Generative AI Adoption in Software Development Through an Extended UTAUT2 Framework with Evidence from Thailand

As generative artificial intelligence (GenAI) diffuses globally, understanding the adoption of AI-assisted software development tools is increasingly important, particularly in emerging digital economies. This study examines GenAI acceptance among individuals in Thailand with varying durations and contexts of software development experience acquired through professional work, education, internships, freelance assignments, independent activities, or personal projects. Anextended UTAUT2 framework was developed by adding perceived trust and perceived risk, with age, gender, and software development experience examined as potential moderators. Partial least squares structural equation modeling was applied to 404 valid responses collected through an online survey. The model explained 42.9% of behavioral intention and 44.8% of use behavior. Hedonic motivation, price value, habit, and perceived risk were significantly associated with behavioral intention, whereas behavioral intention and habit were significantly associated with use behavior. The remaining direct relationships and all moderating effects, including those involving software development experience, were nonsignificant. Contrary to the hypothesized direction, perceived risk showed a small positive association with behavioral intention, suggesting that stronger usage intentions and greater risk awareness may coexist within the present sample. These findings provide context-specific evidence of GenAI adoption in Thailand and offer practical insights for organizations and policymakers seeking to support responsible adoption in comparable settings.