, mental well-being, and financial access emerge as critical concerns. The study further explores how TOE dimensions interact with sustainability and ethical considerations, conceptualized as ESG + E (Environmental, Social, Governance, and Economic). This expanded lens offers a more comprehensive understanding of responsible AI adoption. A novel integrative framework is proposed, providing actionable insights for SMEs, policymakers, and technology providers. The findings contribute to the broader discourse on AI adoption by advancing a sustainability- and ethics-oriented perspective relevant to emerging economies. © 2025 KW - Adoption KW - Artificial intelligence KW - Challenges and opportunities KW - Ethics KW - Sustainability KW - Decision making KW - Economics KW - Ethical technology KW - Sustainable development KW - Adoption KW - Challenge and opportunity KW - Data driven decision KW - Decisions makings KW - Ethical practices KW - Implementation cost KW - Organisational KW - Small and medium-sized enterprise KW - Sustainable practices KW - Technical capacity KW - artificial intelligence KW - ethics KW - small and medium-sized enterprise KW - sustainability KW - technology adoption KW - Artificial intelligence CY - Thailand, United Kingdom ER - TY - JOUR TI - Artificial Intelligence Capability and Firm Performance: A Sustainable Development Perspective by the Mediating Role of Data-Driven Culture AU - Fosso Wamba S. AU - Queiroz M.M. AU - Pappas I.O. AU - Sullivan Y. PY - 2024 JO - Information Systems Frontiers VL - 26 IS - 6 SP - 2189 EP - 2203 DO - 10.1007/s10796-023-10460-z AB - Artificial Intelligence (AI) tools, applications, and capabilities have received tremendous attention from industry practitioners, scholars, and policymakers. Despite the substantial progress of the literature on AI, there is a considerable scarcity of research investigating the effects of AI capability, considering the importance of a data-driven culture and whether a data-driven culture truly mediates the relationship between AI capability and firm performance from a sustainable development perspective. Anchored by the resource-based theory (RBT), we developed a high-order model of AI capability and its resources (tangible, intangible, and human). We used a two-stage approach, with PLS-SEM in the first and fsQCA in the second. The findings from the first step suggest that AI capability directly impacts firm performance and that data-driven culture mediates the relationship between AI capability and firm performance. The results from the second step indicated that different configurations of AI resources could be considered for firms to achieve high performance but that AI infrastructure is a crucial resource. Our study advances the literature on AI capability and sustainable development goals. Similarly, it contributes to moving the RBT theory forward by suggesting that AI capability is a paramount variable that substantially influences firm performance. Simultaneously, it is harmoniously connected with SDG 9 (industry, innovation, and infrastructure) and SDG 12 (responsible consumption and production). © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. KW - Artificial intelligence capability KW - Data-driven culture KW - Firm performance KW - SDG 12 KW - SDG 9 KW - Sustainable development KW - Artificial intelligence KW - Artificial intelligence capability KW - Artificial intelligence tools KW - Data driven KW - Data-driven culture KW - Firm Performance KW - Mediating roles KW - Resource-based theory KW - SDG 12 KW - SDG 9 KW - Tool applications KW - Sustainable development CY - France, Brazil, Norway, United States ER - TY - JOUR TI - How AI capabilities enable business model innovation: Scaling AI through co-evolutionary processes and feedback loops AU - Sjödin D. AU - Parida V. AU - Palmié M. AU - Wincent J. PY - 2021 JO - Journal of Business Research VL - 134 SP - 574 EP - 587 DO - 10.1016/j.jbusres.2021.05.009 AB - Artificial intelligence (AI) is predicted to radically transform the ways manufacturing firms create, deliver, and capture value. However, many manufacturers struggle to successfully assimilate AI capabilities into their business models and operations at scale. In this paper, we explore how manufacturing firms can develop AI capabilities and innovate their business models to scale AI in digital servitization. We present empirical insights from a case study of six leading manufacturers engaged in AI. The findings reveal three sets of critical AI capabilities: data pipeline, algorithm development, and AI democratization. To scale these capabilities, firms need to innovate their business models by focusing on agile customer co-creation, data-driven delivery operations, and scalable ecosystem integration. We combine these insights into a co-evolutionary framework for scaling AI through business model innovation underscoring the mechanisms and feedback loops. We offer insights into how manufacturers can scale AI, with important implications for management. © 2021 KW - Artificial intelligence KW - Business model innovation KW - Digital servitization KW - Digital transformation KW - Digitalization KW - Platform CY - Sweden, Norway, Switzerland, Finland ER - TY - JOUR TI - Balancing innovation and integrity: AI in tax administration and taxpayer rights AU - Guglyuvatyy E. PY - 2025 JO - Humanities and Social Sciences Communications VL - 12 IS - 1 SP - 1818 DO - 10.1057/s41599-025-06099-7 AB - Artificial intelligence (AI) is transforming tax administration by improving efficiency, compliance, and decision-making. However, this shift raises critical concerns about transparency, accountability, and taxpayer rights. This paper examines how AI-driven systems impact legal fairness, due process, and the integrity of tax procedures. It highlights risks such as algorithmic bias, opacity, and weakened procedural safeguards, while acknowledging AI’s potential to streamline enforcement. To safeguard taxpayer rights, the paper proposes an independent AI oversight mechanism to explain and review tax decisions. This system would enhance transparency, reinforce trust, and ensure legal accountability. The paper calls for regulatory frameworks that embed oversight, uphold public trust, and balance innovation with fundamental legal protections. © The Author(s) 2025. CY - Malaysia, Australia ER - TY - JOUR TI - Inside-out AI strategy at Microsoft: From capability building to commercialization AU - Durairaj M. AU - Bagilesh K. AU - Sathyamoorthy A. AU - Shanmugam K. PY - 2025 JO - Journal of Information Technology Teaching Cases SP - 20438869251383032 DO - 10.1177/20438869251383032 AB - As organizations adjust to the changing landscape of artificial intelligence (AI), a critical strategic question arises: Should companies prioritize internal transformation or lead with customer-driven innovation? This case investigates the ‘inside-out’ approach to AI adoption, where internal transformation functions as the foundation for market-facing solutions. The case illustrates how internal AI tool deployments, such as Microsoft 365 Copilot and Dynamics 365, enhanced operational efficiency and led to the development of scalable products for commercial clients, with Microsoft Corporation serving as the central organization. With quantifiable gains in efficiency and productivity, Microsoft must now decide how quickly to expand its AI products worldwide. The study highlights internal improvements such as enhanced operational reliability, alongside external milestones including Azure AI’s role in retail optimization and the widespread adoption of GitHub Copilot across enterprise clients. In comparing Microsoft’s strategy to rivals such as Google, Amazon, and Salesforce, the case highlights market potential as well as ethical, pricing, and regulatory issues. This case offers a framework for analysing commercialization of AI, capability building, and innovation strategy, while drawing on organizational theories such as the Resource-Based View, dynamic capabilities, and responsible innovation to evaluate Microsoft’s long-term strategic options. © Association for Information Technology Trust 2025 KW - AI capability KW - AI commercialization KW - azure AI KW - competitive strategy KW - digital transformation KW - enterprise AI adoption KW - inside-out strategy KW - Microsoft 365 Copilot KW - responsible AI CY - India ER - TY - JOUR TI - HOW DOES AI CAPABILITY ENABLE DIGITAL PRODUCT INNOVATION? A MIXED METHODS DESIGN AU - Li S. AU - Liu J. AU - Yang X. PY - 2025 JO - International Journal of Innovation Management VL - 29 IS - 03n04 SP - 2550017 DO - 10.1142/S1363919625500173 AB - Despite the fact that Artificial Intelligence (AI) in innovation management has been a topic of interest for several decades, little is known throughout the literature about how and why AI capability creates product value. In this work, we proposed a dual process model to explore the effects of AI capability on digital product innovation and tested it using quantitative and qualitative methods. In quantitative analysis, based on AI-Open Innovation matrix and dynamic capability theory, we tested the model using a total of 314 managers from 127 firms in the Chinese mainland. We found that AI capability enables digital product innovation by enhancing online value co-creation (a process of external asset) and digital resilience (a process of internal asset). Moreover, socio-cognitive sensemaking strengthens the mediation process of digital resilience but has no significant moderating effect on the mediation process of online value co-creation. The qualitative analysis enables us to better interpret the reasons why sensemaking plays different roles in mediation processes and suggests that it strengthens the effects of online value co-creation and digital resilience on digital product innovation through the external loop (time effect) and the internal loop (interactive effect), respectively. Our findings provide insights into how firms can scale digital product innovation using AI, with important implications for management. © 2025 World Scientific Publishing Europe Ltd. KW - AI capability KW - digital product innovation KW - digital resilience KW - online value co-creation KW - socio-cognitive sensemaking CY - China ER - TY - JOUR TI - Artificial Intelligence in Bioanalytical Chemistry: A Review of Algorithms, Applications, and Future Prospects AU - Garugu S. AU - Saxena K. AU - Zare K.B. AU - Kumar M. AU - Mukherjee B. AU - Shenoy A.V. AU - Rani S.K. PY - 2025 JO - Journal of Applied Bioanalysis VL - 11 IS - 4 SP - 661 EP - 677 DO - 10.53555/jab.v11i4.410 AB - The emergence of artificial intelligence (AI) as a data-centric tool is redefining bioanalytical chemistry by offering enhanced capabilities for automation, precision, and intelligent decision-making. As analytical laboratories face increasing demands for higher throughput, greater reproducibility, and real-time data processing, AI offers a strategic framework to address these challenges across diverse platforms, including spectroscopy, chromatography, and mass spectrometry. This review critically explores the current landscape of AI applications in bioanalytical workflows, encompassing key algorithmic approaches such as supervised and unsupervised learning, deep learning, and explainable AI (XAI). Emphasis is placed on practical implementations for spectral deconvolution, chromatographic peak detection, retention time prediction, and mass spectral interpretation. The review highlights AI-driven innovations in sample preparation, quality assurance, and method development optimization, supported by automation and Internet of Things (IoT) integration. Emerging concepts such as digital twins, self-learning adaptive systems, and blockchain-enabled traceability are also discussed as future enablers of smart analytical laboratories. While AI demonstrates transformative potential, significant challenges remain in areas of data quality, model reproducibility, interpretability, and regulatory alignment. Addressing these gaps through cross-disciplinary collaboration and the development of standardized validation frameworks is essential for ensuring trustworthy and compliant AI adoption in bioanalytical settings. This review provides a comprehensive synthesis of AI’s capabilities, limitations, and innovation directions, offering valuable insights for researchers, analysts, and regulatory professionals seeking to navigate the evolving intersection of artificial intelligence and analytical chemistry. © 2025, Green Publication. All rights reserved. KW - Artificial Intelligence KW - Bioanalytical Chemistry KW - Chromatography KW - Machine Learning KW - Spectroscopy CY - India ER - TY - JOUR TI - Technologies of improving the university efficiency by using artificial intelligence: Motivational aspect AU - Vinichenko M.V. AU - Melnichuk A.V. AU - Karácsony P. PY - 2020 JO - Entrepreneurship and Sustainability Issues VL - 7 IS - 4 SP - 2696 EP - 2714 DO - 10.9770/jesi.2020.7.4(9) AB - The aim of this study was to identify the most appropriate technologies to improve the university efficiency by using the motivational artificial intelligence (AI). Methods of the study were as follows: the questionnaire survey by using the Google Chrome electronic service, content analysis, methods of statistical analysis, and a focus group. The authors’ version of the questionnaire was made by using the Likert methodology taking into account indicators of the QS World University Rankings rating system. The data obtained during the three stages were generalized and analyzed by using the descriptive statistics. The regression analysis was used to study the relationship between the motives of the academic staff (AS) and the nature of the stimulating effect of the university authorities on the staff of the university. Results: The discrepancy between the AS motivation structure and the range of stimulation methods applied by the university authorities, a continuous increase in the burden from introduced innovations, and the formal style for employees to fulfill new tasks have been revealed. The analysis of the results on using the techniques and methods by the university authorities to motivate and stimulate the staff has shown the need in new combinatorics, an innovative system that harmoniously combines the advantages of natural and artificial intelligence to motivate the AS in training HR for the digital economy of the 21st century. The new system should be universal and flexibly respond to constant changes in the socio-economic environment. It is important to timely eliminate the contradictions in needs and teachers’ opinion on the ideal assessment system of their activities and offered forms of stimulation by universities authorities. The vectors of their activities must be constantly coordinated, based on the AI capabilities. The introduction of AI in the activities of universities improves the competitiveness of promising, innovative teachers and has positive impact on the image, efficiency, academic reputation, and citation index of universities. The authors for the first time ever have studied the problems of using the AI in the motivational system of the university’s AS and offered technologies to improve the efficiency of universities by using the motivational AI. The practical importance of solving the problem is related to the real possibility of applying the offered technologies by the university authorities that strive to improve their efficiency and competitiveness in the educational market. The main advantage of the work is related to the advanced solutions of the emerging problems on using the AI in motivating the university staff identified during the three-stage study. The interdisciplinary nature of the study and the offered technologies can serve as the basis for the further study and an additional element that expands the views, approaches, and the framework of categories and concepts of the world science. Conclusion: The most suitable technologies for the university that strives to be efficient include the elimination of the imbalance in the system of staff motives – incentives of the university (employer) authorities, the harmonious use of the AI in educational activities and the system of motivation and stimulation of staff where the natural intelligence prevails, and the improvement of the staff’s publication and grant activities by using the AI with a synergistic effect due to efficient team building. © 2020 by author(s) and VsI Entrepreneurship and Sustainability Center. KW - Artificial intelligence KW - Efficiency KW - Higher education KW - Motivation and stimulation KW - Technologies CY - Slovakia ER - TY - JOUR TI - Exploring the artificial intelligence integration in top management team decision-making: an empirical analysis AU - Bevilacqua S. AU - Ferraris A. AU - Kozel R. AU - Vincurova Z. PY - 2025 JO - Business Process Management Journal VL - 31 IS - 5 SP - 1763 EP - 1784 DO - 10.1108/BPMJ-07-2024-0659 AB - Purpose Drawing on upper echelons theory (UET), this study empirically explores how artificial intelligence (AI) has influenced the top management team’s (TMT) decision-making process in business management. Design/methodology/approach This article is based on 21 semi-structured interviews with top managers leading AI integration in their organizations. It adopts an inductive approach and applies the Gioia methodology. Findings The research identifies four primary areas of impact of AI for TMTs in managing digital business processes: (1) hybrid decision-making process, (2) AI’s ethical implications, (3) TMT governance through AI, and (4) AI-driven competitive advantage. Also, a framework has been developed that provides an initial understanding of how integrating AI in organizations affects the TMT’s decision-making process. Practical implications The study provides practical insights for the TMT leveraging AI technologies to enhance decision-making in managing business processes. Additionally, it offers helpful guidance for organizations to stay at the forefront of innovation and adaptability in an ever-evolving world. Originality/value Our findings highlight the critical role of TMT’s decision-making in managing business processes transformed by AI. Moreover, the study extends the UET, highlighting how the integration of AI influences the TMT’s decision-making process and how ethical implications impact these decisions and business management. © 2025 Emerald Publishing Limited KW - Artificial intelligence KW - Decision-making KW - Leadership KW - Top management team KW - Upper echelons theory CY - Slovakia ER - TY - JOUR TI - From discovery to delivery: Governance of AI in the pharmaceutical industry AU - Pasas-Farmer S. AU - Jain R. PY - 2025 JO - Green Analytical Chemistry VL - 13 SP - 100268 DO - 10.1016/j.greeac.2025.100268 AB - Artificial Intelligence (AI) is revolutionizing the pharmaceutical industry, significantly enhancing drug discovery, patient care, and operational efficiency. Key AI technologies like machine learning, deep learning, natural language processing, and computer vision are transforming pharmaceutical practices. Despite the promising potential, AI implementation faces numerous challenges such as technical complexity, ethical concerns, regulatory hurdles, and a shortage of skilled professionals. Governance frameworks are essential to ensure AI technologies are ethically developed and deployed, balancing innovation with safety and transparency. Key components of AI governance include regulatory compliance, data governance, algorithm transparency, and continuous system monitoring. However, the fast pace of technological advancements, global regulatory discrepancies, and the need for stakeholder collaboration present ongoing challenges. Best practices for AI governance, such as promoting transparency, fostering multidisciplinary collaboration, and adhering to robust data management standards, are critical for ensuring the ethical and effective use of AI. Addressing these challenges will enable the pharmaceutical industry to fully harness the power of AI, ensuring patient safety and promoting innovation in healthcare. © 2025 KW - AI governance KW - Analytical chemistry KW - Artificial intelligence KW - Ethical considerations KW - Pharmaceutical industry KW - Regulatory challenges KW - Transparency ER - TY - JOUR TI - AI Startup Business Models: Key Characteristics and Directions for Entrepreneurship Research AU - Weber M. AU - Beutter M. AU - Weking J. AU - Böhm M. AU - Krcmar H. PY - 2022 JO - Business and Information Systems Engineering VL - 64 IS - 1 SP - 91 EP - 109 DO - 10.1007/s12599-021-00732-w AB - We currently observe the rapid emergence of startups that use Artificial Intelligence (AI) as part of their business model. While recent research suggests that AI startups employ novel or different business models, one could argue that AI technology has been used in business models for a long time already—questioning the novelty of those business models. Therefore, this study investigates how AI startup business models potentially differ from common IT-related business models. First, a business model taxonomy of AI startups is developed from a sample of 100 AI startups and four archetypal business model patterns are derived: AI-charged Product/Service Provider, AI Development Facilitator, Data Analytics Provider, and Deep Tech Researcher. Second, drawing on this descriptive analysis, three distinctive aspects of AI startup business models are discussed: (1) new value propositions through AI capabilities, (2) different roles of data for value creation, and (3) the impact of AI technology on the overall business logic. This study contributes to our fundamental understanding of AI startup business models by identifying their key characteristics, common instantiations, and distinctive aspects. Furthermore, this study proposes promising directions for future entrepreneurship research. For practice, the taxonomy and patterns serve as structured tools to support entrepreneurial action. © 2021, The Author(s). KW - Artificial intelligence KW - Business model KW - Entrepreneurship KW - Machine learning KW - Pattern KW - Taxonomy CY - Germany ER - TY - JOUR TI - UNLOCKING AI POTENTIAL: EFFORT EXPECTANCY, SATISFACTION, AND USAGE IN RESEARCH AU - Izhar N.A. AU - Teh W.V.Y. AU - Adnan A. PY - 2025 JO - Journal of Information Technology Education: Innovations in Practice VL - 24 SP - 5 DO - 10.28945/5450 AB - Aim/Purpose This study investigates the key factors influencing the adoption and use of artificial intelligence (AI) applications among researchers, focusing on effort expectancy, satisfaction, perceived ease of use, and perceived usefulness, which shaped attitudes and drove AI adoption as a research assistant. Background AI tools have rapidly become game-changers in academic research, transforming tasks such as literature retrieval, writing, editing, and data analysis. Despite their potential, barriers like high effort expectancy, inconsistent user satisfaction, and ethical concerns regarding over-reliance and plagiarism continue to hinder widespread adoption. A pressing gap exists in understanding how AI impacts the efficiency and integrity of academic research workflows. Methodology A quantitative approach using structural equation modeling (SEM) was employed. Data was collected from 120 active researchers who use AI tools for academic tasks, including literature reviews, writing support, and data visualization. Contribution This study contributes to the understanding of how key factors, such as effort expectancy and satisfaction, affect AI adoption in academic research. It emphasizes the importance of reducing cognitive load and improving user satisfaction to promote widespread AI adoption. It also underscores the importance of intuitive AI design and institutional support in shaping researchers’ engagement with AI tools, which could enhance productivity and research outcomes. Findings The findings reveal that effort expectancy, satisfaction, perceived ease of use, and perceived usefulness significantly influence attitude and actual use of AI tools, with attitude serving as a key mediator. The model demonstrated moderate to high explanatory power (R2 = 0.409 to 0.459) and predictive relevance (Q2 = 0.171 to 0.409), highlighting the substantial role of effort expectancy and satisfaction in shaping perceived ease of use and usefulness. These findings emphasize the importance of reducing cognitive load and improving user satisfaction to encourage the adoption of AI tools in research. Recommendations Institutions and AI developers should focus on reducing the learning curve of for Practitioners AI tools by enhancing their intuitiveness and providing targeted training and technical support. Ethical AI use should also be promoted to address concerns about over-reliance and plagiarism. Institutions should foster a culture that normalizes AI integration in research practices. Recommendations Researchers should be informed of the long-term effects of AI adoption on for Researchers research quality and integrity and how institutional support can foster positive attitudes toward AI tools in academic research. Impact on Society The broader adoption of AI tools in academic research could enhance productivity and efficiency, leading to more breakthroughs in various fields and benefiting society by accelerating research and innovation. Additionally, AI can democratize access to research resources, particularly for underfunded institutions and early-career researchers, by enabling broader participation in cutting-edge research and fostering equity and diversity in academic contributions. Future Research Future studies should focus on the role of user experience in AI adoption, particularly how different user groups interact with AI tools. Longitudinal studies could provide insights into how attitudes toward AI change as users become more familiar with the tools. © (2025), (Informing Science Institute). All rights reserved. KW - academic research KW - AI adoption KW - artificial intelligence KW - effort expectancy KW - perceived ease of use KW - research assistance KW - satisfaction KW - technology acceptance CY - Malaysia ER - TY - JOUR TI - Multi-Stakeholder Agile Governance Mechanism of AI Based on Credit Entropy AU - Cheng L. AU - Chen W. AU - Li R. AU - Zhang C. PY - 2025 JO - Sustainability (Switzerland) VL - 17 IS - 20 SP - 9196 DO - 10.3390/su17209196 AB - Driven by the rapid evolution of AI technology, compatible management mechanisms have become a systematic project involving the participation of multiple stakeholders. However, constrained by the rigidity and lag of traditional laws, the “one-size-fits-all” regulatory model will exacerbate the vulnerability of the complex system of AI governance, hinder the sustainable evolution of the AI ecosystem that relies on the dynamic balance between innovation and responsibility, and ultimately fall into the dilemma of “chaos when laissez-faire, stagnation when over-regulated”. To address this challenge, this study takes the multi-stakeholder collaborative mechanism co-established by governments, enterprises, and third-party technical audit institutions as its research object and centers on the issue of “strategic fluctuations” caused by key factor disturbances. From the perspective of the full life cycle of technological development, the study integrates the historical compliance performance of stakeholders and develops a nonlinear dynamic reward and punishment mechanism based on Credit Entropy. Through evolutionary game simulation, it further examines this mechanism as a realization path to promote the transformation from passive campaign-style AI supervision to agile governance of AI, which is characterized by rapid response and minimal intervention, thereby laying a foundation for the sustainable development of AI technology that aligns with long-term social well-being, resource efficiency, and inclusive growth. Finally, the study puts forward specific governance suggestions, such as setting access thresholds for third-party institutions and strengthening their independence and professionalism, to ensure that the iterative development of AI makes positive contributions to the sustainability of socio-technical systems. © 2025 by the authors. KW - collaborative governance KW - credit entropy KW - dynamic reward and punishment KW - full life cycle KW - tripartite evolutionary game KW - compliance KW - governance approach KW - life cycle analysis KW - sustainability KW - sustainable development CY - China ER - TY - JOUR TI - Legislation on the Use of Artificial Intelligence in European Union Countries AU - Kolomoiets T. AU - Velykanova M. AU - Kravets M. AU - Blinova H. AU - Posykaliuk O. PY - 2026 JO - Nusantara: Journal of Law Studies VL - 5 IS - 1 SP - 269 EP - 287 DO - 10.66325/nusantaralaw.v5i1.233 AB - This study analyzes the legislative framework governing the use of artificial intelligence (AI) in the European Union, focusing on patterns of legal convergence and divergence, as well as the governance challenges arising from its implementation. The research aims to examine how the EU constructs a harmonized yet flexible regulatory regime capable of addressing the multifaceted risks of AI while promoting innovation. Methodologically, this study employs a qualitative approach through doctrinal legal analysis and policy review, drawing on primary legal instruments, including the EU AI Act, as well as secondary sources such as policy reports and academic literature. The findings indicate that the EU adopts a risk-based regulatory model that classifies AI systems into low, medium, and high-risk categories. While most AI applications fall into low-or medium-risk categories, high-risk systems—particularly those used in sensitive sectors such as healthcare, justice, employment, and finance—pose significant legal and ethical challenges. The study identifies key risks, including algorithmic bias, data privacy violations, and a lack of transparency, alongside broader concerns about accountability and the protection of fundamental rights. Furthermore, although legal convergence is evident in the establishment of uniform EU standards, divergence persists in national implementation, enforcement practices, and institutional readiness across member states. This study contributes to the existing literature by providing a comprehensive analysis of the interplay between harmonization and fragmentation in EU AI regulation. It also highlights the need for adaptive governance mechanisms that balance regulatory consistency with contextual flexibility. Ultimately, the research underscores that effective AI legislation must strengthen accountability, ensure ethical compliance, and foster public trust, thereby aligning technological development with the core values of the European Union. © 2026, PT. Islamic Research Publisher. All rights reserved. KW - Administrative Legal Protection KW - Civil Legal Protection KW - Intellectual Property Law KW - Liability KW - Object of Legal Relations CY - Ukraine ER - TY - JOUR TI - From automation to cognition: a contextual design approach for enhancing elderly patron services with proactive and memory-aware library robots AU - Yueh H.-P. AU - Lin W. AU - Chen C.-H. AU - Chang F.-H. AU - Fu L.-C. PY - 2025 JO - Electronic Library SP - 1 EP - 23 DO - 10.1108/EL-06-2025-0244 AB - Purpose – This study aims to explore the feasibility and effectiveness of integrating a cognitively-enabled robot into public library services specifically tailored for elderly patrons. It aims to demonstrate how a user-centred and contextually designed robotic system can meet the multifaceted needs of this demographic, moving beyond traditional automated functions to provide more interactive and personalized information services. Design/methodology/approach – Adopting a user-centred design research approach, this study developed “OREO”, a cognitive library robot featuring three key innovations: proactive engagement capabilities, intelligent navigation and recommendation and memory-aware dialogue built upon multiple visits. A field study was conducted in the senior zone of a national public library with elderly patrons, and both quantitative usability test data and qualitative interview data were collected and analysed. Findings – The cognitive robot, OREO, successfully demonstrated its designed functions in a real-world library setting. Three main functions targeted for verification – proactive greeting, intelligent guidance and memory-aware dialogue – were all confirmed with positive evaluation feedback. Originality/value – This study offers novel empirical insights into the deployment of a cognitive embodied robot that integrates advanced artificial intelligence (AI) capabilities of proactive engagement, intelligent navigation and memory-aware dialogue into a public library’s elderly services. It uniquely uses a user-centred and humanity-centred design approach within an authentic field setting, providing first-hand evidence and a crucial understanding of human-robot interaction in community contexts. The findings demonstrate the transformative potential of moving library robots from mere automation to intelligent, socially aware agents, offering actionable design principles for future AI-driven information services. © 2025 Emerald Publishing Limited KW - Artificial intelligence KW - Cognitive robotics KW - Elderly patrons KW - Generative AI KW - Human-robot interaction KW - Information access KW - Library services KW - Public libraries KW - User-centred design CY - Taiwan ER - TY - JOUR TI - EXPLORING RESPONSIBLE INNOVATION WITH PRIVACY PRESERVATION: FEDERATED LEARNING POLICIES FOR DIGITAL FINANCE SERVICES IN ASIA AU - Tam P. AU - Corrado R. AU - Pham T.T. PY - 2025 JO - Asia Pacific Sustainable Development Journal VL - 32 IS - 2 SP - 215 EP - 241 DO - 10.18356/26178419-32-2-10 AB - Advancements in artificial intelligence (AI) and financial technologies (fintech) are transforming digital finance with innovations in personalized products, fraud detection, accessibility and risk management. However, these innovations require sensitive customer data, raising privacy and security concerns. Federated learning (FL) offers a solution by enabling institutions to train AI models locally, sharing only model updates and minimizing data-sharing risks. This paper contains an exploration of how FL can advance AI-driven innovation while ensuring privacy compliance, in particular in Asia, by analysing FL key use cases, including personalized recommendations, fraud detection and credit scoring. We then propose frameworks for FL platform assessments and stakeholder analysis for policy recommendations to enhance data security, regulatory compliance and ethical guidelines for responsible innovation in digital finance. © 2025, UNESCAP. All rights reserved. KW - Asia KW - digital finance KW - federated learning KW - responsible innovation KW - stakeholder analysis CY - Cambodia, Netherlands ER - TY - JOUR TI - Flipping the odds of AI-driven open innovation: The effectiveness of partner trustworthiness in counteracting interorganizational knowledge hiding AU - Arias-Pérez J. AU - Huynh T. PY - 2023 JO - Industrial Marketing Management VL - 111 SP - 30 EP - 40 DO - 10.1016/j.indmarman.2023.03.005 AB - This paper aims to analyze the negative effect of knowledge hiding on the relationship between artificial intelligence (AI) capability and open innovation (inbound and outbound) when partner trustworthiness (benevolence, integrity, and ability) is high. Structural equations were used to test this three-way interaction with survey data from a sample of 229 firms, mainly from highly digitalized sectors. The findings indicate that interorganizational knowledge hiding affects only the relationship between AI capability and outbound open innovation and that partner ability is the only factor that will counteract this negative effect. Therefore, co-exploitation of AI-based solutions with external allies is the sole scenario to encourage knowledge hiding by increasing employees' perceptions of the likelihood of AI negatively impacting their personal interests at work. Moreover, when trustworthiness is at the forefront of the intraorganizational discussion, the findings downplay the significance of benevolence and integrity as traits that significantly reduce knowledge hiding. In contrast, at the interorganizational level, knowledge hiding is lessened only when employees perceive that co-exploitation with external partners represents an opportunity to learn and capture crucial AI knowledge. © 2023 KW - Artificial intelligence capability KW - Digital transformation KW - Knowledge hiding KW - Open innovation KW - Partner ability KW - Partner trustworthiness CY - Colombia, United Kingdom ER - TY - JOUR TI - The more capability, the better behavioural intention? Empirical evidence on the relation between institutes’ artificial intelligence capability and pre-service teachers’ behavioural intentions to design artificial intelligence assisted teaching AU - Wang K. AU - Shen L. AU - Duan B. AU - Zhang C. AU - Yuan X. PY - 2026 JO - Asia Pacific Journal of Education DO - 10.1080/02188791.2026.2625132 AB - The field of education has witnessed a rapid expansion in the utilization of Artificial Intelligence (AI) technologies, fundamentally transforming classroom instruction. Thus, it is critical for pre-service teachers to implement AI-powered technology in their future teaching. This study was conducted in six higher education institutions (HEIs) in China and is grounded in resource-based theory, the technology acceptance model (TAM), and relevant literature. SmartPLS 4.0 was utilized to develop a partial least squares structural equation model (PLS-SEM) to examine the relationships among AI capability (AIC), creativity, self-efficacy, Technological Pedagogical Content Knowledge (TPACK), and pre-service teachers’ behavioural intentions towards AI-assisted teaching. The findings indicated that HEIs’ AIC is a significant predictor of pre-service teachers’ behavioural intentions towards designing AI-assisted teaching. It also predicts their creativity, self-efficacy, and TPACK. Furthermore, creativity, self-efficacy, and TPACK were found to mediate the relationships between HEIs’ AIC and pre-service teachers’ behavioural intentions. These findings suggest that HEIs should support the development of pre-service teachers by enhancing AIC, including resources (data and technology) and awareness (reform and innovation), providing insights into AI integration in higher education within the Asia-Pacific context. © 2026 National Institute of Education, Singapore. KW - AI-assisted teaching KW - Artificial intelligence capability KW - behavioural intention KW - higher education institute KW - pre-service teachers CY - China, Belgium ER - TY - JOUR TI - Towards a Multi-Stakeholder process for developing responsible AI governance in consumer health AU - Rozenblit L. AU - Price A. AU - Solomonides A. AU - Joseph A.L. AU - Srivastava G. AU - Labkoff S. AU - deBronkart D. AU - Singh R. AU - Dattani K. AU - Lopez-Gonzalez M. AU - Barr P.J. AU - Koski E. AU - Lin B. AU - Cheung E. AU - Weiner M.G. AU - Williams T. AU - Thuy Bui T.T. AU - Quintana Y. PY - 2025 JO - International Journal of Medical Informatics VL - 195 SP - 105713 DO - 10.1016/j.ijmedinf.2024.105713 AB - Introduction: AI is big and moving fast into healthcare, creating opportunities and risks. However, current approaches to governance focus on high-level principles rather than tailored recommendations for specific domains like consumer health. This gap risks unintended consequences from generic guidelines misapplied across contexts and from providing answers before agreeing on the questions. Objective: Our objective is to explore pragmatic multi-stakeholder approaches to govern consumer-facing health AI. The aims are to (1) establish an approach tailored for consumer health AI governance and (2) identify key constraints and desirable model characteristics. Methods: This paper synthesizes insights informed by a 4-month multidisciplinary expert consensus process with nearly 200 participants. The deliberations provided guidance for the development of the proposed governance models in consumer health AI. Results: (1) A Shared View of Consensus: A process for consumer health AI governance should limit the scope and incorporate multi-stakeholder perspectives centered on patient needs. Desirable model characteristics include adaptability, patient empowerment, and transparency. (2) Recommended Collaborative Process: A pathway for effective governance should begin by forming a Health AI Consumer Consortium (HAIC2) representing patients and aligning incentives across stakeholders. Conclusions: While examples focus on the United States healthcare system, core themes around incorporating consumer voices, enabling transparency, and balancing innovation with thoughtful oversight while avoiding overambitious scope will have relevance globally. As consumer AI spreads worldwide, the multi-stakeholder alignment and patient empowerment principles proposed here may offer productive ways to ensure AI for consumers is safe, effective, equitable, and trustworthy (SEET). © 2024 KW - Artificial Intelligence KW - Consumer Health Informatics KW - Humans KW - Stakeholder Participation KW - 'current KW - Consumer healths KW - Governance models KW - Key constraints KW - Multi-stakeholder KW - Multi-stakeholder approach KW - Multi-stakeholder perspectives KW - Patient empowerments KW - Stakeholder process KW - Unintended consequences KW - adult KW - article KW - clinical practice guideline KW - consensus KW - consumer KW - female KW - health care system KW - human KW - patient empowerment KW - practice guideline KW - responsible artificial intelligence KW - artificial intelligence KW - consumer health informatics KW - stakeholder participation KW - Electronic health record CY - United States, United Kingdom, Canada ER - TY - JOUR TI - Polycentric Power Plays: Gulf Agency and the Dynamics of China’s AI Diplomacy AU - Tran E. AU - Gulrez T. PY - 2026 JO - Communication and the Public DO - 10.1177/20570473261438564 AB - This article offers the first systematic, comparative analysis of China’s AI diplomacy across all six Gulf Cooperation Council (GCC) states – Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates – foregrounding Gulf governments’ agency in negotiating the intersection of Chinese digital infrastructure and global AI governance. Challenging portrayals of the region as a monolithic or passive recipient of external influence, the study advances a polycentric negotiation framework that integrates theories of norm diffusion, strategic hedging, and co-production to reveal how Gulf states selectively absorb, filter, or recalibrate Chinese technological offerings and regulatory models. Drawing on systematic documentary analysis and comparative case studies, the research uncovers significant intra-GCC divergence: while some states exhibit high infrastructure dependence with limited normative alignment, others compartmentalize Chinese participation and prioritize regulatory convergence with Western frameworks. The findings highlight Gulf states’ strategic use of digital sovereignty and alignment flexibility to maximize autonomy amid intensifying US–China competition. Ultimately, the analysis demonstrates how Gulf agency and institutional innovation shape the global diffusion and localization of AI norms, providing a replicable model for understanding digital diplomacy in other geopolitical contexts. © The Author(s) 2026. This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage). KW - China-GCC AI diplomacy KW - digital sovereignty KW - norm diffusion and localization KW - polycentric negotiation KW - strategic hedging CY - Qatar ER - TY - JOUR TI - Legitimating entrepreneurship through generative AI: The reproduction of visual stereotypes AU - Hormiga E. AU - Jonckers G. AU - Urbano D. PY - 2026 JO - Technology in Society VL - 86 SP - 103278 DO - 10.1016/j.techsoc.2026.103278 AB - Generative artificial intelligence (AI) tools are transforming visual production across multiple domains, including entrepreneurship. However, their influence on constructing cultural imaginaries and legitimating symbols remains insufficiently examined. This study analyzes how text-to-image systems visually represent entrepreneurship and success, using a focused sample of 24 images generated with the Midjourney platform. Through critical visual discourse analysis, the research identifies recurring aesthetic codes, symbolic patterns, and temporal framings that demonstrate how generative AI replicates established cultural narratives of entrepreneurship. The results indicate a consistent depiction of solitary, self-assured individuals who embody control, ambition, and transcendence. In contrast, representations of collaboration, diversity, and social contribution are largely absent. The visual grammar emphasizes formality, isolation, and monumental composition, while temporal orientations favor immediacy and permanence rather than process and collective effort. These algorithmic representations reinforce narrow ideals of entrepreneurial legitimacy and perpetuate masculine-coded notions of success and authority. To synthesize these findings, the study introduces a conceptual model of the sociotemporal automation of legitimacy in generative AI entrepreneurial imaginaries. This model connects algorithmic infrastructures, aesthetic and temporal representations, and cultural circulation through the feedback loop of human-AI co-production. The paper contributes to understanding the aesthetic mechanisms by which generative AI consolidates dominant entrepreneurial ideals and considers implications for critical visual literacy, responsible AI deployment, and inclusive innovation policy. © 2026 The Authors. KW - Algorithmic bias KW - Diversity and inclusion KW - Entrepreneurship KW - Generative artificial intelligence KW - Legitimation KW - Sociotechnical imaginaries KW - Visual stereotypes KW - Vision KW - Algorithmic bias KW - Algorithmics KW - Diversity and inclusion KW - Entrepreneurship KW - Generative artificial intelligence KW - Imaginaries KW - Legitimation KW - Sociotechnical KW - Sociotechnical imaginarie KW - Visual stereotype KW - artificial intelligence KW - entrepreneur KW - innovation KW - Artificial intelligence CY - Spain, Belgium ER - TY - JOUR TI - Transforming early childhood education in Saudi Arabia: AI’s impact on emotional recognition and personalized learning AU - Aldhilan D. AU - Rafiq S. PY - 2025 JO - International Journal of Evaluation and Research in Education VL - 14 IS - 4 SP - 2473 EP - 2486 DO - 10.11591/ijere.v14i4.32660 AB - Artificial intelligence (AI) technologies are increasingly integrated into early childhood education (ECE) worldwide, promising to revolutionize learning experiences for young children. ECE in Saudi Arabia faces challenges in addressing diverse learning needs and fostering socio-emotional development. This qualitative study investigates the role of AI in enhancing emotional recognition, promoting socio-emotional development, and addressing associated challenges in the context of Saudi Arabian schools. A total of 55 ECE teachers in Jeddah were interviewed using purposive sampling, with data saturation achieved at 50 interviews. Themes emerging from the data highlight AI’s effectiveness in personalizing learning experiences based on individual needs and learning styles, fostering empathy and social interaction among children, and enhancing classroom management. Challenges identified include data privacy concerns, cultural adaptation of AI tools, and ensuring equitable access to technology. The study highlights the importance of comprehensive teacher training, ethical guidelines, and robust policy frameworks to support responsible AI integration in Saudi Arabian education. Implications for practice include enhancing educational practices through AI while emphasizing the human role of educators, and the need for ongoing research to inform future innovations in ECE. © 2025, Institute of Advanced Engineering and Science. All rights reserved. KW - AI technologies KW - Data privacy KW - Early childhood education KW - Personalized learning KW - Socio-emotional development CY - Saudi Arabia, Pakistan ER - TY - JOUR TI - AI FOR ACCESSIBILITY IN DIGITAL MEDIA EDUCATION AU - Babu M.R.N. AU - Tungoe C. AU - Vasanthan R. AU - Pimple J. AU - Khandare K.S. AU - Kalyani L.K. PY - 2025 JO - ShodhKosh: Journal of Visual and Performing Arts VL - 6 IS - 2s SP - 67 EP - 77 DO - 10.29121/shodhkosh.v6.i2s.2025.6704 AB - Artificial Intelligence (AI) is transforming the digital media education field to be more accessible and inclusive to various learners. This paper discusses how AI based technologies can be implemented in digital media learning environment to help students with various types of physical, cognitive and sensory disabilities. The study is based on the principles of Universal Design of Learning (UDL) and the author is investigating the possibility of breaking down the barriers to content delivery and participation through the use of adaptive systems (-speech recognition, text to speech (TTS) and image recognition) that assist students with disabilities in their learning process. The study assesses the practical benefits and disadvantages of AI in educational accessibility using a mixed-method approach, which is a combination of classroom observation and comparative study of the AI (accessibility tools) and non-AI (accessibility tools). The results point to the role played by AI-driven applications in ensuring fair interaction whereby they can be used to create more personalized learning experience, enhance understanding and promote communication between students and teachers. This paper highlights that it is necessary to have open AI systems that consider fairness data protection and inclusivity in the design of education. The current study can be added to the current discussion about inclusive pedagogy in which responsible AI involves can improve access, as well as creativity and innovation in digital media education. The paper ends with policy suggestions on how policy makers, educators and technologists can come up with sustainable AI access models in future learning environments. © 2025 The Author(s). KW - Accessibility KW - Artificial Intelligence (AI) KW - Design for Learning (UDL) KW - Digital Media Education KW - Inclusive Technology KW - Universal CY - India ER - TY - JOUR TI - AI, Ethics, and Human-Centered Policy in Albanian Education AU - Hodaj A. AU - Laçka S. PY - 2025 JO - Interdisciplinary Journal of Research and Development VL - 12 IS - 3 SP - 156 EP - 163 DO - 10.56345/ijrdv12n318 AB - The integration of Artificial Intelligence (AI) into education is reshaping pedagogical practices, policy frameworks, and ethical standards worldwide. This paper explores how AI is transforming educational policy and teaching in Albania, a country still adapting to the post-pandemic digital era. Drawing upon comparative policy analysis and global frameworks from UNESCO, the European Union, and the OECD, the study identifies significant gaps between Albania’s current legal structures and international standards. While European education systems emphasize ethical governance, teacher AI literacy, and digital inclusion, Albania’s higher education law (Law No. 80/2015) remains silent on online learning, AI ethics, and data protection. The COVID-19 crisis accelerated technological adoption but revealed the absence of systemic readiness. The paper argues for a human-centered AI policy that links innovation to ethics, proposing reforms for 2025–2030 to align with European directives. Additionally, the research highlights the critical role of teachers as ethical mediators in AI-supported classrooms, emphasizing that technological integration must be accompanied by moral awareness and cultural adaptation. The findings also suggest that Albania’s alignment with the EU Artifi cial Intelligence Act (2024) could foster a coherent framework for accountability, transparency, and fairness in digital education. Through a multidisciplinary approach, this study provides both analytical and policy-oriented insights into how developing educational systems can balance innovation with human dignity, ensuring that Artificial Intelligence becomes a catalyst for inclusive, equitable, and ethically grounded education. © 2025 Albana Hodaj and Senada Laçka. KW - Albania KW - Artificial Intelligence KW - Digital Transformation KW - Educational Policy KW - Ethics KW - Higher Education KW - Teacher Training CY - Albania ER - TY - JOUR TI - AI capability, knowledge integration, and cognitive barriers: Innovation pathways for circular economy practices in construction AU - Soomro M.A. AU - Khan A.N. AU - Khahro S.H. AU - Javed Y. PY - 2026 JO - Journal of Innovation and Knowledge VL - 14 SP - 100948 DO - 10.1016/j.jik.2026.100948 AB - The transformative potential of artificial intelligence (AI) is prompting construction organizations to redefine the concept of progress. However, the transition from digital capability to a higher-level sense of innovation is not inevitable. This research delves into the effect of AI capability on innovation-driven circular economy (CE) practices, providing evidence that technological advances alone do not drive change without an enabling cognitive and organizational environment. We adopt the lens of sociotechnical systems (STS) theory to conceptualize knowledge