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 the Privacy Calculus Theory, Trust Theory, and the Stimulus–Organism–Response framework, the study investigates whether perceived GenAI-based advertising personalization simultaneously creates perceived personalization value and privacy concerns, 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 reflect 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 concerns. Personalization value strengthens algorithmic trust, whereas privacy concerns weaken it. Algorithmic trust, in turn, strongly enhances social commerce purchase intention. The mediation results show that personalization value and privacy concerns transmit the dual effect of perceived GenAI-based advertising personalization to algorithmic trust. In contrast, algorithmic trust transmits these effects to purchase intention. Perceived transparency disclosure does not significantly reduce privacy concerns, but it strengthens the positive relationship between personalization value and algorithmic trust. This study contributes to digital marketing and social commerce research by showing that GenAI-personalized advertising can be perceived as both useful and intrusive and that perceived transparency disclosure may support trust formation without necessarily eliminating privacy concerns.