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Service Failure by Human, Service Recovery by AI Chatbot: The Impact of Justice, AI Efficacy on Recovery Effort

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  • Won-Jun Lee

    (Cheongju University, Cheongju, Korea)

Abstract

Artificial intelligence has led to significant innovation in the marketing field, with intelligent chatbots increasingly involved in both customer service provision and service failure recovery processes. While recent research on AI services and intelligent chatbots has been increasing, there has been little research on the collaborative efforts between AI chatbots and human agents for service failure recovery and users' perceptions of the process. Accordingly, the aim of this study is to assess the impact of AI chatbot intervention in recovering from service failures made by human agents on key service recovery outcomes. To achieve this, we model the influence of perceived justice in AI chatbot service recovery processes on outcomes such as customer forgiveness and post-recovery customer satisfaction mediated through efficacy towards AI chatbots. Additionally, drawing on anthropomorphism theory, we seek to verify the moderation effect of humanness, representing the degree to which AI chatbots resemble humans. To test the hypotheses, a total of 187 respondents who had experienced service failure by human agents and service recovery through AI chatbots were surveyed. The collected data were then validated for reliability and validity, and the hypotheses were tested using PLS analysis. Empirical analysis results confirm the significance of all hypotheses and moderation effects. The findings of this study hold academic significance as an exploration of the proactive role of AI chatbots in service recovery processes, providing both theoretical insights and practical implications for companies intending to implement chatbots in service settings in the future.

Suggested Citation

  • Won-Jun Lee, 2024. "Service Failure by Human, Service Recovery by AI Chatbot: The Impact of Justice, AI Efficacy on Recovery Effort," Virtual Economics, The London Academy of Science and Business, vol. 7(4), pages 48-63, December.
    • Handle: RePEc:aid:journl:v:7:y:2024:i:4:p:48-63
    DOI: 10.34021/ve.2024.07.04(3)
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