Factors Affecting Behavior, Perceived Impact of AI on Work Engagement, and AI Application

This study aimed to elaborate on the predictors of behavioral intention, actual behavior, perceived impact of AI on work engagement, and the perceived impact of AI on teaching and learning in Asian higher education institutions (HEIs). We extended the Unified Theory of Acceptance and Use of Technology (UTAUT) to test the relationships among these variables. In total, 516 lecturers from three different universities contributed to the dataset. Partial least squares structural equation modeling (PLS-SEM) was employed to analyze the respondents’ data through both measurement and structural models. The findings indicate that actual behavior has a significant positive relationship with the perceived impact of AI on teaching and learning and the perceived impact of AI on work engagement. Behavioral intention is strongly correlated with actual behavior. Performance expectancy is the most prominent link to behavioral intention, followed by social influence and facilitating conditions. The results of the study underscore the importance of fostering a conducive environment through adequate facilitating conditions and aligning performance expectations to drive behavioral intention and perceived engagement with AI technologies in HEIs.