Emotional Resonance or Rational Guidance? Exploring the Mechanisms Through Which Generative AI Recommendation Roles Shape Consumers' Adoption Intentions

Generative artificial intelligence (GenAI) is reshaping e‐commerce by enabling conversational recommenders that can explain their advice and flexibly enact social roles (e.g., assistant vs. friend). Yet it remains unclear how these roles affect consumers' adoption of GenAI recommendations, through which trust pathways, and under what message designs. Drawing primarily on role theory and trust theory, with product evaluability and dual‐process logic specifying the relevant conditions, we examine how recommender role interacts with product type and message framing. Across three experiments, Study 1 ( n = 263) demonstrates a role–product fit: assistant‐type recommendations generate higher adoption intentions for search goods, whereas friend‐type recommendations generate higher adoption intentions for experience goods. Study 2 ( n = 293) identifies the underlying trust pathways: assistant‐type recommendations increase adoption of search goods primarily through cognitive trust, whereas friend‐type recommendations increase adoption of experience goods primarily through affective trust. Study 3 ( n = 367) shows that message framing is a boundary condition: loss framing strengthens the assistant–search‐good fit, whereas gain framing strengthens the friend–experience‐good fit. Together, the findings show that GenAI role plasticity—the capacity of the same system to enact different socially meaningful roles—shapes persuasion through role‐contingent trust and context‐dependent message processing. The research offers practical guidance for aligning GenAI roles and framing strategies with product evaluability in e‐commerce.