Supply chain management in the era of generative AI (ChatGPT) : technology fit and psychological drivers of adoption

Abstract

The rise of generative AI (Gen-AI), particularly ChatGPT, is reshaping the landscape of supply chain management (SCM) by enabling interactive, real-time, and language-based intelligence. Unlike traditional AI systems that operate on structured data and predefined rules, ChatGPT introduces a conversational interface that supports decision-making, problem-solving, and coordination across various SCM functions. This study examines the adoption of ChatGPT by assessing its alignment with four key supply chain tasks: optimization, adaptability, sustainability, and coordination. To explain the mechanisms driving adoption, we integrate the Task-Technology Fit (TTF) theory with the Stimulus-Organism-Response (SOR) framework, modeling ChatGPT as a stimulus that influences user trust, satisfaction, and technology anxiety—cognitive and emotional responses that shape behavioral intention. Empirical data were collected from 382 SCM professionals across diverse industries and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results demonstrate that perceived task-technology fit significantly enhances trust and satisfaction, both of which have a positive influence on the intention to adopt ChatGPT. Importantly, the study reveals that technology anxiety moderates these relationships, diminishing the strength of trust and satisfaction in driving adoption. This finding highlights the importance of addressing psychological resistance in conjunction with the deployment of technology. By offering a dual-theoretical lens and empirical validation, this research contributes to the emerging literature on Gen-AI adoption, providing actionable insights for practitioners seeking to integrate ChatGPT into their supply chain operations.