eISSN: 2093-8462 http://jesk.or.kr
Open Access, Peer-reviewed
eISSN: 2093-8462 http://jesk.or.kr
Open Access, Peer-reviewed
GUO MENGLING,
, Jinho Yim
10.5143/JESK.2026.45.3.213 Epub 2026 July 05
Abstract
Objective: The purpose of this study is to investigate users' dual-path behavioral decision-making—continuing AI service use versus switching to human support— under AI service failure conditions, and to examine the role of cognitive evaluations in this process.
Background: Prior research has primarily focused on single behavioral intentions, such as continuance intention, while overlooking the trade-off and transition between alternative service channels under failure conditions. In problem-solving contexts, users often face a decision conflict between persisting with AI services and seeking human assistance.
Method: This study adopts a two-stage mixed-method approach. In the first stage, Latent Dirichlet Allocation (LDA) topic modeling was applied to analyze 1,500 real user complaints to identify six data-driven dimensions of AI service failure. In the second stage, attribution theory was used to screen the dimensions and select four constructs. Based on the Technology Acceptance Model (TAM), structural equation modeling (SEM) was employed to examine how these failure dimensions influence users' behavioral decisions through cognitive evaluation pathways.
Results: Perceived context discontinuity (PCD) exerts the strongest negative effect on perceived ease of use. Among cognitive evaluations, perceived ease of use has a stronger influence on switching intention than perceived usefulness. Multi-group analysis further reveals significant differences across groups with varying levels of AI usage frequency.
Conclusion: Users' responses to AI service failure reflect a cognitive cost-based decision-making process rather than simple withdrawal behavior. By adopting a datadriven approach based on real user complaints, this study enhances the contextual validity of failure dimensions and extends the applicability of TAM to failure contexts.
Application: Reducing interaction costs and maintaining conversational continuity are critical for preventing user switching and improving AI service retention. Enterprises should prioritize the development of context-aware dialogue systems and implement differentiated hybrid AI–human service recovery strategies based on users' experience levels.
Keywords
AI service failure Technology acceptance model Cognitive evaluation Switching intention LDA topic modeling
인공지능(AI)과 자연어 처리(NLP) 기술의 발전에 따라, AI 챗봇은 24시간 서비스 제공, 즉각적인 응답, 그리고 비용 절감의 장점을 바 탕으로 전자상거래, 금융 등 다양한 분야에서 널리 활용되고 있다(Huang and Rust, 2018). 특히 포스트 코로나 시대에서 AI 챗봇은 기 업이 디지털 전환과 '비용 효율적 서비스 우수성(cost-effective service excellence)'을 달성하기 위한 핵심 수단으로 자리잡고 있다(Wirtz and Zeithaml, 2018). 그러나 다양한 소비 상황에서 AI 챗봇은 주관성이 높거나 맥락이 불명확한 사용자 요구를 정확히 이해하는 데 여전히 한계를 보인다. 시스템이 사용자의 문제에 적절히 대응하지 못하는 경우 서비스 실패(Service Failure)가 발생하며, 이는 사용자 경험에 부정적인 영향을 미친다(Huang and Dootson, 2022).
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