The personalization–privacy paradox in social commerce: Why privacy concern does not deter willingness to be profiled on Tiktok shop
Abstract
Purpose – This study examines how online personalization, technical protection, and privacy awareness are associated with privacy concern, customer trust, and willingness to be profiled online for personalization among TikTok Shop users in Vietnam. Integrating Privacy Calculus Theory (PCT) and the Stimulus–Organism–Response (SOR) framework, the study explores how personalization-related stimuli shape users’ privacy evaluations and acceptance of profiling in the context of short-video social commerce.Design/methodology/approach – Data from 300 TikTok Shop users were analyzed using PLS-SEM in SmartPLS 4.0 with 5,000 bootstrap resamples.Findings – Online personalization increased privacy concern, customer trust, and willingness to be profiled. Technical protection strengthened customer trust but did not reduce privacy concern. Contrary to the proposed negative relationship, privacy awareness increased privacy concern, while also strengthening customer trust. Privacy concern was positively related to willingness to be profiled, also contrary to the hypothesized direction. Customer trust was the strongest predictor of profiling acceptance.Originality/value – The study extends the personalization–privacy paradox to TikTok Shop in an emerging market. It shows that personalization can simultaneously increase privacy concern, trust, and profiling acceptance, while customer trust serves as the dominant predictor and mediating mechanism.Practical implications – TikTok Shop and similar platforms should strengthen customer trust by clearly communicating data-protection practices, explaining how personal data are used, and providing meaningful consent and user-control options. Personalization should remain valuable without becoming overly intrusive.Social implications – Greater transparency, informed consent, and user control can support more informed data-sharing decisions and more responsible algorithmic profiling in social commerce.