The power of explanations in mitigating the ill‐effects of service failures
Abstract
Purpose: To examine the role of explanations in influencing customer perceptions of service failures, this study investigated the impact of two types of explanations: retrospective excuses and anticipatory excuses on customers' fairness perceptions and their tipping behaviors. Design/methodology/approach: A 3 (explanation: absent, anticipatory, retrospective) × 2 (service recovery effort: no tangible compensation or 20 percent off the total bill) between‐participant design was used to test the hypotheses. Simulated dining experiences were enacted and videotaped to represent the six conditions. Findings: The findings of this study imply that customer‐contact employees might be able to influence customer impressions by offering a causal explanation for a service failure. Research limitations/implications: Owing to the restaurant‐oriented focus of this study, these results may not be generalizable to other service industries. Second, our stimuli involved service encounters that were clearly inadequate in terms of interactional treatment. Third, due to the research method employed in this study the dyadic nature of the service encounter is minimized. Fourth, the student sample somewhat limits the generalizability of the results. Practical implications: The results indicate that retrospective excuses might enhance customers' fairness perceptions more than anticipatory excuses. Yet, it is important to keep in mind that explanations for failures, even when combined with tangible compensation, are poor substitutes for inadequate interpersonal treatment. Originality/value: The findings of this study add to the evidence that offering an explanation for a service failure can positively influence customer perceptions. Moreover, the paper introduces tipping as a surrogate of satisfaction to the service recovery literature.
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Only very few studies use the split of interactional justice into interpersonal and informational justice.
“However, only very few studies use these dimensions (e.g., Kau and Loh 2006; Mattila 2006).”
From A Meta-Analysis of Organizational Complaint Handling and Customer Responses (2011) · cites this paper as Mattila 2006
Cites
Justice at the millennium: A meta-analytic review of 25 years of organizational justice research.
Colquitt, J. A., Conlon, D. E., Wesson, M. J., Porter, C. O. L. H., & Ng, K. Y. (2001) · Journal of Applied Psychology · doi:10.1037/0021-9010.86.3.425 · Cited by 5,942 · see citing works
Goodwin, C., & Ross, I. (1992) · Journal of Business Research · doi:10.1016/0148-2963(92)90014-3 · Cited by 777 · see citing works
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To Err Is Human(-oid): How Do Consumers React to Robot Service Failure and Recovery?
Choi, S., Mattila, A. S., & Bolton, L. E. (2021) · Journal of Service Research · doi:10.1177/1094670520978798 · Cited by 502 · see citing works
Self-citationA Meta-Analysis of Organizational Complaint Handling and Customer Responses
Gelbrich, K., & Roschk, H. (2011) · Journal of Service Research · doi:10.1177/1094670510387914 · Cited by 481 · see citing works
- Authors
- Mattila, A. S.
- Affiliations
- Pennsylvania State University (US)
- Corresponding author
- Mattila, Anna S.
- ORCID
- Mattila, Anna S.
- Venue
- Journal of Services Marketing
- Publisher
- Emerald
- Published
- 2006-12-01
- Volume
- 20
- Issue
- 7
- Pages
- 422-428
- ISSN
- 0887-6045 (ISSN-L)
- OpenAlex
- W2032299294
- Cited by
- 122
- References
- 2
- Citations by year
- 2026: 7 · 2025: 8 · 2024: 4 · 2023: 6 · 2022: 6 · 2021: 4 · 2020: 7 · 2019: 5 · 2018: 4 · 2017: 6 · 2016: 4 · 2015: 9 · 2014: 13 · 2013: 7 · 2012: 9
- FWCI
- 6.37
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Services Marketing (publishedVersion)
- Topics
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › Customer Service Quality and Loyalty
- Social Sciences › Business, Management and Accounting › Marketing › Service and Product Innovation
- Physical Sciences › Computer Science › Artificial Intelligence › AI in Service Interactions
- Keywords
- service failure, explanation, perceived fairness, service recovery, tipping, customer satisfaction, restaurant service, service encounters
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