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

Yasheng Chen, Yuhong Tu () and Siyao Zeng
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Yasheng Chen: School of Management, Xiamen University, Xiamen 361005, China
Yuhong Tu: School of Management, Xiamen University, Xiamen 361005, China
Siyao Zeng: School of International Law, East China University of Political Science and Law, Shanghai 200050, China

Sustainability, 2024, vol. 16, issue 18, 1-19

Abstract: 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.

Keywords: product recommendation; self-disclosure; emotional support; transaction cost; return intention (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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