What Factors Influence the Churn Intention in the Context of Online Learning Platform

Abstract

Purpose - With the rapid development of Internet technology, the large-scale popularization of intelligent terminals, and the influx of users into online learning platforms caused by COVID-19, online education has become a new outlet for the internet. However, with the control of COVID-19, online learning platforms face problems of low user stickiness and severe loss. Therefore, it is necessary to discuss the factors that affect users’ willingness to pay for online learning platforms.
 Design/Methodology/Approach - Using Unified Theory of Acceptance and Use of Technology and Regulatory Focus theory, this study constructed the influencing factor model of paying user turnover intention of online learning platform. This study collected 332 valid sample data through a questionnaire survey, and tested the main effect and moderating effect by adopting regression analysis. This study explored the factors affecting the churn intention of paying users of online learning platforms and the influence degree of each factor, and explored the influencing factors of the churn intention of paying users on online learning platforms from multiple angles.
 Findings - The results identified that the influencing factor model of user turnover intention constructed in this paper is practical, seven hypotheses are tenable, and three hypotheses are partially supported. Finally, based on the above research conclusions, this study puts forward strategies to reduce the churn intention of paying users on online learning platforms on three aspects: user segmentation, continuous use process, and influencing factors of online learning platform users, to provide some reference value for online learning platform operators and designers.
 Research Implications - Based on the previous analysis results, this study puts forward corresponding improvement strategies from three aspects: user segmentation, continuous use process, and influencing factors in order to provide a specific reference value for reducing the churn of paying users’ online learning platforms.