The increased use of service robots has caused remarkable changes in service delivery across various contexts. Service robots are “system-based autonomous and adaptable interfaces that interact, communicate and deliver service to an organization’s customers” (Wirtz et al. 2018, p. 4). Unlike self-service technology, service robots are empowered with artificial intelligence to deliver socially and emotionally interactive services. In human-robot interactions, consumers can feel they are interacting with another social agent (Doorn et al., 2017). Thus, robots’ adoption can not only be for their competence but also their emotional and social presence (Wirtz et al. 2018). The global market for robots is growing (Xiao and Kumar 2021) and robots are expected to be used massively in delivering frontline hospitality services by 2025 (Huang and Rust, 2018). However, research on service robots is mainly conceptual and focuses on the drivers of their adoption (Filieri et al., 2022). Empirical research on how consumers respond to unintended human-robot interaction failure and how firms can mitigate these unintended consequences is scant (Doorn, 2017; Honig and Oron-Gilad 2018). Thus, understanding how consumers respond to different attributes of human-robot interaction failure is needed. Human-robot interaction failure can influence consumers’ intentions to reuse the service and perceptions of service robots (Gompei and Umemuro, 2015). Thus, mitigating these unintended consequences can have substantial managerial and academic contributions. Human-robot interactions failure has rarely been studied in services marketing and is considered a future developmental plan for services research.
RQ1: How would consumers respond to unintended service failure caused by a robot vs caused by a human misuse of a robot?
RQ2: Do consumers’ responses to an unintended human-robot interaction failure differ based on service failure type, service failure stability, robot design and the social presence of others?
RQ3: What is the most effective recovery strategy in handling service failures caused by a robot vs caused by human misuse of a robot?
In line with the premises of attribution theory, we argue that failure causal attributions are related to consumers’ cognitive and emotional reactions (Vaerenbergh et al., 2014). Consumers’ reactions to failure caused by robots will be stronger than those caused by human misuse of robots due to the external vs internal locus of causality (Weiner 1985). We expect to make several theoretical and managerial contributions to the literature on human-robot interactions in the service failure and recovery context. Specifically, this study responds to services scholars’ calls (Pitardi et al., 2022) for additional research on the unintended consequences of service delivered by smart technologies such as service robots to improve consumers’ overall service experience. Meaningful technological engagement that can develop lasting relationships with humans has substantial implications for customers’ experiences (Doorn, 2017).
Figure 1 provides an organizing framework that captures our conceptual and empirical work. Study 1 examines how failure caused by a robot versus a human misuse of a robot affects consumers’ cognitive, emotional and behavioral reactions following a technical versus interactional failure (testing Hypotheses 1). Study 2 examines how failure caused by a robot versus a human misuse of a robot) affects consumers’ reactions following a permanent versus temporary failure (testing Hypothesis 2). Study 3A examines the moderating effect of interacting with a very human-like vs a mechanical robot (testing hypothesis 3a). Study 3B examines the moderating effect of interacting with thinking vs relational robot (testing hypothesis 3b). Study 3C tests the moderating effect of the social presence of others (testing hypothesis 3c). Study 4A examines the moderating effect of expressing regret by a robot vs a human (testing hypothesis 4a). Study 4B examines the moderating effect of offering options to the consumers by a robot vs a human (testing hypothesis 4b), and study 4C tests the moderating effect of offering a collaborative solution with the help of a human (testing hypothesis 4c).
Studies 1, 2, 3 and 4
Participants and procedures
Study 1 design is a 2 (service failure source: robot vs. human misuse of a robot) x 2 (failure type: technical vs. interactional). Study 2 design is a 2 (service failure source: robot vs. human misuse of a robot) x 2 (failure stability: permanent vs temporary) between-subjects design. Study 3A design is a 2 (service failure source: robot vs. human misuse of a robot) x 2 (robot appearance: very human-like vs. mechanical) between-subjects design. Study 3B design is a 2 (service failure source: robot vs. human misuse of a robot) x 2 (robot type: relational vs. thinking) between-subjects design. Study 3C design is a 2 (service failure source: robot vs. human misuse of a robot) x 2 (social presence of others: present vs. absence) between-subjects design. Studies 4A, 4B and 4C designs are a 2 (recovery source: robot vs. human) x 3 (recovery type: expressing regret vs. offering options for the consumer vs. collaborative solutions with the help of a human). Participants will first read a scenario describing a service failure at a restaurant. Failure source, failure type, and failure stability will be manipulated by pictorial stimuli that will be presented along with written scenarios adopted from previous empirical studies (e.g., Choi et al. 2021). These scenarios will be verified and pretested in separate pretests before using them to manipulate our stimuli. A random sample of UK adult consumers will be recruited from Amazon MTurk’s consumer panel and randomly assigned to one of our experimental conditions. For example, participants in study 1 will be asked to imagine themselves in a restaurant scenario where either a technical failure (robot malfunction caused by a robot vs a human misuse of a robot) or social norm violation (inappropriate social behavior by a robot vs. a human) has occurred. Participants will be asked to indicate their perceived cognitions, emotions and behavioral outcomes. They will also be asked to respond to background information, including the frequency of dining out, failure controllability, failure severity and consumers’ technology readiness (used as potential control variables in a subsequent analysis).
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Filieri, R., Lin, Z., Li, Y., Lu, X., & Yang, X. (2022). Customer Emotions in Service Robot Encounters: A Hybrid Machine-Human Intelligence Approach. Journal of Service Research, 10946705221103937.
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