Integrating Unified Theory of Acceptance and Use of Technology (UTAUT) and Theory of Planned Behavior (TPB): Generational mediation in the intention and behavior toward Artificial Intelligence (AI) use in higher education

Abstract

Artificial Intelligence (AI) is transforming higher education, yet adoption varies across demographic lines. While previous research has applied either the Unified Theory of Acceptance and Use of Technology (UTAUT) or the Theory of Planned Behavior (TPB) to model AI adoption, few studies have explored the mediating role of generational differences. This study addresses this gap by integrating UTAUT and TPB into a unified model that examines how age-related cohorts influence AI adoption intention and behavior. A quantitative design was employed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to analyze data from 385 students in higher education institutions in Cebu City, Philippines. Constructs included performance expectancy, effort expectancy, social influence, facilitating conditions, attitude, subjective norms, and perceived behavioral control. The generational cohort was tested as a mediating variable. Findings indicate that all proposed constructs significantly influenced AI adoption intention, with performance expectancy and attitude emerging as the strongest predictors. Generational differences significantly mediated the relationships between key UTAUT variables and intention, confirming distinct adoption pathways across age groups. Younger cohorts demonstrated higher digital readiness, while older groups relied more on external support and institutional guidance. Barriers to adoption included limited AI literacy, ethical concerns, and unclear institutional policies. This study is the first empirical investigation to incorporate generational mediation into an integrated UTAUT-TPB framework. The results underscore the importance of demographic sensitivity in designing AI strategies. Educational policymakers and administrators should implement targeted AI literacy programs, ethical guidelines, and age-responsive interventions to promote responsible and inclusive AI adoption in academic environments.