Abstract
This study examines how perceived AI recommendation design characteristics shape trust, privacy concern, and purchase intention on social media platforms. Drawing on the Stimulus–Organism–Response framework, we introduce PRDCs as a unified construct capturing users’ perceptions of personalization, transparency, fairness, and intrusiveness in AI recommendation systems. Using survey data from Gen Z social media users, with strong representation from Indonesian and Chinese respondents, we test how these perceptions influence psychological and behavioral outcomes on platforms such as TikTok and Xiaohongshu. The study extends prior research by integrating multiple recommendation design features into one framework and offers insights into how AI-driven platform design shapes consumer responses in social commerce contexts.
Recommended Citation
Valencia, Lawrence and Deng, Kailing, "AI Recommendation Design in Social Media: Trust, Privacy, and Purchase Intentions among Gen Z Users" (2026). ICEB 2026 Proceedings (Beijing, China). 7.
https://aisel.aisnet.org/iceb2026/7