Testing the effects of personalized recommendation service, filter bubble and big data attitude on continued use of TikTok

Abstract

Purpose

This study attempts to identify the determinant factors for Chinese users’ intention of continuous use of TikTok.

Design/methodology/approach

Drawing on the theoretical background and previous literature, this study proposes the research model followed by research hypotheses. For the purpose of empirical analysis of this study, we conducted a questionnaire survey on current Chinese TikTok platform users. Factor analysis was conducted to verify the reliability and validity of the measurement variables; Confirmatory factor analysis was conducted to ensure that the structural dimensions of the measurement are effective; the analysis results of the structural equation model for hypothesis test.

Findings

The result of the study indicates that the core service traits (entertainment, informativeness and convenience of use) of TikTok all have a positive effect on user engagement and perceived usefulness. In addition, the study found that both personalized recommendation services and filter bubble, which are features of TikTok's service recommendation system, have a positive effect on user engagement and perceived usefulness of TikTok. In addition, it was found that both user engagement and perceived usefulness have a significant positive effect on continuous use intention. Finally, users’ attitude toward big data indicating privacy concern only had a positive moderating effect on the personalized recommendation service but not on filter bubble.

Originality/value

As consumers’ demand for services tailored to their needs and preferences fast increased, it calls for growing concern over the safety of personal data being used by the platforms. Although this paradox over increased user convenience versus privacy protection issue has been persistently recognized, no previous research empirically addressed this issue in the context of social media platforms.

Citation

Tan, Y. and Yoon, S. (2024), "Testing the effects of personalized recommendation service, filter bubble and big data attitude on continued use of TikTok", Asia Pacific Journal of Marketing and Logistics, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/APJML-06-2024-0738