Use of Latent Dirichlet Allocation and Structural Equation Modeling in Determining the Factors for Continuance Intention of Knowledge Payment Platform

Abstract

Knowledge payment is a new type of E-learning that has developed in the era of social media. With the influence of the COVID-19 epidemic, the knowledge payment market is developing rapidly. Exploring the influencing factors of users’ continuance intention is beneficial for the sustainable development of knowledge payment platforms. Our study took “Himalayan FM” as an example and included two studies: Study 1 used latent dirichlet allocation (LDA) to explore the main factors affecting the users’ willingness to continue use, through mining user comment data on the knowledge payment platform; Study 2 constructed the conceptual model by integrating the technology acceptance model (TAM) and IS success model (IS) and carried out empirical analysis by SPSS and AMOS using the data that were collected through the questionnaire. The results show that: (1) perceived usefulness, user satisfaction, and spokesperson identity have a direct positive impact on users’ willingness to continuous use, while perceived cost has a direct negative impact on users’ willingness to continue use; (2) perceived ease of use, content quality, and system quality of knowledge payment platforms impacted user satisfaction directly, then affected users’ willingness to continue use indirectly; (3) users’ perceived enjoyment, membership experience, auditory experience, and other factors also directly impacted user satisfaction, affecting users’ willingness to continue use indirectly. This study effectively expands the factors influencing knowledge payment users’ willingness to continue use and provides a useful reference for the sustainable development of knowledge payment platforms.