integration as the mechanism by which AI capability leads to CE practice adoption and cognitive rigidity as the inhibitor that reduces the positive effect of knowledge integration. Structural equation modeling and moderated mediation tests are applied to a survey of 414 construction professionals. Results suggest that AI capability promotes new ways of working and CE practices indirectly through its influence on knowledge integration; however, this influence is attenuated when cognitive rigidity hampers knowledge integration. The research extends STS theory into a domain of innovation management by integrating its cognitive, technical, and organizational elements. Our findings offer practical implications for industry practitioners, suggesting that building CE capacity goes beyond adopting digital technology; it also involves fostering cognitive agility and robust knowledge exchange mechanisms to enable those technologies to translate into innovation. Copyright © 2026. Published by Elsevier B.V. KW - AI capability KW - Circular economy practice KW - Cognitive rigidity KW - Construction industry KW - Knowledge integration KW - Sociotechnical systems CY - China, Saudi Arabia ER - TY - JOUR TI - Revisiting patent law paradigms: legal, economic, and ethical implications of AI-driven inventions in the biosciences: introducing the universal model of augmented invention AU - Levy H.V. PY - 2025 JO - Law, Ethics and Technology VL - 2 IS - 2 SP - 0006 DO - 10.55092/let20250006 AB - The rapid advancements in artificial intelligence (AI) are transforming numerous sectors, particularly biotechnology, where AI systems now play an active role in autonomous research and invention generation. This paper introduces, for the first time, the universal model of augmented invention—a hybrid legal category recognizing patentable outputs co-produced by human agents and AI under meaningful human oversight—and examines its implications for patent law in the biosciences. Focusing on inventorship, ownership, and patentability, the paper highlights the need to adapt patent frameworks to reflect AI’s accelerating impact on research timelines and innovation processes. It analyzes the economic incentives and ethical dimensions of AI-generated inventions, including equity, access, and accountability challenges, and proposes targeted reforms: statutory recognition of augmented inventorship, AI-specific disclosure requirements, and adaptive compulsory licensing triggers. These reforms aim to ensure that patent law both fosters technological progress and safeguards the public interest. ©2025 by the authors. Published by ELSP. KW - AI-driven inventions KW - augmented inventorship KW - compulsory licensing KW - drug discovery and development KW - economic incentives KW - enablement and written description (patent disclosure) KW - ethical AI governance (biomedicine) KW - patent law KW - patentability KW - technology transfer and licensing CY - Israel ER - TY - JOUR TI - ChatGPT’s crystal ring: simulating auditors’ use of machine learning in stock price prediction AU - Arabiat O. AU - Alshurafat H. PY - 2024 JO - Journal of Decision Systems DO - 10.1080/12460125.2024.2371670 AB - This study investigates the influence of technological factors on the intent to use Machine Learning (ML) tools such as Python for the purpose of predicting stock prices. Further, it investigates the moderate impact of Artificial Intelligence (AI) models usage, in particular ChatGPT, on these associations. The outcomes of a simulation involving 400 auditors, accounting for the heterogeneity of their competencies, were obtained through code utilisation based on the Python programming language. The technological factors drawn from diffusion of innovation theory (DOI), including relative advantages, Complexity, compatibility, observability, and triability, all showed positive associations with behavioural intent. The use of ChatGPT significantly fortified these connections. These results suggest a fruitful symbiotic outcome may be achieved by combining AI capabilities with these variables. The findings underscore the significance of planning for the adoption of AI in financial decision-making and auditing and also illustrate the potential of AI in these areas. © 2024 Informa UK Limited, trading as Taylor & Francis Group. KW - auditor KW - ChatGPT KW - DOI KW - Python KW - simulation KW - stock prices KW - Computer simulation languages KW - Computer software KW - Costs KW - Crystals KW - Decision making KW - Electronic trading KW - Financial markets KW - Machine learning KW - Problem oriented languages KW - Auditor KW - ChatGPT KW - Crystal rings KW - Diffusions of innovation theories KW - Learning tool KW - Machine-learning KW - Simulation KW - Stock price KW - Stock price prediction KW - Technological factors KW - Python CY - Jordan ER - TY - JOUR TI - Vulnerabilities and Defenses: A Monograph on Comprehensive Analysis of Security Attacks on Large Language Models AU - Balakrishnan P. AU - Leema A.A. PY - 2025 JO - Indian Journal of Information Sources and Services VL - 15 IS - 2 SP - 442 EP - 467 DO - 10.51983/ijiss-2025.IJISS.15.2.54 AB - This research mainly focused on highly developed natural language processing capabilities, such as large language models (LLMs), which can generate code and power chatbots, among many other uses. Their growing use, though, has put them under many security risks. This work thoroughly investigates LLM vulnerabilities, including adversarial attacks, data poisoning, prompt injection, privacy leaking, and model exploitation via jailbreak. Though there is an increasing corpus of defensive tactics, most still have limited reach, potency, or adaptability. The paper lists ideas for the following studies and emphasizes the requirement for strong, generalizable, explainable security solutions. Creating uniform evaluation standards, adaptive defense mechanisms, more transparent models, automated threat detection, and frameworks for ethical integration are all part of the approach. Ensuring LLMs calls for a multidisciplinary strategy that strikes a compromise between responsible government and technology innovation. © The Research Publication, www.trp.org.in. KW - AI Governance KW - Data poisoning KW - Defense Mechanism KW - Explainability KW - Jailbreaking KW - large Language models KW - LLM Security KW - Model Robustness KW - prompt Injection CY - India ER - TY - JOUR TI - Strategic green entrepreneurship for business sustainability of Batik SMEs in Indonesia: the role of knowledge and ambidextrous innovation AU - Dewi Anjaningrum W. AU - Sudiro A. AU - Setiawan M. AU - Aisjah S. PY - 2025 JO - International Journal of Business Innovation and Research VL - 38 IS - 10 SP - 1 EP - 20 DO - 10.1504/IJBIR.2025.151278 AB - Batik SMEs in today’s highly competitive landscape encounter numerous obstacles concerning their sustainability. This study employs an approach that integrates green knowledge management (GKM) and ambidextrous green innovation (AGI) as sequential mediating factors to investigate the link between green entrepreneurial orientation (GEO) and sustainable performance (SP). We employed an accidental-purposive sampling method to collect quantitative data, specifically selecting 401 respondents from 253 batik SMEs in Indonesia that are actively engaged in green entrepreneurship and innovation. A second-order PLS-SEM analysis yielded multiple findings. Initially, we found that GEO had an insignificant direct effect on SP. Furthermore, GKM and AGI have been established as sequential mediators in this relationship. Lastly, the dimensional analysis indicated that batik SMEs appear to prioritise exploitative green innovation over exploratory innovation. Future research should examine the relationships among these dimensions for more profound insights and investigate how AI capabilities can enhance the GKM process. Copyright © 2025 Inderscience Enterprises Ltd. KW - GEO KW - GKM KW - green business KW - green entrepreneurial orientation KW - green innovation KW - green knowledge management KW - Indonesia KW - sustainability performance CY - Indonesia ER - TY - JOUR TI - AI characteristics and competitive advantage: the moderating role of resource allocation AU - Jabbouri R. AU - Issa H. AU - Truong Y. PY - 2025 JO - International Journal of Entrepreneurial Behaviour and Research SP - 1 EP - 23 DO - 10.1108/IJEBR-08-2024-0814 AB - Purpose – This research explores how artificial intelligence's (AI’s) distinct capabilities (interactivity, autonomy, inscrutability and abstraction), manifested as unique characteristics, impact decision-making in resource-limited social entrepreneurship, assessing their effect on competitive advantage with resource allocation as a key moderator. By analyzing such tensions, this research aims to bridge critical gaps in understanding how emerging technologies influence decisions in social entrepreneurship. Design/methodology/approach – By adopting a dual theoretical framework (next-generation perceived characteristics of innovations [PCI] and resource-based view), this research employs a quantitative empirical approach by gathering data through e-surveys (n = 269) from a professional database and two prominent conferences in AI and social entrepreneurship. Findings – Linear and nonlinear relationships among AI characteristics and decision-making emerge, with the potential for moderation effects influenced by resource allocation. Originality/value – This research makes four key contributions: empirically examining how distinct AI capabilities, manifested through unique characteristics, influence decision-making in the social entrepreneurship context; conceptually introducing “abstraction” as a novel AI capability; theoretically integrating the next-generation PCI framework with the resource-based view for a novel theoretical lens and practically developing a calibration graph as a “prototype” tool to quantify AI abstraction for resource-limited social entrepreneurship, thus potentially enabling optimal decision-making and consequently competitive advantage. © 2025 Emerald Publishing Limited KW - Abstraction KW - AI KW - Next-generation PCI KW - Resource-based view KW - Social entrepreneurship CY - United Arab Emirates, France ER - TY - JOUR TI - AI meets engineering ingenuity: how AI capability enhances innovation performance through decision-making quality AU - Xiang L. AU - Li F. PY - 2026 JO - Engineering, Construction and Architectural Management SP - 1 EP - 21 DO - 10.1108/ECAM-06-2025-0987 AB - Purpose – This study aims to examine the relationships among AI capability, decision-making quality, and innovation performance, and to investigate the moderating effect of algorithmic transparency on the relationship between AI capability and decision-making quality. Design/methodology/approach – A questionnaire survey was conducted using the Credamo data platform. To reduce common method bias, a time-lagged survey design was adopted. Data on AI capability, algorithmic transparency, decision-making quality, and innovation performance were collected from 435 participants. Established scales from authoritative foreign journals were used for measurement, and appropriate translation and verification procedures were carried out. Findings – (1) AI capability is positively associated with innovation performance. Decision-making quality mediates the relationship between AI capability and innovation performance. (2) Algorithmic transparency positively moderates the relationship between AI capability and decision-making quality. Originality/value – This study enriches AI capability research by incorporating engineering perspectives. It extends organizational learning theory by examining how AI capability shapes decision-making processes within engineer–AI collaboration contexts, identifying decision-making quality as a mediator and algorithmic transparency as a moderator. The findings offer practical insights for construction firms to enhance innovation performance through effective AI integration while helping engineers better leverage AI tools in design and project management workflows. © Emerald Publishing Limited KW - AI capability KW - Algorithmic transparency KW - Decision-making quality KW - Innovation performance KW - Behavioral research KW - Construction industry KW - Decision theory KW - Engineering education KW - Project management KW - Transparency KW - AI capability KW - Algorithmic transparency KW - Algorithmics KW - Decision-making quality KW - Decisions makings KW - Design/methodology/approach KW - Innovation performance KW - Moderating effect KW - Quality performance KW - Questionnaire surveys KW - Decision making CY - China ER - TY - JOUR TI - Can artificial intelligence bridge the diversity, equity, and inclusion gap for a sustainable future? A PRISMA systematic review AU - Chedrawi C. AU - Haddad G. AU - Haddad G. AU - ElAli R. PY - 2026 JO - Journal of Innovation and Knowledge VL - 15 SP - 100986 DO - 10.1016/j.jik.2026.100986 AB - This study addresses a critical gap in understanding how artificial intelligence (AI) adoption influences workplace diversity, equity, and inclusion (DEI) initiatives in advancing organizational sustainability. Through a systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, we synthesize and analyze 59 peer-reviewed articles (2019–2025) to examine the intersection of AI implementation and DEI practices. This study reveals how AI technologies can systematically address DEI challenges through algorithmic decision-making frameworks that mitigate unconscious bias, enhance representational parity, and foster inclusive organizational cultures. We develop an integrative theoretical framework that delineates the mechanisms through which AI adoption builds organizational capabilities across technical, managerial, and cultural dimensions while identifying key contingencies that influence DEI outcomes. These findings advance theory by conceptualizing AI-enabled DEI transformation as a dynamic process rather than a static outcome, contributing to the technology adoption and organizational sustainability literature streams. This analysis provides evidence-based insights for implementing AI-driven DEI initiatives while highlighting critical considerations regarding algorithmic fairness and ethical deployment. This study offers significant insights for organizational theorists examining technological innovation in social sustainability contexts and practitioners seeking to leverage AI capabilities for advancing workplace equity and inclusion. © 2026 The Authors. KW - Artificial intelligence adoption KW - Equity and inclusion (DEI) KW - PRISMA Systematic review KW - Social sustainability KW - Workplace diversity CY - Lebanon, Cyprus, France ER - TY - JOUR TI - Overview of the Application of Generative Artificial Intelligence in Film Production: Algorithms, Tools, and Future Trends AU - Li L. AU - Mat Desa M.A.B. AU - Li T. AU - Li W. PY - 2026 JO - Studies in Media and Communication VL - 14 IS - 2 SP - 22 EP - 39 DO - 10.11114/smc.v14i2.8095 AB - The rapid evolution of generative artificial intelligence (GenAI) is transforming the film industry. This article reviews key GenAI algorithms, including GANs, VAEs, diffusion models, and transformer-based architectures, and explores their application across various stages of film production, from scriptwriting to post-production. Through case studies such as The Frost and Netflix's AI-assisted projects, the study illustrates GenAI's creative potential and workflow innovations. It also addresses critical ethical and legal concerns, including authorship disputes, deepfakes, and algorithmic bias. Finally, the paper outlines future directions, such as multimodal model integration and AI-human co-creation, advocating for a responsible and human-centered implementation of these technologies. Copyright for this article is retained by the author(s), with first publication rights granted to the journal. This is an open-access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/4.0/). KW - AI in cinema KW - creative automation KW - diffusion models KW - ethical AI KW - film production KW - generative AI KW - multimodal models CY - Malaysia, China ER - TY - JOUR TI - Revolutionizing dentistry: the integration of artificial intelligence and robotics AU - Veseli E. PY - 2024 JO - Khyber Medical University Journal VL - 16 IS - 4 SP - 352 EP - 353 DO - 10.35845/KMUJ.2024.23729 AB - Technology is rapidly transforming traditional practices in modern healthcare. One area that stands out is the convergence of Artificial Intelligence (AI) and robotics, revolutionizing dentistry. This powerful combination enhances precision, efficiency, and patient outcomes in oral health care while reducing potential errors. AI, with its ability to analyze large amounts of data and identify intricate patterns, has found a place in dentistry. Its applications range from diagnostic tools and treatment planning to personalized medicine and patient management.1 By utilizing advanced imaging techniques, AI assists in the early detection of oral diseases,2 enabling proactive intervention and improving prognosis. The integration of AI into orthodontics and endodontics has radically transformed the field of dental care. In orthodontics, AI and Machine Learning systems support orthodontists in making informed decisions, particularly regarding tooth extraction. AI-driven custom orthodontic treatments minimize subjectivity and improve decision-making processes by utilizing neural networks to predict the extraction outcomes. AI is used throughout orthodontic procedures, from diagnosis to personalized treatment planning, utilizing 3D scans and virtual models to assess abnormalities, produce aligners, and optimize tooth removal strategies.3 Similarly, in endodontics, AI enhances root canal therapy by enabling precise anatomical analysis, lesion detection, fracture identification, stem cell viability prediction, and assessment of treatment efficacy.4 The contributions of AI in both orthodontics and endodontics have resulted in increased efficiency, accuracy, and improved patient outcomes, showcasing significant advancements in dental healthcare. AI also plays a critical role in posttreatment patient monitoring, ensuring timely intervention, and improving recovery. Through continuous data analysis and feedback, AI facilitates long-term oral health management, empowering patients and practitioners with proactive insights into sustained well-being. By integrating AI, dental experience is enhanced by combining cutting-edge technology with personalized care that redefines standards in dental health and treatment protocols. Furthermore, AI algorithms streamline administrative processes, optimize scheduling, and enhance patient experience, thereby improving the overall operational efficiency.5 The capabilities of robotics in dentistry have complemented those of AI, unlocking new frontiers in precision and minimally invasive procedures.6 Robotics provide unparalleled dexterity and control during surgery, resulting in superior outcomes and quicker recovery times for patients. These technological marvels not only enhance the skill set of dental professionals, but also expand access to care in remote or underserved areas. Advancements in technology and computer science have pushed the integration of robotics into navigational surgery in various medical fields. This progress is now being extended to dentistry, where innovative technologies are revolutionizing traditional dental procedures. Robotics-assisted dentistry, employing nanomaterials, nanorobots, and advanced diagnostic tools, is evolving to address the complex procedures necessary for oral healthcare maintenance and lesion removal. These advanced systems are reshaping conventional practices in dentistry, particularly implant therapy, challenging existing paradigms, and expanding the capabilities of practitioners.7 One notable development in robot-assisted dentistry is the creation of micro robots (MR), designed to enhance the precision and efficiency of endodontic treatments, specifically root canal therapy. These advanced robots autonomously perform tasks such as drilling, cleaning, shaping, and filling the root canal system under the supervision of cutting-edge computer-assisted technologies. By integrating various components, such as micro position controllers, sensors, and automated tools, the MR ensures error-free procedures, reduces discomfort for dentists, and enhances treatment outcomes with unparalleled accuracy.8 Furthermore, nanomaterials and nanorobots play a crucial role in enabling the creation of nanorobots for various dental applications such as tooth repair, drug delivery, orthodontic adjustments, and cavity treatments. These minuscule robots offer swift and precise dental care interventions, illustrating their potential to revolutionize traditional dental practices. Additionally, robotic applications in oral and maxillofacial surgery enhance surgical precision by allowing surgeons to program robots for specific tasks, such as bone surgeries and plate positioning.9 As technology continues to advance, the integration of robotics into dentistry promises to reshape the field, offering new possibilities for enhanced patient care and treatment outcomes. The fusion of robotics with AI algorithms holds promise for a future in which complex dental procedures are conducted with unprecedented accuracy and safety. Although the potential benefits of AI and robotics in dentistry are immense, their integration is not devoid of challenges. Ensuring data security, maintaining patient privacy, and addressing ethical concerns surrounding autonomy and decision making are crucial considerations in this rapidly evolving landscape. With appropriate regulations and ethical guidelines in place, the dental community can harness the full potential of these technologies, while upholding the highest standards of patient care and professional integrity. As we stand on the cusp of a new era in oral healthcare, characterized by the symbiotic relationship between human expertise and technological prowess, it is imperative for stakeholders to embrace innovation responsibly.10 Collaborative efforts among researchers, clinicians, technologists, and policymakers will be vital in harnessing the transformative power of AI and robotics to chart a course towards a future where dental treatments are not only effective but also personalized, efficient, and accessible to all. The integration of AI and robotics in dentistry heralds a paradigm shift in the delivery and reception of oral healthcare services. By leveraging these cutting-edge technologies thoughtfully and ethically, the dental community can elevate standards of care, expand treatment options, and improve patient outcomes, as well as redefine the future of dentistry. © 2024, Khyber Medical University. All rights reserved. KW - Artificial intelligence KW - dentistry KW - robotics CY - India ER - TY - JOUR TI - Executives’ perspectives on the impact of generative AI in business: a qualitative study of strategic, ethical and organizational transformations AU - Varouchas E. PY - 2026 JO - Journal of Science and Technology Policy Management SP - 1 EP - 34 DO - 10.1108/JSTPM-10-2025-0493 AB - Purpose – Generative Artificial Intelligence (GenAI) is rapidly transforming business strategy, innovation processes and governance practices. While prior research has focused primarily on technological implementation and performance outcomes, limited attention has been paid to how senior executives interpret, frame and adapt to GenAI as a strategic and ethical phenomenon. This study aims to explore executives’ sensemaking and adaptive responses to GenAI and to develop a conceptual model that captures this process. Design/methodology/approach – The study adopts a qualitative exploratory design based on semi-structured interviews with 15 senior executives from diverse industries in Greece. Data were analyzed using thematic analysis to identify recurring patterns in executives’ perceptions, decision rationales and ethical considerations related to GenAI adoption and integration. Findings – The analysis reveals four interrelated dimensions shaping executive adaptation: strategic integration and decision-making, business value and innovation, human–AI collaboration and workforce transformation and ethics, governance and adoption barriers. Cross-thematic synthesis indicates that executives perceive GenAI as a decision-support and innovation amplifier rather than an autonomous decision-maker. These findings inform an Emergent Integrative Model of Executive Adaptation to GenAI, conceptualized as a dynamic cycle comprising strategic sensemaking (Curate), operational experimentation (Create) and ethical consolidation (Consolidate). Research limitations/implications – Several executives cited technical and resource limitations as obstacles to effective GenAI implementation. Challenges include legacy systems, lack of skilled artificial intelligence (AI) engineers and limited integration between AI platforms and enterprise software. Practical implications – This model advances understanding of executive cognition and adaptive intelligence in the AI era, positioning leadership as a process of continuous learning, sensemaking and ethical stewardship. Practically, the research offers a roadmap for organizations and policymakers for aligning GenAI-driven innovation with responsible governance and leadership development. Social implications – By highlighting the role of executive stewardship, the study underscores how ethical leadership in GenAI adoption influences public trust, workforce well-being and organizational legitimacy. Originality/value – Existing research on AI in business has predominantly focused on technological implementation, efficiency gains and economic outcomes. Studies emphasize measurable benefits such as productivity enhancement, improved decision-making speed and customer experience optimization. However, fewer studies explore how executive cognition and strategic reasoning shape the trajectory of AI adoption – particularly regarding GenAI technologies that introduce new forms of creative automation. The study advances leadership and sensemaking research by shifting the focus from GenAI adoption outcomes to executive cognition and ethical stewardship. It offers a novel integrative model that explains how strategy, innovation and governance co-evolve in the GenAI era. © 2026 Emerald Publishing Limited KW - Adaptive leadership KW - AI governance KW - Digital transformation KW - Executive cognition KW - Generative artificial intelligence KW - Responsible innovation CY - Greece ER - TY - JOUR TI - The Impact of Artificial Intelligence on Corporate Governance AU - Kalkan G. PY - 2024 JO - Journal of Corporate Finance Research VL - 18 IS - 2 SP - 17 EP - 25 DO - 10.17323/j.jcfr.2073-0438.18.2.2024.17-25 AB - The advent of artificial intelligence (AI) marks a pivotal shift in the landscape of corporate governance, catalyzing a reevaluation of traditional frameworks and necessitating a forward-looking approach to decision-making, risk management, and ethical considerations. This study explores the multifaceted impact of AI on corporate governance, offering a nuanced analysis of how AI technologies are transforming the operational, strategic, and ethical dimensions of organizations. The research underscores the potential of AI to enhance decision-making processes, optimize operational efficiencies, and foster innovation by providing advanced analytical capabilities and predictive insights. However, it concurrently highlights the emergence of unprecedented challenges, including data privacy concerns, algorithmic bias, and the need for robust regulatory frameworks to mitigate risks associated with AI deployment. The article advocates for a proactive stance in redefining corporate governance models to accommodate the disruptive nature of AI, emphasizing the integration of ethical considerations and transparency in AI applications. It calls for a collaborative effort among corporate leaders, policymakers, and stakeholders to develop governance structures that not only leverage AI’s potential but also safeguard against its inherent risks. The study’s recommendations include the establishment of ethical AI guidelines, the adoption of transparent AI practices, and the continuous monitoring of AI systems to ensure their alignment with corporate governance objectives and societal values. However, it is important to note that the approach and methods used in this study are based on a qualitative literature review and, therefore, the generalization of the findings across different sectors and corporate governance frameworks may be limited. Additionally, the rapidly evolving nature of AI technologies poses inherent challenges to keeping up with emerging trends and potential risks. © 2024, National Research University, Higher School of Econoimics. All rights reserved. KW - artificial intelligence KW - corporate governance KW - decision-making KW - digital transformation KW - ethical considerations KW - legal and regulatory challenges KW - transparency CY - Turkey ER - TY - JOUR TI - Governance of Personal Information Security in the Iteration of Generative AI: From the Perspective of the Technological Evolution of Large Models; [生成式人工智能迭代中的个人信息安全治理：基于大模型技术演进视角] AU - An L. PY - 2026 JO - Journal of Library and Information Science in Agriculture VL - 38 IS - 4 SP - 61 EP - 70 DO - 10.13998/j.cnki.issn1002-1248.25-0750 AB - [Purpose/Significance] The rapid advancement of generative artificial intelligence (AI) is driving societal digital transformation, yet it simultaneously poses unprecedented systemic risks to personal information security due to the large-scale, automated, and complex nature of its data processing. Previous research has lacked exploration of governance pathways that consider endogenous technological evolution and specific model iterations. This paper takes the technological evolution of mainstream, large-scale generative AI models, both domestically and internationally as a starting point, and systematically reveals the impact of generative AI on personal information protection principles across the stages of data collection, model operation, and content generation. The focus is on analyzing how technological innovations in China's DeepSeek, including open-source traceability, decision transparency, and flexible deployment, lay the groundwork for risk-graded governance. This study not only broadens the theoretical perspective on AI governance and promotes the formation of a "technology-institution" collaborative governance paradigm, but also offers innovative and actionable insights for building an agile and effective personal information protection system in China amidst the rapid adoption of generative AI. [Method/Process] This study employs a comparative analysis and inductive research approach. First, it systematically compares the core technological differences among mainstream generative AI models, both domestic and international, across three dimensions: model ecosystem, model capabilities, and deployment methods. Through this comparison, it analyzes the challenges generative AI poses to personal information protection at various stages, including data collection, model operation, and content generation. Second, the study systematically examines the differentiated impacts brought about by DeepSeek's technological iterations on personal information security governance. Building on this foundation, the research proposes a comprehensive governance strategy centered on the principles of inclusiveness and prudence, guided by risk grading, and covering all operational stages of generative AI. This strategy emphasizes the critical role of DeepSeek's technical characteristics in supporting the implementation of this framework. [Results/Conclusions] The research indicates that constructing a risk-graded governance system based on the sensitivity of personal information is an effective approach to balancing security and innovation in generative AI. This system emphasizes distinguishing between sensitive and general information during data collection, achieving traceability and purpose control during model operation, and implementing differentiated security safeguards during content generation. With its technical advantages, including open-source traceability, decision transparency, and flexible deployment, DeepSeek provides technical validation and practical possibilities for graded governance. This facilitates the protection of sensitive personal information in high-risk scenarios while simultaneously fostering technological iteration and application innovation in medium- to low-risk contexts. Future research should further incorporate multi-dimensional governance elements such as industry self-regulation, social coordination, and international collaboration. Empirical analysis should also be conducted to test the applicability and effectiveness of the governance framework, thereby gradually developing a well-rounded personal information security governance scheme that adapts to the dynamic evolution of technology. © 2026, Agricultural Information Institute, Chinese Academy of Agricultural Sciences. All rights reserved. KW - deepseek KW - generative artificial intelligence KW - personal information security KW - risk classification CY - China ER - TY - JOUR TI - The Convergence of Artificial Intelligence and Sustainability Reporting: A Systematic Review of Applications, Challenges and Future Directions AU - Mustafa F. AU - Smolarski J. AU - Elamer A. PY - 2025 JO - Business Strategy and the Environment VL - 34 IS - 8 SP - 9761 EP - 9784 DO - 10.1002/bse.70090 AB - This research examines the potential of artificial intelligence (AI) to improve sustainability reporting, particularly in relation to environmental, social and governance (ESG) issues. Despite growing interest in the field, the integration of AI in sustainability remains underexplored, especially in terms of its impact on data accuracy, transparency and sustainability reporting effectiveness. This study conducts a systematic literature review (SLR) of 135 peer-reviewed articles to identify significant research gaps and presents a comprehensive framework that integrates AI technologies, such as machine learning, Industry 4.0 innovations and decision support systems (DSS), with sustainability reporting practices. The findings support the need for stronger theoretical and practical frameworks to effectively leverage AI's capabilities in sustainability reporting. The originality of this study is found in its innovative approach to connecting AI technologies with sustainability reporting, a field characterised by fragmentation and underdevelopment in research. This study introduces a broad framework and takes a critical look at the unintended externalities of AI, such as increased inequality and environmental costs. It does this by challenging existing sustainability frameworks, like the GRI and SASB, to change with the times and keep up with new technologies. The emphasis on both the advantages and possible drawbacks of AI in sustainability reporting substantiates the study's publication, providing fresh insights into AI's role in enhancing ethical, transparent and effective ESG disclosures. The study offers recommendations for managers and policymakers aimed at improving the accuracy, transparency and credibility of ESG disclosures via AI-driven solutions, thereby promoting more effective sustainability practices. This paper provides a framework for future research and practical application of AI in sustainability reporting, with the goal of enhancing academic knowledge and real-world practices in the pursuit of sustainable development. © 2025 The Author(s). Business Strategy and the Environment published by ERP Environment and John Wiley & Sons Ltd. KW - artificial intelligence KW - decision support systems KW - environmental impact KW - innovation KW - machine learning KW - sustainability reporting KW - artificial intelligence KW - decision support system KW - environmental economics KW - environmental impact KW - literature review KW - machine learning KW - sustainability KW - sustainable development CY - Egypt ER - TY - JOUR TI - MANAGEMENT STRATEGIES FOR AI-INTEGRATED CRAFT INDUSTRIES AU - Kale C.D. AU - Kaur G. AU - Sule B. AU - Jarad R.S. AU - Prabha D. AU - Bawa D.S. AU - Priyadharshini K. PY - 2026 JO - ShodhKosh: Journal of Visual and Performing Arts VL - 7 IS - 1s SP - 305 EP - 314 DO - 10.29121/shodhkosh.v7.i1s.2026.7086 AB - Traditional craft industry is crucial in preserving culture, rural livelihoods, and creative economies but continues to encounter the same issues with fragmentation of value chains, lack of market confidence, loss of skills and the inability to be sustainable. Artificial intelligence (AI) brings fresh possibilities to overcome these issues, but there is a need to use it in craft ecosystems mindfully, so as to prevent a degradation of culture and marginalization of artisans. This paper looks at AI implementation in craft industries and how to manage these industries, presenting the argument that the main challenge of implementing AI in craft industry is management and governance-related, and not a technological issue. The paper suggests a conceptual model that places strategic management in the mediating role between the traditional craft foundations and the AI capabilities. In the systematic discussion on the strategic alignment, human and AI work, operational integration, and ethical governance, the study proves that AI could be used to improve coordination, quality assurance, market responsiveness, and sustainability without compromising cultural authenticity. The operational mappings and pictorial performance analyses also indicate that the balanced improvement of supply-chain functions through the use of phased and collaborative AI adoption models is still possible without losing handcrafted variability. The results reported add up to strategic management and creative industry publications by reshaping AI as a supplementary technology that enhances the power of artisans instead of eliminating them. The article also brings out the significance of governance systems in respect to intellectual property, ownership of data and representation of cultures. Further studies must apply the suggested frameworks to different craft settings empirically and research involved design strategies of participatory AI development to create inclusive innovation. © 2026 The Author(s). This work is licensed under a Creative Commons Attribution 4.0 International License. KW - Artificial Intelligence KW - Craft Industries KW - Cultural Heritage Preservation KW - Human–AI Collaboration KW - Strategic Management KW - Supply Chain Management CY - India ER - TY - JOUR TI - Artificial Intelligence in Smart City Governance: Case Studies of Singapore and Barcelona AU - Zheng H. PY - 2026 JO - Journal of Urban Technology DO - 10.1080/10630732.2026.2644136 AB - Gen AI is transforming city and urban governance in a way that enhances decision-making, management of infrastructure, and delivery of services to the population. This article considers the role of Gen AI in Singapore and Barcelona and determines that AI has a positive effect in Singapore in terms of traffic movement and healthcare and improving waste management and energy consumption in Barcelona. Governance models have a significant impact on the efficiency of AI: a centralized governmental body in Singapore would allow achieving the desired effect more quickly, and the participatory benefit of Barcelona can decelerate its implementation. Using the Diffusion of Innovations (DOI) Theory, the adoption behaviors and ethical consequences of AI were determined. Although the efficiency gains, which are made by AI, are impressive, there are instances where its impact is excessive to the extent of putting issues of trust in AI and fairness. Some of the recommendations are to enhance AI risk assessment, empower the people and hold the algorithms accountable in order to bring future smart city projects to democratic values and social justice. © 2026 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. KW - generative AI KW - responsible innovation KW - smart cities KW - urban governance CY - China ER - TY - JOUR TI - Advancing Human-AI Collaboration in Small and Medium-Sized Enterprises: A Systems Engineering Approach AU - Ortolano L.F. AU - Gallegos E.E. PY - 2026 JO - Systems Engineering VL - 29 IS - 3 SP - 477 EP - 494 DO - 10.1002/sys.70031 AB - The integration of Artificial Intelligence (AI) into organizational processes presents unique challenges for Small and Medium-sized Enterprises (SMEs), particularly in fostering effective human-AI collaboration. Unlike large corporations with extensive resources for AI adoption, SMEs require adaptable frameworks tailored to their specific constraints and operational needs. This paper introduces the novel Human-AI Collaboration Maturity Model (HAIC-MM), which is a systems engineering framework designed to assess, guide, and enhance AI integration within SMEs. Developed through the synthesis of AI maturity models, digital transformation frameworks, and human-machine teaming research, HAIC-MM identifies seven dimensions and 32 capabilities across five maturity levels that are essential for successful AI adoption in SME contexts. Empirical validation through survey analysis (N = 100) confirmed the model's robustness. Subsequent focus group analyses (N = 10, repeated across five sessions) further validated HAIC-MM's practical utility and alignment with the operational realities of SMEs, emphasizing its relevance to everyday challenges faced by these organizations. Pilot testing with industry practitioners (N = 3) confirmed the usability and usefulness of the final HAIC-MM tool. HAIC-MM provides SME leaders with a structured, human-centered, and systematic approach to evaluate and cultivate human-AI collaboration, addressing key areas such as resource optimization, workforce empowerment, ethical AI oversight, and adaptive organizational culture. This research contributes to AI-enabled systems engineering by offering a practical framework for harmonizing human and AI capabilities within resource-constrained environments, ultimately supporting SMEs in achieving sustainable and ethically grounded AI integration across the organization. Summary: This paper introduces the Human-AI Collaboration Maturity Model (HAIC-MM), a framework designed to address the unique AI adoption challenges faced by Small and Medium-sized Enterprises (SMEs). The model identifies critical dimensions and capabilities needed to foster effective collaboration between humans and AI systems. The model also defines five maturity levels within each capability, allowing a granular assessment within the holistic framework. HAIC-MM provides a practical, step-by-step guide to assess and enhance AI integration for SMEs. The model emphasizes ethical AI oversight, workforce empowerment, and adaptive organizational culture, while addressing key challenges like resource constraints. HAIC-MM represents a significant contribution to the fields of systems engineering and organizational behavior, offering researchers investigating socio-technical systems, AI integration processes, and SME innovation strategies a rigorous framework for both theoretical advancement and practical implementation. With its focus on real-world application, HAIC-MM equips practitioners with actionable insights to build trust, optimize collaboration between human and AI capabilities, and achieve sustainable, ethically sound AI adoption, ensuring their organizations remain competitive in an increasingly digital economy. © 2025 The Author(s). Systems Engineering published by Wiley Periodicals LLC. KW - digital transformation KW - human-AI teaming KW - human-machine KW - maturity model KW - trust in AI KW - Artificial intelligence KW - Ethical aspects KW - Integration KW - Personnel KW - Digital transformation KW - Human-artificial intelligence teaming KW - Human-machine KW - Intelligence integration KW - Intelligence oversight KW - Maturity levels KW - Maturity model KW - Organizational cultures KW - Small and medium-sized enterprise KW - Trust in artificial intelligence KW - Man machine systems CY - United States ER - TY - JOUR TI - ARTIFICIAL INTELLIGENCE REVOLUTION IN INDONESIAN ISLAMIC HIGHER EDUCATION: HOW IT AFFECTS STUDENTS’ SELF-EFFICACY, CREATIVITY, AND LEARNING PERFORMANCE AU - Megawati S. AU - Alfarizi M. AU - Wahyuni J. PY - 2025 JO - Journal of Educators Online VL - 22 IS - 4 DO - 10.9743/JEO.2025.22.4.11 AB - Higher education plays a crucial role in society through research and innovation, but research indi-cates there is resistance to adopting new technologies. The presence of artificial intelligence (AI) offers an innovative approach to learning, information retrieval, and decision-making. AI has garnered global attention for its ability to generate output based on external stimuli. Islamic Higher Education Institutions (IHEIs) also adopt AI for personalized learning and operational efficiency. While international research focuses on AI trends, there is a need for further research on the capacity of AI in education. Literature has yet to explore the role of AI in, and its relationship to, student creativity and learning performance, particularly in IHEIs. This study aimed to analyze AI components at IHEIs and its implications for student creativity and learning performance. The study developed a theoretical model built on the resource-based view theory and tested it through a survey involving 373 faculty members from IHEIs. The data analysis used Partial Least Squares-Structural Equation Modeling (PLS-SEM). Results indicate that the resources and skills possessed by IHEIs and their faculty can predict AI capability. The impact of AI capability on student creativity and self-efficacy is significant and a factor in enhancing learning performance. However, there is no significant impact on student learning performance, highlighting the need for IHEIs to integrate AI more effectively. These findings encourage IHEIs to develop holistic strategies to maximize AI potential for student needs and higher education management efficiency. © 2025, Grand Canyon University. All rights reserved. KW - artificial intelligence KW - capability KW - IHEIs KW - performanc CY - Indonesia ER - TY - JOUR TI - Perceptions of health data commodification in AI-driven healthcare systems in Saudi Arabia AU - Al Qwaid M. PY - 2025 JO - Frontiers in Artificial Intelligence VL - 8 SP - 1559302 DO - 10.3389/frai.2025.1559302 AB - Introduction: Artificial Intelligence (AI) is transforming healthcare service delivery through predictive analytics, precision medicine, and advanced diagnostics. However, the commodification of health data introduces complex ethical and social challenges related to privacy, ownership, and consent. This study explores perceptions of health data commodification within AI-driven healthcare systems, focusing on Saudi Arabia’s rapidly evolving digital healthcare landscape. Methods: A mixed-methods approach was employed, combining quantitative surveys and in-depth qualitative interviews. The study included 42 patients, 8 healthcare professionals, 3 insurance representatives, and 4 AI experts. Data were collected across three main themes: data privacy, perceived benefits of AI, and attitudes toward data commodification. Quantitative data were analyzed descriptively, while qualitative responses were examined thematically. Results: Findings reveal that 61.9% of patients consider health data a form of personal property, while 59.5% feel they have limited control over how their data are used. A significant trust deficit was observed, with 50% expressing low confidence in AI systems’ ability to protect privacy, particularly among older participants. Financial incentives strongly influenced willingness to share data, with 81% agreeing to share their data if compensated. Furthermore, 64.3% supported the sale of anonymized data by healthcare providers to technology companies, provided adequate safeguards are in place. Discussion: These insights underscore the urgent need for robust regulatory frameworks emphasizing informed consent, transparency, and ethical governance in AI healthcare systems. The study highlights the importance of patient-centered policies, equitable compensation mechanisms, and enhanced training and awareness programs to build public trust and ensure responsible AI adoption. By addressing these ethical and governance challenges, policymakers can align technological innovation with equity, privacy, and the principles of ethical healthcare delivery. Copyright © 2025 Al Qwaid. KW - AI KW - AI-driven healthcare KW - digital healthcare KW - health data commodification KW - trust in AI systems CY - Saudi Arabia ER - TY - JOUR TI - Nurse educators' experiences integrating artificial intelligence in teaching and practice: A descriptive phenomenological study AU - Kulintang M.B.M. AU - Ngo A.D. AU - Salas C.J.C. AU - Sumaoy K.C. AU - Alquwez N. PY - 2026 JO - Nurse Education Today VL - 162 SP - 107050 DO - 10.1016/j.nedt.2026.107050 AB - Backgrounds: Artificial intelligence (AI) is rapidly reshaping nursing education by transforming teaching strategies, student engagement, and clinical learning. Despite its growing influence, there is limited evidence exploring how nurse educators experience and navigate the integration of AI, particularly within the Philippine academic context. Aim: To explore the lived experiences of nurse educators integrating AI in classroom and clinical instruction, focusing on adaptation processes, perceived benefits, ethical considerations, and evolving professional roles. Design: Descriptive phenomenological study grounded in Husserlian philosophy. Methods: Thirteen nurse educators with direct experience in AI-related teaching were purposively selected from nursing schools across the Philippines. Semi-structured interviews were conducted face-to-face and online. Data were analyzed using Colaizzi's seven-step method. Trustworthiness was ensured through bracketing, member checking, reflexive journaling, and audit trails. Results: Six themes emerged: (1) navigating AI adoption and readiness; (2) transforming pedagogy through AI; (3) reimagining nurse educators' identity; (4) adaptive practices and institutional support; (5) ethical stewardship and nursing values; and (6) human–technology partnership for the future. Educators perceived AI as a transformative yet ethically sensitive tool that enhances teaching efficiency, supports personalized learning, strengthens student engagement, and reshapes their professional roles. However, they emphasized the need for institutional readiness, faculty development, and clear guidelines to ensure the responsible and value-aligned use of AI. Conclusions: AI integration redefines nursing education by fostering innovation, adaptability, and reflective teaching. Responsible adoption necessitates human-centered approaches, robust ethical safeguards, and organizational support. Implications for nursing education: Developing structured AI policies, providing continuous faculty training, and aligning technological integration with nursing values may promote safe, ethical, and effective AI-enhanced pedagogy in both classroom and clinical settings. © 2026 Elsevier Ltd KW - Artificial Intelligence KW - Digital Pedagogy KW - Ethics KW - Nurse Educators KW - Nursing Education KW - Phenomenology KW - Adult KW - Artificial Intelligence KW - Education, Nursing, Baccalaureate KW - Faculty, Nursing KW - Female KW - Humans KW - Interviews as Topic KW - Male KW - Middle Aged KW - Philippines KW - Qualitative Research KW - Teaching KW - article KW - artificial intelligence KW - clinical audit KW - clinical practice guideline KW - female KW - human KW - male KW - nurse KW - nursing education KW - pedagogics KW - personal experience KW - phenomenology KW - Philippines KW - practice guideline KW - professional standard KW - semi structured interview KW - student engagement KW - teaching KW - trustworthiness KW - adult KW - interview KW - middle aged KW - procedures KW - psychology KW - qualitative research CY - Saudi Arabia, Philippines ER - TY - JOUR TI - AI-driven framework for enhancing water quality engineering experimentation AU - Bai F. AU - Liu S. AU - Yuan Y. AU - Zhang Y. AU - Zhou J. PY - 2026 JO - Results in Engineering VL - 30 SP - 110732 DO - 10.1016/j.rineng.2026.110732 AB - Artificial Intelligence (AI) holds transformative potential for cultivating high-quality talent in higher education, particularly in engineering experimental pedagogy. However, traditional water quality engineering courses face challenges in dynamic responsiveness, personalized learning, and data-intensive experimental workflows. This study addresses these gaps by establishing an integrated AI-enhanced framework for water quality engineering experimental courses. A mixed-methods approach was employed across eight laboratory modules. The framework combined project-based learning, Bloom’s taxonomy, and AI-driven tools (SHAP-optimized Extreme Trees/Random Forest/Decision Tree algorithms, NLP/image recognition, closed-loop feedback systems). Undergraduate cohorts using AI-integrated methods (n = 64) were compared with conventional method (CM) cohorts (n = 67). Metrics included technical proficiency, data accuracy, innovation capability, engagement, error rates, and teacher workload. AI integration significantly enhanced pedagogical efficacy, evidenced by a 41% increase in experimental design efficiency alongside 33.8% (p < 0.01) and 35.6% (p < 0.01) improvements in hypothesis formulation and data interpretation accuracy, respectively. Data processing accuracy exceeded 85%, accompanied by a 57.9% reduction in processing time, while innovation capability rose by 28.3% (p < 0.01) and operational errors decreased by 40%. Concurrently, student engagement increased by 62.5% (p < 0.05) with significant metacognitive skill gains (Cohen’s d = 0.72), and teacher workload declined by 37.1%, freeing 3.1 weekly hours per instructor. These outcomes were driven primarily by real-time closed-loop feedback and personalized learning pathways. This study provides a replicable “AI + Experimental Courses” paradigm that synergizes human expertise with AI capabilities to overcome data robustness and emotional intelligence challenges. It advances sustainable AI-education integration, offering a scalable model for engineering education reform aligned with sustainable development goals. Copyright © 2026. Published by Elsevier B.V. KW - Artificial intelligence KW - Closed-loop feedback KW - Experimental pedagogy KW - Personalized learning pathways KW - Water quality engineering KW - Curricula KW - Data accuracy KW - Data integration KW - Engineering education KW - Learning systems KW - Students KW - Teaching KW - Technical presentations KW - Water quality KW - Closed-loop feedback KW - Experimental course KW - Experimental pedagogy KW - Innovation capability KW - Learning pathway KW - Personalized learning KW - Personalized learning pathway KW - Quality engineering KW - Teachers' KW - Water quality engineering KW - Artificial intelligence CY - China ER - TY - JOUR TI - AI Insights for Wind Speed Retrieval From GNSS Reflectometry AU - Xiao T. AU - Wickert J. AU - Asgarimehr M. PY - 2026 JO - IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing VL - 19 SP - 13693 EP - 13713 DO - 10.1109/JSTARS.2026.3681975 AB - Artificial intelligence (AI) models developed for Global Navigation Satellite System Reflectometry (GNSS-R) observations have demonstrated competitive performance in estimating geophysical parameters, especially ocean surface wind speeds. However, the transition from transparent physical scattering models to complex deep learning architectures raises concerns regarding reduced model transparency and trust. Understanding the decision-making processes of these 'black-box' models is essential for assessing model behavior, detecting anomalies, and ensuring reliability in AI-based Earth observations. In this study, we investigate the role of explainable artificial intelligence (XAI) in addressing the transparency gap for hybrid deep learning models designed for GNSS-R observations. Focusing on ocean wind speed retrieval as a well-characterized benchmark, our study is structured around three primary objectives: first, assessing the robustness and efficiency of XAI explainers, second, interpreting a benchmark hybrid model trained using a manually selected feature set with Shapley additive explanations (SHAP) and gradient-weighted class activation mapping (Grad-CAM), which provide quantitative branchwise attribution and qualitative spatial saliency, and third, proposing an XAI-based feature selection pipeline that leverages SHAP-based ranking and exclusion, comparing its efficacy against conventional statistical methods. The results demonstrate that SHAP is effective not only for model interpretation but also for supporting computationally efficient feature selection and model debugging. Meanwhile, Grad-CAM offers complementary spatial interpretability by highlighting salient regions in the delay-Doppler map inputs. This study demonstrated the potential of integrating XAI as a diagnostic and validation tool into the model development cycle, enabling more transparent, robust, and trustworthy AI models for upcoming GNSS-R missions and future applications. © 2026 IEEE. KW - Deep learning KW - explainable artificial intelligence (XAI) KW - feature engineering KW - Global Navigation Satellite System Reflectometry (GNSS-R) KW - ocean wind speed Al for Remote Sensing KW - Behavioral research KW - Benchmarking KW - Communication satellites KW - Decision making KW - Doppler effect KW - Feature extraction KW - Global positioning system KW - Learning systems KW - Navigation KW - Oceanography KW - Reflection KW - Reflectometers KW - Salinity measurement KW - Transparency KW - Wind KW - Deep learning KW - Explainable artificial intelligence KW - Feature engineerings KW - Global navigation satellite system reflectometry KW - Global Navigation Satellite Systems KW - Ocean wind speed KW - Ocean winds KW - Reflectometry KW - Shapley KW - Wind speed KW - Deep learning CY - Germany ER - TY - JOUR TI - A framework for AI-powered service innovation capability: Review and agenda for future research AU - Akter S. AU - Hossain M.A. AU - Sajib S. AU - Sultana S. AU - Rahman M. AU - Vrontis D. AU - McCarthy G. PY - 2023 JO - Technovation VL - 125 SP - 102768 DO - 10.1016/j.technovation.2023.102768 AB - Artificial intelligence (AI)-powered service innovation (e.g., OpenAI's ChatGPT, Google's Bard and Microsoft's Sydney) has become one of the most significant determinants of firms' success in the Fourth Industrial Revolution. However, extant studies on this topic show that research studies hitherto have been ad-hoc, lacking a conceptual framework for the strategic management of AI-powered service innovation capability in dynamic markets. Thus, this study synthesises the current body of knowledge, proposes a framework, and develops an agenda to advance our knowledge. The findings reveal: (1) AI-market capability relates to customer orientation, industry orientation, and cross-functional orientation; (2) AI-infrastructure capability relates to data, business models, and ecosystem; and (3) AI-management capability relates to AI-orientation, organisational learning, and AI ethics which are crucial determinants of forming AI-powered service innovation capability. These capabilities for the strategic management of AI-powered service innovations play an essential role in achieving organizational agility and competitive advantage. © 2023 Elsevier Ltd KW - AI ethics KW - AI infrastructure Capability KW - AI management Capability KW - AI market Capability KW - AI-Powered service innovation KW - Innovation capability KW - Organizational agility KW - Sustainable competitive advantage KW - Commerce KW - Philosophical aspects KW - Strategic planning KW - Artificial intelligence ethic KW - Artificial intelligence infrastructure capability KW - Artificial intelligence management capability KW - Artificial intelligence market capability KW - Artificial intelligence-powered service innovation KW - Innovation capability KW - Intelligence management KW - Management capabilities KW - Organizational agility KW - Service innovation KW - Sustainable competitive advantages KW - Competition CY - United Arab Emirates ER - TY - JOUR TI - A stochastic production frontier model for evaluating the performance efficiency of artificial intelligence investment worldwide AU - Sun Y.