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When Does an AI Outperform a Human in Service Recovery? A Comparison Between Two Compensation Strategies.

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Title: When Does an AI Outperform a Human in Service Recovery? A Comparison Between Two Compensation Strategies.
Authors: Yao, Tang (AUTHOR), Xue, Hanyue (AUTHOR), Xiao, Weiqun (AUTHOR), Qiu, Qi (AUTHOR)
Source: International Journal of Human-Computer Interaction. Dec2025, Vol. 41 Issue 23, p14643-14656. 14p.
Subjects: Artificial intelligence, Empathy, Wages, Fairness, Consumer attitudes, Compensation management, Client satisfaction
Abstract: Artificial intelligence (AI) has been widely adopted across various service industries. However, consumers often feel that current AI lacks the capability to understand emotions, which can impede its effectiveness in service recovery following service failures. To explore this issue, we examine customer reactions to two compensation strategies—symbolic and utilitarian—provided by different recovery agents (employee vs. AI). A survey and three scenario-based experiments revealed that customers prefer symbolic compensation when it is provided by an employee rather than an AI. Interestingly, this preference gets reversed when utilitarian compensation is provided. Moreover, our research verified that perceived empathy and perceived fairness serve as mediators for this effect, while failure attribution acts as a moderator. These findings enhance our understanding of service recovery and offer important implications for the integration of AI in service industries. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Human-Computer Interaction is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: When Does an AI Outperform a Human in Service Recovery? A Comparison Between Two Compensation Strategies.
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  Data: <searchLink fieldCode="AR" term="%22Yao%2C+Tang%22">Yao, Tang</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Xue%2C+Hanyue%22">Xue, Hanyue</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Xiao%2C+Weiqun%22">Xiao, Weiqun</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Qiu%2C+Qi%22">Qiu, Qi</searchLink> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Human-Computer+Interaction%22">International Journal of Human-Computer Interaction</searchLink>. Dec2025, Vol. 41 Issue 23, p14643-14656. 14p.
– Name: Subject
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  Data: <searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Empathy%22">Empathy</searchLink><br /><searchLink fieldCode="DE" term="%22Wages%22">Wages</searchLink><br /><searchLink fieldCode="DE" term="%22Fairness%22">Fairness</searchLink><br /><searchLink fieldCode="DE" term="%22Consumer+attitudes%22">Consumer attitudes</searchLink><br /><searchLink fieldCode="DE" term="%22Compensation+management%22">Compensation management</searchLink><br /><searchLink fieldCode="DE" term="%22Client+satisfaction%22">Client satisfaction</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Artificial intelligence (AI) has been widely adopted across various service industries. However, consumers often feel that current AI lacks the capability to understand emotions, which can impede its effectiveness in service recovery following service failures. To explore this issue, we examine customer reactions to two compensation strategies—symbolic and utilitarian—provided by different recovery agents (employee vs. AI). A survey and three scenario-based experiments revealed that customers prefer symbolic compensation when it is provided by an employee rather than an AI. Interestingly, this preference gets reversed when utilitarian compensation is provided. Moreover, our research verified that perceived empathy and perceived fairness serve as mediators for this effect, while failure attribution acts as a moderator. These findings enhance our understanding of service recovery and offer important implications for the integration of AI in service industries. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of International Journal of Human-Computer Interaction is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1080/10447318.2025.2486434
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      – Code: eng
        Text: English
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        Type: general
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      – SubjectFull: Wages
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              Text: Dec2025
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