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Article
Publication date: 13 April 2023

Yimin Zhu, Jiemin Zhang and Jifei Wu

This study aims to explore the recovery performances of chatbots (vs human employees) and help firms use chatbots to carry out effective service recovery.

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Abstract

Purpose

This study aims to explore the recovery performances of chatbots (vs human employees) and help firms use chatbots to carry out effective service recovery.

Design/methodology/approach

Two experiments were conducted to test the proposed hypotheses.

Findings

The results show that compared with human employees’ recovery, chatbots’ recovery leads to lower customer satisfaction and revisit intention. This effect is more significant for symbolic recovery instead of economic recovery. Perceived distributive and interactional justice mediate the interaction effect of recovery provider and recovery strategy on recovery performance. Using immediate recovery rather than delayed recovery can attenuate chatbots’ poor performances in symbolic recovery.

Originality/value

This study enriches the chatbot research and the service recovery literature by deploying chatbots into the service recovery setting. Using an integrated theoretical model including recovery strategy and recovery timing, this study provides substantive insight into how firms can enhance chatbots’ recovery performances.

研究目的

本研究旨在探索聊天机器人(与人类员工相比)的服务补救表现, 并帮助公司使用聊天机器人进行有效的服务补救。

研究设计/方法/途径

本研究进行了两个实验来检验提出的理论假设

调查发现

结果表明, 与人类员工的服务补救相比, 聊天机器人的服务补救导致顾客满意度和再惠顾意愿降低。 这种效应对于象征补救而非功利补救更为显著。 分配公平和互动公平在服务补救提供者和补救策略的交互作用对补救表现的影响中起到了中介作用。 使用立即补救而不是延迟补救可以减轻聊天机器人象征补救方面的不良表现。

研究原创性/价值

本研究通过将聊天机器人部署到服务补救环境中丰富了聊天机器人研究和服务补救文献。 本研究通过构建包括服务补救策略和补救时机在内的综合理论模型, 为企业如何提高聊天机器人的服务补救表现提供了实质性的见解。

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