-C. AU - Cosgun O. AU - Sharman R. AU - Mulgund P. AU - Delen D. PY - 2024 JO - Decision Analytics Journal VL - 12 SP - 100504 DO - 10.1016/j.dajour.2024.100504 AB - As artificial intelligence (AI) begins to take center stage in technological innovations, it is essential to understand the business value of AI innovation efforts and investments. While some early work at the firm level exists, there is a shortage of literature that takes a larger country-level perspective. This study investigated the effect of AI innovation efforts on production efficiency across countries using stochastic production frontier approaches. In addition, our model also included the traditional economic inputs of capital and labor. We used both the Cobb–Douglas function and Constant Elastic Substitution model specifications. The significant findings of this study are as follows: Innovation efforts in AI measured by the number of AI-related patents and capital investment in AI have a substantial effect on economic output. The significance of AI investments indicates the need for a robust digital infrastructure as a prerequisite for harnessing AI capabilities. The complementary relationship between labor and AI-related patents implies that high-skilled labor is often necessary to incorporate AI inputs into production. However, as AI capabilities develop, they weaken the effect on labor input. The study also distinguishes between AI innovation (research and development activities indicated by AI patents) and the production efficiency of AI investments (return on every dollar invested), highlighting that more AI innovation does not always translate into better production efficiency. The findings indicate that while the United States leads innovation in AI, the UK has the best production efficiency. China ranked fourth in AI innovation and has the lowest production efficiency among the countries included in the study. © 2024 The Author(s) KW - Artificial intelligence KW - Cobb–Douglas function KW - Innovation KW - Production efficiency KW - Stochastic production frontier CY - United States, Turkey ER - TY - JOUR TI - Artificial intelligence for information-driven resilience: Enhancing strategic adaptation and entrepreneurial success AU - Wang S. AU - Zhang H. PY - 2026 JO - International Journal of Information Management VL - 89 SP - 103057 DO - 10.1016/j.ijinfomgt.2026.103057 AB - This study investigates how artificial intelligence (AI) innovation capability—the systematic integration, management, and application of AI-driven information—enhances strategic resilience and entrepreneurial success under conditions of market volatility and resource constraints. We conceptualize information-driven resilience as the organizational capacity to convert AI-enabled information flows into adaptive responses that sustain competitive functioning. Drawing on dynamic capability theory, we examine how early-stage ventures transform AI capabilities into adaptive capacity and competitive advantage. Using a multi-method approach, we collected two-wave survey data from 357 early-stage ventures in China and Europe, complemented by importance-performance map analysis (IPMA), fuzzy-set qualitative comparative analysis (fsQCA), and semi-structured interviews. Our convergent multi-method design provides complementary evidence: PLS-SEM establishes the mediation hypothesis, IPMA identifies actionable managerial priorities, fsQCA reveals equifinal configurational pathways to high performance, and interviews illuminate the underlying mechanisms. Specifically, AI innovation capability is significantly associated with strategic resilience, which is positively related to entrepreneurial success. The mediating effect of strategic resilience is robust and fully accounts for the AI capability–success relationship, indicating that the mechanism through which AI investments generate performance returns is fundamentally organizational rather than technological in nature. The value derived from AI is unlocked by first building organizational capacity for adaptive learning and agile decision-making. The study contributes to information management literature by clarifying how AI-enabled information processing capabilities enhance adaptive responses in uncertain environments, while providing practitioners with an actionable framework for developing information-driven resilience through strategic AI implementation. © 2026 Elsevier Ltd KW - Artificial intelligence KW - Digital transformation KW - Entrepreneurial performance KW - Information management KW - Knowledge management KW - Strategic resilience KW - Competition KW - Decision making KW - Investments KW - Knowledge management KW - Uncertainty analysis KW - Adaptive response KW - Digital transformation KW - Entrepreneurial performance KW - Entrepreneurial success KW - Innovation capability KW - Map analysis KW - Organisational KW - Performance KW - Performance maps KW - Strategic resilience KW - Artificial intelligence CY - China ER - TY - JOUR TI - Modeling AI-driven inequality and adaptive governance: A system dynamics approach to U.S. Socioeconomic futures AU - Moosavihaghighi M. PY - 2026 JO - Sustainable Futures VL - 11 SP - 101746 DO - 10.1016/j.sftr.2026.101746 AB - Artificial Intelligence is reshaping socioeconomic systems by enhancing productivity while intensifying concerns about inequality, unemployment, and policy responsiveness. This study employs a System Dynamics model to simulate the U.S. socioeconomic landscape from 2000 to 2035, focusing on the interdependencies between AI investment, income distribution, and adaptive policy design. Given data constraints, AI investment is modeled as a uniform labor market driver, with international competition introduced via the DeepSeek stress test. The model integrates political feedback loops linking wealth concentration to reform inertia. Three policy scenarios are evaluated: (1) baseline U.S. AI adoption, (2) competitive pressure from a low-cost foreign platform under varying regulations, and (3) adaptive reforms coupling AI taxation and redistribution to real-time inequality and unemployment metrics. Results reveal that while AI-driven productivity may reduce unemployment and cost of production initially, it exacerbates inequality without responsive governance. Adaptive mechanisms, such as dynamic reskilling and AI-linked fiscal tools, outperform static interventions in promoting equity and competitiveness. However, entrenched political influence constrains reform unless public dissatisfaction crosses critical thresholds. These findings highlight the urgent need for anticipatory, adaptive policy frameworks that align technological innovation with inclusive and sustainable socioeconomic outcomes. © 2026 The Author. Published by Elsevier Ltd. This is an open access article under the CC BY license. http://creativecommons.org/licenses/by/4.0/ KW - Adaptive Governance KW - AI Governance KW - Artificial Intelligence KW - Income Inequality KW - Labor Market KW - System Dynamics CY - Iran ER - TY - JOUR TI - The Impact of VAT Credit Refunds on Enterprises’ Sustainable Development Capability: A Socio-Technical Systems Theory Perspective AU - She J. AU - Sun M. AU - Yan H. PY - 2025 JO - Systems VL - 13 IS - 8 SP - 669 DO - 10.3390/systems13080669 AB - We investigate whether China’s Value-Added Tax (VAT) Credit Refund policy influences firms’ sustainable development capability (SDC), which reflects innovation-driven growth and green development. Exploiting the 2018 implementation of the VAT Credit Refund policy as a quasi-natural experiment, we employ a difference-in-differences (DID) approach and find causal evidence that the policy significantly enhances firms’ SDC. This suggests that fiscal instruments like VAT refunds are valued by firms as drivers of long-term sustainable and high-quality development. Our mediating analyses further reveal that the policy promotes firms’ SDC by strengthening artificial intelligence (AI) capabilities and facilitating intelligent transformation. This mechanism “AI Capability Building—Intelligent Transformation” aligns with the socio-technical systems theory (STST), highlighting the interactive evolution of technological and social subsystems in shaping firm capabilities. The heterogeneity analyses indicate that the positive effect of VAT Credit Refund policy on SDC is more pronounced among small-scale and non-high-tech firms, firms with lower perceived economic policy uncertainty, higher operational diversification, lower reputational capital, and those located in regions with a higher level of marketization. We also find that the policy has persistent long-term effects, with improved SDC associated with enhanced ESG performance and green innovation outcomes. Our findings have important implications for understanding the SDC through the lens of STST and offer policy insights for deepening VAT reform and promoting intelligent and green transformation in China’s enterprises. © 2025 by the authors. KW - AI KW - socio-technical systems theory KW - sustainable development capability KW - value-added tax KW - Green development KW - Sustainable development KW - System theory KW - Taxation KW - Difference-in-differences KW - Differences-in-differences KW - Intelligent transformations KW - Natural experiment KW - Refund policies KW - S values KW - Sociotechnical systems theory KW - Sustainable development capability KW - Tax credits KW - Value-added tax KW - Artificial intelligence CY - China ER - TY - JOUR TI - Ethics dumping in artificial intelligence AU - Bélisle-Pipon J.-C. AU - Victor G. PY - 2024 JO - Frontiers in Artificial Intelligence VL - 7 SP - 1426761 DO - 10.3389/frai.2024.1426761 AB - Artificial Intelligence (AI) systems encode not just statistical models and complex algorithms designed to process and analyze data, but also significant normative baggage. This ethical dimension, derived from the underlying code and training data, shapes the recommendations given, behaviors exhibited, and perceptions had by AI. These factors influence how AI is regulated, used, misused, and impacts end-users. The multifaceted nature of AI’s influence has sparked extensive discussions across disciplines like Science and Technology Studies (STS), Ethical, Legal and Social Implications (ELSI) studies, public policy analysis, and responsible innovation—underscoring the need to examine AI’s ethical ramifications. While the initial wave of AI ethics focused on articulating principles and guidelines, recent scholarship increasingly emphasizes the practical implementation of ethical principles, regulatory oversight, and mitigating unforeseen negative consequences. Drawing from the concept of “ethics dumping” in research ethics, this paper argues that practices surrounding AI development and deployment can, unduly and in a very concerning way, offload ethical responsibilities from developers and regulators to ill-equipped users and host environments. Four key trends illustrating such ethics dumping are identified: (1) AI developers embedding ethics through coded value assumptions, (2) AI ethics guidelines promoting broad or unactionable principles disconnected from local contexts, (3) institutions implementing AI systems without evaluating ethical implications, and (4) decision-makers enacting ethical governance frameworks disconnected from practice. Mitigating AI ethics dumping requires empowering users, fostering stakeholder engagement in norm-setting, harmonizing ethical guidelines while allowing flexibility for local variation, and establishing clear accountability mechanisms across the AI ecosystem. Copyright © 2024 Bélisle-Pipon and Victor. KW - accountability KW - AI ethics KW - AI governance KW - artificial intelligence KW - ethical guidelines KW - ethics dumping CY - Canada ER - TY - JOUR TI - Enhancing university education with AI: a Telegram bot leveraging RAG and external APIs for secure knowledge retrieval AU - Bashurov V. AU - Safonov P. PY - 2025 JO - Issues in Information Systems VL - 26 IS - 3 SP - 413 EP - 420 DO - 10.48009/3_iis_2025_2025_133 AB - This paper presents a novel AI-powered Telegram bot designed to enhance university information services by securely integrating external AI capabilities with institutional private data. The system leverages Retrieval-Augmented Generation (RAG) to transform structured university data (faculty profiles, schedules, lecture notes) into vectorized embeddings, which are dynamically retrieved and combined with responses from a general-purpose AI API (e.g., GPT-4). This hybrid approach ensures accurate, context-aware answers while preserving data privacy — raw institutional information is never exposed directly to third-party systems. Implemented at Comtrade University, the bot demonstrates significant outperforming standalone AI models for domain-specific questions. Key innovations include a scalable pipeline for embedding private data, seamless Telegram-based access, and cost-efficient prompt engineering via RAG. The solution addresses critical challenges in educational technology: balancing AI augmentation with data security and providing 24/7 conversational access to institutional knowledge. We discuss architectural decisions, privacy safeguards, and empirical results, offering a replicable framework for other universities. © 2025 International Association for Computer Information Systems. All rights reserved. KW - educational chatbots KW - hybrid AI systems KW - LLM vector embeddings KW - privacy in EdTech KW - retrieval-augmented generation (RAG) KW - telegram API CY - Serbia, United States ER - TY - JOUR TI - Generative AI-driven sustainability in supply chains: A micro foundation of dynamic capability towards a socially responsible supply chain to achieve greater societal change AU - Yadav S. AU - Samadhiya A. AU - Kumar A. AU - Pandey K.K. AU - Luthra S. AU - El jaouhari A. PY - 2026 JO - Technological Forecasting and Social Change VL - 229 SP - 124726 DO - 10.1016/j.techfore.2026.124726 AB - The application of Gen AI (Generative AI) across multiple sectors like manufacturing and service domains, shows transformative effects to improve socially responsible decision-making and collaborative efforts. Yet it remains insufficiently investigated in the context of a socially responsible supply chain (SRSC) towards sustainable supply chain management (SSCM) in a wider context. Gen AI enables faster reporting and adaptive responses to enhance decision-making, which together improve supply chain flexibility while promoting social responsibility. Although previous research recognizes Gen AI's contribution to social functionality within a supply chain, it does not provide a full theoretical structure for analyzing how Gen AI solutions develop and function in SSCM. Prior research stresses the importance of making people and communities central elements in SSCM from the outset. To address this gap, this research conducts a rigorous qualitative study by analyzing 82 exemplary SSCM cases from manufacturing and service sectors through content analysis. The research explores how organizations can leverage dynamic capability theory (DCT) to adopt and integrate Gen AI systems. The findings demonstrate the stakeholder role in SSCM: 1) NGOs and universities provide essential knowledge and skills together with resources which support sustainable practices; 2) active collaboration with external stakeholders creates competitive benefits while promoting wider implementation of sustainability efforts through imitation. This research delivers a conceptual framework, showing how dynamic supply chain capabilities enabled by Gen AI affect stakeholder alignment towards sustainability goals while mobilizing stakeholders towards SSCM practices; this creates positive effects for wider communities in dynamically evolving Gen AI based SC systems. Our study utilizes micro-foundations of dynamic capabilities to deliver actionable recommendations for managers and outlines future research paths for expanding sustainability practices across multiple dimensions using Gen AI. This study provides helpful insights for professionals, researchers, and leaders to achieve Sustainable Development Goals (SDGs). © 2026 Elsevier Inc. KW - Capability reconfiguration KW - Digital sustainability transition KW - Responsible AI adoption KW - Social change KW - Socio-technical innovation KW - Supply chain resilience KW - Behavioral research KW - Competition KW - Decision making KW - Economic and social effects KW - Social aspects KW - Supply chain management KW - Supply chains KW - Sustainable development KW - Sustainable development goals KW - Capability reconfiguration KW - Digital sustainability transition KW - Responsible AI adoption KW - Social changes KW - Socio-technical innovation KW - Sociotechnical KW - Supply chain resiliences KW - Sustainability transition KW - Sustainable supply chains KW - Technical innovation KW - artificial intelligence KW - competition (economics) KW - decision making KW - innovation KW - manufacturing KW - service sector KW - social change KW - supply chain management KW - sustainability KW - technology adoption KW - theoretical study KW - Industrial research CY - India, United Kingdom, Morocco ER - TY - JOUR TI - Advancements in Artificial Intelligence-based prescriptive and cognitive analytics for business performance: a special issue editorial AU - Charles V. AU - Emrouznejad A. AU - Kunz W.H. PY - 2025 JO - Journal of Business Research VL - 200 SP - 115576 DO - 10.1016/j.jbusres.2025.115576 AB - The rapid advancement of Artificial Intelligence (AI) is transforming business decision-making across industries. AI-based prescriptive and cognitive analytics offer significant potential to enhance decision-making, optimise performance, and create new avenues for value creation. This special issue explores the state-of-the-art advancements in these analytics and their business implications. We introduce the Analytics Onion as a conceptual foundation, comprising three interrelated layers: Perspective Analytics, Responsible Analytics, and the Descriptive-Diagnostic-Predictive-Prescriptive-Cognitive Analytics framework. The Analytics Onion captures the interplay between human judgment, ethics, analytical rigour, and AI techniques. The featured papers exemplify these layers through various topics, namely humanoid service robots, business location optimisation, ESG evaluation, energy efficiency, customer churn, prediction-led prescription, innovation culture, user satisfaction with AI, responsible AI in business models, and executives’ emotions influencing firm value. We highlight emerging opportunities and challenges and offer a forward-looking research agenda to guide future developments in this evolving field. © 2025 Elsevier Inc. KW - Artificial intelligence KW - Business performance KW - Cognitive analytics KW - Decision-making KW - Perspective analytics KW - Prescriptive analytics KW - Responsible analytics CY - United Kingdom, United States ER - TY - JOUR TI - Responsible Artificial Intelligence Attention and Firm Innovation: An Attention-Based View AU - Xiong M. AU - Xu H. AU - Ji J. AU - Zuo R. AU - Wang Y. AU - Olya H. PY - 2026 JO - Journal of Product Innovation Management VL - 43 IS - 1 SP - 186 EP - 214 DO - 10.1111/jpim.70015 AB - Academic Summary: This article draws on the attention-based view (ABV) to examine whether, how, and under what conditions top management team (TMT) attention to responsible artificial intelligence (AI) influences firm innovation. We developed a 480-word responsible AI dictionary grounded in 155 academic sources and 527 corporate case descriptions, and applied it to 2452 S&P 500 earnings call transcripts (2011–2021) using natural language processing (NLP) and large language model (LLM) techniques, yielding 2670 firm-year observations. Linking these measures to US patent data, we find that greater responsible AI attention predicts more and higher-impact patents. The effect is stronger in low-technology industries and under short-term investor pressure, while the presence of a chief technology officer (CTO) does not amplify it. Mechanism analyses reveal that responsible AI attention fosters innovation by increasing investment in AI-relevant human capital and mitigating innovation risk. Theoretically, this article enriches the AI and innovation management literature by positioning responsible AI attention as a dynamic strategic asset that mobilizes resources, reduces risk, and enables contextual adaptation. Practically, findings suggest that firms can strengthen innovation by prioritizing managerial attention to responsible AI, distributing responsibility beyond technical specialists, balancing ethical safeguards with strategic flexibility, and aligning governance with investor and industry conditions. Managerial Summary: This article examines how managerial attention to responsible artificial intelligence (AI) can enhance firm innovation. Using text analytics on 2452 earnings call transcripts from S&P 500 firms (2011–2021) and a panel of 2670 firm-year observations linked to patent outcomes, we show that firms whose top management teams (TMT) devote greater attention to responsible AI produce more and higher-impact patents. This effect is stronger in low-technology industries and when firms face short-term investor pressure; it is not amplified by having a chief technology officer (CTO). In practice, sustained attention to responsible AI tends to build AI-related skills and reduce project risk, thereby supporting a more reliable innovation pipeline. Executives should treat responsible AI as a strategic priority rather than a compliance task by establishing cross-functional governance, investing in role-based governance training, and sharing accountability across the C-suite. Innovation managers can embed ethics checkpoints (bias audits, design reviews) into project workflows to enhance stability and organizational learning. Policymakers can reinforce responsible innovation by providing clear regulatory frameworks and incentives that align ethical safeguards with competitiveness. Together, these actions can help build more durable organizational capability for responsible innovation and support long-term performance and adaptation to ongoing technological change. © 2025 The Author(s). Journal of Product Innovation Management published by Wiley Periodicals LLC on behalf of Product Development & Management Association. KW - attention-based view (ABV) KW - firm innovation KW - large language model (LLM) KW - natural language processing (NLP) KW - responsible artificial intelligence (AI) KW - Investments KW - Natural language processing systems KW - Patents and inventions KW - Product development KW - Attention-based view KW - Attention-based views KW - Condition KW - Firm innovation KW - Language model KW - Language processing KW - Large language model KW - Natural language processing KW - Natural languages KW - Responsible artificial intelligence KW - Human resource management CY - United Kingdom, China, South Korea ER - TY - JOUR TI - Governing AI virtual anchors in China's live streaming E-commerce ecosystem: Policy challenges and global implications AU - Meng Y. PY - 2026 JO - Telecommunications Policy VL - 50 IS - 2 SP - 103109 DO - 10.1016/j.telpol.2025.103109 AB - The rapid advancement of generative artificial intelligence (AI) has fundamentally reshaped the traditional media value chain, transforming the processes of content production, distribution, and consumption. Among these developments, AI virtual anchors have significantly reduced operational costs and enabled the large-scale creation of content. However, their widespread adoption has also raised complex legal, ethical, and regulatory challenges. This paper investigates the governance of AI virtual anchors from three key dimensions. First, it examines how AI technologies are restructuring the media ecosystem, particularly in the realm of live-streaming e-commerce, by displacing human labour and creating new market dynamics. Second, it examines the associated legal and ethical concerns, including intellectual property disputes, the under-recognized rights of “ghost performers”, risks of misinformation, and consumer protection issues. Third, it evaluates China's evolving governance responses, highlighting both proactive regulatory innovations and ongoing challenges. Starting from platform governance theories, this paper develops a China-specific regulatory narrative and identifies a multi-tiered governance system that involves the government, platforms, and public participation, and reveals the underlying logic that redefines platform roles in China's digital governance architecture. This paper argues that China's evolving governance of AI virtual anchors illustrates a distinct institutional model and aims to situate this experience within global discussions, offering comparative reference points for AI governance, particularly regarding platform responsibility, adaptive regulation, and public participation. © 2025 The Author KW - AI KW - China KW - Live streaming KW - Media regulation KW - Value chain KW - Chains KW - Consumer protection KW - Ecosystems KW - Electronic commerce KW - Government data processing KW - Media streaming KW - Philosophical aspects KW - Public policy KW - China KW - Content consumption KW - Content distribution KW - Content production KW - E-commerce ecosystems KW - Live streaming KW - Medium regulation KW - Production distribution KW - Public participation KW - Value chains KW - Artificial intelligence CY - China ER - TY - JOUR TI - Ethical Leadership Challenges in the Age of Artificial Intelligence: An In-depth Analysis AU - Bannor F.O. AU - Baysah J.O. PY - 2025 JO - Pan-African Journal of Education and Social Sciences VL - 6 IS - 2 SP - 76 EP - 88 DO - 10.56893/pajes2025v06i02.06 AB - Artificial intelligence (AI) is rapidly transforming decision-making across various sectors, introducing both opportunities and ethical challenges for leadership. While AI enhances efficiency and innovation, concerns, such as algorithmic bias, transparency deficits, and accountability gaps, pose significant risks to governance. This study examines these ethical dilemmas through real world cases, including Amazon’s recruiting tool, Olay’s algorithmic audit, IBM Watson for Oncology, and predictive policing via COMPAS, to assess their impact on leadership frameworks and the necessity for proactive ethical oversight. Through a comprehensive interdisciplinary analysis, this paper explores traditional ethical leadership models alongside emerging AI governance frameworks, notably the Ethical Management of Artificial Intelligence (EMMA) model. By synthesizing research across ethics, psychology, and management, this study demonstrates how leaders must integrate technical expertise with ethical sensitivity to align AI adoption with organizational values and societal expectations. These findings underscore the crucial need for explainable AI (XAI), bias audits, and transparent accountability structures to promote trust in AI systems. To address these challenges, this study recommends a multi-stakeholder approach that prioritizes interdisciplinary collaboration, continuous ethical monitoring, and enforceable AI governance policies. Ethical AI leadership necessitates adaptive oversight to ensure that AI innovation benefits humanity without perpetuating systemic biases or ethical blind spots. © 2025, Adventist University of Africa. All rights reserved. KW - accountability KW - AI ethics KW - bias KW - ethical leadership KW - governance KW - transparency CY - Liberia ER - TY - JOUR TI - The impact of design thinking and artificial intelligence capabilities on performance: The role of new product development decision-making agility AU - Kyriakopoulos N. AU - Kim E. AU - Hultink E.J. AU - Santema S. PY - 2025 JO - Journal of Business Research VL - 200 SP - 115633 DO - 10.1016/j.jbusres.2025.115633 AB - Design thinking and artificial intelligence (AI) capabilities are gaining prominence in today's dynamic markets. However, research gaps remain regarding their influence on the outcomes of new product development (NPD), such as decision-making agility, and the structural conditions facilitating or impeding their effective implementation. Considering design thinking as a dynamic capability and AI capabilities as technology-driven innovation enablers, this study examines their impact on NPD performance via NPD decision-making agility. An empirical investigation using data collected from 230 U.S. firms shows that design thinking and AI capabilities positively influence agility, which in turn drives NPD performance. This study also uncovers that the moderating role of organizational formalization attenuates the impact of design thinking on NPD decision-making agility but strengthens the impact of AI capabilities on NPD decision-making agility. These findings provide NPD managers with insights into using these capabilities to enhance agility and improve NPD performance in the organizational context. © 2025 The Author(s) KW - Agility KW - Artificial intelligence KW - Design thinking KW - NPD performance KW - Organizational formalization CY - Netherlands ER - TY - JOUR TI - Artificial intelligence (AI) for social innovation in health education: promoting health literacy through personalized ai-driven learning tools – a systematic review AU - Tbaishat D.M. AU - Elfadel M.W. PY - 2026 JO - BMC Medical Education VL - 26 IS - 1 SP - 123 DO - 10.1186/s12909-025-08462-3 AB - Background: Artificial Intelligence (AI) is transforming health education by enabling personalized, adaptive, and scalable approaches that may enhance aspects of health literacy. Despite rapid adoption, comprehensive synthesis of AI tools’ impact on health literacy as social innovation is limited. Understanding these effects guides educators, developers, and policymakers in designing potentially effective, inclusive, and ethical AI interventions. This review examines generative AI models, chatbots, and adaptive learning systems in supporting health literacy globally. Methods: A systematic review was conducted following PRISMA guidelines. Literature was identified primarily through PubMed/Medline, Scopus, and ScienceDirect. Connectedpapers.com was used exclusively as a citation chasing tool, performing both backward and forward reference searches to identify thematically linked studies not captured by database searches. All records retrieved via Connected Papers were subjected to the same eligibility criteria as database-sourced studies, covering publications from 2000–2025. A total of 75 peer-reviewed empirical and theoretical studies focusing on AI tools for health literacy and social innovation were included. Titles, abstracts, keywords, and full texts were screened using predefined criteria. Data were managed and de-duplicated using Zotero. Screening and eligibility decisions were recorded in Excel spreadsheets. Thematic synthesis was conducted manually. PRISMA 2020 and PRISMA-S checklists were used to ensure transparent reporting. Results: AI research in health education was minimal until 2020 but rose sharply from 2021, peaking in 2023–2024 with generative AI (e.g., ChatGPT). Of the 75 included studies, 68 (90.7%) were co-authored by two or more researchers, 54 (72.0%) were published as Open Access, and review articles dominated with 41 studies (54.7%), while empirical research was limited, highlighting moderate to weak evidence. Research focused on personalized AI tools and learning effectiveness, with limited exploration of ethics, technical barriers, or social innovation. Findings suggest that AI interventions may improve readability, metacognitive engagement, cultural accessibility, and learner autonomy in the short term, particularly when multifaceted. However, evidence for long-term behavior change and real-world impact is sparse, indicating caution in generalizing results. Challenges include algorithmic bias, digital inequity, and lack of transparency, emphasizing the need for inclusive, equity-driven AI strategies. Conclusion: AI-powered tools have potential to support health literacy and learner-centered innovation, while contributing to social impact. Multifaceted, adaptive interventions may offer greater benefits than single-tool approaches. Findings provide preliminary guidance for standardized training, AI literacy integration, and policy frameworks, while acknowledging the current limitations in evidence, generalizability, and long-term outcomes. © The Author(s) 2025. KW - Artificial intelligence (AI) KW - Digital health education KW - Health education KW - Health literacy promotion KW - Personalized learning tools KW - Systematic literature review KW - Artificial Intelligence KW - Health Education KW - Health Literacy KW - Humans KW - artificial intelligence KW - health education KW - health literacy KW - human KW - procedures CY - United Arab Emirates, Jordan ER - TY - JOUR TI - Transforming Healthcare in India: The Role of Artificial Intelligence and Regulatory Frameworks for Sustainable Growth AU - Ghosh A. AU - Saini A. AU - Barad H. PY - 2025 JO - World Medical and Health Policy VL - 17 IS - 3 SP - 475 EP - 490 DO - 10.1002/wmh3.70015 AB - Artificial intelligence (AI) has quickly emerged as a game changer in healthcare, providing innovative ways to improve patient care, enhance processes, and reduce expenses. AI could solve important healthcare issues in India, including increasing service demand, a lack of trained medical personnel, and notable geographical differences, especially in rural areas. AI can aid in addressing this gap by offering scalable, affordable regulatory framework that enhance diagnosis, treatment planning, and suitable resource allocation. This paper examines the impact of AI on healthcare, considering its benefits, challenges, and ethical implications for improving the healthcare delivery system. The paper also explores global regulatory frameworks and their implications for AI in healthcare, focusing on the roles of the United States, United Kingdom, European Union, India, and other prominent organizations. Additionally, the paper explores the opportunity to develop a robust AI policy framework for healthcare in India, drawing from global approaches. The study emphasizes ethical, interoperable AI and outlines a roadmap for India's healthcare sector-integrating risk-based regulations, enhanced digital infrastructure, ethical AI policies, and private entrepreneurship via public–private partnerships—to position India as a leader in AI-driven healthcare regulation. As India continues to invest in digital health infrastructure, a comprehensive, ethically sound regulatory framework will be crucial in ensuring that AI-powered healthcare is accessible, affordable, and inclusive for all citizens. By learning from global best practices and focusing on equitable healthcare, India can lead the way in AI-driven healthcare innovation. © 2025 Policy Studies Organization. KW - affordable healthcare KW - AI regulatory framework KW - artificial intelligence (AI) KW - digital health infrastructure KW - article KW - artificial intelligence KW - best practice KW - diagnosis KW - digital health KW - disease management KW - entrepreneurship KW - European Union KW - health care KW - health care cost KW - health care delivery KW - health infrastructure KW - human KW - India KW - infrastructure KW - medical personnel KW - patient care KW - pharmacoeconomics KW - public-private partnership KW - resource allocation KW - rural area KW - sustainable growth KW - treatment planning KW - United Kingdom KW - United States CY - India ER - TY - JOUR TI - Advancements in Information Technology and Tourism Management: Four decades on the Internet AU - Li K.Q. AU - Zhang K. AU - Law R. PY - 2026 JO - Journal of Smart Tourism VL - 6 IS - 1 SP - 29 EP - 43 DO - 10.1177/27652157251393805 AB - The swift advancement of information and communication technologies (ICTs) over the past four decades has fundamentally reshaped tourism, transforming business models, consumer behavior, and value creation. Adopting a sociotechnical perspective, this paper analyzes the historical trajectory and socio-economic implications of technological shifts in tourism across three phases: technological penetration, data reciprocity, and algorithmic dominance. By conceptualizing technology as a socially constructed phenomenon, it highlights the interplay between consumers, businesses, and technology (C-B-T). The findings reveal a shift from technology-driven control to algorithmic governance, redefining supply–demand relationships and operational strategies while raising ethical concerns. To address these challenges, the study proposes a dynamic power equilibrium framework underpinned by governance mechanisms such as consumer transparency, algorithmic accountability, and business auditing. This framework aims to balance value creation, power distribution, and responsibility allocation, offering strategic insights for sustainable smart tourism development. © The Author(s) 2025 KW - information and communication technologies KW - smart tourism KW - sociotechnical perspective KW - technological evolution KW - value creation CY - China ER - TY - JOUR TI - Driving Sustainable Entrepreneurship Through AI and Knowledge Management: Evidence from SMEs in Emerging Economies AU - Alshammakhi Q.M. AU - Sheikh R.A. PY - 2025 JO - Sustainability (Switzerland) VL - 17 IS - 24 SP - 10928 DO - 10.3390/su172410928 AB - This study investigates how artificial intelligence (AI) capabilities shape sustainable entrepreneurship (SE) among small and medium-sized enterprises (SMEs) in emerging economies. Focusing on knowledge management (KM) as a mediator, entrepreneurial orientation (EO) as a moderator, and government policy support (GPS) as an enabler, the research draws upon the Knowledge-Based View, Dynamic Capabilities Theory, and Institutional Theory. Using data from Saudi Arabian SMEs operating within the Vision 2030 agenda, the structural model demonstrates that AI primarily influences sustainability when firms possess robust KM systems capable of translating digital insights into actionable practices. Both EO and GPS strengthen the conversion of knowledge into sustainable outcomes, where EO fosters innovation and proactivity, and GPS provides essential resources and legitimacy. Nevertheless, excessive reliance on policy incentives may divert firms toward compliance rather than substantive transformation. Conceptually, this paper situates KM at the core of sustainability transformation, with policy support shaping the institutional context. The findings offer actionable guidance for SME managers and policymakers seeking to advance the United Nations Sustainable Development Goals (SDGs) through strategic engagement with AI and KM. © 2025 by the authors. KW - artificial intelligence KW - entrepreneurial orientation KW - government policy support KW - knowledge management KW - sustainable entrepreneurship KW - vision 2030 KW - artificial intelligence KW - economic analysis KW - entrepreneur KW - government relations KW - GPS KW - innovation KW - Sustainable Development Goal KW - United Nations CY - Saudi Arabia ER - TY - JOUR TI - Efficacy of Intelligent Consulting Services in Libraries at Home and Abroad under the Background of AI Large Model Driving; [AI大模型驱动背景下国内外图书馆智能咨询服务效能研究] AU - Song L. AU - Zhang X. PY - 2026 JO - Journal of Library and Information Science in Agriculture VL - 38 IS - 4 SP - 99 EP - 111 DO - 10.13998/j.cnki.issn1002-1248.25-0524 AB - [Purpose/Significance] This study investigates the operational practices and strategic development pathways of intelligent consultation services in libraries globally, specifically under the impetus of artificial intelligence (AI) large language models (LLMs). By conducting a systematic analysis of representative case studies, we examine the applied technologies, emerging service models, and measurable efficacy of these AI-enhanced services. The research holds significance in offering actionable insights for the effective implementation of AI within the library sector. It aims to guide the evolution of intelligent consultation toward greater innovation and cultural-contextual adaptability, thereby providing both theoretical underpinning and practical guidance for the localized development of smart library ecosystems. [Method/Process] Employing a comparative case study methodology, this research selected 30 representative libraries from diverse international and domestic contexts as its subjects. Data were primarily gathered through structured online surveys and content analysis of publicly available service interfaces, systematically capturing the scope, functionality, and operational status of their intelligent consultation services. The analysis focused on characterizing technological applications-identifying core LLM integrations, typical functionalities, and architectural highlights. It further integrated findings to compare and contrast prevailing service models and implementation variances. Subsequently, the study conducted a multidimensional comparative assessment of the practical service effectiveness enabled by AI large models, evaluating performance across four key areas: service response efficiency and accuracy; capabilities in resource organization and structured knowledge management; tangible improvements in user service experience; and degree of service model innovation. [Results/Conclusions] The findings indicate that AI large model-driven intelligent consulting services exhibit pronounced advantages in key operational metrics, including enhanced response efficiency, superior knowledge synthesis and management capabilities, enriched user interaction experiences, and the facilitation of novel service paradigms. However, a comparative analysis reveals significant disparities among libraries concerning the depth of technological integration, the sophistication of service offerings, and the level of cultural and linguistic adaptation achieved. In response, the study proposes targeted strategic recommendations from three interrelated perspectives: nuanced technological application, user-centered service design, and collaborative ecosystem construction. It advocates for libraries to prioritize the synergistic balance between technological capability and humanistic service values, to achieve deeper integration with localized and institutional knowledge repositories, and to institute mechanisms for continuous service evaluation and iterative optimization. These approaches are essential for fostering more efficient, inclusive, and sustainable development of intelligent consultation services. Future research directions should encompass longitudinal studies on service effectiveness, the integration of multimodal interactive capabilities, and the formulation of ethical guidelines and governance frameworks for AI deployment in library services. © 2026, Agricultural Information Institute, Chinese Academy of Agricultural Sciences. All rights reserved. KW - AI large model-driven KW - efficiency research KW - intelligent consulting services KW - library CY - China ER - TY - JOUR TI - Big data artificial intelligence to promote new product performance: the role of electronic-supply chain collaboration in B2B firms AU - Tao Y. AU - Muhammad Muneeb F. AU - Wanke P.F. AU - Tan Y. PY - 2025 JO - Journal of Business and Industrial Marketing SP - 1 EP - 14 DO - 10.1108/JBIM-09-2024-0706 AB - Purpose – This study aims to examine how big data analytical intelligence (BDAI) assimilation promotes new product performance (NPP) in business-to-business (B2B) manufacturing firms. This study tests the intermediary role of artificial intelligence (AI) capabilities and the contingency impact of electronic supply chain collaboration (ESCC) in the relationship between BDAI assimilation and NPP. Design/methodology/approach – Drawing on the dynamic capabilities theory (DCT), this study tests the moderated-mediation model using multi-wave, multi-source data collected from 291 Chinese B2B manufacturing firms. Structural equation modeling was applied to test the proposed hypotheses. Findings – The results demonstrate that BDAI assimilation significantly promotes NPP, and AI capabilities act as an intermediary bridge – empowering B2B firms to convert BDAI assimilation into enhanced NPP. This study further found that this mediation model is strengthened through the contingency impact of ESCC and increases its indirect effect on NPP. Practical implications – This study suggests that B2B managers and policy architects should recognize that investment in BDAI assimilation is not sufficient. However, building AI capabilities might fully support BDAI assimilation to gain innovation outcomes such as NPP. This study further suggests the practical implications of ESCC in achieving higher returns through AI capabilities in the B2B context. Originality/value – This study has threefold contributions to the existing literature on big data innovation and B2B firms. This study contributes to extend DCT by emphasizing AI capabilities as an intermediary channel and ESCC as a vital contingency – strengthening the relationship between BDAI assimilation and NPP. We contributed and understand how Chinese B2B firms strategically used big data and AI strategies to reach firms’ competitive advantage. © 2025 Emerald Publishing Limited KW - Artificial intelligence capabilities KW - Big data analytical intelligence KW - Electronic supply chain collaboration KW - New product performance CY - China, Brazil, United Kingdom ER - TY - JOUR TI - Reaching new frontiers in nanoelectronics through artificial intelligence AU - Sivasubramani S. AU - Prodromakis T. PY - 2025 JO - Frontiers in Nanotechnology VL - 7 SP - 1627210 DO - 10.3389/fnano.2025.1627210 AB - Artificial Intelligence (AI) is revolutionizing industries worldwide, delivering unprecedented productivity gains across diverse sectors, from healthcare to manufacturing. Recent advances in generative AI models have particularly accelerated innovation, enabling more efficient execution of complex tasks such as drug discovery, autonomous driving, and predictive maintenance. In the areas of electronics manufacturing: a sector crucial to the advancement of modern technologies, the impact of AI is profound, with the potential to transform every stage of the supply chain. This perspective investigates the role of AI in reshaping the electronics and semiconductor industries, exploring how it integrates into various stages of production and development. The approach to AI integration is structured and methodical, addressing both challenges and opportunities across five key nanotechnology areas: materials discovery, device design, circuit and system design, testing/verification, and modeling. In materials discovery, AI aids in identifying new, more efficient and sustainable materials. In device design, it enhances the functionality and integration of components. AI’s capabilities in circuit and system design enables more complex and precise electronic systems. During the testing and verification stage, AI contributes to more rigorous and faster testing processes, ensuring reliability before market release. Finally, in modeling, AI’s predictive capabilities allow for accurate simulations, crucial for anticipating performance under various scenarios. Each pillar of this electronics supply chain underscores AI’s ability to accelerate processes, optimize performance, and reduce costs. Supported by case studies of AI-driven breakthroughs, this perspective provides a comprehensive review of current AI applications across the entire electronic supply chain, illustrating improvements in yield and sustainable manufacturing practices. Copyright © 2025 Sivasubramani and Prodromakis. KW - artificial intelligence KW - electronics supply chain KW - nanoelectronics manufacturing KW - nanotechnology applications KW - semiconductor design KW - sustainable engineering CY - United Kingdom ER - TY - JOUR TI - Artificial Intelligence and Digital Marketing: Ethical Challenges of Digital Influence on Public Perception and Consumer Behavior in the Law of the UAE AU - Yas H. AU - Abdalaziz M.M.O. AU - Dafri W. AU - Al-Falahi Q. AU - Kashmoola B. AU - Allouzi A.S. PY - 2025 JO - Research Journal in Advanced Humanities VL - 6 IS - 3 DO - 10.58256/5hjmrw16 AB - This paper discusses the ethical and legal considerations of artificial intelligence (AI) in digital marketing in the fast-changing regulatory environment of the United Arab Emirates (UAE). Through the secondary research methodology, the article examines 85 documents comprising academic publications, government reports, and legal texts, providing a thorough review of the nexus between AI capabilities and regulatory frameworks in the UAE. The results show that although the use of AI has increased at a faster pace, especially among various generational groups of consumers, there is a need to transform the legal and ethical framework to address the arising risks. Ethical AI governance is based on the main regulations, including the UAE Personal Data Protection Law, Cybercrime Law, Consumer Protection Law, and Digital Commerce Law. Such laws are focused on transparency, consent, and accountability in AI-driven marketing activities. Additionally, explainability and fairness are key factors that make consumers trustful, but AI is usually too technical to provide meaningful transparency. The paper concludes that a moderate solution is the key, which is to incorporate technological innovation with moral governance. It demands enhanced regulation enforcement, industry self-regulation, and cultural change within organizations to guarantee responsible AI use. The future of ethical digital marketing and consumer protection in the era of intelligent automation will be influenced by the changing legal framework of the UAE. © 2025 The Author(s). KW - AI Ethics KW - Artificial Intelligence (AI) KW - Consumer Protection KW - Data Privacy KW - Digital Marketing KW - Ethical Governance KW - Law KW - Legal Framework CY - Malaysia, United Arab Emirates ER - TY - JOUR TI - From Triumph to Uncertainty: The Journey of Software Engineering in the AI Era AU - Mastropaolo A. AU - Escobar-Velásquez C. AU - Linares-Vásquez M. PY - 2025 JO - ACM Transactions on Software Engineering and Methodology VL - 34 IS - 5 SP - 131 DO - 10.1145/3709360 AB - Over the last 10 years, the realm of AI has experienced an explosion of revolutionary breakthroughs, transforming what seemed like a far-off dream into a reality that is now deeply embedded in our everyday lives. AI’s widespread impact is revolutionizing virtually all aspects of human life, and software engineering (SE) is no exception. As we explore this changing landscape, we are faced with questions about what the future holds for SE and how AI will reshape the roles, duties, and methodologies within the field. The introduction of these groundbreaking technologies highlights the inevitable shift toward a new paradigm, suggesting a future where AI’s capabilities may redefine the boundaries of SE, potentially even more than human input. In this article, we aim at outlining the key elements that, based on our expertise, are vital for the smooth integration of AI into SE, all while preserving the intrinsic human creativity that has been the driving force behind the field. First, we provide a brief description of SE and AI evolution. Afterward, we delve into the intricate interplay between AI-driven automation and human innovation, exploring how these two components can work together to advance SE practices to new methods and standards. © 2025 Association for Computing Machinery. All rights reserved. KW - AI4SE KW - Artificial Intelligence KW - History KW - LLM4Code KW - Software engineering KW - Application programs KW - Embedded software KW - Human engineering KW - Software design KW - Software packages KW - Software quality KW - Verification KW - AI4SE KW - Driving forces KW - Human creativity KW - Human lives KW - Key elements KW - Llm4code KW - Software engineering practices KW - Two-component KW - Uncertainty KW - Computer operating systems CY - United States, Colombia ER - TY - JOUR TI - Harnessing AI capabilities and green entrepreneurial orientation for sustainable SME performance using SEM analysis approach AU - Alwakid W.N. AU - Dahri N.A. PY - 2025 JO - Technology in Society VL - 83 SP - 103007 DO - 10.1016/j.techsoc.2025.103007 AB - The growing focus on sustainability and technological innovation has encouraged small and medium-sized enterprises (SMEs) to adopt artificial intelligence (AI) capabilities and a green entrepreneurial orientation as central drivers of sustainable performance. This research investigates the contribution of AI capabilities in promoting green innovations and creativity in SMEs, which collectively lead to long-term sustainability. The study is based on the Resource-Based View (RBV) theory, which offers a theoretical framework for investigating how AI-based competencies and green entrepreneurial strategies can promote SME performance. The research model comprises infrastructure, business integration, and a proactive attitude influencing AI capabilities, affecting SME creativity and green innovations. Moreover, green risk-taking, innovativeness, and proactiveness significantly contribute to green entrepreneurial orientation, affecting SME creativity and green innovations, ultimately resulting in sustainable performance. A quantitative research design was applied, utilizing survey data gathered from SME managers and business owners functioning in the manufacturing and service sectors in Saudi Arabia. 