OVERCOMING BREAKDOWNS IN CUSTOMER-CHATBOT INTERACTION: DESIGN AND IMPACT OF COLLABORATIVE REPAIR STRATEGIES

• 成果类型: Article
• 署名作者: Gnewuch, Ulrich; Reinkemeier, Fabian
• 署名单位: University of Passau; University of Gottingen
• 刊物名称: MIS QUARTERLY
• ISSN/ISSBN: 0276-7783
• DOI: 10.25300/MISQ/2025/18742
• 发表日期: 2026-06
• 页码: 497-526
• 关键词: Customer service chatbot; conversational breakdown; repair strategy; service failure; human-AI; interaction; theory of least collaborative effort; design science research; field experiment; SCIENCE RESEARCH; FRAMEWORK; KNOWLEDGE; SYSTEM

摘要

When chatbots are deployed to automate customer service, it is nearly inevitable that situations will arise in which they struggle to understand customer requests. Unfortunately, the onus of resolving such conversational breakdowns tends to fall on either the customer or the chatbot alone, turning customerchatbot interaction into a frustrating and often unsuccessful guessing game. Despite indications that customers would be open to collaboration, we know little about repair strategies that involve the customer and chatbot working together to resolve breakdowns. Our research addresses this gap by investigating the design and impact of collaborative repair strategies in customer-chatbot interaction. Drawing upon an integration of the theory of least collaborative effort with research on human-machine communication and customer service chatbots, we propose a novel repair strategy design; we instantiated it in the chatbot of a large insurance company and conducted a naturalistic summative evaluation through a randomized field experiment. Overall, our results suggest that a collaborative repair strategy can lead to more breakdowns being resolved and mitigate the negative impacts of breakdowns on key customer outcomes. Our research offers a new way of thinking about customer-AI service interactions by shifting the narrative from confrontation to collaboration, extends the theory of least collaborative effort by integrating the perspective of customer-chatbot interaction, and provides in-depth insights into breakdown and repair in real-world conversations between customers and chatbots.