Understanding AI Adoption in the Logistics and Supply Chain Industry in Thailand: An Integrated TOE–TTF–UTAUT Framework

Artificial intelligence (AI) is rapidly transforming logistics and supply chain management by enhancing operational efficiency, predictive analytics, and decision-making capabilities. Despite increasing digital transformation initiatives, the determinants of AI adoption in emerging logistics ecosystems remain insufficiently understood. (1) This study aims to develop and empirically examine an integrated framework explaining AI adoption by combining the Technology–Organization–Environment (TOE) framework, Task–Technology Fit (TTF) theory, and the Unified Theory of Acceptance and Use of Technology (UTAUT). (2) Using survey data collected from 500 logistics and supply chain professionals in Thailand, covariance-based structural equation modeling (SEM) was employed to validate the measurement model and test the structural relationships among technological, organizational, environmental, operational, and behavioral factors. (3) The findings indicate that technological, organizational, and environmental contexts significantly influence task–technology fit, while task and technology characteristics strengthen operational alignment between AI systems and logistics activities. Furthermore, performance expectancy, effort expectancy, and social influence significantly enhance behavioral intention, which subsequently drives AI adoption, with facilitating conditions also playing an important supporting role. (4) These results demonstrate that AI adoption in logistics organizations operates through a multi-level mechanism in which structural readiness and operational alignment shape behavioral intention prior to technology implementation, providing theoretical insights and practical guidance for accelerating digital transformation in emerging logistics ecosystems.