Predicting Continuance Intention to Use Learning Management Systems among Undergraduates: The Moderating Effect of Intrinsic Motivation

Learning Management Systems (LMS) are crucial in modern educational technology, enhancing education through personalized support, efficient resource management, and data-driven decision-making. LMS holds a pivotal position in contemporary higher education. This research explores undergraduate students' continued learning intentions, grounded in the Expectation-Confirmation Model and Flow Theory, while assessing the moderating effect of intrinsic motivation within this context. From January to August 2023, an online survey gathered self-reported data on satisfaction, confirmation, perceived value, continued intention, flow experience, and intrinsic motivation from 232 undergraduate students across three universities in Henan Province using the Questionnaire Star platform. Analysis using Partial Least Squares Structural Equation Modeling (PLS-SEM) confirmed all research hypotheses except for the insignificant impact of flow on satisfaction and continued intention, demonstrating the model's significant explanatory power for continued intention, explaining 90.8% of the variance. The adjusted R2 was 90.6%, and the Q2 value reached 78.5%. Intrinsic motivation was found to moderate the relationship between satisfaction and continued intention positively, but it did not affect the relationship between perceived value and continued intention. The findings underscore the importance of LMS in educational settings and provide insights into enhancing user experience, student engagement, and satisfaction. Recommendations include the need for developers to improve the LMS interface and functionalities, for educators to enrich learning resources, and for students to recognize the value of LMS and set clear goals to foster their intrinsic motivation. Exploring Why University Students Want to Keep Using Online Learning Platforms: How Big of an Influence Does Personal Interest Have?Learning Management Systems (LMS) are crucial in modern educational technology, enhancing education through personalized support, efficient resource management, and data-driven decision-making. LMS holds a pivotal position in contemporary higher education. This research explores undergraduate students' continued learning intentions, grounded in the Expectation-Confirmation Model and Flow Theory, while assessing the moderating effect of intrinsic motivation. The analysis was conducted using Smart PLS. Smart PLS offers a myriad of benefits for data analysis, including handling non-normal data, accommodating small sample sizes, maximizing explanatory power for the variance of endogenous latent variables, and the capacity to process complex models. The objective of this study is to discern the maximum explanatory power of CI variance influenced by FLOW, CON, PV, SAT, and SAT. With a sample size of just 232 and encompassing six constructs, the study adopts a complex model framework. Given these conditions, Smart PLS is an apt choice for data analysis in this context. The primary contribution of this research is developing a multi-dimensional and comprehensive model for assessing college students' CI to use LMS. Moreover, our findings suggest that the flow experience does not significantly affect CI use and satisfaction, differing from previous studies where FLOW significantly impacted university students' CI to use e-learning.