Exploring customers' reuse intention to robots under different service failures: A mind perception perspective

Abstract

Artificial intelligence-based service robot failures (hereafter referred to as robot service failures) are inevitable in service practice, making the mitigation of their adverse effects a critical concern for service managers. The present paper investigates the unique classification of robot service failures with the help of mind perception theory and a consumer-centered perspective. Moreover, we further examine the impact of robot service failures on consumer behavioral responses (i.e., reuse intention), the mediating role of negative emotions, and the moderating effect of service robot anthropomorphism. Using a mixed-methods approach, Study 1, based on robot service failure reviews from Ctrip and word co-occurrence network analysis, reveals a two-dimensional classification of robot service failures: agential failures and experiential failures. Furthermore, leveraging the same dataset, Study 2 calculates negative emotions in the text and uses consumer evaluations as a proxy for reuse intention. The results indicate that agential failures (compared to experiential failures) exert a more significant negative impact on consumers, and this relationship is mediated by negative emotions. Study 3 employs a behavioral experiment to further validate the findings of Study 2 and additionally reveals that service robot anthropomorphism moderates the relationship between service failures, negative emotions, and reuse intention, leading to more adverse consequences for experiential failures. This paper makes a valuable contribution to the emerging literature on robot service failures by exploring the distinctiveness of robot services. To the best of our knowledge, this is the pioneering empirical study that explores the unique dimensions of robot service failures. Practically, the findings provide actionable insights. Understanding the classification of robot service failures, which differs from human service failures, allows for a deeper comprehension of AI-powered services and offers effective intervention strategies for consumer recovery following service failures.