Based on the new era of digital sports development, self-media platforms have become an important carrier of sports teaching communication; however, few studies have investigated the relationship between self-aggrandizing behavior during sports teaching on self-media platforms and platform users' learning intention. In this study, we take Jittery self-media platform as an example to explore the chain path that creators' boasting behavior (H1) positively affects users' learning intention (H2) by enhancing users' trust (H3), and to verify the mediating effect of trust in the relationship between boasting behavior and learning intention, as well as the two-way influence between badminton short video creators' boasting behavior and platform users' willingness to learn on self-media platforms. Method: Fifty badminton instructional creators with 10,000-100,000 followers on the Jieyin platform were randomly selected from 200, and their badminton instructional videos published from October to December 2024 were collected. Ext was extracted by speech recognition, and the frequency of self-aggrandizing words (times/minute) was quantified by the LIWC tool. The willingness to learn index was calculated by normalizing the number of likes, comments, and shares. The study design included (1) negative binomial regression to test the inverted U-shaped relationship, (2) SPSS path analysis to test the mediating effect of trust, and (3) hierarchical regression to analyze the heterogeneity of video type (entertainment/competitive) and creator gender. Quantitative analyses revealed (1) a significant inverted U-shaped relationship between self-aggrandizing behavior and willingness to learn (β=0.32 to -0.21, p ² =0.12, p=0.008). This study confirms that moderate self-aggrandizing behaviors of short instructional sports video creators can influence users' trust in self-media platforms and increase willingness to learn, while excessive self-aggrandizing decreases users' willingness to learn. In the future, it can be extended to multi-sports categories, combined with user behavior data, and longitudinal data can be introduced to verify the effect.
Yi et al. (Thu,) studied this question.