From cafés to clinics: Consumer attitudes toward human-like and machine-like service robot failures
通过两个实验，研究了服务场景（医疗vs餐饮）和机器人外观（类人型vs机器型）如何影响消费者对机器人故障的态度，发现医疗场景中类人型机器人故障更不被容忍，且该效应受个体拟人化倾向调节。
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.