Paper Number
ICIS2025-1770
Paper Type
Short
Abstract
The integration of AI into customer service has transformed business-customer interactions, leading to the rise of hybrid service systems where AI and human agents collaborate to deliver services. While such systems aim to combine AI’s efficiency with human judgment, their effectiveness can be undermined by uncoordinated collaboration. This study explores the blame-shifting behavior of human agents in assemblage-based hybrid service systems, where humans and AI act as equal collaborators. Using a rich dataset from a Chinese company that deployed a chatbot on the Taobao platform, we examine how human agents’ blame-shifting behavior affects service effectiveness, i.e. service evaluation and purchase decisions. To address endogeneity concerns, we implement an instrumental variable approach. Our findings will shed light on the behavioral dynamics in human-AI collaboration and highlight a novel challenge in hybrid service delivery: the misuse of AI as a scapegoat by human agents.
Recommended Citation
Chen, Junhang; Yang, Cenying; and Fu, Xin, "AI as A Scapegoat: Impact of Blame-shifting Behavior of Human Agents in Hybrid Service System" (2025). ICIS 2025 Proceedings. 14.
https://aisel.aisnet.org/icis2025/gen_ai/gen_ai/14
AI as A Scapegoat: Impact of Blame-shifting Behavior of Human Agents in Hybrid Service System
The integration of AI into customer service has transformed business-customer interactions, leading to the rise of hybrid service systems where AI and human agents collaborate to deliver services. While such systems aim to combine AI’s efficiency with human judgment, their effectiveness can be undermined by uncoordinated collaboration. This study explores the blame-shifting behavior of human agents in assemblage-based hybrid service systems, where humans and AI act as equal collaborators. Using a rich dataset from a Chinese company that deployed a chatbot on the Taobao platform, we examine how human agents’ blame-shifting behavior affects service effectiveness, i.e. service evaluation and purchase decisions. To address endogeneity concerns, we implement an instrumental variable approach. Our findings will shed light on the behavioral dynamics in human-AI collaboration and highlight a novel challenge in hybrid service delivery: the misuse of AI as a scapegoat by human agents.
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