From cafés to clinics: Consumer attitudes toward human-like and machine-like service robot failures

This study examines consumer evaluations of robotic service failures caused by human interference by integrating service context, robot appearance, and individual anthropomorphism tendencies into a unified model. Two between-subjects experiments were conducted. In Study 1 (N = 402), participants interacted with a healthcare or food-service bot that failed due to verbal interference. Healthcare service failure elicited significantly more negative attitudes and lower failure tolerance than food service failure, and failure tolerance fully mediated the relationship between context and attitudes. In Study 2 (N = 213), we employed a 2 × 2 design (healthcare vs. food services × human-like vs. machine-like robot) and measured perceived deservingness and trait anthropomorphism. Human-like robots were judged most harshly when failing in healthcare (vs. food) services, whereas machine-like robots received similar evaluations across contexts. Perceived deservingness of the robot mediated this interaction. Moreover, the moderated-mediation effect occurred only among individuals with low to medium anthropomorphism tendencies. By positioning failure tolerance and deservingness judgments as core mechanisms in human–robot interaction, our findings advance theoretical understanding of moral attributions in service failure. Practically, they highlight the importance of matching robot anthropomorphic cues to service criticality: less human-like designs in high-stakes environments, while more human-like appearances may be appropriate in lower-stakes settings. • Consumers less tolerant of failures in health vs. food service bots. • Human-like robots face stricter judgments in health vs. food services. • Service context guides effective robot deployment in health vs. food sectors.