Factors Influencing Behavioral Intention to Adopt Artificial Intelligence Among HEI Administrative Personnel: An Extended UTAUT Structural Equation Model

The study examined the determinants of behavioral intention to use artificial intelligence (AI) among administrative personnel in selected private higher education institutions (HEIs) in Cebu, Philippines, using an extended Unified Theory of Acceptance and Use of Technology (UTAUT) framework. The model incorporated trust in AI and AI job insecurity to provide a context-specific understanding of AI adoption in higher education administration. Using a quantitative, cross-sectional research design, data were collected from 243 administrative personnel and analyzed through structural equation modeling. The findings revealed that performance expectancy, effort expectancy, and social influence positively and significantly influenced behavioral intention, with social influence demonstrating the strongest relationship. Conversely, facilitating conditions and AI job insecurity did not significantly predict behavioral intention. Trust in AI exhibited a significant negative relationship with behavioral intention, contrary to the hypothesized positive direction. Multigroup analysis further indicated that gender significantly moderated the relationship between performance expectancy and behavioral intention, while institutional tenure moderated the relationship between social influence and behavioral intention. Age did not significantly moderate the relationship between effort expectancy and behavioral intention. The proposedTargeted Administrative Change Management framework provides a foundation for institutional strategies supporting responsible AI adoption, while its effectiveness warrants further empirical validation.