SHS Web of Conferences (Jan 2022)
Research on customer relational bonds, affective commitment and payment intention for online education platform of traditional culture Based on the moderation of cultural identity
Abstract
This study focuses on the online education platform of traditional culture, a new product of the Internet era. At present, the traditional culture online education platform is facing many difficulties and challenges. Its poor market performance and poor retention of the industry’s overall users are difficult to solve, which has aroused the common concern of the industry and academia. The existing research on online education platform is mainly based on Technology Acceptance Model and motivation theory to explore the influence of platform attribute and platform content on consumer perception. It explores the use behavior of online education users from the perspective of “technology use”, but ignores the “virtual community” attribute of online education platform. Therefore, based on social exchange theory, combined with the “virtual community” attribute of traditional culture online education platform, this paper uses the theoretical framework of relationship bond affective commitment payment willingness to explore the influencing factors of users’ payment intention for traditional culture online education platform. According to the characteristics of traditional culture online education platform user groups, this study also introduces cultural identity as a potential moderator, trying to improve the theoretical framework more systematically. All the above variables were measured by the maturity scale developed by previous scholars. The research model established in this paper can provide a new perspective for the traditional culture online education platform industry to promote users’ payment intention, and also provide a basic framework for future research in the field of traditional cultural online education. In order to verify the hypotheses, this study will use the judgment sampling method to collect 655 questionnaires from the existing individual user groups of relevant online platforms, and use SPSS and Amos statistical software for empirical analysis.
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