Costly “Greetings” from AI: Effects of Product Recommenders and Self-Disclosure Levels on Transaction Costs

Companies are increasingly using artificial intelligence (AI) to provide users with product recommendations, but its efficacy is inconsistent. Drawing upon social exchange theory, we examine the effects of product recommenders and their levels of self-disclosure on transaction costs. Specifically, we recruited 78 participants and conducted a 2 × 2 online experiment in which we manipulated product recommenders (human versus AI) and examined how self-disclosure levels (high versus low) affect consumers’ return intentions. We predicted and found that a low level of self-disclosure from human recommenders instead of AI counterparts results in higher emotional support, which leads to lower transaction costs. However, under high levels of self-disclosure, consumers’ emotional support and subsequent transaction costs do not differ between human and AI recommenders. Accordingly, we provide theoretical insights into the roles of self-disclosure and emotional support in human–machine interactions, and we contribute to sustainable AI practices by enhancing the efficiency of business operations and advancing broader sustainability objectives.