As AI-driven personalized recommendations become integral to social commerce platforms, understanding the balance between user engagement and privacy concerns is essential. This study, conducted within the Vietnamese market using quantitative research methods, examines this balance through the lenses of the Privacy Calculus Theory, Technology Acceptance Model, and Social Exchange Theory. Data from 574 valid responses, gathered online, were analyzed using SPSS 27 for descriptive statistics and SmartPLS 4 for Partial Least Squares Structural Equation Modeling (PLS-SEM). The results demonstrate that higher personalization intensity increases privacy concerns, while data transparency by itself is insufficient to reduce these concerns. Privacy concerns adversely affect user engagement, but this impact is moderated by user awareness and the perceived value of personalization. This research contributes to the literature by offering empirical evidence on the management of trade-offs between personalization and privacy in AI-driven systems. Furthermore, the insights indicate that social commerce platforms should enhance transparency, educate users, and effectively communicate the benefits of personalization to sustain trust and user engagement.

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