Understanding continuance intention towards e-learning platforms: a structural model based on expectation confirmation and individual innovativeness
TL;DR
It is indicated that satisfaction is the strongest predictor of continuance intention toward e-learning platforms, and individual innovativeness and demographic characteristics shape continuance behaviour in online learning environments.
Abstract
The rapid growth of e-learning platforms in higher education has increased the need to understand the factors influencing students’ continuance intention toward online learning systems. Drawing upon Expectation Confirmation Theory and integrating individual innovativeness, this study examines the relationships among expectation confirmation, perceived usefulness, satisfaction, and continuance intention, with gender as a moderating variable. A quantitative research design was employed. Data were collected from 166 students with prior e-learning experience in Indian higher education institutions using a structured questionnaire. Partial Least Squares Structural Equation Modeling (PLS-SEM) was used to analyse the measurement and structural models. The findings indicate that satisfaction is the strongest predictor of continuance intention toward e-learning platforms. Expectation confirmation and perceived usefulness significantly enhance satisfaction; however, their direct effects on continuance intention were not significant. Mediation analysis revealed that satisfaction fully mediates the relationships between expectation confirmation, perceived usefulness, and continuance intention. Additionally, individual innovativeness demonstrated a significant negative direct effect on continuance intention, while gender significantly moderated the relationship between innovativeness and continuance intention. The study extends post-adoption e-learning research by highlighting the central role of satisfaction in sustaining continued usage of e-learning platforms. The findings further suggest that individual innovativeness and demographic characteristics shape continuance behaviour in online learning environments. The study offers theoretical contributions to continuance intention literature and practical implications for higher education institutions and e-learning platform developers seeking to improve long-term learner engagement.