Does Gender and Faculty Background Determine the Sustainability of GenAI Adoption in Higher Education? A Revised UTAUT Perspective - Mizhael Parubak; Ratna Juita; Dedi Iskandar Inan; Muhamad Indra; Nanes Fitri Rahmawati (JUSIFO : Jurnal Sistem Informasi, 2025)
The rapid integration of generative artificial intelligence (GenAI) in higher education has transformed learning practices, yet the sustainability of its adoption remains uneven across student groups. This study examines the determinants of sustained GenAI adoption in university settings, with particular attention to the roles of gender and faculty background. Drawing on an extended Unified Theory of Acceptance and Use of Technology (UTAUT) framework, the study employs a quantitative approach using survey data collected from 184 university students. Partial least squares structural equation modelling (PLS-SEM) is applied to evaluate the proposed relationships. The results indicate that performance expectancy, facilitating conditions, attitude toward use, and behavioural intention significantly influence sustained ChatGPT usage. In contrast, effort expectancy and social influence show limited direct effects. Multi-group analysis further reveals notable differences across gender and faculty background, with female students and those from exact science faculties demonstrating higher levels of sustained GenAI adoption. These findings extend the applicability of UTAUT to GenAI contexts and highlight the importance of demographic and disciplinary factors in designing inclusive and sustainable GenAI adoption strategies in higher education.