Robots or frontline employees? Exploring customers’ attributions of responsibility and stability after service failure or success
By Daniel Belanche, Luis Vicente Casaló, Carlos Flavián, Jeroen J. L. Schepers. Journal of Service Management, 2020
254 citations recorded by the source corpus.
PurposeService robots are taking over the organizational frontline. Despite a recent surge in studies on this topic, extant works are predominantly conceptual in nature. The purpose of this paper is to provide valuable empirical insights by building on the attribution theory.Design/methodology/approachTwo vignette-based experimental studies were employed. Data were collected from US respondents who were randomly assigned to scenarios focusing on a hotel’s reception service and restaurant’s waiter service.FindingsResults indicate that respondents make stronger attributions of responsibility for the service performance toward humans than toward robots, especially when a service failure occurs. Customers thus attribute responsibility to the firm rather than the frontline robot. Interestingly, the perceived stability of the performance is greater when the service is conducted by a robot than by an employee. This implies that customers expect employees to shape up after a poor service encounter but expect little improvement in robots’ performance over time.Practical implicationsRobots are perceived to be more representative of a firm than employees. To avoid harmful customer attributions, service providers should clearly communicate to customers that frontline robots pack sophisticated analytical, rather than simple mechanical, artificial intelligence technology that explicitly learns from service failures.Originality/valueCustomer responses to frontline robots have remained largely unexplored. This paper is the first to explore the attributions that customers make when they experience robots in the frontline.
Subfields: Artificial Intelligence, Cognitive Neuroscience, Social Psychology
View Paper: https://www.semanticscholar.org/paper/01f1c6c00f88d4fdad9c0fb71ff92536d80ab85d
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Citations in the corpus
- Examining the effects of robots' physical appearance, warmth, and competence in frontline services: The Humanness‐Value‐Loyalty model (2021, background): “This requires less cognitive and behavioral adaptation on the customer side and deemphasizes any cost considerations (Belanche et al., 2020c).”