Less is More or More is Better? Comparing Static and Generative AI-Based Recovery for Chatbot Service Breakdowns
Journal
International Conference on Information Systems (ICIS)
Type
conference paper
Date Issued
2025-12-14
Author(s)
Research Team
IWI6
Abstract
Conversational breakdowns in chatbot-based customer service interactions can impair service quality and brand perception. Our study investigates how static versus generative AI (GAI)-based recovery affects user experience in such scenarios. Using a dual-study design, Study 1 examines two static strategies: Inform (explanation-focused) and Repair (action-focused). We find that Repair significantly enhances recovery quality and service perception, while Inform offers limited benefits when used alone. Study 2 explores the potential of GAI-driven recovery, employing a chatbot capable of nuanced, interactive support. Preliminary findings suggest that GAI-based recovery may increase resolution success, particularly in complex interactions, but could demand higher cognitive effort from users. By comparing both approaches and introducing a sequencing approach, our study provides novel insights into how recovery design and GAI influence service outcomes. Our results inform the design of future chatbot service systems, suggesting that adaptive, generative recovery may complement traditional static recovery strategies under specific conditions.
Language
English
Keywords
Conversational Breakdown
Breakdown Recovery
Generative AI
HSG Classification
contribution to scientific community
Refereed
Yes
Publisher place
Nashville, USA
Pages
9
Event Title
International Conference on Information Systems (ICIS)
Event Location
Nashville, USA
Event Date
14.12.2025
Subject(s)
Division(s)
File(s)![Thumbnail Image]()
open.access
Name
JML_1045.pdf
Size
601.43 KB
Format
Adobe PDF
Checksum (MD5)
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