An Integrated Model of ChatGPT Acceptance and Continuance in Cambodian Higher Education

Abstract

            This study examines an integrated model of ChatGPT acceptance and continuance among Cambodian undergraduate students by combining UTAUT constructs, including performance expectancy, effort expectancy, social influence, and facilitating conditions, with expectation confirmation and satisfaction. Data were collected from 421 students across seven public and private universities in Phnom Penh, Cambodia using a structured online survey and analyzed through PLS-SEM. Findings reveal that social influence and satisfaction significantly drive students' behavioral intention, while confirmation strongly affects both performance expectancy and satisfaction. Facilitating conditions and behavioral intention positively influence actual use behavior, highlighting the importance of both structural support and motivational factors. The study contributes to theory by integrating acceptance and post-adoption perspectives, offers empirical evidence from a developing higher education context, and provides insights for designing effective strategies to promote responsible and sustained engagement with generative AI in undergraduate learning.