250 SMEs were examined using “Structural Equation Modeling (SEM)” analysis to validate the proposed hypotheses and evaluate these relationships. The results indicate that AI capabilities significantly impact SME creativity and green innovations. Moreover, green entrepreneurial orientation positively influences SME creativity and green innovations, which in turn facilitate sustainable performance. This research identifies the importance of SMEs allocating resources to AI infrastructure, proactive business strategies, and entrepreneurial risk-taking to foster green innovation and sustainability. Moreover, policymakers must facilitate AI-based green initiatives by implementing incentives and regulatory policies to promote sustainable development. By integrating AI capabilities and green entrepreneurship, SMEs can achieve a competitive advantage while achieving world sustainability goals. © 2025 Elsevier Ltd KW - AI capabilities KW - Green entrepreneurial orientation KW - Green innovations KW - SME creativity KW - Sustainable performance KW - Saudi Arabia KW - Green development KW - Green manufacturing KW - Industrial research KW - Sustainable development KW - Sustainable development goals KW - Artificial intelligence capability KW - Enterprise performance KW - Entrepreneurial orientation KW - Green entrepreneurial orientation KW - Green innovations KW - Modeling analyzes KW - Small and medium-sized enterprise KW - Small and medium-sized enterprise creativity KW - Structural equation models KW - Sustainable performance KW - artificial intelligence KW - business KW - entrepreneur KW - green economy KW - industrial performance KW - infrastructure KW - innovation KW - small and medium-sized enterprise KW - sustainability KW - Competition CY - Saudi Arabia, Malaysia, Oman ER - TY - JOUR TI - AIoT-enabled platform urbanism for smart city management: a demonstration of building footprint extraction AU - Hossain S.T. AU - Yigitcanlar T. AU - Ye X. PY - 2026 JO - Computational Urban Science VL - 6 IS - 1 SP - 30 DO - 10.1007/s43762-026-00267-4 AB - Urban environments are increasingly complex, dynamic, and data-intensive, requiring advanced spatial intelligence to support proactive, evidence-based governance. Current smart city and urban informatics platforms are limited by static datasets, siloed architectures, and underutilised AI capabilities. This study proposes and demonstrates a novel AIoT-enabled platform architecture for built environment mapping and spatial decision support. Anchored in platform urbanism, the architecture integrates high-resolution imagery, pretrained deep learning models from the ArcGIS Living Atlas, iterative workflows in ArcGIS Pro, and interactive dissemination via ArcGIS Experience Builder. The platform is demonstrated through building footprint detection in three Brisbane suburbs using the Building Footprint Extraction Australia model. Suburb-level processing enhances computational efficiency, while analytical extensions support footprint change detection, flood exposure assessment, and land-use zoning overlays. Results indicate that the platform transforms manual, fragmented processes into automated, reproducible, and dynamic workflows directly applicable to urban planning. Although demonstrated for building footprints, the architecture is scalable to other urban features, including roads, parcels, and solar panels. Limitations include dependence on high-resolution imagery and pretrained models, highlighting opportunities for future work in multi-model integration, real-time data streams, and developing AI models tailored to diverse urban contexts. By bridging cutting-edge AI innovation with operational governance needs, the proposed platform offers a replicable pathway for embedding AI-enabled spatial intelligence into smart city management. © The Author(s) 2026. KW - Artificial-intelligence-of-things KW - Brisbane KW - Built environment KW - Platform urbanism KW - Smart city KW - Urban analytics KW - Architecture KW - Automation KW - Computational efficiency KW - Computer aided software engineering KW - Deep learning KW - Iterative methods KW - Land use KW - Smart city KW - Urban planning KW - Artificial-intelligence-of-thing KW - Brisbane KW - Building footprint KW - Built environment KW - City management KW - High resolution imagery KW - Platform urbanism KW - Spatial intelligence KW - Urban analytic KW - Urban environments KW - Extraction CY - Australia, South Africa, United States ER - TY - JOUR TI - Research on the Integration and Innovation Path of Artificial Intelligence and the Real Economy AU - Qinghua L.I. AU - Meiyan S.U.I. AU - Aining L.I. AU - Zhang Y. AU - Hongmei X.U. PY - 2026 JO - Tehnicki Vjesnik VL - 33 IS - 2 SP - 719 EP - 731 DO - 10.17559/TV-20250612002741 AB - This study aims to explore the impact of artificial intelligence (AI) integration on economic performance, focusing on the roles of frugal innovation and business model innovation as mediators. Utilizing a quantitative research design, data were collected from secondary sources, including industry reports and databases like the World Bank and OECD. The sample comprised 177 firms across various sectors, with a focus on small and medium-sized enterprises (SMEs). The findings reveal that AI-driven innovation significantly enhances economic performance, both directly and through its positive impact on real economy innovation. AI Integration Readiness (AIR) amplifies the AIDI→REI link yet shows a non-significant total moderation on EP (p = 0.15; BF10 = 1.8, anecdotal evidence for H0). Challenges such as the negative impacts of AI readiness on performance highlight the need for targeted support for SMEs. The study concludes that fostering AI capabilities and readiness is crucial for overcoming bottlenecks and achieving optimal economic outcomes, emphasizing the importance of supportive policies and infrastructure for broad-based AI adoption. These insights provide valuable implications for policymakers and business leaders aiming to leverage AI for sustainable economic growth and innovation. © 2026 Strojarski Facultet. All rights reserved. KW - AI integration readiness KW - artificial intelligence KW - business model innovation KW - economic performance KW - frugal innovation KW - medium-sized enterprises (SMEs) KW - small KW - Economic analysis KW - Industrial economics KW - Integration KW - Artificial intelligence integration readiness KW - Business model innovation KW - Economic performance KW - Frugal innovations KW - Integration readiness KW - Intelligence integration KW - Medium sized enterprise KW - Medium-sized enterprise KW - Medium-sized enterprise (small and medium-sized enterprise) KW - Small KW - Small and medium-sized enterprise KW - Artificial intelligence CY - China ER - TY - JOUR TI - Artificial Intelligence, Lean Startup Method, and Product Innovations AU - Wang X. AU - Wu L. PY - 2025 JO - Management Science VL - 72 IS - 1 SP - 756 EP - 782 DO - 10.1287/mnsc.2022.03905 AB - Although artificial intelligence (AI) has the potential to drive significant business innovation, many firms struggle to realize its benefits. We investigate why some firms succeed in using AI for innovation, whereas others fail, focusing on the organizational support necessary for leveraging AI in both novel and incremental innovation. Specifically, we examine how the lean startup method (LSM) influences the impact of AI on product innovation in startups. Analyzing data from 1,800 Chinese startups between 2011 and 2020, alongside policy shifts by the Chinese government in encouraging AI adoption, we find that companies with strong AI capabilities produce more innovative products. Moreover, our study reveals that AI investments complement LSM in innovation, with effectiveness varying by the type of innovation and AI capability. We differentiate between discovery-oriented AI, which reduces uncertainty in novel areas of innovation, and optimization-oriented AI, which refines and optimizes existing processes. Within the framework of LSM, we further distinguish between prototyping—focused on developing minimum viable products—and controlled experimentation—focused on rigorous testing such as A/B testing. We find that LSM complements discovery-oriented AI by utilizing AI to expand the search for market opportunities and employing prototyping to validate these opportunities, thereby reducing uncertainties and facilitating the development of the first release of products. Conversely, LSM complements optimization-oriented AI by using A/B testing to experiment with the universe of input features and using AI to streamline iterative refinement processes, thereby accelerating the improvement of iterative releases of products. As a result, when firms use AI and LSM for product development, they are able to generate more high-quality products in less time. These findings, applicable to both software and hardware development, underscore the importance of treating AI as a heterogeneous construct because different AI capabilities require distinct organizational processes to achieve optimal outcomes. © 2025 INFORMS KW - artificial intelligence KW - innovation KW - lean startup method KW - product development KW - startup KW - Investments KW - Iterative methods KW - Reactor startup KW - Business innovation KW - Incremental innovation KW - Innovation KW - Lean startup method KW - Optimisations KW - Organizational support KW - Policy shift KW - Product innovation KW - Startup KW - Uncertainty KW - Artificial intelligence KW - Product development CY - United States ER - TY - JOUR TI - Measuring Customer Experience in AI Contexts: A Scale Development AU - Li C. AU - Hao R. AU - Li N. AU - Zhang C. PY - 2025 JO - Journal of Theoretical and Applied Electronic Commerce Research VL - 20 IS - 1 SP - 31 DO - 10.3390/jtaer20010031 AB - With the advent of the digital intelligence era and the rapid evolution of emerging technologies, Artificial Intelligence (AI) is fundamentally transforming the way consumers and businesses interact, gradually becoming one of the primary tools for companies to continuously improve customer experience and maintain competitiveness. However, existing research on customer experience largely overlooked the disruptive changes brought by the widely applied AI technologies. Therefore, this paper focuses on customer AI experience in the new context, using a mixed research method combining qualitative and quantitative approaches to explore the connotation, measurement, formation mechanism, and related action mechanisms of this construct. This study finds the following: (1) the customer AI experience is an intrinsic and subjective response generated by customers after interacting with AI capabilities, mediated by AI. It specifically includes five dimensions: social experience, intellectual experience, classification experience, exploitation experience, and service experience; (2) its formation and development is a cyclical model comprising three stages: expectation, realization, and reflection, corresponding to the mechanisms of contact, interaction, and comparison; (3) the perceived innovative characteristics of AI technology help customers to have a better AI experience, thereby stimulating customer engagement behavior. This provides certain guidance and reference for enterprises to better understand and utilize AI’s innovative characteristics to improve the customer experience, promote customer engagement, seize opportunities in AI technology development, and maintain a competitive advantage. © 2025 by the authors. KW - customer AI experience KW - customer engagement KW - digital interaction platform KW - perceived AI innovative characteristics CY - China ER - TY - JOUR TI - Empirical Validation of Measurement Scales: AI Capabilities, Cybernetic Thinking, Organizational Ambidexterity, and Employee Wellbeing AU - Bibi M. AU - Tan T.G. AU - Yao H. PY - 2026 JO - SAGE Open VL - 16 IS - 1 DO - 10.1177/21582440251382640 AB - In the technological era, changes are happening around the globe at a fast rate. In this regard, healthcare organizations are implementing changes to improve their process. Hence, to manage implemented changes, there is a need to assess AI capabilities, cybernetic thinking (CT), organizational ambidexterity (OA), and employee wellbeing (EWB). However, no validated scale exists specifically to measure the aspects mentioned earlier in the context of healthcare organizations (HCO). Accordingly, our study attempted to validate existing scales of AI capabilities, CT, OA, and EWB in the context of HCO. Besides, to attain this purpose, a pilot study was led on a sample of 150 doctors employed in private sector hospitals in Pakistan, and data were analyzed using CB-SEM. This study confirms the validity and reliability of the refined scale in the context of a Pakistani healthcare setting. From the practical context, healthcare organizations can use the validated scale to assess their capacity towards adopting emerging technologies. These scales can be used to formulate strategies for managing technological change from both organizational and employee perspectives in healthcare settings. In addition, this study offers a multidimensional perspective by integrating diffusion of innovation theory (DOIT) with AI capabilities, EWB, CT, and OA to specify how innovation diffuses across complex systems, such as healthcare settings. © The Author(s) 2026. This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage). KW - AI capabilities KW - cybernetic thinking KW - diffusion of innovation theory KW - employee wellbeing KW - organizational ambidexterity KW - scales validation CY - Malaysia, Pakistan, China ER - TY - JOUR TI - The Implications of Artificial Intelligence for Small and Medium-Sized Enterprises’ Sustainable Development in the Areas of Blockchain Technology, Supply Chain Resilience, and Closed-Loop Supply Chains AU - Khan S.A.R. AU - Sheikh A.A. AU - Shamsi I.R.A. AU - Yu Z. PY - 2025 JO - Sustainability (Switzerland) VL - 17 IS - 1 SP - 334 DO - 10.3390/su17010334 AB - In today’s fast-paced business settings, the metaverse as a shared marketplace has gained popularity and is helping businesses to develop crucial business strategies in their pursuit of sustainable performance. However, a lack of understanding and knowledge about the effectiveness of the metaverse and its related technologies creates a barrier. Therefore, the current study fills this gap and uses organizational information-processing theory to develop the theoretical framework to examine metaverse-related technologies (artificial intelligence and blockchain technology—BCT) and their direct and indirect effects on sustainable business performance, which no other study has examined. Using purposive sampling, the sample data from 326 SMEs were gathered and analyzed using a partial least square structural equation modeling (PLS-SEM). This study’s findings revealed that AI capabilities are vital for information gathering, analyzing, and decision-making in the metaverse context. BCT facilitates ensuring a transparent, visible, traceable, and immutable supply chain, which helps make it more resilient and improves the closed-loop supply chain (CLSC) system with positive technological advancements and significant effects on increasing sustainable business performance (SBP). This study’s findings help organizations understand the potential benefits of AI-enabled SMEs’ presence in the metaverse. The current investigation provides a strategy for managers to gain a competitive advantage, make the supply chain more robust, and enhance overall business performance. © 2025 by the authors. KW - adaptive capabilities KW - artificial intelligence KW - blockchain technology KW - closed-loop supply chain KW - metaverse KW - supply chain resilience KW - sustainable business performance KW - artificial intelligence KW - business KW - decision making KW - numerical model KW - performance assessment KW - small and medium-sized enterprise KW - supply chain management KW - sustainable development CY - China, Oman ER - TY - JOUR TI - Corporate AI Living Labs: A Structured Approach to Accelerating AI Adoption and Transforming Towards AI-Empowered Employees for Operational Excellence AU - Son A. AU - Apachite C. AU - Petcu A. AU - Schuurman D. PY - 2024 JO - Journal of Innovation Management VL - 12 IS - 3 SP - XV EP - XX DO - 10.24840/2183-0606_012.003_L002 AB - The integration of artificial intelligence (AI) into corporate environments becomes essential for enhancing efficiency, decision-making, and fostering innovation. While AI adoption has successfully optimized specific operational processes, the holistic deployment of AI aimed at empowering employees remains underdeveloped. This letter introduces the concept of the “AI Living Lab” within corporate environments, designed to accelerate AI adoption, foster innovation, and enhance employee productivity and satisfaction. The concept appeared as a response in Continental Automotive to the problem of faster adoption time and scaling of AI-Empowered Employee solutions inside the company. Through a current observation on the state of the art, Continental Automotive’s AI Living Lab as a case study, and identification of existing gaps, this letter suggests future research areas for a scalable AI Living Lab framework in corporate settings. © 2024 Universidade do Porto - Faculdade de Engenharia. All rights reserved. KW - academia-industry Collaboration KW - AI adoption KW - AI-empowered employees KW - automotive industry KW - co-creation with employees KW - corporate AI living labs KW - employee empowerment KW - employee-driven AI development KW - ethical AI governance KW - framework development for scalability KW - impact assessment metrics KW - knowledge transfer models KW - operational transformation KW - real-world experimentation KW - scalability of AI solutions CY - Romania, Germany, Belgium ER - TY - JOUR TI - From smart infrastructure to regenerative destinations: a tri-country study of tourism digital capabilities, innovation and ethical AI in Southeast Asia AU - Ali Mari M. AU - Ahmad W. PY - 2026 JO - Tourism Review SP - 1 EP - 22 DO - 10.1108/TR-11-2025-1289 AB - Purpose – Tourism destinations increasingly pursue digital transformation, yet most initiatives remain efficiency-focused. Existing research provides a limited empirical explanation of how digital capability, environmental literacy and ethical artificial intelligence (AI) jointly enable regeneration beyond sustainability. To address this gap, this study aims to develop and test a smart regenerative tourism transformation model explaining how digital readiness, organizational capability and ethical governance support net-positive destination renewal. Design/methodology/approach – Survey data were collected from 543 tourism managers in Malaysia, Singapore and Thailand. Partial least squares structural equation modeling (PLS-SEM) was used to test eight hypothesized relationships linking smart tourism infrastructure, digital accessibility and inclusion, eco-literacy and net-zero commitment and regenerative destination governance to tourism digital transformation capability (TDTC), regenerative tourism innovation and regenerative destination transformation, with AI and data-ethics climate as a moderator. Findings – The results indicate that smart infrastructure, inclusivity and governance strengthen TDTC, which, in turn, supports regenerative tourism innovation and regenerative destination transformation. A strong AI and data-ethics climate amplifies these relationships. These findings are based on managerial perceptions and suggest, rather than confirm, destination-level regenerative progress. Research limitations/implications – This study’s cross-sectional design limits causal inference, as relationships remain correlational despite procedural and statistical checks. Future research should adopt longitudinal, experimental or panel data approaches to track TDTC over time. Additionally, incorporating objective indicators, such as AI ethics audits and digital investment records, can enhance validity. Expanding the sample to include diverse cultural contexts and adopting multi-stakeholder approaches will provide richer insights. Finally, dynamic-system modeling can better capture feedback loops between digitalization, governance and regeneration, advancing the understanding of regenerative tourism in evolving destinations. Practical implications – This study provides evidence for advancing regenerative digital transformation in Southeast Asian tourism. It confirms that smart tourism infrastructure, digital accessibility, eco-literacy and regenerative governance collectively enhance digital transformation outcomes. AI and data ethics climate play a crucial moderating role, ensuring ethical digital progress. Tourism boards must invest in inclusive digital ecosystems, ensuring that technology empowers local communities and businesses. Governments should integrate eco-literacy and sustainability into programs, while transparent AI frameworks should be adopted to ensure fairness. The findings support the creation of an Association of Southeast Asian Nations (ASEAN) regenerative tourism data network to align digital standards and promote sustainable, inclusive tourism. Social implications – This study highlights the role of digital transformation in fostering social inclusion and community well-being. By ensuring that technology enhances accessibility and empowers local communities, destinations can reduce socioeconomic inequalities and promote equitable growth. Regenerative governance frameworks and AI ethics are crucial for building trust and accountability in digital tourism, ensuring that innovations benefit all stakeholders. The integration of eco-literacy and sustainability practices further supports societal regeneration, encouraging active participation in conservation efforts and sustainable tourism practices. The proposed ASEAN regenerative tourism data network offers a model for fostering inclusive, responsible digital engagement across tourism destinations. Originality/value – This study offers a tri-country empirical examination of how ethical AI conditions the transformation of digital capability into regenerative value creation. It advances tourism theory by positioning digital transformation as a morally governed organizational capability that supports socio-ecological renewal rather than efficiency or harm reduction alone. © 2026 Emerald Publishing Limited KW - Digital transformation KW - Ethical artificial intelligence KW - Gobernanza inteligente KW - Inteligencia artificial ética KW - Regenerative tourism KW - Smart governance KW - Southeast Asia KW - Sudeste Asiático KW - Transformación digital KW - Turismo regenerativo KW - 东南亚 KW - 伦理人工智能 KW - 再生型旅游 KW - 数字化转型 KW - 智慧治理 CY - Malaysia ER - TY - JOUR TI - Are Universities Becoming Obsolete in the Age of Artificial Intelligence? AU - Mili K. AU - Abdelaziz K. PY - 2026 JO - TEM Journal VL - 15 IS - 1 SP - 828 EP - 841 DO - 10.18421/TEM151-76 AB - This paper examines whether traditional university education faces displacement by artificial intelligence technologies. As AI systems democratize knowledge access, personalize learning, and offer scalable skills training, universities' core value proposition is challenged. The analysis explores technological, economic, and social drivers behind this potential shift while acknowledging aspects resistant to replication. While universities historically dominated higher learning, research, and credentialing, AI technologies fundamentally alter how knowledge is accessed, created, and validated. Many core functions can now be achieved through AI-powered alternatives, potentially rendering conventional models obsolete for many learners. However, this analysis examines which functions might be better served by emerging technologies versus which unique values universities continue to provide. Findings reveal AI displacement potential varies substantially across university functions: administrative tasks face 75-80% disruption risk while mentorship and social development remain largely human-dependent at 25-30% substitutability. Knowledge transmission shows 75-80% AI substitutability, while research literature synthesis demonstrates 70-75% automation potential. Conversely, critical thinking development and ethical reasoning cultivation retain 70-75% human centrality. The transformation requires governments to redesign accreditation frameworks and quality assurance mechanisms. Workforce development systems need lifelong learning infrastructure and dynamic credentialing for continuous reskilling. Societally, knowledge democratization through AI may reduce educational inequality yet risk exacerbating digital divides and eroding universities' social mobility function. The analysis provides strategic recommendations emphasizing hybrid models integrating AI capabilities while preserving irreplaceable human elements. Successful adaptation requires neither wholesale abandonment of traditional models nor uncritical technological embrace, but deliberate institutional redesign balancing technological innovation with preservation of core academic values. © 2026 Khaled Mili & Khaled Abdelaziz; published by UIKTEN. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 License. KW - Artificial Intelligence KW - education KW - higher education KW - obsolescence KW - universities CY - Saudi Arabia, Tunisia ER - TY - JOUR TI - A.I. INTO FASHION PROCESSES LAYING THE GROUNDWORK AU - Rizzi G. AU - Casciani D. PY - 2023 JO - Fashion Highlight VL - 2023 IS - 2 SP - 12 EP - 20 DO - 10.36253/fh-2490 AB - The article aims to provide a comprehensive understanding of Artificial Intelligence (AI) and its integration into fashion processes, focusing on the research, design, development, and manufacturing stages. First, it offers an overview of AI evolution, from its early developments to the contemporary advanced Machine and Deep learning models, attempting to tackle the challenge of ambiguous terminology and aiming to deal with the different interpretations of AI capabilities. Subsequently, a review of the perspectives on the integration of AI tools within fashion processes will be presented. This overview will underscore the growing need for industries to undergo a conscious technological transformation, adopting AI toward a more sustainable and responsible fashion evolution. © 2023, Firenze University Press. All rights reserved. KW - Artificial Intelligence KW - Fashion Processes Transformation KW - Sustainable Fashion KW - Technological Innovation CY - Italy ER - TY - JOUR TI - Digitalisation and AI adoption as drivers of market share in GCC banking AU - Albaker Y. AU - Khalaf B.A. PY - 2025 JO - Journal of Asian Scientific Research VL - 16 IS - 1 SP - 146 EP - 160 DO - 10.55493/5003.v16i1.5869 AB - This study investigates the impact of digitalization and AI adoption on the market share of banks operating in the Gulf Cooperation Council's (GCC) region, drawing upon the resource-based view (RBV) and dynamic capabilities theory (DCT). In the current context of digital transformation and AI-driven innovation reshaping the banking sector, it is crucial to understand the role of these technologies in driving competitive advantage. The study constructs novel composite indices for digitalization and AI adoption using secondary data from 400 bank-year observations across five GCC countries between 2015-2024. Employing a dynamic panel estimation technique, the analysis reveals that both digitalization and AI adoption significantly and positively influence bank market share, even after controlling for profitability, bank size, and macroeconomic conditions. These results hold strong across different models, supporting the idea that improving and adapting technological skills is key to enhancing the market share of banks. The study offers theoretical contributions by operationalizing digital and AI capabilities as strategic resources and practical implications for bank executives and policymakers aiming to strengthen digitalization in the financial sector. It also provides one of the first empirical validations of the digitalization–market share nexus in the GCC context, thereby filling an important gap in the literature on technology-enabled market performance. © 2026 AESS Publications. All Rights Reserved. KW - Artificial intelligence KW - Banking sector KW - Digital transformation KW - Digitalisation KW - Dynamic capabilities KW - GCC KW - Market share KW - Resource-based view KW - System GMM CY - Qatar ER - TY - JOUR TI - Artificial intelligence capabilities for circular business models: Research synthesis and future agenda AU - Madanaguli A. AU - Sjödin D. AU - Parida V. AU - Mikalef P. PY - 2024 JO - Technological Forecasting and Social Change VL - 200 SP - 123189 DO - 10.1016/j.techfore.2023.123189 AB - This study explores the interlink between AI capabilities and circular business models (CBMs) through a literature review. Extant literature reveals that AI can act as efficiency catalyst, empowering firms to implement CBM. However, the journey to harness AI for CBM is fraught with challenges as firms grapple with the lack of sophisticated processes and routines to tap into AI's potential. The fragmented literature leaves a void in understanding the barriers and development pathways for AI capabilities in CBM contexts. Bridging this gap, adopting a capabilities perspective, this review intricately brings together four pivotal capabilities: integrated intelligence capability, process automation and augmentation capability, AI infrastructure and platform capability, and ecosystem orchestration capability as drivers of AI-enabled CBM. These capabilities are vital to navigating the multi-level barriers to utilizing AI for CBM. The key contribution of the study is the synthesis of an AI-enabled CBM framework, which not only summarizes the results but also sets the stage for future explorations in this dynamic field. © 2024 The Authors KW - AI future research agenda KW - Artificial intelligence KW - Business model innovation KW - Circular business models KW - AI future research agenda KW - Business model innovation KW - Business models KW - Circular business model KW - Literature reviews KW - Model contexts KW - Process automation KW - Research agenda KW - Research synthesis KW - ]+ catalyst KW - artificial intelligence KW - business KW - innovation KW - literature review KW - modeling KW - research work KW - Artificial intelligence CY - Sweden, Norway, Finland ER - TY - JOUR TI - Government-guided funds and the rise of corporate AI: Evidence from China AU - Lu J. AU - Gao H. AU - Yang L. AU - Liu Z. PY - 2026 JO - Pacific Basin Finance Journal VL - 95 SP - 103004 DO - 10.1016/j.pacfin.2025.103004 AB - We investigate how Government-Guided Funds (GGFs) influence corporate artificial intelligence (AI) development in China. Using panel data for Chinese A-share listed firms from 2012 to 2023, we find that GGFs significantly promote AI advancement through three complementary mechanisms: resource empowerment, signalling certification, and facilitation of firms' integration into platform ecosystems. This effect remains robust across multiple empirical tests. The positive impact of GGFs is stronger among firms facing more intense market competition, in regions with higher levels of intellectual property protection, and in areas where governments place greater policy emphasis on AI. Further analyses show that GGFs do not crowd out non-AI innovation; instead, they generate an innovation diffusion effect. By advancing firms' AI capabilities, GGFs contribute to employment growth, workforce upgrading, and higher labour income shares, highlighting their dual role in promoting technological innovation and improving social welfare. © 2025 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license. http://creativecommons.org/licenses/by/4.0/ KW - Artificial intelligence KW - G24 KW - G28 KW - Government-guided funds KW - Innovation diffusion KW - O32 KW - O38 KW - Platform integration KW - Resource empowerment KW - Signalling certification KW - Workforce upgrading CY - China, Australia ER - TY - JOUR TI - Direct and indirect effects of supply chain plasticity and AI capability on business performance: the moderating role of network embeddedness AU - Aboelmaged M. AU - Hashem G. AU - Mady K. PY - 2025 JO - Business Process Management Journal SP - 1 EP - 29 DO - 10.1108/BPMJ-08-2025-1392 AB - Purpose – This study examines how artificial intelligence (AI) capability and supply chain (SC) plasticity jointly influence business performance, while considering the moderating role of network embeddedness. Design/methodology/approach – Drawing on network theory and the dynamic capability (DC) view, the study develops and tests a conceptual model using survey data collected from 453 managers in manufacturing and service firms, analyzed with the PLS-SEM approach. Findings – The findings show that SC plasticity is the strongest driver of business performance, enabling rapid reconfiguration of processes and networks while mediating the impact of AI capability. Relational embeddedness positively moderates the effect of AI capability on SC plasticity but weakens the link between SC plasticity and performance, reflecting the paradox of embeddedness. Structural embeddedness weakens the effect of AI capability on SC plasticity but shows no significant effect on the SC plasticity-performance relationship. Practical implications – Managers should view SC plasticity as a strategic capability that converts digital resources into performance. While network embeddedness can strengthen adaptability, over-embeddedness may limit its benefits. Managers must also balance the benefits of embedded networks with the risks of over-embeddedness, ensuring flexibility in partnerships while leveraging trust-based ties for adaptability. Originality/value – This study is among the first to link AI capability with SC plasticity and provides empirical evidence on the boundary conditions of digital transformation and adaptability in emerging economies. © 2026 Emerald Publishing Limited KW - Artificial intelligence KW - Business performance KW - Dynamic capability KW - Network embeddedness KW - Relational embeddedness KW - Structural embeddedness KW - Supply chain plasticity CY - United Arab Emirates, Egypt, Oman ER - TY - JOUR TI - AI in strategic management and organizational agility of SMEs: leadership, policy environment, and adaptive capability AU - Zhong L. AU - Ady S.U. AU - Indrasari M. PY - 2026 JO - Future Technology VL - 5 IS - 2 SP - 1 EP - 12 DO - 10.55670/fpll.futech.5.2.1 AB - This study investigates how artificial intelligence strategic capabilities, transformational leadership, and policy environments collectively influence organizational agility in small and medium-sized enterprises through dynamic capability mechanisms. Employing a mixed-methods design, the research analyzes survey data from 300 SMEs across manufacturing, service, and technology sectors, complemented by qualitative case studies. Structural equation modeling reveals that AI strategic capabilities constitute the strongest predictor of organizational agility (β=0.42, p<0.001), with digital dynamic capabilities mediating 67% of this total effect. Technology-management fit emerges as a critical boundary condition, amplifying AI effectiveness by 123% under high alignment scenarios (β=0.58 versus β=0.26 in low alignment contexts). Transformational leadership exhibits dual mechanisms through direct positive effects on agility (β=0.28, p<0.001) and moderating influences on AI-agility relationships (β=0.21, p<0.01). Notably, AI capabilities demonstrate buffering properties against policy environment uncertainty (β=0.12, p<0.05), transforming institutional constraints into manageable strategic variables. Machine learning analyses reveal nonlinear effects with diminishing returns beyond the 75th percentile of AI adoption. The structural model explains substantial variance in organizational agility (R²=0.64) and firm performance (R²=0.52). These findings extend dynamic capability theory to digital contexts, reconceptualize AI as a strategic capability rather than an operational tool, and illuminate digital leadership dimensions, offering evidence-based guidance for SME managers, technology vendors, and policymakers navigating digital transformation challenges. © 2026, Future Publishing LLC. All rights reserved. KW - Artificial intelligence capability KW - Digital transformation KW - Dynamic capabilities KW - Organizational agility KW - Small and medium-sized enterprises CY - Indonesia ER - TY - JOUR TI - Intrinsic Motivation and the Use of Artificial Intelligence (AI) in the Public Sector: Evidence from Indonesia; [Motivação Intrínseca e o Uso da Inteligência Artificial (IA) no Setor Público: Evidências da Indonésia] AU - Chaniago H. AU - Hidayat H. AU - Efawati Y. PY - 2025 JO - Revista Brasileira de Politicas Publicas VL - 15 IS - 2 SP - 412 EP - 427 DO - 10.5102/rbpp.v15i2.10066 AB - This study is motivated by the importance of integrating humans and Artificial Intelligence (AI) within the public sector, particularly in promoting the efficiency and innovation of public services. The adoption of AI not only depends on technological readiness but also on the intrinsic motivation of employees. This study aims to understand how intrinsic motivation influences the utilization of AI in government work environments. The research was conducted between February to April 2024 in West Java Province, Indonesia, using an explanatory survey method. Data were collected through questionnaires administered to 150 randomly selected respondents from various local government agencies. The study identified two main dimensions of AI utilization: AI Capabilities & Users (AICU) and Benefits of AI (BAI). The findings reveal that Intrinsic Motivation of Employees (IME) has a partial effect on both AICU and BAI. Moreover, IME and AICU simultaneously have a positive impact on BAI. These results suggest that enhancing the benefits of AI in government is highly influenced by psychological factors and individual readiness. Therefore, governments should develop strategies that focus not only on technological aspects but also on strengthening users’ motivation and competencies. The study recommends replication in other developing country contexts to test and further develop the AICU model as a framework for AI adoption in the public sector. © 2025, Centro Universitario de Brasilia. All rights reserved. KW - AI capabilities & users KW - artificial intelligence KW - benefits of AI KW - government KW - motivation of employee CY - Indonesia ER - TY - JOUR TI - The Human-GenAI Value Loop in Human-Centered Innovation: Beyond the Magical Narrative AU - Grange C. AU - Demazure T. AU - Ringeval M. AU - Bourdeau S. AU - Martineau C. PY - 2026 JO - Information Systems Journal VL - 36 IS - 1 SP - 29 EP - 51 DO - 10.1111/isj.12602 AB - Organisations across various industries are still exploring the potential of Generative Artificial Intelligence (GenAI) to automate a variety of knowledge work processes, including managing innovation. While innovation is often viewed as a product of individual creativity, it more commonly unfolds through a collaborative process where creativity intertwines with knowledge. However, the extent and effectiveness of GenAI in supporting this process remain open questions. Our study investigates this issue using a collaborative practice research approach focused on three GenAI-enabled innovation projects conducted within different organisations. We explored how, why, and when GenAI could effectively be integrated into design sprints—a highly structured, collaborative process enabling human-centred innovation. Our research identified challenges and opportunities in synchronising AI capabilities with human intelligence and creativity. To translate these insights into practical strategies, we propose four recommendations for organisations eager to leverage GenAI to both streamline and bring more value to their innovation processes: (1) establish a collaborative intelligence value loop with GenAI; (2) build trust in GenAI; (3) develop robust data collection and curation workflows; and (4) embrace a craftsman's discipline. © 2025 The Author(s). Information Systems Journal published by John Wiley & Sons Ltd. KW - design thinking KW - generative AI KW - human-centered innovation KW - Collaborative practices KW - Collaborative process KW - Design thinking KW - Generative AI KW - Human-centered innovation KW - Individual creativity KW - Knowledge work process KW - Managing innovation KW - Practice researches KW - Research approach KW - Information use CY - Canada ER - TY - JOUR TI - Artificial Intelligence and Its Influence on Dental Hygiene AU - Hurlbutt M. PY - 2025 JO - Journal of Dental Hygiene VL - 99 IS - 5 SP - 49 EP - 58 AB - Artificial intelligence (AI) including generative AI, analytical AI, predictive AI, prescriptive AI, and hybrid AI, is rapidly evolving and continues to expand its influence across dental hygiene, transforming clinical care, education, research, public health, corporate operations, administration, and entrepreneurship. In clinical practice, AI is advancing diagnostic accuracy for radiographic interpretation, periodontal assessment, and early detection of oral pathology, while enhancing decision-making and personalized care planning. In education, AI enables adaptive learning, intelligent tutoring, predictive analytics, and generative content creation, enriching both didactic and clinical training. In research and public health, Artificial intelligence supports large-scale data analysis, disease surveillance, teledentistry, and targeted prevention strategies, with a growing emphasis on equity and inclusivity. Corporate and administrative applications include AI-driven product development, market analysis, workflow optimization, and performance management. Entrepreneurial uses span idea generation, content creation, branding, and market engagement. As AI capabilities advance, dental hygienists must balance innovation with ethical oversight, digital literacy, and equitable access. Ensuring AI is integrated through evidence-based practices, transparent decision-making, and patient-centered values will be essential to realizing its benefits while preserving the integrity of the profession. © 2025, American Dental Hygienists' Association. All rights reserved. KW - AI KW - artificial intelligence KW - dental hygienists KW - digital literacy KW - evidence-based practice KW - Artificial Intelligence KW - Dental Hygienists KW - Humans KW - Oral Hygiene KW - artificial intelligence KW - dental hygienist KW - education KW - human KW - mouth hygiene CY - United States ER - TY - JOUR TI - Transforming learning experiences and assessments through AI-empowered cocreation of quality feedback AU - Coenen C. AU - Pfenninger M. PY - 2025 JO - New Directions for Teaching and Learning VL - 2025 IS - 182 SP - 59 EP - 65 DO - 10.1002/tl.20628 AB - This article examines the transformative impact of generative artificial intelligence (GenAI) in enhancing feedback quality in a Bachelor of Science course. It the challenges of providing personalized, timely feedback to students in larger educational settings, focusing on the use of GenAI to analyze and respond to student logbooks. These logbooks are key to reflective learning and capture students’ insights and progress. The study explores the integration of GenAI in feedback provision, which moves beyond traditional automated processes to tailor feedback to individual student journeys, thus fostering a growth mindset. GenAI shows potential for revolutionizing learning feedback, with positive effects on student engagement and learning outcomes. The article details the innovation, implementation, and context of this approach, reflecting on the learning coach's experience and successes. It links these findings to educational theories, discussing the broader implications for educators, future AI integration in education, and policy considerations. The conclusion outlines the benefits of GenAI in feedback processes and future directions for exploration and ethical guidelines. © 2024 Wiley Periodicals LLC. CY - Switzerland ER - TY - JOUR TI - The Role of Artificial Intelligence in Drug Discovery and Development AU - Ozaybi M.Q.B. AU - Madkhali A.N.M. AU - Alhazmi M.A.M. AU - Faqihi H.M.A. AU - Alanazi M.M. AU - Siraj W.H.Y. AU - Zalah A.H.A. AU - Abdu Khormi M.M. AU - Al Salem A.M.A. AU - Mashragi T.Q.M. AU - Alotaibi A.N. AU - Naji A.A.M. AU - Abdo Bagal R.M. AU - Maswdi A.M.M. AU - Marwee H.A.A. PY - 2024 JO - Egyptian Journal of Chemistry VL - 67 IS - 13 SP - 1541 EP - 1547 DO - 10.21608/ejchem.2024.337877.10835 AB - Background: The drug discovery and development process has traditionally been one of the most challenging and resource-intensive endeavours in the pharmaceutical industry. On average, bringing a single drug from concept to market takes over a decade and costs approximately $2.6 billion. These processes are further hindered by high attrition rates, particularly in clinical trials, which contribute to the escalating cost and time. This inefficiency is largely attributed to the complexity of biological systems and the limitations of existing empirical methodologies. Over recent years, Artificial Intelligence (AI) has emerged as a powerful tool capable of transforming the drug development landscape. AI leverages computational algorithms, machine learning models, and data-driven approaches to overcome traditional bottlenecks in drug discovery. With capabilities spanning target identification, lead optimization, drug repurposing, and clinical trial design, AI is reshaping the future of pharmaceutical innovation. Aim: This paper provides a comprehensive examination of the role of AI in drug discovery and development. It explores the methodologies and tools employed by AI, evaluates key successes achieved in real-world applications, and examines challenges associated with its adoption. By synthesizing advancements and analyzing their impact, this paper aims to illuminate the transformative potential of AI in revolutionizing the pharmaceutical industry. Methods: The study adopts a robust methodological approach, relying on a critical review of recent literature published between 2015 and 2024. It integrates findings from academic