Expectation–Confirmation and Satisfaction in E-Learning Continuance

Abstract

The study ecplores the main determinants of students’ satisfaction and their willingness to maintain e-learning use in Indonesian higher education institutions. Based on the Expectation–Confirmation Model (ECM), the study extends the framwork by incorporating information quality from the Information Systems Success Model (ISSM) and considers both information quality and perceived usefulness as major contributors to satisfaction, which subsequently shapes continuance intention. AI literacy is incorporated as an antecedent of information quality, reflecting users’ capacity to engage critically with AI-enabled content in modern e-learning environments. The research used a quantitative method, employing a structured online questionnaire distributed to university students and working learners who actively use e-learning platforms. Data collection yielded 271 valid responses. The study utilized Partial Least Squares–Structural Equation Modeling (PLS-SEM). The results indicate that AI literacy significantly influences information quality, which has a significant impact on both perceived usefulness and satisfaction. The results demonstrate that perceived usefulness and satisfaction have an important roles in shaping continuance intention, with satisfaction serving as the most influential factor. These findings emphasize the importance of information quality and individual AI-related capabilities in fostering effective and sustainable digital learning experiences.