Leveraging Generative AI for sustainable supply chain: adoption challenges and strategic insights
研究运用TOEH框架和DEMATEL方法，识别并分析生成式AI在可持续供应链中采纳的障碍，发现人力障碍源于技术、法规和组织准备不足，为管理者提供克服阻力、释放AI潜力的战略建议。
Generative AI (GAI) holds transformative potential for supply chain management (SCM) by improving efficiency, minimising inefficiencies, and advancing sustainability. Yet, its adoption remains hindered by complex barriers spanning technological, ethical, regulatory, and organisational domains. This study applies the Technology-Organisation-Environment-Human (TOEH) framework to classify these barriers and utilises the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method to explore their interdependencies. Expert insights reveal that human-centric obstacles—such as workforce resistance and skill deficits—are primarily rooted in gaps in technological readiness, regulatory clarity, and organisational alignment. Ethical concerns, financial limitations, and conflicting strategic priorities further exacerbate these challenges. The findings underscore the need for a comprehensive strategy encompassing AI ethics, regulatory harmonisation, and structured management change. This study contributes actionable insights by promoting interdisciplinary collaboration, robust data governance, and enhanced explainability, enabling organisations to overcome resistance and unlock the full potential of GAI in fostering resilient, adaptive, and sustainable supply chains.