ABSTRACT

Artificial intelligence (AI)-powered chatbots are changing the nature of service interfaces from being human-driven to technology-dominant. As a result, customers are expected to resolve issues themselves before reaching out to customer service representatives, ultimately becoming a central element of service production as co-creators of value. However, AI-powered interactions can also fail, potentially leading to anger, confusion, and customer dissatisfaction. We draw on the value co-creation literature to investigate the process of co-destruction in AI-powered service interactions. We adopt an exploratory approach based on in-depth interviews with 27 customers who have interacted with AI-powered chatbots in customer service settings. We find five antecedents of failed interactions between customers and chatbots: authenticity issues, cognition challenges, affective issues, functionality issues, and integration conflicts. We observe that although customers do accept part of the responsibility for co-destruction, they largely attribute the problems they experience to resource misintegration by service providers. Our findings contribute a better understanding of value co-destruction in AI-powered service settings and provide a richer conceptualization of the link between customer resource loss, attributions of resource loss, and subsequent customer coping strategies. Our findings also offer service managers insights into how to avoid and mitigate value co-destruction in AI service settings.

摘要

人工智能 (AI) 驱动的聊天机器人正在将服务界面的性质从人类驱动转变为技术主导。因此，客户应该在联系客户服务代表之前自行解决问题，最终成为服务生产的核心要素，成为价值的共同创造者。然而，人工智能驱动的交互也可能失败，可能导致愤怒、困惑和客户不满。我们利用价值共创文献来研究人工智能驱动的服务交互中的共破坏过程。我们采用基于对 27 位在客户服务设置中与人工智能聊天机器人交互的客户进行深入访谈的探索性方法。我们发现了客户与聊天机器人之间互动失败的五个前因：真实性问题、认知挑战、情感问题、功能问题和整合冲突。我们观察到，虽然客户确实接受了共同销毁的部分责任，但他们在很大程度上将他们遇到的问题归因于服务提供商的资源错误整合。我们的研究结果有助于更好地理解 AI 驱动的服务环境中的价值共毁，并为客户资源损失、资源损失归因和后续客户应对策略之间的联系提供更丰富的概念化。我们的发现还为服务经理提供了有关如何避免和减轻 AI 服务设置中的价值共同破坏的见解。他们在很大程度上将他们遇到的问题归咎于服务提供商的资源错误整合。我们的研究结果有助于更好地理解 AI 驱动的服务环境中的价值共毁，并为客户资源损失、资源损失归因和后续客户应对策略之间的联系提供更丰富的概念化。我们的发现还为服务经理提供了有关如何避免和减轻 AI 服务设置中的价值共同破坏的见解。他们在很大程度上将他们遇到的问题归咎于服务提供商的资源错误整合。我们的研究结果有助于更好地理解 AI 驱动的服务环境中的价值共毁，并为客户资源损失、资源损失归因和后续客户应对策略之间的联系提供更丰富的概念化。我们的发现还为服务经理提供了有关如何避免和减轻 AI 服务设置中的价值共同破坏的见解。