Title:
Balancing Algorithmic Personalization and Privacy Concerns: Trust Formation and Perceived Data Control in TikTok Commerce (Generation Z Users in West Java, Indonesia)

Authors:
Ayuningtyas Yuli Hapsari, Ni Putu Nurwita Pratami Wijaya, Indonesia

Abstract:

The rapid growth of social commerce on algorithmic platforms has introduced a fundamental tension between personalization-driven engagement and user privacy expectations, particularly among younger digital natives. This study examines how TikTok's recommendation algorithm shapes consumer trust formation and perceived data control within a commerce context, with a focus on Generation Z users (born 1997–2012) across ten cities in West Java, Indonesia. Drawing on the Technology Acceptance Model (TAM) and Information Boundary Theory (IBT), this research investigates how users navigate the trade-off between hyper-personalized product recommendations and concerns over data surveillance, behavioral profiling, and consent transparency. A quantitative approach was employed using a structured questionnaire distributed to 250 respondents across ten big city at West Java Province. Data were analyzed using Partial Least Squares – Structural Equation Modeling (PLS-SEM) via SmartPLS. Findings reveal that perceived data control significantly mediates the relationship between privacy concerns and purchase intention, while algorithmic transparency and platform trust emerge as key antecedents of positive commerce behavior. Digital literacy moderates users' tolerance for personalization. The study contributes to the literature on algorithm-mediated social commerce and provides implications for platform designers, marketers, and digital policymakers.

DOI: https://doi.org/10.51505/IJEBMR.2026.10508