Is compensation necessary to recover robotic service failures? The role of attribution and rapport in restaurants

Purpose The adoption of service robots in the hospitality and tourism industries has transformed service encounters into human–robot interactions. This study aims to investigate whether service providers can benefit from increasing recovery effort when service robots are involved. Design/methodology/approach Drawing on the theory of mind perception, the study employed field and online experiments to examine the effects of robotic service recovery strategies on customer forgiveness and satisfaction. This mixed-methods approach ensures robust and ecologically valid findings. Findings The results reveal that higher recovery effort by a service robot positively influences customer forgiveness, which in turn increases customer satisfaction. Interestingly, when customers attribute less blame to the robot and perceive stronger rapport with it, increasing recovery effort does not yield additional positive effects. Practical implications This study offers practical guidance for restaurant managers on managing robotic service failures and improving the effectiveness of recovery efforts and outcomes. Originality/value This research offers novel insights into the dynamics of service recovery in human–robot interactions. It demonstrates that service robots can foster customer forgiveness through cost-effective recovery strategies, offering both theoretical and practical implications for the restaurant industry. Also, the findings advance our understanding of how mind perception attribution shapes robotic service recovery outcomes and provide actionable guidance for optimizing service robot recovery strategies in real-world contexts.

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Publication Details

Journal
International Journal of Contemporary Hospitality Management
Published
2025-12-13
DOI
https://doi.org/10.1108/ijchm-02-2025-0218
Citations
1
Primary Topic
AI in Service Interactions
Type
article
Field-Weighted Citation Impact
2.48
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article

Is compensation necessary to recover robotic service failures? The role of attribution and rapport in restaurants

Yu‐Shan Huang, Chen-Wei Tao, Wei‐Kang Kao, Seonwoo Ko
1 citations
International Journal of Contemporary Hospitality Management
AI in Service Interactions
2.48
article

Is compensation necessary to recover robotic service failures? The role of attribution and rapport in restaurants

Yu‐Shan Huang, Chen-Wei Tao, Wei‐Kang Kao, Seonwoo Ko
article en
1 citations

Abstract

Purpose The adoption of service robots in the hospitality and tourism industries has transformed service encounters into human–robot interactions. This study aims to investigate whether service providers can benefit from increasing recovery effort when service robots are involved. Design/methodology/approach Drawing on the theory of mind perception, the study employed field and online experiments to examine the effects of robotic service recovery strategies on customer forgiveness and satisfaction. This mixed-methods approach ensures robust and ecologically valid findings. Findings The results reveal that higher recovery effort by a service robot positively influences customer forgiveness, which in turn increases customer satisfaction. Interestingly, when customers attribute less blame to the robot and perceive stronger rapport with it, increasing recovery effort does not yield additional positive effects. Practical implications This study offers practical guidance for restaurant managers on managing robotic service failures and improving the effectiveness of recovery efforts and outcomes. Originality/value This research offers novel insights into the dynamics of service recovery in human–robot interactions. It demonstrates that service robots can foster customer forgiveness through cost-effective recovery strategies, offering both theoretical and practical implications for the restaurant industry. Also, the findings advance our understanding of how mind perception attribution shapes robotic service recovery outcomes and provide actionable guidance for optimizing service robot recovery strategies in real-world contexts.

International Journal of Contemporary Hospitality ManagementVol. 38(2)
Oklahoma State University (US), University of Southern Maine (US), Virginia Tech (US), Texas A&M University – Corpus Christi (US)
Openalex Percentile: Top 8%
AI in Service Interactions
2.48
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