When Is AI Recommendation Transparency Read as Promotion? Disclosure Elaboration, Motive Attribution, and Consumer Trust

Online platforms explain algorithmic recommendations to build trust, yet detailed disclosures may appear promotional. Drawing on uncertainty management theory, the persuasion knowledge model, and perceived control theory, we conducted a sequential multi-study design comprising two online between-subjects scenario experiments with adult consumers recruited from a South Korean research panel; data were analyzed with planned contrasts, analysis of variance, and regression-based bootstrap (moderated) mediation analysis. In Study 1 (N = 392; disclosure level crossed with explanation type and product context), trust was higher when a brief, one-sentence explanation accompanied the recommendation than when no explanation was given or when technical and procedural details were added. The trust advantage of a brief explanation over no explanation was accompanied by lower AI-washing skepticism, reflecting doubts about AI claims, whereas Study 1 provided no clear evidence that skepticism accounted for the lower trust under elaborated disclosure. Perceived control was associated with higher purchase intention, and evidence that preference-based explanations outperformed procedural explanations for a high-priced product was modest. In Study 2 (N = 220; 2 × 2 design), identical high-disclosure content lowered perceived authenticity and trust, through heightened skepticism, when framed as image-oriented rather than consumer-serving. Disclosure motives shape consumer responses.