research, industrial case studies, and regulatory perspectives to provide a holistic understanding of AI's impact across the drug development pipeline. Comparative analysis highlights the efficiencies of AI-driven approaches relative to traditional methods, with an emphasis on specific applications such as deep learning, reinforcement learning, and natural language processing (NLP). Results: AI applications have demonstrated measurable success across multiple domains of drug development. Machine learning models have expedited the identification of novel drug targets by analyzing high-dimensional omics data. Deep learning algorithms have revolutionized lead optimization by accurately predicting molecular properties and their pharmacological profiles. AI-driven platforms have also advanced drug repurposing, as evidenced by rapid therapeutic identification during the COVID-19 pandemic. Furthermore, in the realm of clinical trials, AI has significantly improved patient stratification, optimized trial protocols, and enhanced predictive analytics for outcomes. These breakthroughs have collectively reduced both the time and cost of drug development while increasing the likelihood of successful outcomes. Conclusion: AI is transforming the pharmaceutical industry, offering unparalleled solutions to challenges that have long plagued drug discovery and development. By integrating large-scale datasets, enhancing chemical design, and optimizing trial processes, AI has established itself as a cornerstone of future innovation. Nevertheless, the successful integration of AI into drug development requires overcoming challenges such as data quality, regulatory compliance, ethical concerns, and the interpretability of AI algorithms. Addressing these barriers is essential to fully realize AI's potential in meeting global healthcare needs. Moving forward, the development of standardized frameworks, interdisciplinary collaborations, and ethical guidelines will be critical in fostering equitable and effective AI-driven drug discovery. ©2024 National Information and Documentation Center (NIDOC) KW - Artificial Intelligence KW - Clinical Trials KW - Computational Biology KW - Drug Development KW - Drug Discovery KW - Drug Repurposing KW - Lead Optimization KW - Machine Learning CY - Saudi Arabia ER - TY - JOUR TI - Artificial intelligence and organisational transformation: technical skills, job insecurity and adoption AU - Arranz Lahuerta L. AU - López Ramajo M.R. AU - Gandía A. PY - 2025 JO - Management Decision SP - 1 EP - 16 DO - 10.1108/MD-05-2025-1462 AB - Purpose – This study explores the impact of Artificial Intelligence (AI) on technical skills development, job insecurity, and system adoption within organisations. It examines how businesses can navigate AI-driven workplace transformations while mitigating workforce challenges and fostering a culture of trust and innovation. Design/methodology/approach – The research adopts a mixed-method approach, combining theoretical analysis with empirical insights. Data were gathered from the AI-driven transformation Scopus database, analysing the relationship between AI implementation, employee perceptions, and organisational strategies for skill development and job security. Findings – (1) AI has a dual impact: it increases demand for advanced technical skills while also heightening job insecurity, particularly in organisations lacking structured reskilling programs. (2) Organisations that integrate transparent governance and employee participation into AI adoption strategies experience lower resistance and higher acceptance. (3) A balance between technological advancement and human capital investment is critical for minimising disruptions and ensuring a smooth transition to AI-driven operations. Research limitations/implications – The study is limited by the scope of available industry data and the generalisability of case study findings. Future research should explore sector-specific AI adoption challenges and long-term workforce adaptation strategies. Practical implications – The findings offer actionable insights for organisational leaders and policymakers, emphasising the need for structured skill enhancement programs, transparent communication, and ethical AI governance frameworks. These measures reduce workforce resistance, enhance innovation, and facilitate equitable AI-driven transformation. Social implications – By addressing concerns about job security and skill obsolescence, the study contributes to a more sustainable AI integration approach that promotes workforce adaptability, inclusion, and ethical decision-making in the digital era. Originality/value – This research provides a novel perspective by integrating AI adoption, skill development, and job insecurity within the broader framework of organisational transformation. It offers a socio-technical view of AI-driven change, highlighting the importance of ethical considerations and participatory decision-making. © 2025 Emerald Publishing Limited KW - AI adoption KW - Artificial intelligence (AI) KW - Ethical AI integration KW - Job insecurity KW - Organisational learning KW - Organisational transformation KW - Socio-technical systems KW - Technical skills development KW - Technology acceptance KW - Workforce adaptability CY - Spain ER - TY - JOUR TI - Empirical analysis of the roles of dynamic sustainable capabilities and artificial intelligence in accelerating circular business model innovation: Insights from Chinese manufacturing firms AU - Renfei C. AU - Zhongwen L. PY - 2026 JO - Technology in Society VL - 85 SP - 103176 DO - 10.1016/j.techsoc.2025.103176 AB - Despite increasing managerial focus on Artificial Intelligence (AI) -enhanced environmental strategies, few studies have examined how dynamic sustainable capabilities (DSC) and AI-enabled circular business model innovation (CBMI) affect sustainability-oriented corporate performance (SOCP). Grounded in dynamic capabilities theory and dynamic managerial capabilities theory, this study develops a unified framework to investigate the mechanisms through which DSC fosters AI capabilities and CBMI, ultimately enhancing SOCP, while examining managerial cognition as a key moderator. This study empirically analyzes data from 289 questionnaires from 120 manufacturing companies through the PLS-SEM method. The findings reveal that DSC has a positive effect on AI capabilities, CBMI, and SOCP. AI capabilities and CBMI partially mediate the DSC-SOCP relationship. Managerial cognition positively contributes to the role of CBMI on SOCP, and AI capabilities have a positive effect on CBMI. This study advances the literature by elucidating the sequential pathways from DSC to AI-driven CBMI, highlighting micro-foundations for circular transitions. Moreover, this study extends managerial cognition to CBMI practices, revealing the synergies between managerial cognition and CBMI and its role in accelerating SOCP, contributing to clarifying the sources of performance differences in circular economy practices. This lays the foundation for future research agendas on AI integration in the circular economy. © 2025 Elsevier Ltd. KW - Artificial intelligence capabilities KW - Circular business model innovation KW - Digital transformation KW - Dynamic sustainable capabilities KW - Managerial cognition KW - Artificial intelligence KW - Environmental management KW - Sustainable development KW - Artificial intelligence capability KW - Business model innovation KW - Circular business model innovation KW - Circular economy KW - Corporate performance KW - Digital transformation KW - Dynamic sustainable capability KW - Empirical analysis KW - Managerial cognitions KW - Manufacturing firms KW - artificial intelligence KW - business KW - empirical analysis KW - industrial performance KW - innovation KW - manufacturing KW - sustainability KW - Circular economy CY - China ER - TY - JOUR TI - Design and Psychometric Evaluation of the Artificial Intelligence Acceptance and Usage in Research Creativity Scale Among Faculty Members: Insights From the Network Analysis Perspective AU - Al-Rousan A.H. AU - Ayasrah M.N. AU - Salih Yahya S.M. AU - Khasawneh M.A.S. PY - 2025 JO - European Journal of Education VL - 60 IS - 1 SP - e12927 DO - 10.1111/ejed.12927 AB - The acceptance of artificial intelligence (AI) in academic settings, particularly in the context of research creativity, is a growing area of interest. This study aimed to design and validate the AI Acceptance and Research Creativity Scale (AIA&RCS) among faculty members. This exploratory mixed-method was conducted among 720 faculty members. A literature review and participant interviews were conducted in the qualitative phase to generate and develop items. In the quantitative phase, face validity, content validity, construct validity, convergent validity and reliability (internal consistency and stability) were used. Exploratory factor analysis (EFA) indicated a 4-factor model of the scale with ‘perceived usefulness and effectiveness of AI in research creativity’, ‘ethical issues in research’, ‘trusted in AI capabilities’ and ‘willingness to use AI’ accounting for 51.6% of the variance. This arrangement was verified by confirmatory factor analysis (CFA), with fit indices that were at suitable levels. Then, the network analysis took into account the four-factor structure of AIA&RCS further. Similarly, the exploratory graph analysis (EGA) indicated the four-factor configuration of the AIA&RCS. The 25-item scale is well-suited for measuring AI acceptance and research innovation among faculty because of its psychometrics. © 2025 John Wiley & Sons Ltd. KW - AI adoption in academia KW - artificial intelligence KW - faculty members KW - psychometrics KW - research creativity KW - technology acceptance CY - Jordan, Saudi Arabia ER - TY - JOUR TI - Artificial Intelligence, ESG Governance, and Green Innovation Efficiency in Emerging Economies AU - Mansour M. AU - Zobi M.A. AU - Alomair M. PY - 2026 JO - Economies VL - 14 IS - 1 SP - 11 DO - 10.3390/economies14010011 AB - Emerging economies confront the dual challenge of accelerating digital transformation while simultaneously mitigating environmental degradation under conditions of institutional and governance heterogeneity. In this context, this study examines how artificial intelligence (AI) capability influences green innovation efficiency (GIE) in emerging Asian economies and investigates whether environmental, social, and governance (ESG) performance conditions this relationship. Using an unbalanced panel of 59,112 firm-year observations from 4926 publicly listed firms across 15 emerging Asian economies over the period 2011–2022, we employ a comprehensive panel-data econometric framework that accounts for unobserved heterogeneity, dynamic effects, endogeneity, and potential self-selection bias. The empirical results indicate that AI capability is positively and significantly associated with higher green innovation efficiency. More importantly, ESG performance strengthens this relationship, suggesting that robust governance frameworks enhance firms’ ability to translate digital intelligence into environmentally efficient innovation outcomes. These findings underscore that AI adoption alone is insufficient to generate sustainable value; rather, its environmental effectiveness depends critically on complementary governance structures that promote transparency, accountability, and responsible risk management. The results remain robust after correcting for endogeneity concerns, alternative model specifications, and extensive sensitivity and heterogeneity analyses. Overall, this study contributes to the literature on digital transformation and sustainability by providing large-scale, multi-country evidence that highlights the pivotal role of ESG in shaping the sustainability returns to AI adoption in emerging economies. © 2025 by the authors. KW - artificial intelligence KW - digital transformation KW - emerging economies KW - ESG governance KW - green innovation efficiency KW - SDGs KW - sustainable development CY - Jordan, Saudi Arabia ER - TY - JOUR TI - Data stewardship and curation practices in AI-based genomics and automated microscopy image analysis for high-throughput screening studies: promoting robust and ethical AI applications AU - Taddese A.A. AU - Addis A.C. AU - Tam B.T. PY - 2025 JO - Human Genomics VL - 19 IS - 1 SP - 16 DO - 10.1186/s40246-025-00716-x AB - Background: Researchers have increasingly adopted AI and next-generation sequencing (NGS), revolutionizing genomics and high-throughput screening (HTS), and transforming our understanding of cellular processes and disease mechanisms. However, these advancements generate vast datasets requiring effective data stewardship and curation practices to maintain data integrity, privacy, and accessibility. This review consolidates existing knowledge on key aspects, including data governance, quality management, privacy measures, ownership, access control, accountability, traceability, curation frameworks, and storage systems. Methods: We conducted a systematic literature search up to January 10, 2024, across PubMed, MEDLINE, EMBASE, Scopus, and additional scholarly platforms to examine recent advances and challenges in managing the vast and complex datasets generated by these technologies. Our search strategy employed structured keyword queries focused on four key thematic areas: data governance and management, curation frameworks, algorithmic bias and fairness, and data storage, all within the context of AI applications in genomics and microscopy. Using a realist synthesis methodology, we integrated insights from diverse frameworks to explore the multifaceted challenges associated with data stewardship in these domains. Three independent reviewers, who systematically categorized the information across critical themes, including data governance, quality management, security, privacy, ownership, and access control conducted data extraction and analysis. The study also examined specific AI considerations, such as algorithmic bias, model explainability, and the application of advanced cryptographic techniques. The review process included six stages, starting with an extensive search across multiple research databases, resulting in 273 documents. Screening based on broad criteria, titles, abstracts, and full texts followed this, narrowing the pool to 38 highly relevant citations. Results: Our findings indicated that significant research was conducted in 2023 by highlighting the increasing recognition of robust data governance frameworks in AI-driven genomics and microscopy. While 36 articles extensively discussed data interoperability and sharing, AI-model explain ability and data augmentation remained underexplored, indicating significant gaps. The integration of diverse data types—ranging from sequencing and clinical data to proteomic and imaging data—highlighted the complexity and expansive scope of AI applications in these fields. The current challenges identified in AI-based data stewardship and curation practices are lack of infrastructure and cost optimization, ethical and privacy considerations, access control and sharing mechanisms, large scale data handling and analysis and transparent data-sharing policies and practice. Proposed solutions to address issues related to data quality, privacy, and bias management include advanced cryptographic techniques, federated learning, and blockchain technology. Robust data governance measures, such as GA4GH standards, DUO versioning, and attribute-based access control, are essential for ensuring data integrity, security, and ethical use. The study also emphasized the critical role of Data Management Plans (DMPs), meticulous metadata curation, and advanced cryptographic techniques in mitigating risks related to data security and identifiability. Despite advancements, significant challenges persisted in balancing data ownership with research accessibility, integrating heterogeneous data sources, ensuring platform interoperability, and maintaining data quality. Ongoing risks of unauthorized access and data breaches underscored the need for continuous innovation in data management practices and stricter adherence to legal and ethical standards. Conclusions: These findings explored the current practices and challenges in data stewardship, offering a roadmap for strengthening the governance, security, and ethical use of AI in genomics and microscopy. While robust governance frameworks and ethical practices have established a foundation for data integrity and transparency, there remains an urgent need for collaborative efforts to develop interoperable platforms and transparent data-sharing policies. Additionally, evolving legal and ethical frameworks will be crucial to addressing emerging challenges posed by AI technologies. Fostering transparency, accountability, and ethical responsibility within the research community will be key to ensuring trust and driving ethically sound scientific advancements. © The Author(s) 2025. KW - Artificial intelligence KW - Data curation KW - Data stewardship KW - Genomics KW - Microscopy image analysis KW - Scoping review KW - Artificial Intelligence KW - Data Curation KW - Genomics KW - High-Throughput Nucleotide Sequencing KW - High-Throughput Screening Assays KW - Humans KW - Image Processing, Computer-Assisted KW - Microscopy KW - algorithm bias KW - Article KW - artificial intelligence KW - automation KW - blockchain KW - cryptography KW - data analysis KW - data extraction KW - data integration KW - data integrity KW - data interoperability KW - data privacy KW - data protection KW - data quality KW - data stewardship KW - federated learning KW - genomics KW - high throughput screening KW - human KW - image analysis KW - information processing KW - information security KW - microscopy KW - proteomics KW - publication KW - systematic review KW - total quality management KW - artificial intelligence KW - ethics KW - high throughput screening KW - high throughput sequencing KW - image processing KW - information processing KW - microscopy KW - procedures CY - Ethiopia ER - TY - JOUR TI - Venus: A RISC-V Domain Specific Architecture Towards Integrated AI and Wireless Baseband Processing for 6G Edge Intelligence AU - Jiang Z. AU - Shi Y. AU - Jiang L. AU - Hu H. AU - Deng Q. AU - Xu S. AU - Liu Y. AU - Yuan F. AU - Cao S. AU - Zhou S. PY - 2025 JO - IEEE Wireless Communications VL - 32 IS - 6 SP - 18 EP - 26 DO - 10.1109/MWC.2025.3600950 AB - Future 6G local area networks (LANs) are expected to inherently feature edge artificial intelligence (AI) capabilities, despite constraints on power consumption and device dimensions. Additionally, the 6G architecture has integrated various AI-based algorithms into wireless baseband signal processing. These developments suggest a move towards a unified AI and wireless baseband architecture in 6G LANs. This article presents a framework from a computing architecture viewpoint, dubbed Venus, which is an integrated AI and wireless baseband domain-specific architecture based on RISC-V instruction extensions. Venus is conceived using a multi-level dataflow-driven approach and executed on a manycore architecture that features non-uniform memory access (NUMA). When compared to prevailing architectures, such as general-purpose processors (GPPs) with specialized accelerators, digital signal processors (DSPs), graphic processing units (GPUs), and field-programmable gate arrays (FPGAs), Venus strikes the optimal balance between programmability and efficiency. This is achieved through a tailored instruction extension for both AI and wireless signal processing, alongside an advanced dataflow-driven, highly parallel manycore architecture. Moreover, it benefits from the open RISC-V ecosystem, enabling scalability for future AI and wireless innovations. © 2002-2012 IEEE. KW - Data flow analysis KW - Digital signal processing KW - Local area networks KW - Memory architecture KW - Network architecture KW - Parallel architectures KW - Program processors KW - Reduced instruction set computing KW - Signal receivers KW - Software architecture KW - Base-band processing KW - Baseband signal processing KW - Dataflow KW - Domain specific architectures KW - Edge intelligence KW - Instruction extensions KW - Local areas KW - Many-core architecture KW - Power KW - Wireless baseband KW - Digital signal processors KW - Field programmable gate arrays (FPGA) CY - China ER - TY - JOUR TI - AI and organizational leadership: bibliometric review and future trends AU - González-Reyes C. AU - Ficapal-Cusí P. AU - Torrent-Sellens J. PY - 2025 JO - Journal of Organizational Change Management SP - 1 EP - 35 DO - 10.1108/JOCM-03-2025-0291 AB - Purpose – This article analyses the evolution of scientific literature at the intersection of artificial intelligence (AI) and organizational leadership. It identifies research trends, theoretical frameworks and emerging lines of inquiry, while addressing the practical, ethical and policy implications of AI integration in leadership settings. Design/methodology/approach – A mixed-methods approach was adopted, combining bibliometric techniques with qualitative content analysis. A total of 304 peer-reviewed articles (2014–2025) were retrieved from the Web of Science Core Collection and screened using a PRISMA-inspired procedure. Data were analysed with VOSviewer and thematic synthesis to identify networks, citation patterns, thematic clusters and theoretical foundations. Findings – The study identifies three dominant thematic areas: (1) decision support, (2) evolving leadership roles, and (3) ethics/sustainability. Influential theoretical perspectives include the dynamic capabilities view, social cognitive theory and sociotechnical systems theory. The review also highlights the emergence of diverse leadership styles (transformational, ethical, empowering and digital) shaped by AI's integration into organizational processes. Overall, the field displays both consolidation and fragmentation, underscoring the need for more integrative sociotechnical frameworks. Research limitations/implications – Beyond its diagnostic contribution, this study emphasizes the strategic, ethical and digital competencies required for AI-driven leadership, providing practical insights for organizations seeking to align governance, talent management and innovation strategies with emerging technological challenges. The research offers a systematic and integrative mapping of the AI–leadership field, combining bibliometric indicators with qualitative insights to identify conceptual trends, research gaps and actionable guidance on competencies, hybrid human–AI structures, and governance for responsible and sustainable adoption. Practical implications – The findings offer practical guidance for organizational leaders navigating AI-driven transformation. They identify key leadership competencies such as ethical reasoning, digital literacy and change management, essential for integrating AI effectively. The study also informs the design of leadership development programs, talent strategies, and governance frameworks that promote responsible AI use. By mapping leadership styles suited to AI-mediated environments, it helps organizations align human and algorithmic decision-making, foster trust, and ensure sustainable performance in increasingly digital contexts. Social implications – The integration of AI into leadership practices raises critical social concerns related to fairness, transparency, and accountability. This study highlights the need for inclusive and ethical governance models to address algorithmic bias, protect employee well-being, and ensure equitable access to AI benefits. Leadership plays a key role in mediating these challenges by fostering human-AI collaboration based on trust and ethical alignment. The findings underscore the importance of preparing leaders to navigate complex sociotechnical systems, influence organizational culture, and contribute to shaping public policies that support responsible and sustainable AI adoption. Originality/value – This study offers a systematic and integrative mapping of the academic landscape on AI and leadership. By combining bibliometric indicators with qualitative insights, it identifies conceptual trends and research gaps, while providing actionable guidance for scholars, organizational leaders and policymakers navigating the complexities of digital transformation. © 2025 Emerald Publishing Limited KW - AI adoption KW - Artificial intelligence KW - Ethics KW - Leadership KW - Leadership styles KW - Organizational transformation KW - Sustainability CY - Spain ER - TY - JOUR TI - Turning Profit Into Sustainability: Evidence on Artificial Intelligence, Education, and Ecological Footprint AU - Balci N. AU - Gürel B. AU - Okur M.R. PY - 2026 JO - Sustainable Development VL - 34 IS - 2 SP - 2697 EP - 2724 DO - 10.1002/sd.70476 AB - This paper examines how profit relates to ecological footprint intensity and how the link is shaped by artificial intelligence capability and education quality. We analyze 53,081 firm year observations from 15 innovation-leading economies during 2003–2022 using system GMM. The findings reveal that (i) profitability is associated with lower footprint intensity (ii) artificial intelligence capability is associated with higher footprint intensity and weakens the footprint-reducing effect of profitability, while education quality is associated with lower intensity and strengthens that channel, (iii) the joint effect of profitability, AI capability, and education quality increases footprint intensity. The findings speak to responsible production and climate action agendas. The study findings indicate that the interactions between profitability, artificial intelligence capability, and education quality have a multi-layered structure in terms of environmental sustainability. In line with sustainable development goals, recommendations focus on subjecting artificial intelligence investments to mandatory environmental impact assessments, and aligning education systems with sustainable production and environmental responsibility awareness. © 2025 ERP Environment and John Wiley & Sons Ltd. KW - artificial intelligence KW - ecological footprint KW - education quality KW - environmental sustainability KW - firm performance KW - artificial intelligence KW - ecological footprint KW - educational development KW - environmental education KW - environmental impact assessment KW - industrial performance KW - profitability KW - sustainability KW - sustainable development CY - Turkey ER - TY - JOUR TI - Enhancing Logistical Efficiency in Public Institutions through AI: A Managerial Framework for Regulatory and Technological Integration AU - Alnajdawi M.H. AU - Raafat R. AU - Aburayya A. AU - Al Ghurabli Z. PY - 2025 JO - International Journal of Industrial Engineering and Production Research VL - 36 IS - 3 SP - 81 EP - 92 DO - 10.22068/ijiepr.36.3.2459 AB - This study investigates regulatory gaps impeding artificial intelligence (AI) integration in public sector logistics, revealing how fragmented legislative frameworks hinder operational efficiency and innovation. Through a quantitative cross-sectional survey of 182 legal professionals, public employees, and AI/legal scholars using stratified purposive sampling and validated instruments (Cronbach’s α=0.985) we identified statistically significant stakeholder divergences (*p*<0.05) via χ² tests and Cramer’s V effect sizes. Key findings demonstrate that: (1) legal experts prioritize regulatory clarity deficits (M=4.62), while public staff emphasize institutional resistance (M=4.41); (2) human capital training is systematically undervalued (M=2.57, V=0.26) despite its theoretical importance; and (3) while regulation enhances operational efficiency (M=4.36), it paradoxically inhibits logistical innovation (M=2.48), exposing a critical innovation-governance disconnect. The study’s core contribution, a Dynamic Institutional Alignment Framework, resolves this tension through three pillars: human-centered regulatory design integrating legal-technical dimensions, adaptive policy sandboxes synchronized with AI advancement cycles, and stakeholder-specific implementation pathways. By embedding institutional adaptability within global compliance standards (EU AI Act, OECD Principles), this framework advances AI governance theory and offers public institutions actionable strategies for balancing technological advancement with accountability. © Iran University of Science and Technology 2025. KW - Artificial intelligence KW - Institutions KW - Law KW - Logistics KW - Technology CY - United Arab Emirates ER - TY - JOUR TI - Transforming Higher Education for the Digital Age: Examining Emerging Technologies and Pedagogical Innovations AU - Chadha A. PY - 2024 JO - Journal of Interdisciplinary Studies in Education VL - 13 IS - S1 SP - 53 EP - 70 DO - 10.32674/em2qsn46 AB - In this study, I explore the transformative potential of artificial intelligence (AI) and emerging technologies in higher education, focusing on case studies and pedagogical innovations that are reshaping the learning experience. Through an in-depth analysis of key initiatives—such as Stanford University's AI-driven personalized learning platform, the AI chatbot implemented at the University of Murcia, Knewton's adaptive learning system, and the intelligent tutoring platform developed by Pai et al.—the study highlights how AI enhances learner engagement, customizes educational experiences, and improves academic outcomes. The research also critically examines the ethical challenges and policy considerations associated with AI integration in educational settings. It emphasizes the need for clear guidelines to ensure responsible and equitable use of AI, particularly in addressing issues of fairness, student welfare, and access. The paper concludes by calling for further research into the long-term implications of AI on educational equity and ethical standards in higher education. © 2024, STAR Scholars Network. All rights reserved. KW - adaptive learning KW - AI KW - AI ethics KW - edtech KW - higher education KW - institutional policy KW - intelligent tutoring systems KW - personalized learning CY - India ER - TY - JOUR TI - AI in education through the learners’ eyes: practical experience, perceptions, and challenges AU - Yotov K. AU - Gaftandzhieva S. AU - Hadzhikolev E. AU - Hadzhikoleva S. AU - Gorgorova M. PY - 2026 JO - Frontiers in Education VL - 11 SP - 1717886 DO - 10.3389/feduc.2026.1717886 AB - Introduction: In order to remain competitive, higher education institutions strive to enhance the student experience by integrating modern technologies for both educational and administrative purposes. This paper presents the results of a study exploring students’ attitudes toward the use of artificial intelligence (AI) in higher education. Methods: The data was collected through a survey conducted among 138 students, who responded to 50 questions regarding their level of awareness, practical experience, perceived benefits of using AI tools, potential issues and challenges, as well as ideas and suggestions for more effective use of AI technologies. The analysis was conducted using both traditional statistical techniques and contemporary machine learning methods. Results: Findings show that students who understand AI capabilities are more confident and proactive in using it for learning purposes. Those who utilize AI believe it enhances their academic performance and recommend its use to their peers. Discussion: Overall, students support the innovative use of AI and believe it will improve the educational process. According to them, the main risks associated with AI use include academic misconduct and the loss of critical thinking skills. The findings can serve as a starting point and foundation for future, more extensive studies exploring the attitudes of students from various academic disciplines and institutions. Copyright © 2026 Yotov, Gaftandzhieva, Hadzhikolev, Hadzhikoleva and Gorgorova. KW - AI in higher education KW - AI literacy KW - educational innovation KW - stem education KW - student perceptions KW - survey CY - Bulgaria ER - TY - JOUR TI - AI governance after MiFID II: beyond (mere) technological neutrality? AU - Azzutti A. PY - 2026 JO - ERA Forum VL - 27 IS - 1 SP - 7 EP - 31 DO - 10.1007/s12027-026-00871-1 AB - This article examines the evolving intersections between artificial intelligence (AI) and EU financial regulation, focusing on the Markets in Financial Instruments Directive II (MiFID II). Grounded in the principle of technological neutrality, MiFID II seeks to enhance investor protection, safeguard market integrity, and ensure that innovation develops within competitive and well-regulated markets across the Union. The article argues, however, that while this neutrality renders the framework functionally enabling, it also leaves it normatively silent in the face of the distinctive and evolving risks introduced by financial AI. As AI applications become increasingly heterogeneous—both across the financial functions in which they are deployed and in their underlying lifecycles and value chains—MiFID II’s activity-based logic increasingly struggles to accommodate their diverse and evolving risk profiles. Reflecting the EU’s broader shift toward risk-based AI governance, the article outlines an initial taxonomy of financial AI applications designed to guide the proportionate alignment of regulatory obligations with AI-related risks, thereby supporting the continued adaptability, coherence, and future-proofing of EU financial services law. © The Author(s) 2026. KW - AI governance KW - Artificial intelligence KW - MiFID II KW - Risk-based regulation KW - Technological neutrality CY - United Kingdom ER - TY - JOUR TI - Resource-Poor, Risk-Rich: Why Small Businesses Struggle to Turn AI Into Strategic Advantage AU - McIlveene T. AU - Nguyen S. AU - Batchelor J. AU - Keller S. AU - Ranelli E. PY - 2026 JO - Journal of Small Business Strategy VL - 36 IS - 2 SP - 19 EP - 26 DO - 10.53703/001c.158957 AB - While many small businesses are adopting artificial intelligence (AI), many struggle to translate this adoption into a lasting and sustainable competitive advantage. This paper posits that this implementation gap is not merely a strategic failure but also introduces significant ethical risks that can harm key stakeholders. Using a dual theoretical framework approach, we employ the resource-based view to identify how “resource poverty,” specifically, gaps in financial, human, technological, and organizational resources, inhibits successful AI implementation. The paper then applies stakeholder theory to illustrate how these internal resource gaps directly lead to external harms, including risks to customer privacy, employee welfare, and broader societal well-being. To address these challenges, a practical four-stage AI capability roadmap is introduced to help small business leaders develop their internal resources and capabilities and establish the necessary ethical safeguards. This roadmap provides a guide for small businesses to navigate the complexities of AI. © 2026 Small Business Institute. All rights reserved. KW - Artificial Intelligence (AI) KW - Ethics KW - Resource-Based View Theory KW - Small Business KW - Stakeholder Theory CY - United States ER - TY - JOUR TI - Impact of artificial intelligence on innovative work behaviour of employees AU - Rajpurohit N. AU - Sharma D. AU - Sharma D.K. AU - Jain T. PY - 2025 JO - International Journal of Process Management and Benchmarking VL - 21 IS - 3 SP - 322 EP - 341 DO - 10.1504/IJPMB.2025.149395 AB - Artificial intelligence (AI) has the potential to boost the efficiency of employees by fostering employees’ creativity and serving as a multipurpose tool for innovation. However, it is uncertain how AI affects employees’ innovative work behaviour. Consequently, this study investigates the impact of artificial intelligence on the innovative work behaviour of employees. A total of 327 responses were obtained from questionnaires administered to software engineers, IT specialists, and employees with other tech-related positions in high-tech organisations in India. The responses were evaluated using the structural equation modelling (SEM) approach. The findings of the study reveal that AI work dynamics and AI capability have a considerable impact on employees’ innovative work behaviour. This study makes a significant contribution to the literature on the effects of AI technology in the workplace and has significant implications relating to the utilisation of AI technology for employees’ innovative work behaviour. Copyright © 2025 Inderscience Enterprises Ltd. KW - AI capability KW - artificial intelligence KW - information technology KW - innovation KW - innovative work behaviour CY - India ER - TY - JOUR TI - Rethinking Competitiveness in the Age of AI: A Comparative Index-Based Approach AU - Jeon G. PY - 2025 JO - Journal of International Development VL - 37 IS - 7 SP - 1525 EP - 1542 DO - 10.1002/jid.70018 AB - This study examines the influence of artificial intelligence (AI) capabilities on national competitiveness through a comparative analysis of the IMD World Competitiveness Index and three major AI indices: Oxford AI Readiness, Tortoise AI Index and Stanford AI Index. Utilizing correlation analysis, multiple regression and K-means clustering across samples of 64, 59 and 35 countries, respectively, the research identifies infrastructure and research capacity as key predictors of national competitiveness, with regression models explaining 52.4%–60.8% of IMD variance and Pearson correlations exceeding 75% for predictive validity. Clustering analysis delineates AI-advanced nations (A2 cluster) with superior AI performance relative to national competitiveness and resource-dependent laggards (C2 cluster) at risk of stagnation without AI investment. The study proposes open innovation strategies, inspired by collaborative ecosystems like shared mobility, leveraging government-industry-academia partnerships and digital public infrastructure (DPI) to address gaps in government policy, research capacity and infrastructure, with case studies of the United States and Singapore. For Least Developed Countries (LDCs), a 2 × 2 strategy matrix outlines low-cost, high-impact AI initiatives to enable a bypass strategy, leveraging open innovation ecosystems to circumvent traditional industrial pathways. Findings underscore AI's transformative role in redefining competitiveness, driven by qualitative capabilities like efficiency, innovation and governance, offering actionable pathways for advanced economies and LDCs to close competitiveness gaps through strategic AI integration and DPI investments. © 2025 John Wiley & Sons Ltd. KW - AI Index KW - clustering analysis KW - least developed countries (LDCs) KW - multiple regressions KW - national competitiveness KW - open innovation KW - Singapore [Southeast Asia] KW - United States KW - artificial intelligence KW - cluster analysis KW - competitiveness KW - correlation KW - digitization KW - infrastructure KW - innovation KW - investment KW - multiple regression KW - public-private partnership CY - South Korea ER - TY - JOUR TI - Examining the role of higher education learning, research excellence, and innovation capacity in driving AI-technological advancements in Nordic countries AU - Zamir S. AU - Mehmood M.S. AU - Abbasi B.N. AU - Li W. AU - Wang Z. PY - 2025 JO - Humanities and Social Sciences Communications VL - 12 IS - 1 SP - 1325 DO - 10.1057/s41599-025-05665-3 AB - Higher education, research, and innovation are essential for advancing a systematic understanding of the responsible deployment and application of AI-driven technologies. These mechanisms facilitate the evaluation of societal impacts, the identification and mitigation of risks associated with misuse, and the enhancement of AI capabilities for specific, practical applications. However, how effective are these mechanisms in achieving these outcomes? This study, therefore, investigates the effectiveness of higher education learning, research excellence, and innovation capacity in relation to AI-driven technology, as well as the moderation effect of good governance on these relationships, using data from Nordic countries spanning from 2009 to 2023. The analysis employs the dynamic common correlated effects (DCCE) model by Chudik and Pesaran (2015) and the panel non-causality test by Juodis et al. (2021). The findings revealed that higher education learning, research excellence, and innovation capacity actively promote the development of AI-driven technology in Nordic countries. Furthermore, good governance positively influences the connection, with the magnitude of the influence being greatest on higher education learning, followed by innovation capacity, and then research excellence. Moreover, there is bidirectional causality between all the variables and AI-driven technology; thus, the variables and AI-driven technology are the determinants of one another. In line with these findings, policy recommendations were proposed. © The Author(s) 2025. CY - China ER - TY - JOUR TI - Service Blueprinting for Better Collaboration in Human-Centric AI: The Design of a Digital Scribe for Orthopedic Consultations AU - Magyari R. AU - Secomandi F. PY - 2023 JO - International Journal of Design VL - 17 IS - 3 SP - 63 EP - 77 DO - 10.57698/v17i3.04 AB - This case study explored the application of the service blueprinting method during the conceptual design of an AI-enabled digital scribe— an intelligent documentation support system—tailored for orthopedic consultations. In this paper, we discuss how this method can be used to enhance collaboration between user experience designers and machine learning engineers. Specifically, we show how service blueprinting can help innovation teams create a common foundation for understanding design challenges, enrich data with user-related insights, and highlight the value of AI capabilities as an organizational resource. Building on recent academic research in the field of human-computer interaction, our findings provide additional insights for addressing the design challenges associated with developing human-centric AI and incorporating service design approaches. © 2023 Magyari & Secomandi. KW - Blueprinting KW - Clinical Documentation KW - Digital Scribe KW - Human-centric AI KW - Service Design KW - UX Design CY - Netherlands ER - TY - JOUR TI - Lessons From One FQHC’s Experience With Artificial Intelligence AU - Wang G. AU - Kennedy S. AU - Johnson M. AU - Avellino L. PY - 2026 JO - Journal of Ambulatory Care Management VL - 49 IS - 1 SP - E31 EP - E38 DO - 10.1097/JAC.0000000000000541 AB - Objective – The rapid evolution of artificial intelligence (AI) presents opportunities and challenges for health systems, especially safety-net providers like Federally Qualified Health Centers (FQHCs). Safety-net systems may need help with structures and processes for assessing AI applications. To address this need, this article describes Moses-Weitzman Health System’s (MWHS) initial steps toward establishing an AI program that defines intentional and informed AI use. Approach – MWHS established two AI-focused workgroups: one of senior leaders and a cross-departmental group, providing a collaborative space for exploring potential applications, creating guidelines, and discussing concerns. With limited existing templates, MWHS crafted an AI policy emphasizing transparency, privacy, and security, outlining the criteria for implementing AI tools that interact with patient data and ensuring compliance with current regulations. Current AI-related projects focus on automating routine tasks, and research interests include evidence frameworks for making decisions about adopting AI tools and evaluating ambient listening technologies. Findings – Lessons learned in building our AI program are that effective implementation requires tech-savvy leadership, cross-department collaboration, and cautious differentiation between general automation and generative AI. Challenges include the need for agile budgeting, careful vendor vetting, and safe testing environments to assess AI benefits and risks responsibly. Conclusions and Action Steps – MWHS’s AI program underscores a cautious but proactive approach to AI, aiming to balance innovation with operational and ethical considerations, and offers a model for other safety-net systems beginning their AI journeys. © 2025 KW - artificial intelligence KW - community health centers KW - organization and administration KW - primary health care KW - Artificial Intelligence KW - Humans KW - Safety-net Providers KW - artificial intelligence KW - human KW - organization and management KW - safety net health care CY - United States ER - TY - JOUR TI - Artificial intelligence and climate risk: Toward sustainable development within a Double Helix framework AU - Tong Z. AU - Tan Z. PY - 2026 JO - Technological Forecasting and Social Change VL - 226 SP - 124592 DO - 10.1016/j.techfore.2026.124592 AB - This study examines the impact of climate risk on corporate performance and investigates whether artificial intelligence (AI) capability moderates this relationship. Drawing on the Double Helix framework, we conceptualize climate resilience as emerging from the co-evolution of institutional pressures and technological capabilities. Using panel data from 10,601 firm-year observations of Chinese A-share listed companies between 2016 and 2023, we employ fixed effects regression, instrumental variable estimation, and difference-in-differences analysis surrounding the 2018 Environmental Tax Reform. Results indicate that climate risk exposure significantly reduces firm performance measured by return on assets and return on equity. Importantly, AI capability weakens this negative effect, with stronger moderating effects observed in high climate risk regions, heavy-polluting industries, and private firms. These findings remain robust across alternative specifications and measurement approaches. This study contributes to climate finance and digital strategy literature by demonstrating how technological capabilities buffer environmental shocks within institutional contexts. The results offer practical guidance for managers seeking to leverage AI for climate adaptation and for policymakers designing digital transformation initiatives that support sustainable development. © 2026 Elsevier Inc. KW - Artificial intelligence KW - Climate risk KW - Corporate performance KW - Double Helix framework KW - Emerging markets KW - China KW - Industrial management KW - Risk assessment KW - Sustainable development KW - Climate risk KW - Co-evolution KW - Corporate performance KW - Double helix KW - Double helix framework KW - Emerging markets KW - Fixed effects KW - Institutional pressures KW - Panel data KW - Technological capability KW - artificial intelligence KW - climate change KW - estimation method KW - institutional framework KW - panel data KW - performance assessment KW - sustainable development KW - Artificial intelligence CY - China ER - TY - JOUR TI - REVOLUTION OR RISK? EXPLORING AI’S ROLE IN ENHANCING MUSEUM VISITOR EXPERIENCE AU - Eduarda M. AU - Wendhausen V. PY - 2025 JO - Journal of Science and Technology of the Arts VL - 17 IS - 1 SP - 122 EP - 135 DO - 10.34632/jsta.2025.17586 AB - As digital technologies redefine cultural engagement, Artificial Intelligence (AI) emerges as a transformative force in museum experiences, balancing innovation and tradition. This paper investigates the dual impact of AI on cultural institutions, analysing its potential to enhance visitor engagement through personalisation, accessibility, and operational efficiency while addressing significant challenges including ethical concerns, data dependency, and risks to cultural authenticity. The study highlights AI’s capability to democratise cultural heritage, drawing from diverse recently implemented examples, such as AI-powered art restoration, immersive VR/AR experiences, and adaptive educational tools. However, limitations surrounding infrastructure, algorithmic bias, and privacy underscore the need for strategic, ethical governance. By navigating these complexities, museums can leverage AI to enrich public interaction with heritage while safeguarding their foundational mission of cultural stewardship. Based on recent European Parliament briefings and regulations, this research offers actionable frameworks for institutions aiming to integrate AI responsibly, to foster innovation that honors technological potential and humanistic values. © 2025, Universidade Catolica Portuguesa. All rights reserved. KW - Aesthetics KW - AI in museums KW - Audience studies KW - Media studies CY - Portugal ER - TY - JOUR TI - Does Knowledge Heterogeneity Always Foster Innovation? The Moderating Role of Artificial Intelligence Capabilities in Value Chain Relationships AU - Huang Y. AU - Yu X. AU - Li Y. AU - Chen D. PY - 2025 JO - Knowledge Management Research and Practice DO - 10.1080/14778238.2025.2572352 AB - Interorganizational knowledge heterogeneity can fuel innovation but also create cognitive burdens–tensions that remain insufficiently understood in value chain contexts amid growing adoption of artificial intelligence (AI). We theorize an inverted U-shaped relationship between value chain knowledge heterogeneity–differences in technological knowledge between a focal firm and its key suppliers or customers–and the focal firm’s technological innovation, reflecting the competing forces of knowledge complementarity and cognitive burden. Using a patent-based knowledge-distance measure and panel data on Chinese listed firms from 2010 to 2020, we find evidence for this effect. We further show that a focal firm’s AI capabilities shifts the turning point rightwards in both supplier and customer contexts, thereby extending the range over which heterogeneity is beneficial; while it does not significantly alter the curvature in supplier contexts, it unexpectedly steepens the inverted U in customer contexts. Theoretical and practical implications are discussed. © 2025 The Operational Research Society. KW - artificial intelligence capability KW - Knowledge heterogeneity KW - technological innovation performance KW - value chain CY - China, United States ER - TY - JOUR TI - AI Readiness and Sustainable Development: A Systems Perspective on Health, Education, Innovation, and Climate Action AU - Köseoglu M.A. AU - Khaki A. AU - Arici H.E. PY - 2026 JO - Business Strategy and Development VL - 9 IS - 1 SP - e70315 DO - 10.1002/bsd2.70315 AB - This study investigates how national artificial intelligence (AI) readiness influences sustainable development performance across four Sustainable Development Goals (SDGs): Good Health and Well-Being (SDG 3), Quality Education (SDG 4), Industry, Innovation and Infrastructure (SDG 9), and Climate Action (SDG 13). Using cross-country data from the Oxford Insights AI Readiness Index and the Sustainable Development Report, we apply ensemble machine learning methods and select boosting based on predictive performance. SHAP values and Partial Dependence Plots reveal nonlinear and interaction effects across institutional, digital, and economic dimensions. The findings indicate that AI readiness functions as a strategic systems-level capability with domain-specific impacts. Data availability and ecosystem maturity shape health outcomes; digital infrastructure and economic scale influence education; technology sector strength supports innovation; and climate performance depends on economic–digital complementarities. The study positions AI capability as a structural enabler of national competitiveness and coordinated development strategy. © 2026 ERP Environment and John Wiley & Sons Ltd. KW - AI readiness KW - climate action KW - digital governance KW - innovation ecosystems KW - machine learning KW - SDGs CY - United States, Kuwait, Turkey, Spain ER - TY - JOUR TI - Wearable IoT (w-IoT) artificial intelligence (AI) solution for sustainable smart-healthcare AU - Singh G. PY - 2025 JO - International Journal of Information Management Data Insights VL - 5 IS - 1 SP - 100291 DO - 10.1016/j.jjimei.2024.100291 AB - Smart technologies, specifically wearables are cutting edge innovation of design science with an emerging Artificial Intelligence (AI) capability for sustainable healthcare. Wearable IoT (w-IoT) applications, solutions and systems can promote early warning measures for physiological parameter monitoring and other vital health observation while addressing, streamlining and enhancing emergency response procedures in the provision and deliverance of healthcare services. These solutions exhibit real-time responses with underlying machine-learning (ML) methodologies alongside ubiquitous, context-aware, pervasive and advance software features. AI frameworks, for the development and implementation of solutions are well covered in this study adopting design science (DS) principles for new product development (NPD), comprising various healthcare scenarios for distributed numbers and environments. Physiological or health activity-related data produced by embedded optical smartwatch sensors can instigate sustainable and economical health-oriented solutions for continuous monitoring, semantic predictions for constrained, intractable and autonomous environments to address cardiac disorders. This paper addresses, the practical implementation of the w-IoT health technology solution prototype for real-time applicability, covering problem identification