Understanding determinants of GenAI usage and its effect on SCM performance using dynamic capability view

Purpose: Generative artificial intelligence (GenAI) can potentially improve supply chain management (SCM) processes across levels and verticals. However, despite its promise, the implementation of GenAI for SCM remains challenging, mainly due to the lack of knowledge regarding its key drivers. To address this gap, this study examines the factors driving GenAI implementation in an SCM environment and how these factors optimize SCM performance. Design/methodology/approach: A thorough literature review was followed to identify the drivers. The resultant model from the drivers was validated using a quantitative study based on partial least squares structural equation modeling (PLS-SEM) that used responses from 315 expert respondents from the field of SCM. Findings: The results confirmed the positive effect of performance expectancy, output quality and reliability, organizational innovativeness and management commitment to GenAI usage. Further, they showed that successful GenAI usage improved SCM performance through improved transparency, better decision-making, innovative design, robust development and responsiveness. Practical implications: This study reports the potential drivers for the contemporary development of GenAI in SCM and highlights an action plan for GenAI’s optimal performance. The findings suggest that by increasing the rate of GenAI implementation, organizations can continuously improve their strategies and practices for better SCM performance. Originality/value: This study establishes the first step toward empirically testing and validating a theoretical model for GenAI implementation and its effect on SCM performance.