Strategic Disclosure of AI Curation: A Boundary Condition on Algorithm Aversion in Hedonic E-Commerce

Algorithm-aversion research predicts that consumers prefer human curators to algorithmic ones in subjective decision domains, including taste-based hedonic recommendation. Drawing on the algorithmic-symbiosis paradigm and on assortment-perception theory, this paper identifies a boundary condition on that prediction: disclosing that recommendations are AI-curated rather than human-curated lifts purchase intention in hedonic e-commerce but not in utilitarian e-commerce. The mechanism is a search-side option-breadth inference—the consumer’s attribution about the size of the option pool the curator considered upstream—which is diagnostic in preference-formative consumption categories where consumers build, rather than match, a preference. Three online experiments deployed through a Chinese consumer panel test the framework. Study 1 (N=228) finds the predicted Disclosure × Product-type interaction (ηp2=0.025) with the AI-versus-human lift confined to the hedonic cell (d=0.65). Study 2 (N=257) isolates the option-breadth pathway against trust and competence as competing mediators. Study 3 (N=519) extends the design to a second hedonic category, decomposes option breadth into search-side and display-side subdimensions through an eight-item bi-factor scale, tests mentalizing alongside option breadth as a competing mediator, and brings the moderated-mediation test by consumer AI familiarity to conventional statistical power (index of moderated mediation =+0.065, 95% CI [+0.014,+0.118]). Implications for interactive-marketing practice and algorithmic-disclosure regulation are discussed.