and utilizing design science guidelines, evaluation and contribution by emphasizing on the experimental stage in general and with specificity. It covers performance results rendering research science communication on machine learning models for time series analysis, regression and classification to implement defined and adaptive thresholds, adopting standard deviation and moving average, computing mean square error (MSE), root mean square error (RSME) and mean absolute error (MAE) values, utilizing exponential moving average results on multiple features, prominently targeting resting heartrate data. Machine Learning algorithms for classification with higher F-score or performance metrics adopted are Decision Trees (DT), K-Nearest Neighbours (KNN), XGboost, One-class SVM and Logistic Regression. In Binary classification, KNN achieved F-score of 91 %, followed by DT at 81 % which seems an effective algorithm with flexibility on overfitting with high accuracy result. This study will cover all stages of design science methodology, guidelines for w-IoT healthcare solution development, by presenting experimental prototype towards pipeline implementation to address healthcare needs, alleviating previously prevalent Body Area Networks (BANs) solutions precision with advancing w-IoT smart technologies or Wireless Body Sensor Networks (WBSNs). © 2024 The Author(s) KW - Artificial intelligence KW - Binary classification KW - Defined-adaptive thresholds KW - Machine learning algorithms KW - Predictive models KW - Real-time monitoring KW - Regression KW - Smart-healthcare KW - Smart-watches KW - Time series analysis KW - Wearable IoT (w-IoT) CY - Australia ER - TY - JOUR TI - AI-powered organizational transformation: the role of digital mindset, change management, and cross-cultural leadership in shaping future business strategies AU - Teng Z. AU - Sukesi H. AU - Purnomo B.R. PY - 2026 JO - Future Technology VL - 5 IS - 2 SP - 49 EP - 59 DO - 10.55670/fpll.futech.5.2.5 AB - This study explores how artificial intelligence reshapes business strategies through synergistic effects between digital thinking, change management, and cross-cultural leadership in organizational transformation processes. Based on multi-source public data from 450 global enterprises across technology, manufacturing, finance, and retail sectors, this research integrates structural equation modeling, in-depth case analysis of 20 extreme cases, and machine learning prediction methods to construct and validate an “AI-Driven Strategic Triple Helix Evolution Framework” through seven interrelated hypotheses. Empirical findings confirm that organizational transformation plays the role of a core mediating hub (R2=0.64), connecting AI capabilities to strategic reconstruction, while the interaction with the three elements of synergy adds an additional 11% of explanatory power to it (ΔR2=0.11, P<0.001). Six strategic paths are differentiated in this research: AI-native (12%), platform transformation (23%), ecosystem orchestration (18%), niche specialization (21%), hybrid innovation (17%), and conservative following (9%), with significant cultural context dependence. Cross-cultural leadership shows the greatest moderating effect on high power distance cultures (β=0.38). The framework goes beyond the traditional technology-organization-environment models in unfolding dynamic co-evolution mechanisms among technological capabilities, cognitive reconstruction, and cultural adaptation. Machine learning models further predict 70% of enterprises participating in ecosystem strategies by 2030, and a digital mindset contributes 34.2% to strategic innovation prediction. © 2026, Future Publishing LLC. All rights reserved. KW - Artificial intelligence KW - Business strategy reconstruction KW - Cross-cultural leadership KW - Digital transformation KW - Organizational transformation CY - Indonesia ER - TY - JOUR TI - AI capability and green innovation impact on sustainable performance: Moderating role of big data and knowledge management AU - Al Halbusi H. AU - Al-Sulaiti K.I. AU - Alalwan A.A. AU - Al-Busaidi A.S. PY - 2025 JO - Technological Forecasting and Social Change VL - 210 SP - 123897 DO - 10.1016/j.techfore.2024.123897 AB - This study addresses the environmental impact of industries by focusing on increased resource consumption and waste generation that lead to ecosystem degradation. It advocates sustainable practices and a circular economy (CE) as strategies to mitigate these effects. Thus, the study examines how Artificial Intelligence (AI) capabilities directly affect green innovations and their subsequent influence on sustainable performance and CE. In addition, it introduces two key moderating factors—big data analytics and knowledge management systems—in the relationship between AI capabilities and green innovation. We validate the model using multi-sectoral population data from various Qatari industries and employ structural equation modeling (SEM) and artificial neural networks (ANN) as analytical approaches. The results indicate the significant impact of AI capability on green innovation, with these innovations critically linked to sustainable performance and CE. Remarkably, interactions with big data analytics and knowledge management systems enhance the positive impact of AI capabilities. Hence, this study emphasizes AI's noteworthy implications for green innovation, shaping sustainable performance, and CE. Identifying big data analytics and knowledge management systems as vital moderators adds complexity. The findings guide industries to integrate AI, big data analytics, and knowledge management systems for practical applications, stressing a holistic approach to promoting environmentally responsible practices across sectors. © 2024 Elsevier Inc. KW - AI capability KW - Big data analytics KW - Circular economy (CE) KW - Environmental sustainability KW - Green innovation KW - Knowledge management systems KW - Qatar KW - Green development KW - Green economy KW - Artificial intelligence capability KW - Big data analytic KW - Circular economy KW - Data analytics KW - Environmental sustainability KW - Green innovations KW - Knowledge management system KW - Resource wastes KW - Sustainable performance KW - artificial intelligence KW - artificial neural network KW - complexity KW - developing world KW - environmental economics KW - green economy KW - industrial development KW - innovation KW - performance assessment KW - sustainability KW - Circular economy CY - Qatar, United Kingdom, Oman ER - TY - JOUR TI - Explainable neural algorithms for corporate sustainability forecasting: A layered predictive model anchored in executive awareness, green finance, and digital innovation AU - Ibrahim Y. AU - Moubarak H. AU - Badawy H. PY - 2026 JO - Innovation and Green Development VL - 5 IS - 1 SP - 100335 DO - 10.1016/j.igd.2026.100335 AB - This study investigates how artificial intelligence (AI) capability drives sustainable performance through the mediating role of Digital Green Innovation (DGI). Grounded in the Resource-Based and Natural Resource-Based Views, survey data from 321 organizations are analyzed using a multi-method approach that integrates partial least squares structural equation modeling (PLS-SEM), machine learning (ML), and explainable AI (XAI). The PLS-SEM results reveal a full mediation effect AI Capability enhances sustainable performance exclusively through DGI highlighting that technological resources must be embedded within innovation processes to generate environmental and social value. To ensure convergent validation and methodological robustness, predictive ML models (random forest, support vector regression, multilayer perceptron, and one-dimensional convolutional neural networks) are applied alongside XAI techniques (SHAP and LIME). These complementary analyses independently converge on the same key drivers DGI and top management environmental awareness providing strong empirical triangulation and interpretive transparency. Theoretically, the study advances the understanding of AI-enabled sustainability by demonstrating that AI resources yield value only when channeled through green innovation capabilities. Methodologically, it contributes by showcasing a convergent SEM–ML–XAI framework that enhances both explanatory and predictive validity. Practically, organizations should strengthen digital innovation systems and employ XAI tools to dynamically monitor and refine sustainability performance drivers. © 2026 The Authors KW - AI capability KW - Digital green innovation KW - Machine learning forecasting KW - Model interpretability KW - SEM KW - Sustainable performance CY - Egypt ER - TY - JOUR TI - Artificial Intelligence (AI)-Aided Collaborative Design in Industrial Design Education for Final Year Projects (FYP) Improving Workflow and Innovation AU - Me R.C. AU - Kamil M.J.M. AU - Razali A.F. AU - Li J. AU - Abidin S.Z. AU - Ramli S.H. PY - 2025 JO - Academic Quarter VL - 31 2025 SP - 28 EP - 46 DO - 10.54337/academicquarter.i31.11269 AB - The integration of Artificial Intelligence (AI) into design education is transforming collaborative learning and creative practice, particularly in Industrial Design. A theoretical framework was developed through the literature review to guide this study, which investigates how AI-assisted tools influence creativity, collaboration, and workflow efficiency in Final Year Projects (FYPs) among 38 Industrial Design students at a Malaysian university. Employing a mixed-methods design, two classes participated in a quasi-experimental comparison: one integrated AI tools throughout the design process, while the other used traditional methods. Students applied AI tools across five project phases: research (Notion AI, Elicit), ideation (DALL·E, MidJourney), design simulation (Fusion 360 AI, Rhino AI), reporting (ChatGPT, Grammarly), and prototyping (generative design tools). Quantitative data from project rubric scores and supervisor evaluations were complemented by qualitative insights from reflective journals and focus group discussions. Results showed that the AI-assisted class achieved higher creativity and design quality, supported by enhanced efficiency and faster iteration. However, students also reported challenges related to over-reliance on AI, ethical concerns about authorship, and reduced hands-on engagement. The study concludes that AI can serve as a valuable cognitive and creative partner in design education when integrated within a reflective and human-centered pedagogical framework that maintains critical thinking, originality, and ethical responsibility. © 2025, Aalborg University. All rights reserved. KW - AI-Aided Design KW - Collaborative Design KW - Final Year Project (FYP) KW - Human-AI Collaboration KW - Human-Centered Design KW - Industrial Design education CY - Malaysia ER - TY - JOUR TI - Youth Expectations and Perceptions of Generative Artificial Intelligence in Higher Education AU - Cotino-Arbelo A.E. AU - González-González C.S. AU - Molina-Gil J. PY - 2025 JO - International Journal of Interactive Multimedia and Artificial Intelligence VL - 9 IS - 2 SP - 84 EP - 92 DO - 10.9781/ijimai.2025.02.004 AB - Artificial Intelligence (AI) is not a recent innovation, what’s new is how accessible its features have become across multiple devices, apps, and services. Sensationalistic news can distort public perception by exaggerating AI’s capabilities and risks. This leads to misconceptions and unrealistic expectations, causing misunderstandings about the true nature and limitation of these tools. Such distortions can undermine trust and hinder the effective adoption and integration of AI into society. This study aims to address this issue by exploring the expectations and perceptions of young individuals regarding Generative Artificial Intelligence (GAI) tools. It explores their understanding of GAI and related devices, such as virtual assistants, chatbots, and social robots, which can incorporate GAI. A total of N=100 university students engaged in this study by completing a digital questionnaire distributed through the virtual campus of the University of La Laguna. The quantitative analysis uncovered a significant gap in participants’ understanding of GAI terminology and its underlying mechanisms. Additionally, it shed light on a noteworthy gender-based discrepancy in the expressed concerns. Participants commonly recognized their ability to communicate effectively with GAI, asserting that such interactions enhance their emotional well-being. Notably, virtual assistants and chatbots were perceived as more valuable tools compared to social robots within the educational realm. © 2025, Universidad Internacional de la Rioja. All rights reserved. KW - Artificial Intelligence KW - Chatbots KW - Generative Artificial Intelligence KW - Higher Education KW - Perceptions KW - Social Robots KW - Virtual Assistants CY - Spain ER - TY - JOUR TI - The AI Integration Matrix: a Framework for Responsible Artificial Intelligence in Mental Health AU - Schneider E.M. AU - E. Ayearst L. PY - 2026 JO - Journal of Technology in Behavioral Science DO - 10.1007/s41347-026-00608-4 AB - Artificial intelligence (AI) is transforming mental health care by enabling new approaches to monitoring, prevention, diagnosis, intervention, and relapse prevention. Yet, digital mental health tools introduce a range of complex challenges, spanning clinical, ethical, regulatory, technical, and contextual dimensions. A person-centered, developmentally informed approach is needed to ensure AI innovation leads to improved outcomes. This paper proposes the AI Integration Matrix (AIM), a framework for the responsible development and implementation of AI in mental health care. It integrates, builds on, and extends current regulatory, implementation science, and ethical frameworks. The Matrix offers systematic, context-sensitive guidance across seven interdependent domains: (1) clinical grounding and application, (2) ethical integrity and trust, (3) regulatory and economic sustainability, (4) user experience, (5) social and cultural impact, (6) evidence and continuous learning, and (7) technical foundations. It provides a holistic foundation for evaluating and optimizing digital mental health innovation across diverse settings and populations and equips users with a mental model for navigating the complexity of digital mental health applications, supporting responsible AI integration that drives meaningful impact. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2026. KW - AI framework KW - Artificial intelligence KW - Digital mental health KW - Person-centered care KW - Responsible AI CY - United States, Canada ER - TY - JOUR TI - Regulating Artificial Intelligence in Democratic Societies: Legal Challenges and Ethical Imperatives for Peace, Development, and Integration AU - Lushka I. PY - 2025 JO - Interdisciplinary Journal of Research and Development VL - 12 IS - 1 S1 SP - 85 EP - 92 DO - 10.56345/ijrdv12n1s110 AB - Artificial Intelligence (AI) is reshaping democratic institutions, offering significant opportunities for innovation while also raising serious legal and ethical concerns. Its use in areas like surveillance, predictive policing, hiring, and healthcare challenges core democratic principles such as transparency, accountability, and the protection of fundamental rights. This paper examines how democratic societies can govern AI effectively, ensuring that its development aligns with civil liberties and human dignity. Existing legal frameworks, often outdated, struggle to address the complexities of AI, including issues of bias, discrimination, and the lack of human oversight in automated decision-making. While regulations like the EU’s General Data Protection Regulation (GDPR) provide some safeguards, they fall short in addressing the full scope of AI’s impact. The proposed EU AI Act represents progress toward a harmonized, risk-based approach but raises questions about enforcement and adaptability. Ethical governance must go beyond voluntary guidelines. Binding legal standards are needed to enforce principles such as fairness, explainability, and human-centric design. Furthermore, international cooperation is essential to prevent regulatory gaps and ensure consistent protections across borders. Participatory oversight is also vital. Public trust depends on involving a broad range of stakeholders—citizens, experts, developers, and civil society—in shaping AI policy. Legal systems must anticipate AI’s broader effects, such as job displacement and social inequality, through proactive measures like retraining programs and social protections. Ultimately, AI governance must safeguard democratic values. Transparent, accountable, and inclusive legal frameworks are essential to ensure that AI strengthens—rather than undermines—freedom, justice, and human dignity. © 2025 Ina Lushka. KW - Artificial Intelligence KW - civil liberties KW - comparative law KW - democracy KW - ethics KW - EU AI Act KW - regulation CY - Albania ER - TY - JOUR TI - Human and machine: CEO greed, artificial intelligence, and corporate green innovation AU - Jin R. AU - Li X. PY - 2026 JO - Asia Pacific Journal of Management DO - 10.1007/s10490-026-10143-8 AB - While greed is an important psychological trait that shapes executives’ decision-making, whether it is beneficial or detrimental to firms remains controversial. Drawing on the attention-based view (ABV), we examine how CEO greed facilitates or impedes firms’ green innovation. Using a panel dataset of listed private firms in China, we find that CEO greed has an inverted U-shaped effect on firms’ green innovation. This effect becomes steeper for firms with high levels of sensing artificial intelligence (AI) adoption, but becomes flatter for firms with high levels of learning AI adoption. By demonstrating a double-edged sword effect of CEO greed, our study helps reconcile inconsistencies regarding the impact of executive greed and offers important implications for firms’ CEO selection, AI governance, and green innovation. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2026. KW - AI adoption KW - Attention-based view KW - CEO greed KW - Green innovation KW - Human-AI interaction CY - China ER - TY - JOUR TI - Crossing The Nexus: Language, Culture, and Technology in a Globalized World AU - Ghania O. AU - Bouguebs R. PY - 2025 JO - Traduction et Langues VL - 24 IS - 1 SP - 12 EP - 15 DO - 10.52919/translang.v24i01.1019 AB - In an era defined by globalization and digital connectivity, the intersections of language, culture, and technology have become central to understanding contemporary communication, education, and identity. Crossing The Nexus: Language, Culture, and Technology in a Globalized World presents a collection of interdisciplinary studies that explore emerging trends and challenges in applied linguistics, literary analysis, translation studies, and pedagogical innovation. Drawing on both theoretical frameworks and empirical research, this volume reflects the evolving landscape of linguistic inquiry across diverse sociocultural and geopolitical contexts. The contributions examine a wide range of topics, from contrastive linguistic analysis and syntactic theory to intercultural education, AI-assisted interpreting, and postcolonial semiotics. Key themes include the complexities of cross-linguistic phenomena and cultural fidelity in translation, investigated through contrastive studies of reduplication in Vietnamese-English literary prose and syntactic exceptions in Arabic and Spanish. Educational innovation is highlighted via the implementation of Collaborative Online International Learning (COIL) for intercultural competence and the Flipped Learning model in specialized contexts, alongside analyses of structural challenges in migrant student integration in Cyprus. The volume critically engages with technology's role, examining AI's capabilities and limitations in interpreting and handling culturally specific translation challenges like verbified proper nouns, while also demonstrating data-driven approaches to public sentiment analysis in digital discourse. Further contributions explore affective factors in language learning, such as test anxiety among Yemeni students, and the influence of gender perspectives in the translation of Qur'anic verses. Multimodal literary analysis reveals the co-construction of meaning through text and image, and postcolonial semiotics examines narratives of exile and identity. Collectively, these studies underscore the necessity of nuanced, culturally sensitive, and interdisciplinary approaches to navigate the evolving landscape of global communication, pedagogical strategies, and technological integration, significantly contributing to scholarly discourse in the humanities and social sciences. © 2025, University of Oran 2 Mohamed Ben Ahmed. All rights reserved. KW - Collaborative Online International Learning (COIL) KW - Culture and Technology KW - Exceptional Constructions in Arabic and Spanish KW - Flipped Learning in an ESP Context KW - Gender Studies KW - Reduplication in Vietnamese Literary Prose KW - Translation CY - Algeria ER - TY - JOUR TI - Transforming the 5G RAN With Innovation: The Confluence of Cloud Native and Intelligence AU - Li N. AU - Xu X. AU - Sun Q. AU - Wu J. AU - Zhang Q. AU - Chi G. AU - Chih-Lin I. AU - Sprecher N. PY - 2023 JO - IEEE Access VL - 11 SP - 4443 EP - 4454 DO - 10.1109/ACCESS.2023.3234493 AB - Intelligence and cloudification are widely recognized as key driving forces in the evolution of 5G radio access network (RAN). This paper presents a promising architecture framework for the evolution of 5G radio access network, enabled by a deep integration with cloudification and artificial intelligence/machine learning (AI/ML) technologies. To accommodate the diversified scenarios and services and handle the complexity of the 5G network in a flexible and efficient manner, the architecture framework highlights three concepts: convergence of RAN and cloud, RAN empowered by hierarchical AI capabilities, and mutual awareness between RAN and services. The key design aspects and technologies that realize those concepts are discussed systematically. Two typical use cases including the RAN slice resource allocation optimization and RAN-aware video service assurance, are demonstrated along with the simulation or lab test results to validate the potential of the architecture framework. © 2013 IEEE. KW - 5G KW - AI/ML KW - cloud-native KW - RAN KW - service-awareness KW - 5G mobile communication systems KW - Computer architecture KW - Network architecture KW - Radio KW - Radio access networks KW - 5g KW - 5g mobile communication KW - Artificial intelligence/machine learning KW - Cloud-computing KW - Cloud-native KW - Machine-learning KW - Mobile communications KW - Optimisations KW - Radio access networks KW - Service-awareness KW - Cloud computing CY - China, Israel ER - TY - JOUR TI - Artificial intelligence in action: shaping the future of public sector AU - Panda M. AU - Hossain M.M. AU - Puri R. AU - Ahmad A. PY - 2025 JO - Digital Policy, Regulation and Governance VL - 27 IS - 6 SP - 668 EP - 686 DO - 10.1108/DPRG-10-2024-0272 AB - Purpose – Artificial intelligence (AI) has transformed various sectors, including automotive, finance, media, travel and retail by leveraging new-age technologies. Education, banking, health care, social policy and regulation, within the public sector have witnessed significant AI applications and substantial benefits. The importance of AI in the public sector includes enhanced efficiency, improved decision-making, cost savings, citizen-centric services, etc. Despite these advancements, a mindful discussion on the societal impact of AI in the public sector demands comprehension regarding its subjugation. Therefore, this study aims to analyze the role of AI in transforming the public sector using a bibliometric analysis of recent trends and challenges. Design/methodology/approach – This study has used bibliometric analysis to trace the intellectual patterns of previous research. It comprises 231 articles from 2000 to 2024 from Scopus through the Scientific Procedures and Rationales for Systematic Literature Reviews protocol. This protocol has adopted a three-step process for identifying articles, i.e. assembling, arranging and assessing. Findings – The publication trend shows an upward trajectory since 2017, whereas network visualization protrudes with the recent trends and thematic expressions, namely, Global AI ethics and policy challenges in public sectors, AI adoption and governance in public sector, challenges and opportunities of implementing AI in public administration and AI’s role in economic and public transformation. Research limitations/implications – The findings suggest AI adoption in the public sector enhances transparency and efficiency but demands ethical guidelines, legal frameworks and stakeholder governance to address challenges such as data privacy, algorithmic bias and public trust. Policies should promote responsible AI use, balancing innovation with accountability to improve public service delivery and uphold democratic values. Originality/value – This paper enhances the limited literature on the integration of AI in the public sector, focusing on emerging themes and trending topics with future research directions to furnish a holistic perspective. It aims to guide researchers and policymakers in exploring areas for further investigation in this domain. © 2025 Emerald Publishing Limited KW - Artificial intelligence KW - Bibliometric analysis KW - Governance KW - Public sector KW - SPAR 4 SLR CY - India ER - TY - JOUR TI - How mindfulness shapes AI competence: a structural equation modeling analysis of mindfulness, AI literacy and behavioral intention in Chinese media students AU - Lan Y. AU - Liu S. AU - Xia L. PY - 2025 JO - Frontiers in Psychology VL - 16 SP - 1652934 DO - 10.3389/fpsyg.2025.1652934 AB - Introduction: Artificial Intelligence (AI) literacy, defined as the knowledge and ability to recognize, apply, and evaluate AI, is a key driving force of digital transformation and technological innovation. In the media industry, the demand for “intelligent+” interdisciplinary talent has prompted universities to embed AI literacy training into talent development programs. While curriculum systems have been progressively refined, the challenge remains on how to activate students’ intention to embrace and effectively utilize AI. Mindfulness, a metacognitive trait that enhances cognitive flexibility, self-regulation, and creativity, may contribute to the development of AI literacy, although its specific impact in this progress remains largely unexplored. Methods: This study constructs the integrated model of “Mindfulness-AI Literacy-Technology Application“. Survey data were collected from 588 media students in China and analyzed using SPSS and SmartPLS to conduct structural equation modeling. AI literacy is comprised of four dimensions: acknowledgment of AI (AAI), AI ethics (AIE), AI collaboration (AIC), and AI self-efficacy (AIS). Results: Mindfulness significantly and positively influenced AAI, AIE, and AIC, but showed no significant relationship with AIS. It also had a significant direct positive effect on AIBI. Furthermore, AAI and AIC partially mediated the relationship between mindfulness and AIBI. Discussion: Results confirm that mindfulness is an effective internal pathway for strengthening key AI literacy dimensions and enhancing media students’ intention to apply AI technologies. Incorporating mindfulness interventions into higher media education, aligned with curriculum and practice, could provide a strategic approach to cultivating AI-ready graduates. Copyright © 2025 Lan, Liu and Xia. KW - AI behavioral intention (AIBI) KW - AI literacy KW - media education KW - media students KW - mindfulness CY - China ER - TY - JOUR TI - Digital innovation and women's entrepreneurship: Integrating fragmented literature through a stage-contingent lens AU - Latif M. AU - Tanveer A. AU - Saeedikiya M. AU - Ullah A. AU - Bilal A. PY - 2026 JO - Digital Business VL - 6 IS - 2 SP - 100174 DO - 10.1016/j.digbus.2026.100174 AB - Women's entrepreneurship is a significant driver of innovation and economic growth; however, research on digital innovation in this context remains fragmented across disciplines and lacks an integrative theoretical foundation. This systematic literature review addresses these deficiencies by analysing how the antecedents, processes, and outcomes of digital innovation vary across three business development stages: new ventures, small and medium-sized enterprises (SMEs), and corporations. A total of 163 peer-reviewed articles published between 2000 and 2024 were analysed, following PRISMA 2020 guidelines and utilizing data from Scopus, Web of Science, and EBSCOhost. The Ability-Motivation-Opportunity (AMO) framework was employed as a multiplicative analytical model, extended to incorporate stage-contingent interactions and platform ecosystem dynamics. The findings indicate that binding constraints shift systematically across business development stages. In new ventures, Opportunity constraints are predominant, as access to funding, platforms, and networks determines the innovation potential. In SMEs, Ability constraints become salient, with leadership capabilities and digital skills limiting the success of scaling. In corporations, Motivation emerges as the primary constraint, with governance structures and strategic commitment influencing innovation outcomes. Digital innovation functions as a critical mediator linking gender-specific resources to venture performance, while platform governance, supply chain integration, and emerging generative AI capabilities reshape these relationships. This review offers five primary contributions: an integrated stage-contingent theoretical model that demonstrates context-dependent gender effects; an extension of AMO theory with explicit classification rules; integration of platform ecosystem and emerging technology perspectives; a reconceptualization of regulation as an enabler rather than a constraint; and evidence-based, stage-specific policy recommendations with documented trade-offs. In addition, nine critical research priorities are identified from synthesis gaps, providing direction for future scholarship and policy development. Copyright © 2026. Published by Elsevier B.V. KW - AMO framework KW - Digital innovation KW - Platform ecosystems KW - Stage-contingent analysis KW - Systematic literature review KW - Women's entrepreneurship CY - Australia ER - TY - JOUR TI - Demand analysis of transitional care for patients undergoing minimally invasive cardiac interventions with AI-driven solutions: a mixed-methods approach AU - Liu Y. AU - Li S. AU - Yu J. AU - Cao J. AU - Ma Q. AU - Li M. AU - Zheng Y. AU - You Y. AU - Lv W. AU - Li Q. AU - Zhang C. AU - Piao M. PY - 2025 JO - BMC Nursing VL - 24 IS - 1 SP - 453 DO - 10.1186/s12912-025-03037-5 AB - Aims: Minimally invasive cardiac intervention (MICI) patients remain at high risk of readmission and mortality during their post-discharge phase, with 30-day readmission rates of up to 10%. Although technological innovations, especially AI-driven solutions, hold promise for improving outcomes, there is a pressing need to clarify the full spectrum of patient demands during the transition from hospital to home. This study aimed to systematically identify these demands to guide the development of AI-driven solutions that reduce readmission rates and improve clinical outcomes. Methods and results: A convergent parallel mixed-methods design was employed to systematically identify patient demands and inform the development of AI-driven interventions in transitional care. Quantitative and qualitative data were collected from 137 MICI patients recruited from four hospitals (June–August 2024). Quantitatively, a 23-item survey was analyzed using the Kano model, revealing no “must-be” demands—indicating that patients were accustomed to a lack of guidance post-discharge. However, health monitoring, medication guidance, symptom management, and personalized exercise plans were identified as “one-dimensional” demands that significantly impact patient satisfaction. Additionally, continuous exercise monitoring and dietary planning emerged as “attractive” features that could enhance care quality without negatively affecting satisfaction if absent. Qualitative interviews uncovered the importance of comorbidity management, psychological support and financial transparency, which were not fully captured in the survey data. The integration of these findings underscores the need for AI-driven personalized health monitoring systems and knowledge-based AI tools to revolutionize the transitional care process for MICI patients. Conclusion: This integrated analysis highlights the significant care demands of MICI patients during the transition from hospital to home. Key recommendations include: (1) deploying AI-driven health monitoring, medication guidance, and symptom management systems, (2) designing personalized exercise and dietary tools, and (3) creating accessible, knowledge-based platforms for reliable medical information. In addition, comorbidity management, psychological support and financial transparency are areas that call for our attention. By aligning with these patient-centered demands and leveraging AI’s capabilities, future transitional care interventions—particularly in China have the potential to address healthcare staffing constraints and improve patient outcomes. However, due to the limitations of our study, these insights require further validation and exploration. © The Author(s) 2025. KW - Artificial intelligence KW - Minimally invasive cardiac interventions KW - Mixed-method KW - Transitional care CY - China ER - TY - JOUR TI - Using the Theoretical-Experiential Binomial for Educating AI-Literate Students AU - Modran H.A. AU - Ursuțiu D. AU - Samoilă C. PY - 2024 JO - Sustainability (Switzerland) VL - 16 IS - 10 SP - 4068 DO - 10.3390/su16104068 AB - In the dynamic landscape of modern education, characterized by an increasingly active involvement of IT technologies in learning, the imperative to transfer to university students the skills necessary to integrate Artificial Intelligence (AI) into the process represents an important goal. This paper presents a novel framework for knowledge transfer, diverging from traditional programming language-centric approaches by integrating PSoC 6 microcontroller technology. This framework proposes a cyclical learning cycle encompassing theoretical fundamentals and practical experimentation, fostering AI literacy at the edge. Through a structured combination of theoretical instruction and hands-on experimentation, students develop proficiency in understanding and harnessing AI capabilities. Emphasizing critical thinking, problem-solving, and creativity, this approach equips students with the tools to navigate the complexities of real-world AI applications effectively. By leveraging PSoC 6 as an educational tool, a new generation of individuals is efficiently cultivated with essential AI skills. These individuals are adept at leveraging AI technologies to address societal challenges and drive innovation, thereby contributing to long-term sustainability initiatives. Specific strategies for experiential learning, curriculum recommendations, and the results of knowledge application are presented, aimed at preparing university students to excel in a future where AI will be omnipresent and indispensable. © 2024 by the authors. KW - AI literacy KW - Artificial Intelligence (AI) KW - experiential learning KW - PSoC 6 KW - sustainability KW - innovation KW - machine learning KW - student KW - sustainability KW - theoretical study KW - university sector CY - Romania ER - TY - JOUR TI - Beyond Replacement: How Large Language Models Influence Dictionary Usage Patterns Among Chinese English Learners AU - Liu R. AU - Chen X. AU - Xu Y. PY - 2025 JO - International Journal of Lexicography VL - 38 IS - 4 SP - 342 EP - 364 DO - 10.1093/ijl/ecaf017 AB - This study investigated how Large Language Models (LLMs) influence Chinese English learners’ dictionary usage patterns. Through a mixed-methods approach combining questionnaire surveys (n = 608) and semi-structured interviews (n = 17), the findings reveal that LLMs reconstruct the language learning tool ecosystem through clear functional divisions rather than simple replacement patterns. Results demonstrate that LLMs predominantly serve complex language tasks including translation, writing assistance, and grammar correction, while traditional dictionaries maintain competitive advantages in providing authoritative information, precise definitions, and structured vocabulary learning tools. Learners have developed sophisticated task-oriented selection strategies, following a ‘dictionaries for discrete knowledge acquisition, LLMs for integrated language application’ usage pattern that maximizes learning efficiency. Beyond behavioural adaptations, this study identified significant demographic stratification in tool adoption, with age, educational background, and English proficiency level significantly influencing usage patterns. The research further revealed cognitive paradigm shifts in language learning conceptualization, exposing tensions between instrumental utility and cultural acquisition perspectives. These findings suggest two critical directions for future lexicographic development: (1) intelligent integration combining authoritative content with interactive AI capabilities, and (2) specialized personalization addressing domain-specific and scenario-based learning needs through enhanced functionality and user-centred design. © The Author(s) 2025. Published by Oxford University Press. All rights reserved. KW - Chinese English learners KW - Dictionary use behaviour KW - Language learning tools KW - Large Language Models KW - Mixed-methods research CY - China ER - TY - JOUR TI - Integrating Artificial Intelligence in dairy farm management − biometric facial recognition for cows AU - Mahato S. AU - Neethirajan S. PY - 2025 JO - Information Processing in Agriculture VL - 12 IS - 3 SP - 312 EP - 325 DO - 10.1016/j.inpa.2024.10.001 AB - The integration of Artificial Intelligence (AI) into dairy farm management through biometric facial recognition of cows marks a significant milestone in livestock care. This comprehensive review explores the development, implementation, and challenges associated with AI-powered biometric facial identification in dairy agriculture. It emphasizes the pivotal role of this innovation in enabling precise monitoring of individual cows, thereby facilitating thorough tracking of their health, behaviors, and productivity levels. Derived from facial recognition technologies originally designed for humans, this approach harnesses distinctive features of cow faces for gentle and immediate observation within large-scale farming operations. The evolution of AI from basic pattern recognition to advanced Convolutional Neural Networks (CNNs) and deep learning frameworks signifies a transition toward data-driven agriculture. This analysis addresses notable challenges such as environmental variability, data collection difficulties, ethical considerations, and technological limitations. Furthermore, it compares various AI frameworks, highlighting their unique advantages and suitability in the dairy farming context. Despite these obstacles, facial recognition technology holds promise for enhancing farm efficiency, improving animal welfare, and promoting sustainable practices, underscoring the need for ongoing research and innovation. We advocate for future investigations focused on enhancing adaptability to diverse environments, ensuring ethical AI deployment, fostering compatibility across different breeds, and integrating with complementary agricultural technologies. Ultimately, this review underscores the transformative impact of AI in advancing dairy farming towards a data-centric future while prioritizing responsible agricultural practices. © 2024 The Author(s). KW - AI-driven livestock Management KW - Animal identification technology KW - Dairy cow biometrics KW - Dairy welfare KW - Digital livestock farming KW - Facial recognition technology KW - Precision dairy farming KW - Sustainable dairy practices KW - Convolutional neural networks KW - Animal identification KW - Animal identification technology KW - Artificial intelligence-driven livestock management KW - Dairy cow KW - Dairy cow biometric KW - Dairy farming KW - Dairy welfare KW - Digital livestock farming KW - Facial recognition KW - Facial recognition technology KW - Identification technology KW - Livestock farming KW - Precision dairy farming KW - Sustainable dairy practice KW - agricultural practice KW - artificial intelligence KW - dairy farming KW - environmental assessment KW - farming system KW - integrated approach CY - Canada ER - TY - JOUR TI - Shame in the machine: affective accountability and the ethics of AI AU - McNealis R. PY - 2026 JO - AI and Society VL - 41 IS - 1 SP - 403 EP - 413 DO - 10.1007/s00146-025-02472-x AB - The cultural weaponization of shame surrounding the use of artificial intelligence (AI) tools like ChatGPT often redirects ethical scrutiny away from systemic concerns and toward individual users. Drawing on Sara Ahmed’s affect theory, this paper argues that cultural narratives of "AI shaming" function as moral displacement that redirects scrutiny away from the environmental costs, exploitative labor practices, and corporate monopolization defining contemporary AI development. The analysis examines how shame operates across academic and professional settings to create "effort anxiety" that demands both visible human labor and accelerated productivity. Current discourse treats AI use as a personal virtue problem and obscures the carbon-intensive data centers, underpaid content moderators, and proprietary knowledge systems that enable these technologies. Instead of eliminating shame, the paper proposes redirecting it toward collective accountability for AI’s systemic harms. Environmental degradation, algorithmic bias, and extractive infrastructures represent the true ethical frontier of artificial intelligence. Policy frameworks, educational interventions, and governance structures offer pathways for transforming shame from individual punishment into institutional reform. The stakes extend beyond AI itself: as emerging technologies reshape society, the patterns of moral responsibility established now will determine whether innovation serves collective flourishing or perpetuates existing inequalities. Shame can become a vehicle for institutional critique and systemic accountability if we redirect its focus from individual users to the powerful corporations, governance structures, and infrastructural systems that profit from AI’s rapid expansion. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025. KW - Affect theory KW - AI Ethics KW - Artificial intelligence KW - Environmental ethics KW - Shame KW - Systemic accountability KW - Employment KW - Ethical technology KW - Public policy KW - Affect theory KW - Artificial intelligence ethic KW - Artificial intelligence tools KW - Corporates KW - Environmental costs KW - Environmental ethics KW - Governance structures KW - Shame KW - Systemic accountability KW - Weaponization KW - Knowledge based systems CY - United States ER - TY - JOUR TI - Artificial intelligence capabilities, open innovation, and business performance – Empirical insights from multinational B2B companies AU - Sahoo S. AU - Kumar S. AU - Donthu N. AU - Singh A.K. PY - 2024 JO - Industrial Marketing Management VL - 117 SP - 28 EP - 41 DO - 10.1016/j.indmarman.2023.12.008 AB - In contrast to the operational nature of business-to-consumer (B2C) enterprises, business-to-business (B2B) organizations emphasizes offering specialized products and services to their clients, which are businesses. Recognizing that B2B organizations are dealing with complex metadata in an ever-changing business context, artificial intelligence (AI) technologies possess the potential in assisting them analyse massive amounts of data, generating actionable insights, and formulating revolutionary ideas, potentially improving collaboration and innovation. As a result, this study adopts an empirical research approach to analyse the relationship among AI capabilities, open innovation, and business performance, with an emphasis on B2B companies, based on the theoretical foundations of social-technical system and contingency theories. This study investigated the relationship between AI capabilities and open innovation practices, as well as the effect they have on business performance, using survey data collected from 398 B2B multinational companies and structural equation modelling. The findings indicate that AI capabilities have a favourable effect on open innovation practices, which subsequently leads to an improvement in business performance. Notably, the impact of AI capabilities on business performance was found to be partially mediated. The examination of the moderating effect of environmental dynamism reveals that it exerts a significant influence on the relationship between AI capabilities and outbound open innovation. However, it does not have a significant moderating impact on the causal interaction of AI capabilities on both business performance and inbound open innovation. The ramifications of these findings are significant for managers and policymakers who are interested in fostering innovation and enhancing competitiveness within the B2B sector. The results underscore the crucial role of cultivating AI capabilities. © 2023 Elsevier Inc. KW - AI KW - Artificial intelligence KW - B2B companies KW - Business performance KW - Inbound open innovation KW - Outbound open innovation CY - India, United States ER - TY - JOUR TI - Pathways to sustainable competitive performance: social entrepreneurship orientation, disruptive innovation and artificial intelligence capabilities AU - Wang C. AU - Zhang Q. AU - Zhang W. PY - 2026 JO - Humanities and Social Sciences Communications VL - 13 IS - 1 SP - 481 DO - 10.1057/s41599-026-06851-7 AB - In the era of artificial intelligence (AI), achieving a sustainable competitive advantage has become a pressing challenge for firms. Social entrepreneurship orientation (SEO) has emerged as a pivotal organizational strategy for advancing long-term sustainability. While existing studies have explored the influence of SEO on various performance outcomes, the underlying mechanisms through which SEO enhances sustainable competitive performance in high-tech firms remain underexplored. Drawing on the resource-based view (RBV) and dynamic capabilities theory (DCT), this study develops a moderated mediation model to investigate the relationship between SEO and sustainable competitive performance. Based on empirical data from 229 high-tech firms, our findings demonstrate that SEO positively influences sustainable competitive performance. Moreover, disruptive innovation mediates the relationship between SEO and sustainable competitive performance, while AI capabilities moderate the impact of SEO on disruptive innovation. This study contributes to the RBV and DCT literature, deepens the understanding of SEO outcomes, and offers valuable managerial insights for high-tech firms to promote sustainable development. © The Author(s) 2026. CY - China ER - TY - JOUR TI - Responsible Artificial Intelligence and Green Innovation Impact on MSMEs’ Sustainable Performance AU - Gupta S. AU - Dhiman A. AU - Singla A. AU - Saini G. PY - 2026 JO - Global Journal of Flexible Systems Management DO - 10.1007/s40171-026-00481-3 AB - This research investigates how MSMEs may become more sustainable by combining responsible AI (RAI) with green innovation. Although AI has revolutionary potential to boost eco-innovation and operational efficiency, the leadership’s role in coordinating new technologies with sustainable goals is not well acknowledged. The study adds to the expanding debate over the sustainable performance of MSMEs by investigating how leadership influences ethical, environmental, and economic responsibility within the organisational landscape. A mixed-methods approach was employed, beginning with quantitative analysis on 321 MSME respondents, followed by qualitative analysis. A disjoint two-stage approach using PLS-SEM is followed by meta-inferences to evaluate the hypothesised model. The findings suggest the RAI significant effect on MSMEs’ sustainable performance as well as green innovation. The influence of green innovation and RAI on sustainability is favourably moderated by sustainable transformational leadership (STL), underscoring the crucial role that leadership plays in promoting environmental and digital change. The research provides implementable recommendations for MSME leaders, industry professionals, and policymakers to incorporate RAI into business strategies, thus fostering green innovation and ensuring long-term sustainability. It affirms the key contribution of STL in overcoming barriers to AI adoption, aligning technology with sustainable goals. The study integrates RAI, green innovation and STL into a single paradigm, particularly in the unexplored setting of MSMEs. It reconceptualises RAI as a strategic factor behind sustainable performance and leadership, providing a novel viewpoint on how responsible behaviour may improve organisation performance and contribute to broader sustainability goals. © The Author(s) under exclusive licence to Global Institute of Flexible Systems Management 2026. KW - Fourth bottom line KW - Green innovation KW - Mixed methods approach KW - MSMEs KW - Responsible AI KW - Strategic flexibility KW - Sustainable transformational leadership KW - TOES framework CY - India ER - TY - JOUR TI - Developing a Consumer Electronics Robotics With a Large Language Model Based on a Trustworthy AI Framework AU - Wu H.-T. AU - Wei W. AU - Li S.-H. AU - Chen M.