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The Impact of AI’s Response Method on Service Recovery Satisfaction in the Context of Service Failure

Author

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  • Zengmao Yang

    (School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing 100876, China)

  • Jinlai Zhou

    (School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing 100876, China)

  • Hongjun Yang

    (School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing 100876, China)

Abstract

In order to perpetuate service sustainability and promote sustainable growth in the service sector, it is important to resolve service failures. AI technology is being applied to service jobs in more and more industries, but AI will inevitably fail while providing service. How to carry out service recovery and obtain the understanding and forgiveness of customers is a problem that urgently needs solving in the practice and research of AI services. The purpose of this study was to explore the artificial intelligence remediation mechanism in the context of service failure and to explore the remedial utility of AI’s self-deprecating humor responses. The study conducted data collection through three experiments to test our hypotheses: study 1 verified the main effect of self-deprecating humor responses and the mediating effect of perceived sincerity and perceived intelligence; study 2 verified the moderated effect of the sense of power; and study 3 verified the moderated effect of failure experience. The experimental results show that, in the context of AI for service recovery, self-deprecating humor responses can improve customers’ willingness to tolerate failure, with perceived intelligence and perceived sincerity found to play a mediating role in this. The sense of power also plays a moderating role by affecting perceived sincerity, and failure experience has a moderate effect by affecting perceived intelligence. The theoretical contribution of the article is to introduce the perspective of AI’s self-deprecating humor service recovery, which complements theoretical research in the field of AI services. The management significance of the article is to provide new AI communication strategies and practical suggestions for enterprises and technical personnel.

Suggested Citation

  • Zengmao Yang & Jinlai Zhou & Hongjun Yang, 2023. "The Impact of AI’s Response Method on Service Recovery Satisfaction in the Context of Service Failure," Sustainability, MDPI, vol. 15(4), pages 1-18, February.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:4:p:3294-:d:1064891
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    References listed on IDEAS

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    1. Tang, Le & Sun, Shiyu, 2021. "How does leader self-deprecating humor affect creative performance? The role of creative self-efficacy and power distance," Finance Research Letters, Elsevier, vol. 42(C).
    2. Sara Kim & Ann L. McGill, 2011. "Gaming with Mr. Slot or Gaming the Slot Machine? Power, Anthropomorphism, and Risk Perception," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 38(1), pages 94-107.
    3. Bo Huang & Matthew Philp, 2021. "When AI-based services fail: examining the effect of the self-AI connection on willingness to share negative word-of-mouth after service failures," The Service Industries Journal, Taylor & Francis Journals, vol. 41(13-14), pages 877-899, October.
    4. Helena González‐gómez & Sarah Hudson & Aude Rychalski, 2021. "The psychology of frustration: Appraisals, outcomes, and service recovery," Post-Print hal-03329187, HAL.
    5. Huang, Ran & Ha, Sejin, 2020. "The effects of warmth-oriented and competence-oriented service recovery messages on observers on online platforms," Journal of Business Research, Elsevier, vol. 121(C), pages 616-627.
    6. Daniel Belanche & Luis V. Casaló & Carlos Flavián & Jeroen Schepers, 2020. "Service robot implementation: a theoretical framework and research agenda," The Service Industries Journal, Taylor & Francis Journals, vol. 40(3-4), pages 203-225, March.
    7. Babin, Barry J. & Zhuang, Weiling & Borges, Adilson, 2021. "Managing service recovery experience: Effects of the forgiveness for older consumers," Journal of Retailing and Consumer Services, Elsevier, vol. 58(C).
    8. Sourav Bikash Borah & Srinivas Prakhya & Amalesh Sharma, 2020. "Leveraging service recovery strategies to reduce customer churn in an emerging market," Journal of the Academy of Marketing Science, Springer, vol. 48(5), pages 848-868, September.
    9. Wei, Chuang & Liu, Maggie Wenjing & Keh, Hean Tat, 2020. "The road to consumer forgiveness is paved with money or apology? The roles of empathy and power in service recovery," Journal of Business Research, Elsevier, vol. 118(C), pages 321-334.
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