Robots or frontline employees? Exploring customers’ attributions of responsibility and stability after service failure or success

Daniel Belanche, Luis V. Casaló, Carlos Flavián, Jeroen Schepers (Corresponding author)

Research output: Contribution to journalArticleAcademicpeer-review

5 Citations (Scopus)

Abstract

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 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 a restaurant’s waiter service.


Findings

Results indicate that respondents make stronger attributions of responsibility for the service performance towards humans than towards 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. 

Original languageEnglish
Pages (from-to)267-289
Number of pages23
JournalJournal of Service Management
Volume31
Issue number2
DOIs
Publication statusPublished - 18 Jul 2020

Keywords

  • Artificial intelligence
  • Customer attributions
  • Frontline robots
  • Responsibility
  • Service failure
  • Service robots
  • Stability

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