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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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-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 - 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 - 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 - 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 - 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-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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 -