Abstract

Generative artificial intelligence (GenAI) is increasingly promoted as a tool for educational administration and administrative decision-making, yet evidence on why educational leaders adopt it remains scarce, particularly in technical and vocational education. Extending the unified theory of acceptance and use of technology (UTAUT) with trust, privacy concern, and institutional policy fit, this study examined the determinants of administrators’ GenAI behavioral intention and use behavior. The study analyzed survey responses from 330 directors and deputy directors of Thai private vocational colleges, selected through two-stage stratified cluster sampling. The data were analyzed using generalized structured component analysis (GSCA) and bootstrapping. The measurement model showed satisfactory reliability and validity. The model fitted the data well and explained 82.9% of the variance in behavioral intention and 79.3% in use behavior. Social influence, trust, lower privacy concern, and institutional policy fit significantly predicted behavioral intention, whereas the unique direct effects of performance expectancy, effort expectancy, and facilitating conditions could not be separated from those of the correlated predictors; dominance analysis showed that these three determinants accounted for about one third of the explained variance in intention, almost all of it shared with the other predictors. Effort expectancy and facilitating conditions also shaped social influence, through which their influence reached intention. Behavioral intention, trust, and lower privacy concern predicted use behavior. Full-collinearity diagnostics indicated a risk of common method variance; a post hoc marker-variable analysis showed that the predictors of intention and the intention-use path were robust, whereas the direct effects of trust and privacy concern on use were not. Administrators’ adoption of GenAI thus appears to depend on institutional legitimacy, normative endorsement, and data-governance assurance, which explain intention beyond what instrumental appraisal explains. Educational authorities seeking to promote GenAI in administration should therefore pair infrastructure investment with explicit institutional GenAI policies, guidelines for verifying GenAI outputs, and data-protection safeguards, because these conditions add to, rather than merely duplicate, beliefs about usefulness.

This work is licensed under a Creative Commons Attribution 4.0 License.