When AI Conversations Become Advertising Data: Algorithmic Trust, Privacy Calculus, and Purchase Intention in GenAI-Personalized Social Commerce

This study examines how consumers respond to personalized advertisements that appear to be derived from prior conversations with generative AI assistants in social commerce settings. Drawing on privacy calculus theory, trust theory, and the stimulus–organism–response framework, the study investigates whether GenAI-based advertising personalization simultaneously creates perceived personalization value and privacy concern, and how these evaluations shape algorithmic trust and social commerce purchase intention. A scenario-based survey was conducted with 435 social commerce users in Jordan. Respondents evaluated a situation in which a product advertisement appeared to match a previous conversation with a generative AI assistant. The data were analyzed using partial least squares structural equation modeling with SmartPLS 4. The findings show that perceived GenAI-based advertising personalization increases both perceived personalization value and privacy concern. Personalization value strengthens algorithmic trust, whereas privacy concern weakens it. Algorithmic trust, in turn, strongly enhances social commerce purchase intention. The mediation results confirm that personalization value and privacy concern explain the dual effect of GenAI-based advertising personalization on algorithmic trust. In contrast, algorithmic trust transmits these effects to purchase intention. Transparency disclosure does not significantly reduce privacy concern, but it strengthens the positive relationship between personalization value and algorithmic trust. The study contributes to digital marketing and social commerce research by showing that conversational-AI-derived advertising data can be perceived as both useful and intrusive. That transparency may support trust formation without necessarily eliminating privacy concerns.