Investigating the actual usage behavior of Thai traditional craftsmen in adopting AI through a hybrid SEM–ANN approach

Artificial intelligence (AI) tools have been widely adopted across various industries, significantly influencing people’s lives and production activities. However, limited attention has been given to the adoption of AI among traditional craftsmen. Integrating the technology acceptance model (TAM) and the unified theory of acceptance and use of technology (UTAUT) model, this study examines the effects of performance expectancy, effort expectancy, social influence, facilitating conditions, perceived risk, and trust on the attitudes and actual usage behavior of Thai traditional craftsmen toward AI adoption. A purposive sampling method was applied to collect 215 valid data from Thai traditional craftsmen in Thailand through both onsite and online channels. The findings of partial least squares structural equation modeling (PLS-SEM) and artificial neural network (ANN) analysis revealed that performance expectancy, effort expectancy, social influence, and trust positively influence the attitudes of Thai traditional craftsmen toward adopting AI, while perceived risk has a negative effect. Moreover, performance expectancy, social influence, and trust positively impact their actual usage behavior, with perceived risk showing a negative effect. Notably, performance expectancy, social influence, perceived risk, and trust significantly affect both attitudes and behavior in AI adoption. These findings provide empirical evidence that integrating TAM and UTAUT effectively explains the attitudes and behaviors associated with new technology adoption among Thai traditional craftsmen. In addition, the study offers practical insights through a hybrid SEM–ANN approach, suggesting that the government and relevant organizations can develop targeted policies and training programs to support AI adoption needs of Thai traditional craftsmen.