-Y. PY - 2025 JO - IEEE Transactions on Consumer Electronics VL - 71 IS - 1 SP - 2027 EP - 2038 DO - 10.1109/TCE.2025.3538785 AB - As many countries face the challenges of an aging population and declining birth rates, the demand for labor, particularly for assisting the elderly, is increasing. Traditional robots, being standardized products, require engineers to adjust parameters to fit the unique lifestyle of each household, which is time-consuming and inconvenient. However, with the rapid development of artificial intelligence and consumer electronics, there is a growing need for home robots with smarter interfaces to achieve the goal of intelligent living. This paper proposes a home robot based on a trustworthy AI framework, integrated with large language models (LLM). These LLM can perform natural language processing and object recognition, allowing users to control the robot’s operations through natural language commands. This innovation further advances consumer electronics. The robot’s arm can remember these actions and operate according to instructions. Additionally, the robot arm is equipped with monitoring functions, capable of overseeing the operation of other robots and using cameras to detect errors. This development is significant in the field of consumer electronics. The robot also uses Long Short-Term Memory (LSTM) networks to predict the motion paths of the robotic arm, ensuring smooth and efficient operation. This integration of AI and robotics aims to enhance the adaptability and functionality of home robots, making them more suitable for the diverse needs of modern households, improving the quality of life for the elderly, and driving innovation in the consumer electronics field. © 1975-2011 IEEE. KW - consumer electronics KW - intelligent automation KW - large language model KW - long short-term memory KW - robotic arm KW - Trustworthy AI KW - Industrial robots KW - Intelligent robots KW - Aging population KW - Home robot KW - Intelligent automation KW - Language model KW - Large language model KW - Model-based OPC KW - Natural languages KW - Robot arms KW - Short term memory KW - Trustworthy AI KW - Robotic arms CY - Taiwan, China ER - TY - JOUR TI - Artificial Intelligence for Remote Sensing Data Analysis: A review of challenges and opportunities AU - Zhang L. AU - Zhang L. PY - 2022 JO - IEEE Geoscience and Remote Sensing Magazine VL - 10 IS - 2 SP - 270 EP - 294 DO - 10.1109/MGRS.2022.3145854 AB - Artificial intelligence (AI) plays a growing role in remote sensing (RS). Applications of AI, particularly machine learning algorithms, range from initial image processing to high-level data understanding and knowledge discovery. AI techniques have emerged as a powerful strategy for analyzing RS data and led to remarkable breakthroughs in all RS fields. Given this period of breathtaking evolution, this work aims to provide a comprehensive review of the recent achievements of AI algorithms and applications in RS data analysis. The review includes more than 270 research papers, covering the following major aspects of AI innovation for RS: machine learning, computational intelligence, AI explicability, data mining, natural language processing (NLP), and AI security. We conclude this review by identifying promising directions for future research. © 2013 IEEE. KW - Data handling KW - Data mining KW - Image processing KW - Learning algorithms KW - Machine learning KW - Natural language processing systems KW - Artificial intelligence algorithms KW - Artificial intelligence techniques KW - Data understanding KW - Images processing KW - Machine learning algorithms KW - Machine-learning KW - Remote sensing data KW - Remote-sensing KW - Research papers KW - Sensing fields KW - artificial intelligence KW - image processing KW - machine learning KW - remote sensing KW - Remote sensing CY - China ER - TY - JOUR TI - Organizational sustainable artificial intelligence capabilities scale development, validation, and implications AU - Qalati S.A. AU - Siddiqui F. PY - 2026 JO - Journal of Innovation and Knowledge VL - 11 SP - 100863 DO - 10.1016/j.jik.2025.100863 AB - As artificial intelligence (AI) adoption accelerates globally, its sustainability implications remain insufficiently integrated into organizational capability frameworks. This study develops and validates the organizational sustainable AI capabilities (OSAIC) construct, extending dynamic capabilities theory by embedding sustainability as a meta-capability in AI governance and innovation processes. OSAIC is conceptualized as a five-dimensional, reflective, higher-order construct, encompassing sustainable AI learning, seizing, sensing, stakeholder integration, and transformation. A multi-phase scale development procedure—including expert Q-sorting, exploratory factor analysis, and confirmatory factor analysis, using partial least squares structural equation modeling—was employed. The scale was assessed and validated using two distinct samples: a pilot study ( n = 188) and a main study ( n = 364), both comprising managers from diverse industries and regions. The findings indicated robust psychometric attributes, characterized by substantial reliability, convergent, discriminant, and predictive validity. A positive and significant relationship between OSAIC and sustainable innovation indicated nomological validity, addressing the AI sustainability paradox by illustrating that sustainability-oriented AI capabilities enhance rather than constrain innovation. By extending the research on dynamic capabilities and paradoxes and presenting a validated measurement tool, this study contributes theoretically and methodologically, respectively, to the literature. Practically, it offers managers a diagnostic framework to align AI implementation with environmental and social accountability while fostering innovation. © 2025 The Author(s). KW - organizational sustainable AI capabilities KW - stakeholder integration KW - sustainable AI learning KW - sustainable AI seizing KW - Sustainable AI sensing KW - sustainable AI transforming CY - China ER - TY - JOUR TI - Revolutionizing urogynecology: Machine learning application with patient-centric technology: Promise, challenges, and future directions AU - Rotem R. AU - Galvin D. AU - Daykan Y. AU - Mi Y. AU - Tabirca S. AU - O'Reilly B.A. PY - 2024 JO - European Journal of Obstetrics and Gynecology and Reproductive Biology VL - 300 SP - 49 EP - 53 DO - 10.1016/j.ejogrb.2024.07.009 AB - In an epoch where digital innovation is redefining the medical landscape, electronic health records (EHRs) stand out as a pivotal transformative force. Urogynecology, a discipline anchored in intricate patient histories and meticulous follow-ups, is on the brink of profound transformation due to these digital strides. While EHRs have unified patient data, challenges related to data privacy, interoperability, and access persist. In response, we present Pelvic Health Place (PHPlace) – a multilingual, patient-centric application. Purposefully designed to bolster patient engagement, PHPlace provides clinicians with essential pre-consultation insights, streamlines the consent process, vividly delineates surgical pathways, and assures comprehensive long-term monitoring. This platform also establishes a foundation for global data amalgamation, promising to invigorate research and potentially harness artificial intelligence (AI) capabilities. With AI integration, we anticipate a more tailored treatment approach and enriched patient education, signaling a pivotal shift in urogynecology and emphasizing the imperative for ongoing academic inquiry. © 2024 Elsevier B.V. KW - Artificial intelligence KW - Digital innovation KW - Electronic health records KW - Global data amalgamation KW - Urogynecology KW - Electronic Health Records KW - Female KW - Gynecology KW - Humans KW - Machine Learning KW - Patient-Centered Care KW - Urology KW - Article KW - artificial intelligence KW - data accuracy KW - data privacy KW - digital health technology KW - electronic health record KW - follow up KW - gynecology KW - human KW - information storage KW - machine learning KW - medical history KW - patient care KW - patient coding KW - patient education KW - patient engagement KW - person centered care KW - urology KW - electronic health record KW - female KW - person centered care CY - Ireland, Israel, Romania ER - TY - JOUR TI - Harnessing Artificial Intelligence and Employee Resilience for Enhanced Business Performance: A Resource-Based and Dynamic Capabilities Perspective AU - Javed M. AU - Švecová L. AU - Danko L. AU - Tučková Z. PY - 2026 JO - Human Behavior and Emerging Technologies VL - 2026 IS - 1 SP - 1701203 DO - 10.1155/hbe2/1701203 AB - A hypercompetitive business environment has put unprecedented pressure on business firms to adopt technologies such as artificial intelligence (AI). Alongside, AI is being recognized as a strategic resource to enhance business performance. However, less is known about the mechanisms of AI capabilities toward performance-enhancing outcomes. Drawing on the resource-based view (RBV), this study explores the role of AI use in the four key organizational dimensions, i.e., supply chain management (SCM), business models (BMs), inventory management (IM), and budgeting toward business performance, along with the mediating role of employee resilience. By employing a cross-sectional research design and using survey data of 307 manufacturing firms in Pakistan, the analysis was carried out through partial least squares–structural equation modeling (PLS–SEM). Results demonstrate that the use of AI across all four domains has a significant positive influence on business performance. In addition, employee resilience partially mediates the relationship between the AI-assisted capabilities and business performance, which underscores the crucial role of human adaptive capacity in harnessing technological resources. This study contributes to the theory by demonstrating that performance improvements of AI use are not only from the technological capabilities but also from the employees’ capabilities through efficient utilization of technologies. Therefore, this study advances the literature by highlighting employee resilience as a pivotal behavioral mechanism coupling AI capabilities and business performance. This study offers practical insights for managers to strategically invest in the use of AI along with human-centered strategies to maximize business performance and long-term competitiveness. Copyright © 2026 Mohsin Javed et al. Human Behavior and Emerging Technologies published by John Wiley & Sons Ltd. KW - artificial intelligence KW - budgeting KW - business models KW - business performance KW - employee resilience KW - inventory management KW - supply chain management CY - Czech Republic ER - TY - JOUR TI - Benefits and Challenges of AI-Based Digital Twin Integration in the Saudi Arabian Construction Industry: A Correspondence Analysis (CA) Approach AU - Alnaser A.A. AU - Elmousalami H. PY - 2025 JO - Applied Sciences (Switzerland) VL - 15 IS - 9 SP - 4675 DO - 10.3390/app15094675 AB - The Fourth Industrial Revolution (4IR) has accelerated the construction industry’s shift toward digital transformation. This progress is mainly driven by the emergence of innovative technologies, including artificial intelligence (AI) and digital twins (DTs). While global research has extensively explored the benefits and challenges of AI-based DTs, the rapid growth of Saudi Arabia’s construction sector—fueled by substantial local investments and international partnerships—underscores the urgent need to examine their specific impact within this context. To address this gap, this study aims to investigate the potential benefits and challenges of integrating AI-driven DTs into Saudi Arabia’s construction industry. To achieve this, a structured literature review and a survey were conducted among architecture, engineering, and construction (AEC) firms, with 106 complete responses analyzed using correspondence analysis (CA). The findings revealed that AI-driven DTs substantially benefit Saudi Arabia’s construction industry. For example, among the 17 identified benefits, the top-ranked ones include AI capabilities to improve analytics, AI’s facilitation of digital twins in modeling complex real-world systems, and the facilitation of strategic decision making. However, several challenges hinder the realization of these benefits, including a lack of standardization of integrated DT and AI in construction projects, a lack of understanding of AI’s capabilities, a lack of logistics and the limited availability of IT infrastructure, and the complexity of AI algorithms. These findings underscore the transformative potential of integrating AI-driven DTs to optimize construction performance, improve decision-making, and address real-world complexities. This study provides actionable insights for stakeholders and recommends future research exploring strategies for overcoming adoption challenges, fostering technological innovation, and capacity building in Saudi Arabia’s construction sector. © 2025 by the authors. KW - artificial intelligence (AI) KW - benefits and challenges KW - construction industry KW - correspondence analysis (CA) KW - digital twins (DTs) KW - Decision making KW - Project management KW - Analysis approach KW - Artificial intelligence KW - Benefit and challenges KW - Construction sectors KW - Correspondence analysis KW - Correspondence analyze KW - Digital transformation KW - Digital twin KW - Industrial revolutions KW - Saudi Arabia KW - Construction industry CY - Saudi Arabia, Australia ER - TY - JOUR TI - Harnessing AI for value: examining the impact of AI capabilities and the mediating role of organizational agility on project value proposition AU - Mariani C. AU - Mancini M. PY - 2025 JO - International Journal of Managing Projects in Business VL - 18 IS - 8 SP - 112 EP - 143 DO - 10.1108/IJMPB-03-2025-0068 AB - Purpose – Recent advancements in artificial intelligence (AI) have transformed it from a mere technological tool to a key strategic asset, able to enhance company value propositions by enabling deeper insights, improved decision-making and innovative business models. This study empirically examines how AI capabilities influence value definition, creation and capture in project-based organizations (PBOs) and evaluates the mediating role of organizational agility. Design/methodology/approach – Drawing on Resource-Based View and Dynamic Capability View, we propose that AI capabilities constitute a unique type of organizational capability, enabling project-based organizations to utilize technological assets and other resources to boost productivity and generate economic value. The paper employs a survey instrument and a partial least squares structural equation modeling (PLS-SEM) to assess how AI capabilities impact project value processes and the mediating role of organizational agility in this relationship. Findings – The results robustly support all proposed hypotheses concerning the direct effects. Additionally, organizational agility is identified as a mediator in the relationship between AI capabilities and project value processes. Our study confirms that developing robust AI capabilities necessitates strategic investment in core AI resources. This offers implications for managers and policymakers aiming to leverage AI for fostering competitive advantage. Originality/value – This paper explores the role of AI capabilities in enhancing project value processes. It provides empirical evidence highlighting the significance of AI capabilities as essential organizational resources that enable the leveraging of AI to generate project value. The study supports the hypothesis that technology alone is insufficient for deriving value from it. This finding underscores the need for strategic investments in AI capabilities to fully capitalize on the potential of technological advancements. © 2025 Costanza Mariani and Mauro Mancini KW - AI capabilities KW - Organizational agility KW - Project value processes CY - Italy ER - TY - JOUR TI - Revolutionising Educational Management with AI and Wireless Networks: A Framework for Smart Resource Allocation and Decision-Making AU - Koukaras C. AU - Hatzikraniotis E. AU - Mitsiaki M. AU - Koukaras P. AU - Tjortjis C. AU - Stavrinides S.G. PY - 2025 JO - Applied Sciences (Switzerland) VL - 15 IS - 10 SP - 5293 DO - 10.3390/app15105293 AB - Educational institutions face growing challenges. Rising enrolment, limited budgets, and sustainability goals demand more efficient resource management and administrative decision-making. To address challenges like these, this work proposes a conceptual framework for smart campus management which integrates Artificial Intelligence (AI) and advanced wireless networks based on 5G. The framework’s design outlines layers for campus data collection (via sensors and connected devices), high-speed communication, and AI-driven analytics for decision support. By leveraging data-driven insights enabled by reliable wireless connectivity, institutions can make more informed decisions, use resources more effectively, and automate routine tasks. Envisioned AI capabilities include forecasting (for predictive maintenance and demand planning), anomaly detection (for fault or irregularity identification), and optimisation (for resource scheduling). Rather than reporting empirical results, the framework is illustrated through hypothetical scenarios (e.g., anticipating equipment maintenance, dynamically scheduling classrooms, or reallocating resources) to present potential benefits and tools for researchers. The discussion also highlights how the framework incorporates data privacy, security, and accessibility considerations to ensure inclusive adoption. Eventually, this conceptual proposal provides a roadmap for administrators and planners, guiding the adoption of AI and wireless innovations in educational management to enable more responsive, efficient governance and, ultimately, improve outcomes for students and staff. © 2025 by the authors. KW - 5G KW - artificial intelligence KW - decision support systems KW - education KW - Internet of Things KW - resource allocation KW - Curricula KW - Distance education KW - Educational robots KW - Enterprise resource planning KW - Human resource management KW - Information management KW - Personnel training KW - Resource allocation KW - Risk perception KW - Teaching KW - 5g KW - Administrative decision making KW - Conceptual frameworks KW - Decision supports KW - Decisions makings KW - Educational institutions KW - Educational management KW - Resource management KW - Resources allocation KW - Support systems KW - Students CY - Greece ER - TY - JOUR TI - Innovation Dynamics and Ethical Considerations of Agentic Artificial Intelligence in the Transition to a Net-Zero Carbon Economy AU - Mondal S. AU - Uyen N.C.T. AU - Das S. AU - Vrana V.G. PY - 2025 JO - Sustainability (Switzerland) VL - 17 IS - 19 SP - 8806 DO - 10.3390/su17198806 AB - As climate action becomes increasingly urgent, nations and institutions worldwide seek advanced technologies for practical mitigation efforts. This study examines how agentic artificial intelligence systems capable of decision-making and learning from experience drive innovation dynamics in climate change mitigation, with a particular focus on ethical considerations during the net-zero transition. The current urgency of climate action demands advanced technologies, yet organisations struggle to effectively deploy agentic AI for climate mitigation due to unclear implementation pathways and ethical consideration. This study examines the relationships among agentic AI capabilities, innovation dynamics, and net-zero transition performance, using survey data from 340 organisations across the manufacturing, energy, and technology sectors, and analysed using structural equation modelling. Based on dynamic capabilities theory, this research proposes a novel theoretical model that examines how agentic AI drives innovation dynamics in climate change mitigation within governance frameworks that encompass transparency, accountability, and environmental justice. Results reveal significant mediation effects of innovation dynamics, dynamic capabilities, and ethical considerations, while environmental context negatively moderates innovation and ethical pathways. Findings suggest that overly restrictive ethical considerations can lead to implementation delays that undermine the urgency of climate action. This study proposes three solutions: (1) adaptive ethical protocols adjusting governance intensity based on climate risk severity, (2) pre-approved ethical templates reducing approval delays by 60%, and (3) stakeholder co-design processes building consensus during development. The research advances dynamic capabilities theory for AI contexts by demonstrating how AI-enabled sensing, seizing, and reconfiguring capabilities create differentiated pathways to climate performance. This study provides empirical validation of the responsible innovation framework, identifies asymmetric environmental contingencies, and offers evidence-based guidance for organisations implementing agentic AI for climate action. © 2025 by the authors. KW - agentic AI KW - climate innovation KW - dynamic capabilities theory KW - ethical considerations KW - artificial intelligence KW - carbon KW - climate change KW - environmental impact KW - environmental justice KW - ethics KW - innovation KW - stakeholder CY - Greece ER - TY - JOUR TI - MANAGEMENT STRATEGIES FOR AI-BASED MUSIC STARTUPS AU - Malik M. AU - Bhatnagar S. AU - Patil V.V. AU - Pallavi M. AU - Khanna L. AU - Gupta V. PY - 2025 JO - ShodhKosh: Journal of Visual and Performing Arts VL - 6 IS - 2s SP - 139 EP - 148 DO - 10.29121/shodhkosh.v6.i2s.2025.6693 AB - The music industry, has been transformed by the introduction of new startups due to the advent of the Artificial Intelligence (AI) where machine learning, deep learning, and natural language processing are used to redefine the music creation, production, and distribution. The paper will discuss the management practices which may be utilized in the success of AI-based music startups such as the organizational models, innovation models and the mechanisms of sustainable growth. Applications of AI to music have touched various themes such as generative music composition, machine learning-based playlist optimization, machine mastering, machine-generated music and emotion-based playlists, and audience analytics. These startups are difficult to administer as they will require a mediating zone between the invention of technology and aesthetic arts and be necessitated by inter-disciplinary management, which will incorporate the engineering precision and aesthetic sense. The strategic aspects found to be agile development cycles, ethical data governance, intellectual property management and collaboration with artists and technologists. The paper is devoted to the dynamic business strategies, such as the so-called AI-as-a-Service (AIaaS) and subscription-based models which can be scaled and made to maintain the relationships with customers. Similarly, strategic cooperation with record labels, streaming applications as well as independent artists are crucial agents of market entry. Acquiring talent strategies should give priority to hybrid skills with data science, good engineering and music theory to be able to maintain product relevance and continuity of innovation. Issues like data bias, ambiguity in copyright and creative ownership are resolved by transparent algorithm design and management practices which are stakeholder-centric. The conclusion of the paper is that effective AI-driven music startups are built on a dynamic leadership, cross-domain partnership, and constant ethical review of AI work. With creative innovation and sustainability of business, these startups can transform the entire music ecosystem across the world, enabling a more personalized, intelligent, and inclusive future of music creation and consumption. © 2025 The Author(s). KW - AI-Based Startup Music KW - Creative Industries KW - Digital Entrepreneurship KW - Ethical AI KW - Generative Composition KW - Ip KW - Machine Learning KW - Management KW - Management of Innovation KW - Music Analytics KW - Music Technology CY - India ER - TY - JOUR TI - Seizing the opportunity window of artificial intelligence in China: Towards an innovation policy mix framework for emerging technologies from an evolution perspective AU - Liu J. AU - Wang M. AU - Kang X. AU - Zhang X. AU - Chen X. PY - 2022 JO - Systems Research and Behavioral Science VL - 39 IS - 3 SP - 397 EP - 414 DO - 10.1002/sres.2875 AB - China's innovation policies for artificial intelligence (AI) are widely considered as having made a remarkable achievement, which offers us a pertinent case to explore how to design and implement an effective innovation policy mix for an emerging technology. On the basis of literature on the characteristics of emerging technologies and the typology of innovation policy, this paper proposes a four-dimensional framework. It then conducts a categorical principal component analysis and a k-prototype cluster analysis by using data on 116 China's AI policy programmes from 2009 to 2021, which show that the characteristics of the innovation policy mix can be captured by the four dimensions. Furthermore, our analysis indicates that China's AI innovation policy mix evolves following the changing characteristics of AI technology over time. This paper has some implications for designing AI innovation policy mixes in other countries and designing innovation policy mixes for other emerging technologies. © 2022 John Wiley & Sons Ltd. KW - artificial intelligence KW - emerging technologies KW - innovation policy mix KW - Cluster analysis KW - Principal component analysis KW - Seizing KW - Artificial intelligence technologies KW - Design and implements KW - Emerging technologies KW - Four dimensions KW - Innovation policies KW - Innovation policy mix KW - K-prototype KW - Opportunity windows KW - Principal-component analysis KW - Typology of innovation KW - Artificial intelligence CY - China ER - TY - JOUR TI - Artificial intelligence transforming healthcare and nursing: A comprehensive bibliometric analysis AU - Balpande V. AU - Rewatkar P. AU - Dhole P. AU - Alwadkar I. AU - Gomase K. PY - 2025 JO - Multidisciplinary Reviews VL - 8 IS - 9 SP - e2025267 DO - 10.31893/MULTIREV.2025267 AB - Artificial intelligence (AI) is revolutionizing healthcare and nursing by enhancing decision-making, streamlining processes, and improving patient outcomes. This bibliometric analysis explores the evolving landscape of AI applications in healthcare and nursing, highlighting key research trends, influential publications, and emerging technologies. The study examines the integration of AI tools, such as machine learning, natural language processing, and predictive analytics, in areas like disease diagnosis, personalized treatment, patient monitoring, and administrative efficiency. It also investigates the role of AI in nursing practice, emphasizing its potential to support clinical decision-making, optimize care delivery, and alleviate workforce challenges. By analyzing a vast corpus of scholarly publications, this study identifies pivotal themes, prominent authors, and leading institutions driving AI innovation in healthcare. Key findings reveal an exponential growth in research output, particularly in leveraging AI for chronic disease management, telemedicine, and predictive risk modeling. Ethical considerations, including data privacy, algorithmic bias, and patient safety, emerge as critical focal points, underscoring the need for robust frameworks to ensure responsible AI adoption. Furthermore, the study highlights interdisciplinary collaboration as a cornerstone for successful AI integration, bridging the gap between technological advancements and clinical practice. Despite its transformative potential, challenges such as skill gaps, resistance to change, and resource constraints remain barriers to widespread AI adoption in healthcare and nursing. This comprehensive analysis provides valuable insights for researchers, practitioners, and policymakers, offering a roadmap for leveraging AI to address current and future healthcare challenges. The findings underscore the transformative role of AI in reshaping healthcare delivery, enhancing nursing practice, and ultimately improving patient care on a global scale. By synthesizing current knowledge and identifying future directions, this study contributes to advancing the understanding of AI’s impact on healthcare and nursing, paving the way for more efficient, equitable, and patient-centered care systems. © 2025, Malque Publishing. All rights reserved. KW - AI applications in nursing KW - AI in nursing care KW - artificial intelligence in healthcare KW - bibliometric analysis KW - digital transformation in medicine KW - healthcare technology trends CY - India ER - TY - JOUR TI - The Computational Study of Old English AU - Martín Arista J. PY - 2025 JO - Encyclopedia VL - 5 IS - 3 SP - 137 DO - 10.3390/encyclopedia5030137 AB - This entry presents a comprehensive overview of the computational study of Old English that surveys the evolution from early digital corpora to recent artificial intelligence applications. Six interconnected domains are examined: textual resources (including the Helsinki Corpus, the Dictionary of Old English Corpus, and the York-Toronto-Helsinki Parsed Corpus), lexicographical resources (analysing approaches from Bosworth–Toller to the Dictionary of Old English), corpus lemmatisation (covering both prose and poetic texts), treebanks (particularly Universal Dependencies frameworks), and artificial intelligence applications. The paper shows that computational methodologies have transformed Old English studies because they facilitate large-scale analyses of morphology, syntax, and semantics previously impossible through traditional philological methods. Recent innovations are highlighted, including the development of lexical databases like Nerthusv5, dependency parsing methods, and the application of transformer models and NLP libraries to historical language processing. In spite of these remarkable advances, problems persist, including limited corpus size, orthographic inconsistency, and methodological difficulties in applying modern computational techniques to historical languages. The conclusion is reached that the future of computational Old English studies lies in the integration of AI capabilities with traditional philological expertise, an approach that enhances traditional scholarship and opens new avenues for understanding Anglo-Saxon language and culture. © 2025 by the author. KW - artificial intelligence KW - computational linguistics KW - corpus lemmatisation KW - digital lexicography KW - historical language processing KW - natural language processing KW - Old English KW - universal dependencies CY - Spain ER - TY - JOUR TI - Generative AI in IoT: transforming cloud services with intelligent automation AU - Raiyani A. AU - Pandya S. AU - Jani K. AU - Vyas Z. PY - 2025 JO - International Journal of Grid and Utility Computing VL - 16 IS - 5-6 SP - 579 EP - 587 DO - 10.1504/IJGUC.2025.148544 AB - This paper explores how Generative AI can revolutionise cloud services, enabling smart automation over the Internet of Things (IoT). It outlines the potential use of Generative AI in smart homes, industrial automation, healthcare and transportation, proposing an architecture that integrates cloud computing, edge computing and IoT computing for Generative AI. The integration allows IoT devices to assess data, make independent decisions and manage themselves based on customised user experiences and functionality. Case studies and experimental evaluations demonstrate significant productivity, efficiency and user satisfaction improvements. However, challenges such as data heterogeneity, security issues and ethical considerations must be addressed for reliable AI-enabled IoT applications. Collaboration with academicians, business experts and governmental representatives is suggested to build trustworthy spaces for integrating AI capabilities into IoT cloud services, leveraging the joint effort between Gen AI and IoT for innovation, productivity and competitiveness. Copyright © 2025 Inderscience Enterprises Ltd. KW - AI-Driven IoT KW - cloud services KW - generative AI KW - intelligent automation KW - internet of things KW - Artificial intelligence KW - Automation KW - Distributed database systems KW - Edge computing KW - Intelligent buildings KW - Smart homes KW - User experience KW - AI-driven internet of thing KW - Case-studies KW - Cloud services KW - Cloud-computing KW - Edge computing KW - Generative AI KW - Industrial automation KW - Intelligent automation KW - Smart homes KW - Users' experiences KW - Internet of things CY - India ER - TY - JOUR TI - Artificial Intelligence-Driven Supply Chain Agility and Resilience: Pathways to Competitive Advantage in the Hotel Industry AU - Elshaer I.A. AU - Azazz A.M.S. AU - Aljoghaiman A. AU - Mansor M. AU - Salama M.A. AU - Fayyad S. PY - 2026 JO - Logistics VL - 10 IS - 1 SP - 5 DO - 10.3390/logistics10010005 AB - Background: The extraordinary disturbances faced by the hotel industry, ranging from worldwide health problems to political instability and climate change, have highlighted the insistent need for more resilient and agile supply chain (SC) systems. This study explored how artificial intelligence (AI) capabilities can generate competitive advantage (CA) through supply chain agility (SCA) and supply chain resilience (SCR) as mediators and competitive pressure (CP) as a moderator. Methods: Drawing on the resource-based view (RBV) framework, we suggested and empirically tested the study model. Using data collected from 432 hotel managers and analyzed using Partial Least Squares Structural Equation Modelling (SEM-PLS). Results: the results reveal that AI-driven SC can significantly strengthen SCA and SCR. Furthermore, SCA and SCR can act as powerful mediators, and CP can strengthen the tested relationships (the links from AI adoption and CA) as a moderator. Conclusions: The study made several theoretical and practical contributions by integrating AI capabilities into SCR and SCA frameworks in the hotel and tourism context, and by providing practical evidence for professionals aiming to leverage AI-driven SC tools to navigate uncertainty and create sustainable CA. © 2025 by the authors. KW - AI KW - competitive advantage KW - competitive pressure KW - resilience KW - supply chain agility CY - Saudi Arabia, Egypt ER - TY - JOUR TI - Responsible AI and employee service innovation behavior: A sequential mediation model of AI self-efficacy and AI crafting AU - Xu Y. AU - Xie P. AU - Naeem R.M. AU - Almugren I. AU - Hameed Z. AU - Agarwal S. PY - 2026 JO - Technological Forecasting and Social Change VL - 224 SP - 124470 DO - 10.1016/j.techfore.2025.124470 AB - While the use of artificial intelligence (AI) has become an effective tool for transforming individuals and organizations, adopting a responsible approach to AI systems is imperative. Drawing on conservation of resources theory and social learning theory, this study examines how responsible AI enhances employees' service innovation behavior via employee AI self-efficacy and employee AI crafting, with a particular focus on the moderating role of leader AI crafting. We tested the proposed relationships using structural equation modeling with data collected from 335 U.S. employees working in various service organizations. The findings demonstrate that the indirect effect of responsible AI on employee service innovation behavior is mediated serially by employee AI self-efficacy and employee AI crafting. Furthermore, leader AI crafting strengthens the positive relationship between responsible AI and employee AI self-efficacy. This study contributes to the AI and management literature by highlighting the importance of responsible AI systems in promoting service innovation behavior among employees. This study addresses both theoretical and practical dimensions, as well as proposing directions for future research. © 2025 Elsevier Inc. KW - Employee AI crafting KW - Employee AI self-efficacy KW - Leader AI crafting KW - Responsible AI KW - Service innovation behavior KW - Artificial intelligence KW - Artificial intelligence systems KW - Conservation of resources theories KW - Effective tool KW - Employee artificial intelligence crafting KW - Employee artificial intelligence self-efficacy KW - Leader artificial intelligence crafting KW - Responsible artificial intelligence KW - Self efficacy KW - Service innovation KW - Service innovation behavior KW - artificial intelligence KW - innovation KW - numerical model KW - social theory KW - theoretical study KW - Human resource management CY - India ER - TY - JOUR TI - ELSA Labs for responsible AI: a novel approach for addressing ethical, legal, social issues AU - Wang H. AU - Blok V. AU - van Hilten M. PY - 2025 JO - Journal of Responsible Innovation VL - 12 IS - 1 SP - 2563944 DO - 10.1080/23299460.2025.2563944 AB - Artificial Intelligence (AI) is rapidly transforming our society, offering remarkable opportunities but also raising significant Ethical, Legal, and Social Aspects (ELSA) that should be addressed for responsible development. Some existing approaches to responsible AI successfully translate ELSA into concrete AI design practices but risk overlooking power dynamics and structural issues, while others excel at fostering dialogue yet struggle to turn insights into real design changes. This paper develops the ELSA Lab approach as a promising way to bridge this gap. Building on research in Responsible Research and Innovation (RRI), Social Labs, and Quadruple Helix (QH) collaboration, we show how this approach combines the strengths of practical, solutionist strategies with sufficient negotiation and reflexivity. We not only outline the key features of this ELSA Lab approach theoretically but also present a hands-on work process for putting it into practice. This approach aims to drive a systemic shift toward more responsible AI. © 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. KW - ELSA KW - Quadruple Helix stakeholder KW - responsible research and innovation KW - social lab KW - trustworthy AI CY - Netherlands ER - TY - JOUR TI - Bragging About Valuable Resources? The Dual Effect of Companies’ AI and Human Self-Promotion AU - Vorobeva D. AU - Pinto D.C. AU - González-Jiménez H. AU - António N. PY - 2025 JO - Psychology and Marketing VL - 42 IS - 6 SP - 1680 EP - 1699 DO - 10.1002/mar.22198 AB - As companies actively invest in self-promotion of Artificial Intelligence (AI) empowered services to sustain their competitive advantage, there is a growing potential for such promotional activities to backfire. Bridging signaling theory with the resource-based view, this research reveals that companies’ self-promotion of AI resources can reduce customers’ willingness to engage with AI-based (vs. human-based) services. Four studies, including text mining and experiments, demonstrate that companies’ self-promotion of AI-based resources has a detrimental effect on willingness to engage, and concurrently perceived as exaggeration. In contrast, companies’ self-promotion about human-related resources yields beneficial outcomes, since such promotional signals contribute to the enhancement of human capital. The findings suggest that self-discrepancy and trust are the key underlying factors driving the effects as customers may experience a discrepancy between their expectations of human-like service interactions and actual AI capabilities. Additionally, findings reveal the moderating effect of honest (vs. self-promotional) framing on the relationship between service type (AI vs. human) and willingness to engage. Customer perceptions of AI appear less influenced by presentation style compared to perceptions of human resources. This research provides valuable insights into how customers respond to companies’ self-promotion of AI resources and emphasizes the need for promotional alignment with customers’ expectations about AI. © 2025 Wiley Periodicals LLC. KW - artificial intelligence KW - bragging KW - competitive advantage KW - self-discrepancy KW - self-promotion CY - Portugal, Spain ER - TY - JOUR TI - Generative AI changes the book publishing industry: reengineering of business processes AU - Ryzhko O. AU - Krainikova T. AU - Vodolazka S. AU - Sokolova K. PY - 2024 JO - Communication and Society VL - 37 IS - 3 Special Issue SP - 255 EP - 271 DO - 10.15581/003.37.3.255-271 AB - The research defines main direction of book publishing houses reengineering based on the analysis of successful cases of AI use in publishing business. The timeline of the research started in August 2023 and was summarised in the beginning of January 2024. The main methods were expert interview, monitoring of international and Ukrainian internet platforms, and document analysis. The study showed that the main aspects of business processes reengineering in publishing houses, based on the use of AI, are: (1) development of business strategies and plans; (2) development of digital spaces in publishing houses; (3) emerging of new professions; (4) discussions and their summaries; (5) received manuscripts check; (6) finding plagiarism; (7) preparation of creative, advertising, and presentation materials; (8) working with numbers and databases. The recommendations on the use of AI in business processes are extracted from the policies of the organisations connected with the book publishing industry. They are presented in the convenient table for further use. One of the study results showed that Ukrainian publishing houses discuss the capabilities of AI for generating different types and formats of content, and based on that, AI capabilities for reengineering are considered. One of the biggest challenges, created by AI, is that the technology develops faster than people can perceive so they struggle to describe the technology itself and its impact. It means that we should adjust to the changes, caused by exponential development of AI, finding resources to overcome unequal access to AI capabilities in the process of specialists’ preparation. © 2024 Communication & Society. KW - book publishing KW - book publishing houses KW - business processes KW - generative AI KW - Reengineering KW - technological innovations CY - Ukraine ER - TY - JOUR TI - A systematic review of generative AI: importance of industry and startup-centered perspectives, agentic AI, ethical considerations & challenges, and future directions AU - Patel K. AU - Shah M. AU - Qureshi K.M. AU - Qureshi M.R.N. PY - 2026 JO - Artificial Intelligence Review VL - 59 IS - 1 SP - 7 DO - 10.1007/s10462-025-11435-z AB - Generative Artificial Intelligence (GenAI) is rapidly redefining the landscape of work organizations and society at large. GenAI has rapidly evolved from rule-based symbolic systems ofThe 1940 s to advanced deep learning architectures capable of producing human-like content across modalities, such as text, images, audio, and video. This review focuses on current emerging trends, such as large concept models and critical comparisons of tools, including ChatGPT, Gemini, and Claude. This study synthesizes evidence of GenAI’s essential role across major industries, revealing transformative applications in the finance, cloud and IT, healthcare, education, and energy sectors. The paper also highlights the unique opportunities GenAI offers for start-ups, enabling agile projects to leverage cutting-edge technology for competitive advantage. However, the deployment of GenAI systems through edge devices also raises critical challenges related to ethics, transparency, bias, accountability, computational issues, and many more. To address these complexities, this paper examines emerging approaches such as AI agents, agentic AI, and multi-agent systems that aim to extend the functionality of GenAI through autonomy, goal-directed behavior, and collaborative intelligence. It discovers novel incorporations with agentic AI architecture, such as BabyAGI, and discusses emerging issues of coordination, hallucination, and security risks. The findings reveal persistent challenges related to scalability, interpretability, and regulatory compliance while identifying future research directions toward developing more sophisticated, ethical, and accessible GenAI systems that will continue to reshape technological landscapes and societal interactions. This systematic review informs researchers, academicians, data scientists, and developers about the latest advancements in GenAI and highlights its applications and role across various industries, as well as supporting practitioners and scholars in staying current with the rapidly evolving landscape of generative technologies. © The Author(s) 2025. KW - Agentic AI KW - AI agents KW - Artificial intelligence KW - Compliance KW - Ethical frameworks KW - GenAI evolution KW - Generative artificial intelligence KW - Industrial GenAI KW - Large concept model KW - Large language model KW - Multi agent systems KW - Autonomous agents KW - Competition KW - Ethical technology KW - Intelligent agents KW - Regulatory compliance KW - Agentic AI KW - AI agent KW - Compliance KW - Concept model KW - Ethical framework KW - Generative artificial intelligence KW - Generative artificial intelligence evolution KW - Industrial generative artificial intelligence KW - Language model KW - Large concept model KW - Large language model KW - Multiagent systems (MASs) KW - Multi agent systems CY - Australia, India, Saudi Arabia ER - TY - JOUR TI - Who legitimises the AI algorithm? Leadership, volatility and the governance of algorithmic authority AU - Anning-Dorson T. PY - 2026 JO - Strategy and Leadership SP - 1 EP - 19 DO - 10.1108/SL-12-2025-0453 AB - Purpose – This paper reconceptualizes ethical AI governance as a leadership problem of legitimacy production rather than technical compliance. Existing frameworks presume stable infrastructure, coherent institutions, and baseline trust − assumptions that frequently fail in volatile environments. Drawing on Africa as a theory-generating extreme context, this paper reframes ethical governance as a legitimacy infrastructure required for governable AI deployment, addressing the question of who legitimizes the AI algorithm. Design/methodology/approach – The paper develops an analytically grounded conceptual framework supported by illustrative vignettes. It specifies volatility as three-dimensional: infrastructural, institutional, and socio-political, showing how each generates distinctive governance breakdowns. Building on legitimacy theory and algorithmic accountability scholarship, the paper derives a Sensing–Stabilizing–Legitimizing (SSL) leadership capability model and three falsifiable propositions that explain how organizations sustain contestability and accountability amid instability. Findings – In times of volatility, ethical AI governance succeeds only when leaders institutionalize legitimacy infrastructure rather than relying on principles alone. Infrastructural volatility produces exclusion-by-fragility; institutional volatility generates compliance theater; socio-political volatility amplifies legitimacy shocks. The SSL capability chain is decisive for sustaining governable algorithmic authority. Practical implications – Leaders should establish clear decision rights, escalation pathways, and recourse mechanisms calibrated to volatility conditions. Organizations should build redundancy into monitoring, prioritize safeguards for vulnerable groups, and treat impact assessments as living governance instruments rather than one-time compliance deliverables. Originality/value – The paper makes an integrative-conceptual contribution by theorizing volatility as an explicit governance condition and positioning ethical AI governance as a strategic leadership capability rather than a delegated technical task. It positions Africa as an extreme context that reveals hidden assumptions in dominant governance models and generates transferable insights for rising global volatility. © 2026 Thomas Anning-Dorson. KW - Algorithmic authority KW - Ethical AI governance KW - Leadership in VUCA environments KW - Legitimacy infrastructure KW - Volatility and innovation CY - South Africa ER - TY - JOUR TI - Integrating AI Ethics and Sustainability Through Experiential and Data-Driven Curriculum Innovation at PCCOE AU - Vivekanandan V. AU - Rajeswari R. PY - 2026 JO - Journal of Engineering Education Transformations VL - 39 IS - Special Issue 2 SP - 213 EP - 222 DO - 10.16920/jeet/2026/v39is2/26026 AB - A novel undergraduate course, Professional Ethics and Sustainability in the Age of AI, bridges critical gaps in engineering education by combining experiential learning with outcome-based assessment. Developed at Pimpri Chinchwad College of Engineering (PCCOE), the curriculum employs four research-grounded activities: historical case analyses of ethical disasters, TARES Test evaluations of AI advertisements, governance quizzes on surveillance systems, and multi-stakeholder role-plays about algorithmic grading. Interim results from 46-60 participants demonstrate significant competencies development: 91.3% of students recognize AI's ethical influence, 95.7% show heightened emotional awareness, with strong performance in persuasion literacy (M=4.40/5) and governance knowledge (M=9.43/10). Structured assessments reveal 81.8% attainment in ethical reasoning and 80.5% in communication/governance skills, while qualitative analysis uncovers sophisticated engagement with fairness, transparency, and accountability principles. Built on Kohlberg's moral development theory, UNESCO's ESD framework, and IEEE's Ethically Aligned Design, the course uniquely integrates macro ethical principles with micro ethical skill-building. Final evaluations of the sustainability-focused AI mini-projects showed attainment levels of 82.4% for CO2 and 84.1% for CO4, completing the comprehensive outcomes-based assessment cycle.. This model offers engineering educators a replicable blueprint for cultivating professional judgment in AI ethics through three key innovations: (1) contextualized historical analogies, (2) measurable persuasion literacy benchmarks, and (3) stakeholder negotiation simulations that mirror real-world tech governance challenges. The demonstrated success of this active learning approach provides empirical support for transforming traditional ethics education in response to emerging technologies. © 2026, Rajarambapu Institute Of Technology. All rights reserved. KW - AI Ethics Education KW - Experiential Learning KW - Governance Competencies KW - Outcome-Based Assessment KW - Sustainability Literacy CY - India ER - TY - JOUR TI - The role of knowledge creation modes in architectural innovation AU - Azzam A. AU - He Q. AU - Sarpong D. PY - 2020 JO - Strategic Change VL - 29 IS - 1 SP - 77 EP - 87 DO - 10.1002/jsc.2312 AB - Knowledge creation modes (especially socialization and internalization) enhance architectural innovation (AI) capability of U.K. manufacturing firms. AI is the reconfiguration of product or process components and creating completely new interfaces between them. Knowledge creation modes enhance firms' AI to create new products while utilizing their architectural knowledge. Knowledge socialization and internalization are the most important modes that affect AI. Socialization helps to share tacit knowledge while internalization enables individuals to absorb and embody accumulated know-how to envision new product ideas. © 2019 John Wiley & Sons, Ltd. CY - United Kingdom ER - TY - JOUR TI - Implementing Artificial Intelligence in Critical Care Medicine: a consensus of 22 AU - Cecconi M. AU - Greco M. AU - Shickel B. AU - Angus D.C. AU - Bailey H. AU - Bignami E. AU - Calandra T. AU - Celi L.A. AU - Einav S. AU - Elbers P. AU - Ercole A. AU - Gómez H. AU - Gong M.N. AU - Komorowski M. AU - Liu V. AU - Park S. AU - Sarwal A. AU - Seymour C.W. AU - Zampieri F.G. AU - Taccone F.S. AU - Vincent J.-L. AU - Bihorac A. PY - 2025 JO - Critical Care VL - 29 IS - 1 SP - 290 DO - 10.1186/s13054-025-05532-2 AB - Artificial Intelligence (AI) is rapidly transforming the landscape of critical care, offering opportunities for enhanced diagnostic precision and personalized patient management. However, its integration into ICU clinical practice presents significant challenges related to equity, transparency, and the patient-clinician relationship. To address these concerns, a multidisciplinary team of experts was established to assess the current state and future trajectory of AI in critical care. This consensus identified key challenges and proposed actionable recommendations to guide AI implementation in this high-stakes field. Here we present a call to action for the critical care community, to bridge the gap between AI advancements and the need for humanized, patient-centred care. Our goal is to ensure a smooth transition to personalized medicine while, (1) maintaining equitable and unbiased decision-making, (2) fostering the development of a collaborative research network across ICUs, emergency departments, and operating rooms to promote data sharing and harmonization, and (3) addressing the necessary educational and regulatory shifts required for responsible AI deployment. AI integration into critical care demands coordinated efforts among clinicians, patients, industry leaders, and regulators to ensure patient safety and maximize societal benefit. The recommendations outlined here provide a foundation for the ethical and effective implementation of AI in critical care medicine. © The Author(s) 2025. KW - Artificial intelligence KW - Critical care medicine KW - Ethics KW - Healthcare innovation KW - Personalized medicine KW - Artificial Intelligence KW - Consensus KW - Critical Care KW - Humans KW - Intensive Care Units KW - Article KW - artificial intelligence KW - clinical practice KW - emergency ward KW - health care KW - human KW - intensive care KW - intensive care medicine KW - intensive care unit KW - multidisciplinary team KW - patient care KW - patient safety KW - person centered care KW - personalized medicine KW - consensus KW - intensive care KW - organization and management KW - procedures CY - Belgium ER - TY - JOUR TI - Emerging AI Regimes and Contemporary Filmmaking in Nigeria: Governance, Practice, and Creative Futures AU - Bello R.