Understanding AI Adoption in the Logistics and Supply Chain Industry in Thailand: An Integrated Technology-Organization-Environment, Task-Technology Fit, and Unified Theory of Acceptance and Use of Technology Framework

Artificial intelligence (AI) is transforming logistics and supply chain management by enhancing operational efficiency, predictive analytics, and decision-making capabilities; however, the determinants of AI adoption in emerging logistics ecosystems remain insufficiently understood. This study develops and empirically examines an integrated framework combining technology-organization-environment (TOE), task-technology fit (TTF), and unified theory of acceptance and use of technology (UTAUT) to explain AI adoption in Thailand. Using survey data from 500 logistics and supply chain professionals, covariance-based structural equation modeling (SEM) was employed to validate the measurement model and test the proposed relationships. The results show that technological, organizational, and environmental factors significantly influence AI adoption at the organizational level, while task and technology characteristics enhance task-technology fit at the operational level. At the behavioral level, performance expectancy, effort expectancy, and social influence positively influence behavioral intention, which in turn drives AI adoption, with facilitating conditions also exerting a direct effect. These findings indicate that AI adoption is shaped by a cross-level mechanism involving structural conditions, operational alignment, and individual acceptance, offering theoretical and practical insights for advancing digital transformation in logistics contexts.