Digital drivers: How the acceptance and use of technology model shapes university students' behavior toward generative AI

This study explores the adoption of generative artificial intelligence (GenAI) among university students in Saudi Arabia through the lens of the Unified Theory of Acceptance and Use of Technology (UTAUT). Specifically, it examines how performance expectancy, effort expectancy, social influence, facilitating conditions, attitude toward GenAI, and perceived risk relate to usage behavior. The study also investigates the mediating role of behavioral intention and the moderating effects of age, gender, and AI familiarity. A quantitative survey of 742 students was conducted, and the data were analyzed using bifactor exploratory structural equation modeling (bi-ESEM). The results revealed that global social influence was the strongest predictor of behavioral intention, followed by attitude toward GenAI, while global expectancy showed no significant effect. Behavioral intention emerged as the most robust predictor of actual usage behavior and significantly mediated the effect of attitude on usage. Attitude toward GenAI also had a direct effect on usage behavior, underscoring the importance of cognitive and emotional engagement. Other indirect effects, including those from global expectancy, global social, and perceived risk, were not significant. Moderation analysis showed that only age had a significant but small influence on behavioral intention, while gender and familiarity with GenAI had no significant moderating effects. These findings highlight the central role of social validation and attitudinal commitment in shaping students' engagement with GenAI, offering practical and theoretical implications for technology adoption in higher education contexts. The findings highlight the critical role of social support and positive attitudes in promoting GenAI adoption in educational settings, and the paper concludes with implications for both research and pedagogical practice.