Responsible Innovation in AI-Driven Operations and Supply Chain Management: An Institutional Theory Framework

Rapid integration of Artificial Intelligence (AI) into Operations and Supply Chain Management (OSCM) presents unprecedented opportunities to enhance efficiency, agility, resilience, and innovation across increasingly complex global networks. AI-driven systems are revolutionizing demand forecasting, logistics optimization, risk management, and sustainability tracking, enabling data-informed decision-making at scales previously unattainable. However, alongside these benefits emerge significant ethical and governance challenges, including algorithmic bias, opacity in decision processes, data privacy risks, workforce displacement, and the environmental costs of computational infrastructures. These challenges stem from the socio-technical complexity of AI systems, the interdependence of global supply networks, and the evolving institutional environments in which OSCM operates. To address these multifaceted concerns, this paper develops a novel institutional theory framework that explicates how regulatory pressures, industry norms, and cognitive frames collectively influence the design, adoption, and governance of responsible, human-centric AI in supply chains. By systematically mapping ethical risks to institutional dynamics, the framework transcends purely technical approaches to bias mitigation, emphasizing instead the deep interplay between formal governance structures, shared social values, and organizational culture. It conceptualizes responsible AI not as a static compliance objective but as a dynamic institutional process requiring alignment between technological affordances and societal expectations. Our findings demonstrate that achieving responsible AI in OSCM necessitates embedding fairness, accountability, and transparency principles throughout the AI lifecycle—from data collection and model development to deployment and feedback loops. Effective implementation demands proactive institutional engagement, interdisciplinary collaboration, and continuous monitoring mechanisms that ensure adaptive governance as technologies evolve. Furthermore, the research underscores the pivotal role of institutional forces in building stakeholder trust, legitimizing AI decision systems, and aligning organizational objectives with broader societal and environmental values. The proposed framework offers actionable insights for organizations, policymakers, and researchers seeking to balance innovation with ethical stewardship. Ultimately, this study contributes a structured, institutionally grounded roadmap for fostering equitable, transparent, and sustainable AI-driven transformation in global operations and supply chain ecosystems.