Understanding Students’ Continuance Intention toward Generative AI Tools in Higher Education: An Integrated ECM and D&M IS Framework

The rapid expansion of generative artificial intelligence (GenAI) tools in higher education has transformed students’ academic practices, shifting attention from initial adoption to sustained use. Despite this growth, empirical research examining the determinants of students’ continuance intention toward GenAI remains limited, particularly within Arab and African higher education contexts, where institutional conditions and digital transformation trajectories differ from those in developed economies. To address this gap, this study develops and empirically tests an integrated post-adoption framework that combines the D&M IS Model with the ECM Model, while extending these perspectives through the inclusion of trust, perceived risk, and price value. Data were collected through a web-based survey administered to 594 undergraduate and postgraduate students with prior experience using GenAI tools for academic purposes. Partial least squares structural equation modelling (PLS-SEM) was used to evaluate the proposed model. The results show that satisfaction is the most powerful predictor of the continuance intention of the students and next is the price value and this shows that perceived benefits are more important than costs of usage. Conversely, the performance expectancy is not found to have a significant direct impact on the continued use of GenAI. Moreover, system quality, information quality and service quality are important in increasing student trust and satisfaction. Confirmation has a positive impact on satisfaction and performance expectancy, and perceived risk is positively correlated with trust in GenAI tools. This study contributes to the existing knowledge about GenAI post-adoption behavior in Arab and African higher education and offers practical implications towards establishing sustainable, trustful, and value-driven application of generative AI in higher education.