How attitude shapes generative AI adoption intentions among graduate students

Nunez N.A.; Salvador-Marquez C.; Poma-Chavez J. · Computers in Human Behavior Reports (2026) · DOI: 10.1016/j.chbr.2026.101257

Resumen

Although generative artificial intelligence (Gen AI) has fully entered graduate business education, the mechanisms by which students form intentions to use these tools are not well understood. Drawing on the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT), this study examines whether attitude toward Gen AI mediates the relationship between performance expectancy and behavioral intention in the context of Gen AI adoption in education. Survey data were collected from 252 MBA students at an internationally accredited business school in Peru. Using ordinary least squares regression with HC3 robust standard errors and nonparametric bootstrap mediation analysis, the results show that performance expectancy significantly predicts both attitude and behavioral intention. More importantly, attitude mediates a substantial share of the effect of performance expectancy on behavioral intention: the indirect effect through attitude constitutes 60.6% of the total effect in the simple mediation model and 46.3% in the controlled model (both are bootstrap estimates of the proportion of the specific PE→BI indirect path mediated by attitude, with confidence intervals excluding zero). The findings suggest that attitude is not a redundant construct in voluntary Gen AI adoption contexts but rather a central evaluative mechanism through which cognitive beliefs translate into intended use. This study contributes to educational technology adoption research by demonstrating that attitude is empirically non-redundant in voluntary Gen AI adoption contexts and should be explicitly modeled as a mediating mechanism in UTAUT-based frameworks applied to educational settings. This study offers practical implications for business schools seeking to integrate Gen AI into MBA learning environments. Copyright

Instituciones: Pontificia Universidad Católica del Perú; Universidad Autónoma del Perú