Generative AI as a Driver of Green Supply Chain Performance: Role of Flexibility, Responsiveness and Risk Mitigation

Abstract

The traditional supply chain model can adapt to changes in environmental regulations in real time through the adoption of artificial intelligence. However, empirical evidence on how GAI‐enabled gradual, multistage capabilities can be built within the framework of green supply chain management (GSCM) by extending the theoretical concept of dynamic capability theory (DCT) remains limited in the existing literature. Three hundred thirty‐eight survey data were collected using a structured questionnaire and a 5‐point Likert scale to quantify responses, which were analysed using SEM. The results have revealed direct correlations among the main study constructs, which show different layers of capabilities in a green supply chain, that is, flexible information system (FIS), responsiveness, risk mitigation competency (RMC) and the final output, GSCM performance. The study findings confirmed a significant enhancement of GSCM performance by FIS through direct and indirect serial mediation effects of responsiveness and RMC, which are consistent with the fundamental concepts of DCT. Additionally, the study has evaluated the moderating effect of GAI convenience (GAIC) and risk avoidance (RA) to establish the pivotal role of GAI in GSCM. The findings of the moderation analysis depict that higher values of GAI convenience and risk avoidance amplify the positive correlation between supply chain capabilities across multiple stages and GSCM performance. The study has significant managerial and theoretical implications that contribute to the existing literature and provide guidelines for managers to achieve high levels of GSCM performance. Additionally, the present study provides in‐depth insights into how GAI can capture environmental and market changes in real time and make quick decisions to adjust the system as early as possible.