A Bibliometric Analysis of Research Trends on Data Privacy Concerns and Consumer Purchase Decision-Making in TikTok Social Commerce

The rise of TikTok as a dominant social commerce platform has transformed online consumer behavior, leveraging algorithm-driven engagement and interactive features such as influencer marketing and live-stream shopping. This study aims to conduct a bibliometric and meta-analysis of existing research on data privacy concerns and consumer purchase behavior in TikTok social commerce. It seeks to identify key research trends, examine theoretical frameworks, and assess the relationship between privacy concerns, trust, and purchase decisions. A systematic bibliometric analysis was conducted using databases such as Web of Science, Scopus, Google Scholar, and IEEE Xplore. Citation analysis, keyword co-occurrence, and co-citation networks were examined. A meta-analysis of empirical studies was performed to measure the effect sizes of privacy concerns on consumer trust and purchase behavior. The study finds that privacy concerns significantly affect consumer trust, which in turn mediates purchase decisions. Algorithmic transparency and ethical marketing strategies enhance consumer confidence. Research trends indicate increasing scholarly interest in regulatory compliance and AI-driven personalization in TikTok commerce. This study provides a comprehensive synthesis of existing literature, integrating Privacy Calculus Theory, Social Exchange Theory, and Social Commerce Engagement Theory. It offers practical insights for platform developers, regulators, and marketers to enhance consumer trust while ensuring ethical data practices in TikTok social commerce.