Journal of Information Resources Management ›› 2026, Vol. 16 ›› Issue (4): 81-93.doi: 10.13365/j.jirm.2026.04.081

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Repairing with Emotion: A Review of Emotional Recovery Strategies in AI Service Failure Contexts

Meng Sujie1 Ye Jianmei1 Gao Shijie1 Ma Ye2 Wang Weijun2   

  1. 1.Key Laboratory of Adolescent Cyberpsychology and Behavior, Central China Normal University, Wuhan, 430079; 
    2. Department of Public Administration, Beijing City University, Beijing, 100094
  • Online:2026-07-26 Published:2026-09-21
  • About author:Meng Sujie, Ph.D candidate, research interests including psychology and behavior of human-AI interaction; Ye Jianmei, Ph.D candidate, research interests including psychology and behavior of human-AI interaction; Gao Shijie, master's student. research interests including psychology and behavior of human-AI interaction; Ma Ye, Ph.D, associate professor, master's supervisor, research interests including social work service management; Wang Weijun (corresponding author), Ph.D, professor, doctoral supervisor, research interests including behavior of human-AI interaction, digital intelligence assessment and services for mental health. Email: wangwj919@163.com.
  • Supported by:
    This is an outcome of the Major Projects "Research on the Reconstruction and Application of Human-Centered AI-Driven Information Service Systems"(22&ZD324) supported by National Social Science Foundation of China.

Abstract: Using a systematic literature review that combines quantitative evidence synthesis and qualitative theoretical reconstruction, this study reviews emotional recovery in AI service failure contexts. Based on the Stimulus-Organism-Response (S-O-R) framework, AI emotional recovery strategies are classified into emotional expression and empathic response. Their mechanisms, effects, and boundary conditions are examined from the perspectives of social cognition, social interaction, emotional and attitudinal outcomes, behavioral intentions, and user, situational, and technological characteristics. The findings show that these strategies can alleviate negative emotions and enhance forgiveness, satisfaction, and continuance intention, although their effectiveness varies across users and service contexts. This study expands the research perspectives on AI service recovery and offers implications for the human-centered design of intelligent service systems.

Key words: Artificial intelligence, Information service, AI service failure, AI service recovery, Human-AI interaction, Affective computing

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