Chatbots as prominent form of conversational agents are increasingly implemented as a user interface for digital customer-firm interactions on digital platforms and electronic markets, but they often fail to deliver suitable responses to user requests. In turn, individuals are left dissatisfied and turn away from chatbots, which harms successful chatbot implementation and ultimately firm’s service performance. Based on the stereotype content model, this paper explores the impact of two universally usable failure recovery messages as a strategy to preserve users’ post-recovery satisfaction and chatbot re-use intentions. Results of three experiments show that chatbot recovery messages have a positive effect on recovery responses, mediated by different elicited social cognitions. In particular, a solution-oriented message elicits stronger competence evaluations, whereas an empathy-seeking message leads to stronger warmth evaluations. The preference for one of these message types over the other depends on failure attribution and failure frequency. This study provides meaningful insights for chatbot technology developers and marketers seeking to understand and improve customer experience with digital conversational agents in a cost-effective way.

聊天机器人作为对话代理的重要形式，越来越多地被用作数字平台和电子市场上数字客户与公司交互的用户界面，但它们往往无法对用户请求提供适当的响应。反过来，个人会感到不满并远离聊天机器人，这会损害聊天机器人的成功实施并最终损害公司的服务绩效。基于刻板印象内容模型，本文探讨了两种普遍可用的故障恢复消息作为保留用户恢复后满意度和聊天机器人重用意图的策略的影响。三项实验的结果表明，聊天机器人的恢复消息对恢复反应有积极的影响，这是由不同的引发的社会认知介导的。特别是，以解决方案为导向的信息会引发更强的能力评估，而寻求同理心的信息会导致更强的温暖评估。对其中一种消息类型相对于另一种消息类型的偏好取决于故障归因和故障频率。这项研究为聊天机器人技术开发人员和营销人员提供了有意义的见解，帮助他们以经济高效的方式了解和改善数字对话代理的客户体验。