CRITICAL FACTORS FOR GENERATIVE AI- DRIVEN GREEN VALUE CREATION IN SUPPLY CHAINS: A HIERARCHICAL FUZZY BEST WORST METHOD APPROACH

Abstract

Green value creation has become a central objective for modern supply chains as organizations increasingly adopt circular and sustainable practices. While prior research has identified multiple environmental and sustainability-related factors across supply chain activities, the role of advanced digital technologies—particularly Generative Artificial Intelligence (GenAI)—in shaping and prioritizing these factors remains insufficiently explored. Moreover, existing studies often rely on conventional analytical approaches and lack structured decision-making frameworks capable of addressing uncertainty and expert subjectivity. This study aims to identify and prioritize the critical factors influencing GenAI-driven green value creation in supply chains using a Hierarchical Fuzzy Best–Worst Method (HFBWM) approach. Based on an extensive review of the literature and expert consultation, five key supply chain dimensions—Supplier, Product, Packaging, Logistics, and Consumption—along with eighteen associated sub-factors are identified and validated. The HFBWM is employed to systematically capture expert judgments under uncertainty and to derive local and global priority weights. The results reveal that product-related factors, particularly design for reuse, modular design, and circular product design, are the most influential drivers of green value creation, followed by sustainable packaging and consumption-oriented factors. Scenario-based analysis further demonstrates that GenAI capabilities—through iv interactive and non-interactive knowledge search—enhance decision-making quality, reduce dependence asymmetry, and strengthen inter-organizational collaboration, thereby reshaping the prioritization of green value creation factors. The study contributes to the literature by integrating fuzzy multi-criteria decision making with GenAI-enabled supply chain capabilities and offers a practical decision support framework for managers seeking to prioritize high-impact sustainability initiatives. The proposed approach provides actionable insights for leveraging GenAI to support strategic green value creation in complex and uncertain supply chain environments.