-W. AU - Ogundokun R.O. AU - Owolawi P.A. AU - Wyk E.A.V. AU - Tu C. AU - Imoru O. PY - 2025 JO - Architecture Image Studies VL - 6 IS - 3 SP - 1056 EP - 1063 DO - 10.62754/ais.v6i3.380 AB - Filmmaking is rapidly transforming by Artificial Intelligence (AI) across the globe, with Nigeria’s vibrant film industry (i.e. Nollywood) emerging as an adopter and a contested site for technological experimentation. How technological, institutional, and regulatory frameworks as emerging AI regimes are reforming creative practices and industry structures in Nigeria is examined in this study. Also, the integration of AI tools was explored in this study across pre-production, production, postproduction, and distribution by situating Nollywood within global discourses of platform capitalism and algorithmic governance. The study highlights several case studies such as AI-enabled editing, subtitling, visual effects, and distribution on digital platforms. It addresses the challenges in governance related to intellectual property, labor, and cultural representation. The study, by drawing on a multi-level framework of AI regimes, emphasizes that in time to come, Nigerian filmmaking will depend on innovation that is balanced with protections for creative labor and cultural integrity. The study concludes by recommending policies and practices for building a comprehensive, sustainable AI-enabled film ecosystem in Nigeria and Africa in general. © by AP2 on Creative Commons 4.0 KW - Artificial Intelligence KW - Filmmaking KW - Nigeria KW - Nollywood KW - Regime. CY - Nigeria, South Africa, Canada ER - TY - JOUR TI - How the First Medical Imaging Cancer Atlas EUCAIM Was Populated: The Experience of a Reference Hospital. AU - Penadés Blasco A. AU - Cerdá Alberich L. AU - de Marco García A. AU - Soler Pons C. AU - Marín Radoszynski I. AU - Martínez R. AU - Segrelles-Quilis D. AU - Blanquer I. AU - Martí-Bonmatí L. PY - 2025 JO - Open Research Europe VL - 5 SP - 310 DO - 10.12688/openreseurope.21016.1 AB - The fragmentation and decentralization of medical data, including radiological imaging, continue to challenge large-scale observational research across Europe. Artificial intelligence (AI) applied to big datasets is transforming diagnosis and treatments towards precision medicine across many diseases, yet the lack of findable, accessible, and interoperable datasets still limits model development, validation, and final clinical translation. The European Federation for Cancer Images (EUCAIM) project was launched in 2023 to address these challenges by establishing a secure centralized and federated infrastructure for the secondary use of large-scale oncological imaging and related clinical data. By consolidating fragmented datasets, EUCAIM lays the groundwork for harmonized data governance and trusted cross-border sharing. Implementing a robust documentation framework is essential to ensure regulatory compliance, safeguard data integrity, and support secure data flows across institutional and national boundaries, fully aligned with European regulations and ethical standards. EUCAIM builds on the AI for Health Imaging (AI4HI) initiative (PRIMAGE, CHAIMELEON, EuCanImage, ProCancer-I, INCISIVE) and integrates over 94 partners and more than 180 stakeholders spanning medical imaging, high performance computing, data standardization, innovation, and legal compliance. This large collaborative ecosystem reinforces EUCAIM’s role as a reference for General Data Protection Regulation (GDPR) and European Health Data Space Regulation (EHDSR) adherence. This publication presents the real-world experience of integrating imaging and clinical data from a reference university hospital into the EUCAIM infrastructure. It outlines the procedural, ethical, and legal challenges encountered, and details the strategies implemented to ensure compliance with data protection regulations, including privacy, security, and ethical standards. These insights offer a practical framework for future large-scale oncological imaging datasets harmonization and AI development, contributing to scalable, reproducible, and legally compliant research that strengthens Europe’s capacity for trustworthy AI-driven oncology solutions. Copyright: © 2025 Penadés Blasco A et al. KW - Artificial Intelligence KW - cancer research KW - data governance KW - federated infrastructures KW - innovation KW - medical imaging KW - sustainability CY - Spain ER - TY - JOUR TI - Adding External Artificial Intelligence (AI) into Internal Firm-Wide Smart Dynamic Warehousing Solutions AU - Hamilton J.R. AU - Maxwell S.J. AU - Ali S.A. AU - Tee S. PY - 2024 JO - Sustainability (Switzerland) VL - 16 IS - 10 SP - 3908 DO - 10.3390/su16103908 AB - This study advances knowledge in the AI field. It provides deep insight into current industry generative AI inclusion systems. It shows both literature and practical leading industry operations can link, overlap, and complement each other when it comes to AI and understanding its complexities. It shows how to structurally model and link AI inclusions towards delivering a suitable sustainability positioning. It shows approaches to integrate external AI contributions from one firm into another firm’s intelligences developments. It shows how to track, and maybe benchmark, the progress of such AI inclusions from either an external or an integrated internal software developer perspective. It shows how to understand and create a more sustainable, AI-integrated business positioning. This study considers firm artificial intelligence (AI) and the inclusion of additional external software developer engineering as another AI related pathway to future firm or industry advancement. Several substantive industrial warehousing throughput areas are discussed. Amazon’s ‘smart dynamic warehousing’ necessitates both digital and generative ongoing AI system prowess. Amazon and other substantive, digitally focused industry warehousing operations also likely benefit from astute ongoing external software developer firm inclusions. This study causally, and stagewise, models significant global software development firms involved in generative AI systems developments—specifically ones designed to beneficially enhance both warehouse operational productivity and its ongoing sustainability. A structural equation model (SEM) approach offers unique perspectives through which substantive firms already using AI can now model and track/benchmark the relevance of their prospective or existing external software developer firms, and so create rapid internal ‘net-AI’ competencies incorporations and AI capabilities developments through to sustainable operational and performance outcomes solutions. © 2024 by the authors. KW - acquisition KW - artificial intelligence KW - assimilation KW - autonomous robots KW - collective knowledge KW - competitiveness KW - deep machine learning KW - digital network KW - generation AI system KW - innovation KW - productive capacities KW - strategic risk KW - sustainable performance KW - transformation KW - Amazonia KW - artificial intelligence KW - business KW - data acquisition KW - digitization KW - innovation KW - knowledge KW - machine learning KW - robotics KW - software KW - strategic approach KW - sustainability CY - Australia ER - TY - JOUR TI - HitHire: The future of ethical, fair, and sustainable AI recruitment – A governance framework AU - Albaroudi E. AU - Mansouri T. AU - Hatamleh M. AU - Alameer A. PY - 2026 JO - Array VL - 29 SP - 100592 DO - 10.1016/j.array.2025.100592 AB - Artificial Intelligence (AI) is transforming recruitment but remains susceptible to algorithmic bias and environmental inefficiencies. This paper presents HitHire, a pilot fairness- and sustainability-aware AI hiring platform tailored to the Saudi Arabian context and aligned with Vision 2030 goals. HitHire integrates large language models (LLMs), adversarial debiasing, Shapley Additive Explanations (SHAP), and real-time carbon tracking to ensure transparent and equitable candidate ranking. Evaluated on 350 anonymized CVs across four job roles (web development, finance, human resources, and data science) using a 70/20/10 train/test/validation split, HitHire achieves notable improvements in fairness metrics—Statistical Parity Difference (SPD) for gender = 0.0156 and Disparate Impact (DI) for nationality = 1.2387—while maintaining strong predictive performance (F1 = 0.96 compared to a baseline of 0.80). The system achieves over a 40% reduction in operational CO2 emissions, with inference energy consumption of 0.003 kWh per query. In a three-month pilot study involving 23 HR professionals within a large Saudi organization, 87% of participants rated system trust at 4 out of 5 or higher. These findings contribute to national digital ethics strategies such as the Saudi Green Initiative, which emphasizes carbon neutrality and sustainable innovation. © 2025 The Author(s) KW - Adversarial Debiasing KW - AI governance KW - Algorithmic Bias KW - Ethical recruitment KW - Explainable AI KW - Fairness in AI KW - Human-in-the-Loop systems KW - Saudi Vision 2030 KW - SHAP explainability KW - Sustainable AI KW - Artificial intelligence KW - Employment KW - Ethical technology KW - Personnel KW - Sustainable development KW - Adversarial debiasing KW - Algorithmic bias KW - Algorithmics KW - Artificial intelligence governance KW - De-biasing KW - Ethical recruitment KW - Explainable artificial intelligence KW - Fairness in artificial intelligence KW - Human-in-the-loop KW - Human-in-the-loop system KW - Loop systems KW - Saudi vision 2030 KW - Shapley KW - Shapley additive explanation explainability KW - Sustainable artificial intelligence KW - Carbon CY - United Kingdom ER - TY - JOUR TI - Exploring perspectives on artificial intelligence: Awareness, attitudes, and knowledge among health majors students at Saudi universities AU - Albaik M. AU - Al-Qahtani S.A. AU - Mantargi M.J.S. AU - Alghamdi A. AU - Sindi I.A. AU - Sheikh R.A. AU - Kamel M. AU - Kurdi L.A.F. PY - 2025 JO - PeerJ Computer Science VL - 11 SP - e3255 DO - 10.7717/peerj-cs.3255 AB - Background: The world is witnessing tremendous development in the field of new digital tools, including artificial intelligence (AI), in all sectors, including the health and medical sectors. However, educational and training opportunities in the field of artificial intelligence remain nascent and limited. Hence, this study aims to assess the awareness, attitudes, and knowledge of artificial intelligence among students of health specialties in Saudi universities and to assess whether artificial intelligence is viewed as a beneficial innovation or a potential threat to healthcare roles. Methods: This cross-sectional study included 498 male and female students from various health colleges at different Saudi universities. The participants completed an online questionnaire adapted from previous studies to assess their awareness, attitudes, and knowledge of artificial intelligence. Descriptive statistics and chi-square analyses were conducted to explore the associations between variables related to artificial intelligence and other factors. Results: Most students showed a high level of awareness of artificial intelligence, with social media being identified as their main source of information about artificial intelligence. While students’ attitudes towards AI were generally positive, for example, 89.2% of the students believed that AI would be crucial to the future of healthcare, 76.7% supported AI education, and 78.3% were keen to increase their knowledge of AI. In terms of assessing students’ knowledge of AI, the study revealed that the participating students had moderate knowledge of AI principles and skills, with significant gaps in understanding specific AI capabilities and functions. Conclusions: While healthcare students in Saudi Arabia demonstrate strong awareness and positive attitudes towards AI, there are significant gaps in practical knowledge. These findings underscore the need for tailored educational strategies to better integrate AI into curricula, thus preparing future healthcare professionals to effectively leverage AI. © Copyright 2025 Albaik et al. Distributed under Creative Commons CC-BY 4.0 KW - Artificial Intelligence KW - Artificial intelligence (AI) KW - Computer Education KW - Education KW - Emerging Technologies KW - Health sciences KW - Human-Computer Interaction KW - Saudi universities KW - Student perceptions KW - Artificial intelligence KW - Education computing KW - Engineering education KW - Health care KW - Health risks KW - Personnel training KW - Artificial intelligence KW - Computer education KW - Computer interaction KW - Cross-sectional study KW - Digital tools KW - Emerging technologies KW - Health science KW - Potential threats KW - Saudi university KW - Student perceptions KW - Students CY - Saudi Arabia, Egypt ER - TY - JOUR TI - How AI innovation shapes supplier concentration under the triple helix framework: Evidence from emerging markets AU - Wu W. AU - Xu J. AU - Li Y. AU - Fan Y. AU - Tang S. PY - 2026 JO - Technological Forecasting and Social Change VL - 226 SP - 124579 DO - 10.1016/j.techfore.2026.124579 AB - This study investigates how artificial intelligence innovation influences supplier concentration and management efficiency among firms in emerging markets. Using patent data from Chinese A share listed companies between 2014 and 2023, we construct firm level measures of AI innovation and examine their association with upstream supply chain outcomes. The empirical analysis employs fixed effects regression models to control for unobserved firm heterogeneity and common temporal shocks. Results indicate that AI related patents are positively and significantly associated with supplier concentration, suggesting that firms with stronger AI capabilities tend to rely on fewer upstream partners. We also find that AI innovation is positively associated with supplier management efficiency, as reflected in higher inventory turnover and faster accounts payable cycles. Further analysis reveals that these relationships are contingent on firm characteristics: digital maturity and operational risk exposure amplify the effects of AI innovation on both supplier concentration and efficiency outcomes. We interpret these findings through the lens of the Triple Helix framework, which emphasizes the institutional context of government policy, academic knowledge production, and industrial application that characterizes innovation ecosystems in emerging markets. The study contributes to the literature by shifting attention from operational performance to structural supply chain outcomes, identifying boundary conditions that shape technology driven supply chain restructuring, and demonstrating the relevance of institutional perspectives for understanding digital transformation in interfirm relationships. Practical implications are discussed for managers seeking to leverage AI for supply chain optimization and for policymakers aiming to balance innovation promotion with supply chain resilience. © 2026 KW - Supplier concentration KW - Supplier management efficiency KW - Triple Helix perspective KW - China KW - Commerce KW - Inventory control KW - Marketing KW - Patents and inventions KW - Public policy KW - Regression analysis KW - Supply chains KW - Technology transfer KW - Emerging markets KW - Empirical analysis KW - Firm heterogeneity KW - Fixed effects regression models KW - Management efficiency KW - Supplier concentration KW - Supplier management KW - Supplier management efficiency KW - Triple helix perspective KW - Triple helixes KW - artificial intelligence KW - boundary condition KW - innovation KW - supply chain management KW - Efficiency CY - China, Australia, United Kingdom ER - TY - JOUR TI - How Does Artificial Intelligence Capability Affect Product Innovation in Manufacturing Enterprises? Evidence from China AU - Gao Y. AU - Liu Y. AU - Wu W. PY - 2025 JO - Systems VL - 13 IS - 6 SP - 480 DO - 10.3390/systems13060480 AB - In today’s fast-changing business environment, artificial intelligence (AI) capability plays a critical role in fostering product innovation (PI). Resource-based theory (RBT) posits that resources and capabilities characterized as valuable, rare, inimitable, and non-substitutable can generate a sustained competitive advantage, providing an appropriate theoretical framework for this study. Using RBT this study examines how business intelligence transforming capability (BITC) mediates the relationship between AI capability and PI and how formal and informal knowledge governance mechanisms (FKGMs and IKGMs, respectively) moderate the effect of AI capability on BITC. Using partial least squares structural equation modeling on 516 Chinese manufacturing enterprises, we empirically test a mediated moderation model. The findings reveal that BITC significantly mediates the relationship between AI capability and PI. Both FKGMs and IKGMs strengthen the effect of AI capability on BITC (with IKGMs showing a stronger influence). This study theoretically contributes by identifying BITC’s mediating role, defining AI capability and BITC boundary conditions, revealing FKGMs’ and IKGMs’ asymmetries, and extending RBT. In terms of practical contributions, the findings emphasize the necessity of developing BITC and strategically applying both FKGMs and IKGMs to maximize AI capability-driven PI benefits. © 2025 by the authors. KW - artificial intelligence capability KW - business intelligence transforming capability KW - formal knowledge governance mechanisms KW - informal knowledge governance mechanisms KW - product innovation KW - Artificial intelligence KW - Competitive intelligence KW - Information analysis KW - Knowledge management KW - Artificial intelligence capability KW - Business intelligence transforming capability KW - Business-intelligence KW - Formal knowledge KW - Formal knowledge governance mechanism KW - Governance mechanisms KW - Informal knowledge governance mechanism KW - Knowledge governance KW - Product innovation KW - Competition CY - China ER - TY - JOUR TI - Artificial Intelligence Fueling Endogenous Innovation: Evidence on Global Value Chain Upgrading in Chinese Manufacturing Firms AU - Yu R. AU - Cheng T.C.E. AU - Xu X. PY - 2026 JO - IEEE Transactions on Engineering Management VL - 73 SP - 2163 EP - 2179 DO - 10.1109/TEM.2026.3658087 AB - This study investigates how artificial intelligence (AI)-driven endogenous innovation enables Chinese manufacturing firms to upgrade their positions in global value chains (GVCs). Based on survey data from 287 firms, we identify a core mechanism through which AI alleviates resource constraints by improving technical efficiency, supporting data-driven decision-making, and facilitating knowledge recombination. This mechanism helps firms overcome low-end lock-in and move toward higher value activities. Our analysis reveals two key findings that contrast with established views. First, the primary internal driver of innovation is organizational innovation culture rather than individual entrepreneurship, refining the traditional Schumpeterian paradigm's emphasis on the entrepreneur. Second, while absorptive capacity strengthens process and product upgrading, it does not support functional upgrading, revealing a disconnect between technological capability and governance power. The study contributes theoretically by clarifying the linkages among AI capabilities, endogenous innovation, and GVC upgrading. For managers, it underscores the importance of cultivating an innovation-oriented culture within the organization, while leveraging external market pressures and policy support to build a robust foundation in data, algorithms, and computing power. All findings are validated through structural equation modeling and robustness checks, providing reliable insights for both research and practice. © 2026 IEEE. KW - Artificial intelligence (AI) KW - endogenous innovation KW - global value chain KW - Artificial intelligence KW - Chains KW - Engineering research KW - Knowledge management KW - Artificial intelligence KW - Core mechanisms KW - Data driven decision KW - Decisions makings KW - Endogenous innovation KW - Global value chain KW - Manufacturing firms KW - Resource Constraint KW - Survey data KW - Technical efficiency KW - Decision making CY - China, United Kingdom ER - TY - JOUR TI - Building capabilities for responsible AI in finance: insights from Fuzzy ISM AU - Kaur J. AU - Kaushik H. AU - Swami S. AU - Srivastava S. AU - Kumari B. PY - 2026 JO - Journal of Ambient Intelligence and Humanized Computing DO - 10.1007/s12652-026-05085-4 AB - The rapid adoption of artificial intelligence (AI) in the financial sector has intensified concerns regarding responsible use, governance, and long-term capability development. While prior studies examine AI adoption, regulation, ethical principles, or performance outcomes, limited empirical research explains how banking and regulated financial service organizations systematically build responsible AI capabilities as part of their strategic management processes. In particular, existing studies do not structurally model the interdependencies among AI management factors nor link them to dynamic organizational capabilities. Addressing this gap, this study presents an original empirical investigation of responsible AI management in commercial banks and regulated financial institutions engaged in risk management, compliance, credit assessment, and customer-facing financial services. The study makes an original theoretical contribution by integrating Fuzzy Interpretive Structural Modelling (FISM) with Dynamic Capabilities Theory (DCT) to develop a new, theory-informed capability-building framework. A three-phase mixed-methods design was employed. In the first phase, open-ended questionnaires and in-depth interviews with 26 domain experts, along with Nominal Group Technique (NGT) sessions involving 11 experts, identified 14 critical factors influencing responsible AI management. In the second phase, FISM was applied to model the hierarchical and contextual interrelationships among these factors. In the final phase, follow-up interviews mapped these factors to the sensing, seizing, and transforming dimensions of DCT. The findings generate new empirical insights demonstrating that responsible AI in banking functions as a second-order dynamic capability extending beyond technological readiness. While financial institutions possess foundational AI resources, they lack specialized skills and governance structures required for responsible AI integration. Ethical governance mechanisms, workforce development, legacy system alignment, and technology partnerships emerge as key driving enablers. The study offers an original, empirically grounded framework that advances responsible AI scholarship by linking structural factor modelling with dynamic capability development, providing a structured roadmap for financial managers and policymakers seeking to align AI innovation with risk management and ethical accountability. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2026. KW - Banking KW - Ethical technology KW - Legacy systems KW - Risk management KW - Capability development KW - Dynamics capability KW - Ethical principles KW - Financial institution KW - Financial sectors KW - Financial service KW - Intelligence management KW - Interpretive structural models KW - Performance outcome KW - Risks management KW - Risk assessment CY - India ER - TY - JOUR TI - Human–AI collaboration in knowledge ecosystems: a multidisciplinary review, integrative framework and future directions AU - Ali I. AU - Nguyen K. AU - Ali A.M. AU - Cui T. PY - 2025 JO - Journal of Knowledge Management SP - 1 EP - 22 DO - 10.1108/JKM-03-2025-0431 AB - Purpose – The advancement of artificial intelligence (AI) is transforming knowledge ecosystems, reshaping the creation, dissemination and application of knowledge. This study aims to delve into the powerful synergy between human expertise and AI, illustrating how computational intelligence amplifies decision-making and sparks groundbreaking innovation in complex and data-rich business environments. Design/methodology/approach – Through a systematic review of 101 scholarly articles, this study synthesizes key insights and presents a comprehensive framework integrating socio-technical, ethical and policy dimensions of AI adoption. Findings – Human–AI collaboration in knowledge ecosystems is shaped by antecedents (trust, AI capabilities, organizational context, user expertise); mediators (cognitive alignment, explanation quality, emotional engagement); and moderators (user attitudes, task complexity, transparency, ethics). Positive configurations enhance decision quality, innovation and user satisfaction, while risks such as power imbalances, deskilling and algorithmic opacity can undermine collaboration and productivity. The authors devise an integrative antecedent–mediator–moderator–outcome framework, emphasizing human-centered design, contextual integration and equity. They also highlight the need for more empirical and theory-driven research in the domain. Originality/value – By bridging fragmented perspectives, this study advances theoretical understanding and illuminates practical pathways for leveraging AI to augment human ingenuity, uphold ethical imperatives and catalyze innovation in rapidly shifting knowledge landscapes. © 2025 Emerald Publishing Limited KW - Antecedent–moderator–mediator–outcome framework KW - Human–AI collaboration KW - Knowledge ecosystems KW - Multidisciplinary review CY - Australia, Saudi Arabia ER - TY - JOUR TI - Human strategic innovation against AI systems - analyzing how humans develop and implement novel strategies that exploit AI limitations AU - Dattijo A. AU - Jo S. PY - 2025 JO - Discover Artificial Intelligence VL - 5 IS - 1 SP - 321 DO - 10.1007/s44163-025-00439-x AB - This paper systematically analyzes documented cases and examines human strategic innovation against artificial intelligence systems. Drawing from peer-reviewed research and verified instances in strategic domains including complex games such as Go (Wang et al. in: Proceedings of the 40th international conference on machine learning, 2023), chess (McIlroy-Young et al. in Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining, 2020), Dota 2 (Berner et al. Dota 2 with large-scale deep reinforcement learning. arXiv preprint arXiv:19106680, 2019), and poker (Brown and Sandholm in Science 359:418–424, 2017), as well as real-world applications including cybersecurity (Comiter Attacking artificial intelligence: AI's security vulnerability and what policymakers can do about it. Belfer Center for Science and International Affairs, Harvard Kennedy School, 2019) and finance (Zhang et al., 2024), we identify patterns in human innovation when confronting AI opponents. Our analysis reveals that humans can achieve notable successes by developing novel strategies operating outside AI training distributions, exploiting specific AI limitations (Gleave et al. in International Conference on Machine Learning, 2020). Key findings demonstrate several critical mechanisms. First, pattern-breaking innovations enable humans to force AI systems into unfamiliar decision spaces where their training becomes insufficient (Comiter Attacking artificial intelligence: AI's security vulnerability and what policymakers can do about it. Belfer Center for Science and International Affairs, Harvard Kennedy School, 2019). Second, exploiting AI's bounded rationality allows strategic actors to leverage artificial systems' inherent computational and representational limitations (Simon, 1972). Third, adaptive resource distribution strategies permit dynamic capabilities reallocation based on real-time AI behavioral pattern assessment (Fatima and Wooldridge. in Proceedings of the Fifth International Conference on Autonomous Agents, 2001). In Go, adversarial policies have achieved win rates exceeding 97% against superhuman AI by forcing the system into unfamiliar game states it cannot correctly evaluate (Wang et al. in Proceedings of the 40th International Conference on Machine Learning, 2023). These attacks succeed not through superior Go play but by exploiting fundamental vulnerabilities in how AI systems process information outside their training distributions. Chess analysis indicates that human strategic choices often diverge from AI preferences, with models like Maia specifically designed to predict human moves achieving accuracies of 46–52% for targeted skill levels, highlighting fundamental differences in strategic evaluation between human and artificial intelligence (McIlroy-Young et al. in Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining, 2020). While AI systems like OpenAI Five have demonstrated overwhelming dominance in Dota 2, achieving a 99.4% win rate in public games under restricted rule sets (Berner et al. Dota 2 with large-scale deep reinforcement learning. arXiv preprint arXiv:19106680, 2019), and Libratus significantly outperformed top poker professionals in heads-up no-limit Texas Hold'em (Brown and Sandholm in Science 359:418–424, 2017), human approaches in these contexts reveal ongoing attempts to identify and exploit AI behavioral patterns. These efforts demonstrate the persistent potential for strategic innovation even against seemingly dominant artificial systems. The implications of these findings extend beyond gaming applications to broader strategic contexts. They suggest fundamental considerations for AI system design, particularly regarding the need for enhanced strategic flexibility and adaptation capabilities when facing novel adversarial approaches (Wang et al. in Proceedings of the 40th international conference on machine learning, 2023). We propose that these insights should inform next-generation AI system development, emphasizing robust strategic frameworks that can better anticipate and respond to human innovations that operate outside conventional training paradigms. Our research contributes to the theoretical understanding of human-AI strategic interaction and provides practical frameworks for developing more resilient AI systems. The broader implications span multiple domains, including AI safety research (Russell in Human compatible: Artificial intelligence and the problem of control, Viking Press, 2019), human-AI collaboration frameworks (Vaccaro et al. in Nat Hum Behav 8:1869–1886, 2024), and strategic decision-making system design (Chen and Kumar in J Artif Intel Res 79:245–278, 2024). © The Author(s) 2025. KW - Adversarial machine learning KW - Behavioral research KW - Complex networks KW - Computation theory KW - Cybersecurity KW - Data mining KW - Deep learning KW - Game theory KW - Learning systems KW - Network security KW - Personnel training KW - AI systems KW - International affairs KW - Large-scales KW - Machine-learning KW - Novel strategies KW - On-machines KW - Policy makers KW - Reinforcement learnings KW - Security vulnerabilities KW - Strategic innovations KW - Autonomous agents CY - South Korea ER - TY - JOUR TI - The twin transition in emerging economies: Synergizing artificial intelligence and sustainable business model innovation in Thailand’s BCG economy AU - Sangnak D. PY - 2026 JO - Sustainable Futures VL - 11 SP - 101789 DO - 10.1016/j.sftr.2026.101789 AB - This study investigates the convergence of digitalization and sustainability—termed the "Twin Transition"—within the context of Thailand’s Bio-Circular-Green (BCG) Economy. While Artificial Intelligence (AI) offers significant potential to accelerate sustainable development, research in emerging economies remains fragmented, often lacking empirical grounding in the specific institutional realities of the Global South. Integrating Institutional Theory and Dynamic Capabilities Theory (DCT), this research employs a rigorous mixed-methods sequential exploratory design (Qual → QUAN) to unpack the mechanisms driving this convergence. Phase 1 involved a reflexive thematic analysis of 30 in-depth interviews with executives. This phase revealed a "Compliance Plus" mindset—where firms evolve from regulatory adherence to competitive innovation—alongside critical structural barriers, such as the "Data-Sustainability Paradox." Subsequently, Phase 2 analyzed survey data from 425 firms using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings confirm that Institutional pressures (INP) (coercive, normative, and mimetic) are strong antecedents of AI Capability (AIC), which in turn significantly drives Sustainable Business Model Innovation (SBMI). Crucially, Environmental Turbulence (ENT) positively moderates this relationship, highlighting AI as a vital mechanism for organizational resilience in volatile markets rather than merely for operational efficiency. The study also addresses the "Dark Side" of AI, proposing a "Net-Positive" framework to mitigate energy consumption and algorithmic bias. These results challenge techno-centric views, offering policymakers actionable insights to bridge the "Data Gap" and "Talent Gap," positioning the Twin Transition as a critical lever for emerging economies to escape the Middle-Income Trap. © 2026 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license. http://creativecommons.org/licenses/by/4.0/ KW - Artificial intelligence capability KW - BCG Economy KW - Institutional theory KW - Sustainable business model innovation (SBMI) KW - Twin transition CY - Thailand ER - TY - JOUR TI - AI-Powered Digital Transformation of Government Human Resource Management: A Bibliometric and Systematic Literature Review AU - Bian X. AU - Panyagometh A. AU - Wang B. AU - Szabó R.Z. PY - 2025 JO - Journal of Innovation Management VL - 13 IS - 3 SP - 66 EP - 95 DO - 10.24840/2183-0606_013.003_0003 AB - Recent developments in modern artificial intelligence (AI) have driven profound changes in public sector human resource management systems, offering remarkable opportunities alongside intricate challenges. Governments across the globe are progressively integrating AI tools to modernize HR operations, enhance workforce planning, and respond to evolving socio-economic demands. This research utilizes the PRISMA framework for systematic literature review to explore the role of AI in transforming government HR practices. By analyzing 47 peer-reviewed articles published from 2019 to 2023, the study identifies five central themes: ethical and governance models for AI in public administration; AI’s influence on HR functions and organizational behavior; implementation barriers and potential benefits; AI applications in digital governance and policy formulation; and innovations in HR technologies driven by big data. The findings highlight critical success factors such as strong data infrastructure, structured employee training initiatives, and well-defined ethical standards. Key challenges identified include concerns around data privacy, biased algorithms, workforce adaptation, and wider societal implications like employment shifts and changing competency needs. The study underscores the importance of: (1) adaptive regulatory frameworks that support innovation while safeguarding public interest; (2) robust data governance strategies to manage confidentiality and cybersecurity risks; (3) tailored training programs aimed at improving AI understanding among government staff; and (4) collaborative efforts across sectors to promote ethical AI adoption and mitigate socio-economic disruptions. © 2025 Universidade do Porto - Faculdade de Engenharia. All rights reserved. KW - artificial intelligence KW - digital transformation KW - ethics KW - government KW - human resource management KW - morality CY - China, Thailand, Hungary ER - TY - JOUR TI - AI on drugs: can artificial intelligence accelerate drug development? evidence from a large-scale examination of bio-pharma firms AU - Lou B. AU - Wu L. PY - 2021 JO - MIS Quarterly: Management Information Systems VL - 45 IS - 3 SP - 1451 EP - 1482 DO - 10.25300/MISQ/2021/16565 AB - Advances in artificial intelligence (AI) could potentially reduce the complexities and costs in drug discovery. We conceptualize an AI innovation capability that gauges a firm’s ability to develop, manage, and utilize AI resources for innovation. Using patents and job postings to measure AI innovation capability, we find that it can affect a firm’s discovery of new drug-target pairs for preclinical studies. The effect is particularly pronounced for developing new drugs whose mechanism of impact on a disease is known and for drugs at the medium level of chemical novelty. However, AI is less helpful in developing drugs when there is no existing therapy. AI is also less helpful for drugs that are either entirely novel or those that are incremental “follow-on” drugs. Examining AI skills, a key component of AI innovation capability, we find that the main effect of AI innovation capability comes from employees possessing the combination of AI skills and domain expertise in drug discovery as opposed to employees possessing AI skills only. Having the combination is key because developing and improving AI tools is an iterative process requiring synthesizing inputs from both AI and domain experts during both the development and the operational stages of the tool. Taken together, our study sheds light on both the advantages and the limitations of using AI in drug discovery and how to effectively manage AI resources for drug development. © 2021 University of Minnesota. All rights reserved. KW - AI capability KW - Artificial intelligence KW - Biotech and pharmaceutical industries KW - Drug discovery KW - IT Innovation KW - Drug products KW - Patents and inventions KW - Personnel KW - Domain expertise KW - Domain experts KW - Drug development KW - Drug discovery KW - Innovation capability KW - Iterative process KW - Operational stages KW - Preclinical studies KW - Artificial intelligence CY - United States ER - TY - JOUR TI - Towards experimental standardization for AI governance in the EU AU - Prifti K. AU - Fosch-Villaronga E. PY - 2024 JO - Computer Law and Security Review VL - 52 SP - 105959 DO - 10.1016/j.clsr.2024.105959 AB - The EU has adopted a hybrid governance approach to address the challenges posed by Artificial Intelligence (AI), emphasizing the role of harmonized European standards (HES). Despite advantages in expertise and flexibility, HES processes face legitimacy problems and struggle with epistemic gaps in the context of AI. This article addresses the problems that characterize HES processes by outlining the conceptual need, theoretical basis, and practical application of experimental standardization, which is defined as an ex-ante evaluation method that can be used to test standards for their effects and effectiveness. Experimental standardization is based on theoretical and practical developments in experimental governance, legislation, and innovation. Aligned with ideas and frameworks like Science for Policy and evidence-based policymaking, it enables co-creation between science and policymaking. We apply the proposed concept in the context of HES processes, where we submit that experimental standardization contributes to increasing throughput and output legitimacy, addressing epistemic gaps, and generating new regulatory knowledge. © 2024 The Author(s) KW - Artificial Intelligence KW - Harmonized European standards KW - Legitimacy KW - Policy experimentation KW - Standardization KW - Artificial intelligence KW - Public policy KW - European Standards KW - Evaluation methods KW - Ex ante evaluation KW - Harmonized european standard KW - Legitimacy KW - Policy experimentation KW - Policy making KW - Policy-based KW - Standards process KW - Test standards KW - Standardization CY - Netherlands ER - TY - JOUR TI - Human-Centric AI Governance: An Adaptive Public International Law Framework for Ethical and Inclusive AI Regulation in Public Health AU - Sedeeq F.S. AU - Arman P. PY - 2025 JO - Journal of Law, Medicine and Ethics VL - 53 IS - 4 SP - 563 EP - 574 DO - 10.1017/jme.2025.10175 AB - Artificial Intelligence (AI) is transforming public health, presenting both opportunities and ethical and legal challenges. This study adopts an interdisciplinary approach, integrating legal doctrinal analysis, public health ethics, AI governance scholarship and a scoping review of international legal instruments to evaluate and operationalize three core pillars: ethical accountability, regulatory adaptability and transparency. Through a scoping review of treaties, regional regulations and policy frameworks, the study maps jurisdictional gaps and proposes an adaptive public law framework that addresses critical shortcomings in existing AI governance models, such as the WHO’s limited enforceability and the GDPR’s rigid data-sharing rules. The framework introduces scalable, region-specific regulations to enhance interoperability while respecting local governance needs. Its human-centric design, modular regulation and accountability mechanisms ensure adaptability across diverse legal, cultural and health system contexts. Informed by case studies and a thematic synthesis of global best practices, this framework offers policymakers and practitioners a structured yet flexible approach to balancing AI-driven innovation with ethical imperatives, promoting equitable public health outcomes. © The Author(s), 2025. Published by Cambridge University Press on behalf of American Society of Law, Medicine & Ethics. KW - Adaptive governance KW - Ethical accountability KW - Public health KW - Regulatory adaptability KW - Transparency KW - Artificial Intelligence KW - Humans KW - Public Health KW - Social Responsibility KW - artificial intelligence KW - ethics KW - human KW - public health KW - social responsibility CY - Cyprus ER - TY - JOUR TI - Unpacking Boundary-Spanning Search and Green Innovation for Sustainability: The Role of AI Capabilities in the Chinese Manufacturing Industry AU - Sun Y. AU - Zhang M. AU - Chang J. AU - Wang C. PY - 2025 JO - Sustainability (Switzerland) VL - 17 IS - 14 SP - 6439 DO - 10.3390/su17146439 AB - Achieving the dual carbon goal and addressing escalating environmental challenges requires that manufacturing enterprises in China must pursue sustainability via green innovation strategies. A key rationale for green innovation is to overcome boundaries and acquire knowledge through boundary-spanning search. Additionally, leveraging artificial intelligence (AI) capabilities provides technical support throughout the innovation process. Thus, both boundary-spanning search and AI capabilities are crucial for achieving sustainability objectives. Drawing on organizational search and knowledge management theories, this paper aims to analyze how dual boundary-spanning search affects sustainability performance and green innovation. It also examines the moderating role of AI capabilities and constructs a moderated mediation model. We analyzed questionnaire data collected from 171 Chinese manufacturing companies over a 13-month period, employing hierarchical regression and bootstrap sampling methods using SPSS 27.0. Our findings reveal that both prospective and responsive boundary-spanning searches significantly enhance corporate sustainability performance. Furthermore, green innovation acts as a positive partial mediator between dual boundary-spanning search and corporate sustainability performance. Notably, AI capabilities positively moderate the relationship between dual boundary-spanning search and green innovation. They also strengthen the mediating effect of green innovation on the link between dual boundary-spanning search and corporate sustainability performance. Based on these findings, more resources should be allocated to boundary-spanning search while encouraging enterprises to pursue green innovation and develop AI capabilities. These efforts will provide robust support for sustainability performance in the manufacturing sector. © 2025 by the authors. KW - AI capabilities KW - green innovation KW - knowledge management KW - prospective boundary-spanning search KW - responsive boundary-spanning search KW - sustainability performance KW - China KW - artificial intelligence KW - innovation KW - knowledge KW - manufacturing KW - performance assessment KW - sampling KW - sustainability CY - China ER - TY - JOUR TI - Artificial intelligence in the informal economy: game changer for microentrepreneurs? AU - Kolade O. AU - Egbetokun A. AU - Owoseni A. AU - Woldesenbet Beta K. PY - 2026 JO - International Journal of Entrepreneurial Behaviour and Research SP - 1 EP - 27 DO - 10.1108/IJEBR-12-2024-1457 AB - Purpose – This article integrates insights from bricolage theory and the dynamic capability (DC) framework to explore the potentialities and dangers of artificial intelligence (AI) in the informal sector, where microenterprises could harness its powers to transform their business models and scale, or risk falling further behind in the wake of AI-enabled disruption. Design/methodology/approach – This article takes a conceptual approach complemented with case illustrations. In the first part, it draws on bricolage and DCs theories to introduce nine new propositions that explicate the dynamic, sometimes bidirectional, relationships, between AI, digital bricolage, DCs and enterprise growth and competitiveness. In the second part, it highlights three illustrative cases of microenterprises to further elucidate these relationships. Findings – This study proposes a novel framework integrating AI, digital bricolage and DCs to enhance the performance of informal microenterprises. It highlights the role of digital bricolage as a mechanism for adapting existing resources to develop AI capabilities, and the complementary role of DC in deploying AI for growth, scaling and competitiveness. The study demonstrates AI's role in strengthening opportunity sensing, seizing and transformative capacities that differentiate struggling enterprises from thriving ones, while also addressing critical limitations such as infrastructural inequities and fragmented skills. Practical implications – The study offers valuable practical implications for fostering inclusive digital transformation in informal microenterprises. It highlights the role of digital bricolage in enabling resource-constrained entrepreneurs to creatively adapt and deploy AI for value creation, operational efficiency and agility. Policymakers and practitioners can leverage these insights to address barriers such as infrastructural inequities and skill gaps, fostering AI adoption. This approach supports sustainable competitiveness and market integration for marginalised enterprises. Originality/value – This study proposes a novel framework integrating AI, digital bricolage and DCs to explicate the mechanisms and processes through which informal microenterprises achieve differential outcomes that propel some microenterprises to growth and scaling, on the one hand, while leaving others to fall further behind. To the best of our knowledge, this is the first article that aims to unpack the double-edged sword of AI as both a potential leveller and stratifier in the informal sector. © Oluwaseun Kolade, Abiodun Egbetokun, Adebowale Owoseni and Kassa Woldesenbet Beta KW - Dynamic capabilities KW - Institutional theory KW - Institutions KW - Resource-based theory KW - Technology CY - United Kingdom ER - TY - JOUR TI - Ubuntu-Ẹ̀kọ́ framework: A four-pillared critical-cultural-contextual-communal model for cultivating, educating and enhancing an AI-ready African workforce AU - Feyijimi T.R. AU - Dansu V. AU - Osunbunmi I.O. AU - Elesemoyo I.O. AU - Olayemi M. AU - Apata O.E. AU - Oyeniran D.O. PY - 2026 JO - Computers and Education Open VL - 10 SP - 100372 DO - 10.1016/j.caeo.2026.100372 AB - The rapid advancement of Artificial Intelligence (AI) presents a profound colonial paradox for the African continent. While offering transformative potential for education, the uncritical adoption of AI systems built on Western, individualistic definitions of intelligence risks perpetuating digital colonialism and enabling deep epistemological capture. This conceptual and theoretical manuscript argues that preparing Africa for an AI-driven future requires a fundamental paradigm shift: from the reactive pursuit of “workforce readiness” to the proactive establishment of Digital Sovereignty. We deconstruct the dominant, functionalist definitions of AI and propose a new, decolonized conceptualization: AI as Augmented Collective Wisdom (ACW). This reconceptualization, grounded in select African epistemologies, reframes AI as a socio-technical vehicle, encapsulated by the Yoruba proverb “òwe lẹsin ọ̀rọ̀” (a proverb is the horse of words), designed to carry, clarify, and enhance a community's collective wisdom and ethical fabric. To operationalize this, we introduce a novel theoretical lens: the Ubuntu-Ẹ̀kọ́ Framework for AI Workforce Readiness. This model synthesizes the communal, relational ethics of Southern African Ubuntu (“I am because we are”) with the West African Yoruba educational philosophy of Ẹ̀kọ́ (Education), which redefines the goal of education as the cultivation of the Ọmọlúàbí – an individual of impeccable character, ethical grounding, and communal responsibility. Built on four interconnected pillars: Critical Consciousness, Cultural Grounding, Contextual Responsiveness, and Communal Capability Enhancement, the framework critically assesses the African educational ecosystem across K–12, Technical and Vocational Education and Training (TVET), and Higher Education sectors. A key novel contribution is the Ẹ̀kọ́-Ọmọlúàbí Procedural Framework, a three-layered procedural template that systematically translates Yoruba moral and educational philosophy into a structured architecture for ethical AI governance, establishing Ìwà Rere (good character) as the foundational design constraint, the Ẹ̀kọ́ holistic learning cycle (Ímọ̀-Òye-Ọgbọń) as the implementation process, and Ọmọlúàbí as the terminal goal for both AI systems and their users. Drawing primarily on a structured synthesis of extant literature, policy documents, and philosophical analysis, the manuscript proposes a holistic competency model that prioritizes ethical character, critical agency, and collaborative problem-solving over purely technical skills. This paper offers actionable recommendations for policymakers, educators, and researchers to build an equitable, sovereign, and sustainable AI future. Its central contribution is a fundamental re-theorization of AI through indigenous philosophies, offering a blueprint for aligning technological innovation with communal values. The model further lays the groundwork for novel, culturally grounded assessment paradigms, such as gbígbọ́ ẹjọ́ (Yoruba restorative justice) scenario analysis, that prioritize collective accountability and communal well-being over individual performance metrics. As a conceptual framework, the Ubuntu-Ẹ̀kọ́ Framework is presented as a foundational and guiding model whose effectiveness warrants future empirical validation across diverse African contexts. © 2026 The Author(s) KW - artificial intelligence KW - workforce readiness KW - african education KW - Ubuntu-Ẹ̀kọ́ framework KW - digital sovereignty KW - decolonizing AI KW - Ọmọlúàbí KW - indigenous knowledge systems (IKS) KW - AI ethics KW - Ubuntu KW - Ẹ̀kọ́, augmented collective wisdom (ACW) CY - United States, Nigeria ER - TY - JOUR TI - Governance of Artificial Intelligence Technologies and Systems in the EU and Ukraine: Legal Foundations and Institutional Mechanisms AU - Kwilinski A. AU - Reznik O. PY - 2025 JO - Forum Scientiae Oeconomia VL - 13 IS - 3 SP - 8 EP - 52 DO - 10.23762/FSO_VOL13_NO3_1 AB - The rapid advancement of artificial intelligence (AI) is transforming economic, managerial, and legal systems, creating new opportunities and risks for sustainable development and social equity. Within global digitalisation and Ukraine’s integration into the European legal and technological framework, the study of AI development management becomes a key element of national digital transformation. The topic holds significant scholarly and practical importance as it bridges law, economics, management, and sustainability. The aim of this study is to identify and systematise the legal and institutional mechanisms governing AI development in the European Union and Ukraine, and to assess their alignment with the global sustainable development agenda, based on three hypotheses: (1) the interdisciplinary nature of AI and law; (2) the integration of the UN Sustainable Development Goals into the EU AI Act (Regulation (EU) 2024/1689); and (3) the normative and institutional convergence between Ukraine and the European Union. The methodology applies a triangulated approach combining bibliometric analysis (PRISMA protocol), formal content analysis, and SWOT analysis, ensuring a comprehensive evaluation of legal and managerial aspects of AI governance. The findings show that the EU model balances innovation and accountability through ESG principles, digital ethics, and human-centred governance. Ukraine demonstrates growing alignment with EU law via strategic documents, enhanced digital governance, and participation in international harmonisation programmes. The theoretical contribution lies in conceptualising AI development management as part of a sustainable digital regulation model, reflecting the evolving roles of the state, academia, and business in responsible technology governance. The practical implications include recommendations for developing a national strategy for AI regulation and management, drafting AI legislation harmonised with the EU AI Act, and improving digital governance in the public sector. © 2025 by the authors. KW - AI Act KW - AI development management KW - artificial intelligence KW - digital governance KW - ESG principles KW - European Union KW - institutional convergence KW - legal regulation KW - sustainable development KW - Ukraine CY - United Kingdom, Poland, Ukraine ER - TY - JOUR TI - From artificial intelligence strategy to strategic resilience: the roles of digital leadership, AI governance, and organizational agility AU - Shatila K. PY - 2026 JO - Strategy and Leadership SP - 1 EP - 30 DO - 10.1108/SL-01-2026-0020 AB - Purpose – The rapid diffusion of artificial intelligence (AI) has intensified organizational efforts to enhance innovation, agility, and long-term resilience. However, many firms struggle to translate AI initiatives into sustained strategic outcomes. This study aims to examine how digital leadership, AI strategic orientation, and AI governance jointly shape digital capability and innovation, and how these capabilities foster organizational agility and strategic resilience. Design/methodology/approach – The study adopts a quantitative research design using survey data collected from 302 managers and professionals across organizations of varying sizes and sectors. Drawing on Dynamic Capabilities Theory and Upper Echelons Theory, the model is empirically tested using PLS-SEM to assess both measurement and structural relationships. Findings – The findings demonstrate that digital leadership and AI strategic orientation play central roles in building digital capability, whereas AI strategic orientation and AI governance significantly enhance innovation. Digital capability and innovation jointly foster organizational agility, which emerges as the key mechanism through which AI-related capabilities translate into strategic resilience. Originality/value – This study contributes to the literature by integrating AI strategic orientation and AI governance into a dynamic capability framework that explains strategic resilience. By empirically positioning organizational agility as the primary transmission mechanism between AI-enabled capabilities and resilience, the research offers a more nuanced understanding of how organizations can move beyond isolated AI initiatives toward sustained, capability-based transformation in turbulent environments. © 2026 Emerald Publishing Limited KW - AI strategic orientation KW - Artificial intelligence KW - Digital leadership KW - Organizational agility KW - Strategic resilience CY - France ER -