With the widespread adoption of digital learning tools and the continuous upgrading of online learning technologies, e-learning has increasingly become a common way for English as Foreign Language (EFL) college students to obtain learning resources and practice English beyond the classroom. Drawing on the Technology Acceptance Model (TAM), Expectation Confirmation Theory (ECT), and Flow Theory, this study examines key factors associated with EFL college students' continuance intention toward e-learning. Using responses from 435 EFL college students, the study applied PLS-SEM (Partial Least Squares Structural Equation Modeling) using SmartPLS to examine the hypothesized relationships. The results indicate that perceived ease of use, perceived usefulness, confirmation, satisfaction, and perceived enjoyment are significantly related to continuance intention, with the model explaining 59.3% of the variance. Among these factors, perceived usefulness and satisfaction show the strongest associations with continuance intention, while perceived enjoyment is positively related to continuance intention and is also associated with perceived ease of use, perceived usefulness, and satisfaction. Furthermore, the multigroup analysis reveals significant differences between higher-grade and first-year students in specific paths: higher-grade students demonstrate stronger associations in the paths from perceived enjoyment to perceived ease of use and from confirmation to perceived enjoyment. These findings underscore the moderating role of learning experience in the associations underlying continuance intention toward e-learning. Overall, the findings deepen theoretical understanding by integrating TAM, ECT, and Flow Theory into the EFL e-learning setting and inform practical efforts aimed at refining platform design, elevating user satisfaction, and maintaining sustained usage.
Building similarity graph...
Analyzing shared references across papers
Loading...
Xuejie Xu
Harbin University
Chunhui Yang
Harbin University
Scientific Reports
Harbin University
Building similarity graph...
Analyzing shared references across papers
Loading...
Xu et al. (Tue,) studied this question.
synapsesocial.com/papers/69e1cdc45cdc762e9d8570d8 — DOI: https://doi.org/10.1038/s41598-026-48311-x