Robots or frontline employees? Exploring customers’ attributions of responsibility and stability after service failure or success
Purpose: Service 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/approach: Two 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. Findings: Results 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 implications: Robots 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/value: Customer 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.
Year of publication: |
2020
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Authors: | Belanche, Daniel ; Casaló, Luis V. ; Flavián, Carlos ; Schepers, Jeroen |
Published in: |
Journal of Service Management. - Emerald, ISSN 1757-5818, ZDB-ID 2495133-X. - Vol. 31.2020, 2 (18.07.), p. 267-289
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Publisher: |
Emerald |
Saved in:
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