Modeling Factors Associated with Continuance Intention to Use E-Learning During and After COVID-19
Modeling Factors Associated with Continuance Intention to Use E-Learning During and After COVID-19

Summary/Abstract: Background/purpose. Widespread adoption of e-learning was triggered worldwide during the COVID-19 pandemic and modern higher education institutions dedicated significant resources to the use of diverse e-learning systems. However, to maximize the benefits of these investments, it is crucial that the degree to which end-users accept and continue using these systems is evaluated. Employing an integrative theoretical framework based on the extended Technology Acceptance Model (TAM) and Expectation Confirmation Model (ECM), this study aimed to scrutinize factors affecting students’ continuance intention to use an e-learning platforms following the pandemic. Materials/methods. The research employed a cross-sectional design, and 343 university students were surveyed. Partial least squares structural equation modeling (PLS-SEM) was employed in the data analysis. Results. The findings indicate perceived usefulness and satisfaction to be direct predictors of e-learning system usage, whilst confirmation of expectation, perceived usefulness, perceived ease of use, and system interactivity were shown as indirect predictors. Conclusion. Discussed along with the literature, the study’s results revealed satisfaction to be the most vital indicator of continuance intention. Some suggestions are put forward, aimed towards helping both instructors and system designers.

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