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Affiliative or self-defeating? Exploring the effect of humor types on customer forgiveness in the context of AI agents’ service failure

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  • Xie, Yuguang
  • Zhou, Peiyu
  • Liang, Changyong
  • Zhao, Shuping
  • Lu, Wenxing

Abstract

Service failures of conversational artificial intelligence (AI) agents are common in real-world interactions. How to mitigate the negative impact of AI agent failures and improve customer forgiveness is imperative. In this study, we construct a model of the influence mechanism of affiliative and self-defeating humor types on customer forgiveness. Through four experimental studies (N = 1919), we find that AI agents expressing humor can effectively enhance customer forgiveness during service failures, with self-defeating humor having the best effect. Moreover, affiliative and self-defeating humor types can improve customers’ positive emotion and allow them to experience relief. The positive impact of AI agents expressing humor persisted in the low-severity failure condition but disappeared in the medium- and high-severity conditions. These findings extend the existing literature on AI agents expressing humor and guide online service providers to mitigate and minimize the negative effects of AI agents’ service failures.

Suggested Citation

  • Xie, Yuguang & Zhou, Peiyu & Liang, Changyong & Zhao, Shuping & Lu, Wenxing, 2025. "Affiliative or self-defeating? Exploring the effect of humor types on customer forgiveness in the context of AI agents’ service failure," Journal of Business Research, Elsevier, vol. 194(C).
    • Handle: RePEc:eee:jbrese:v:194:y:2025:i:c:s0148296325002048
    DOI: 10.1016/j.jbusres.2025.115381
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