Paper Type
Complete
Abstract
Although artificial intelligence (AI) systems such as ChatGPT offer transformational potential for knowledge work, user resistance to their adoption remains substantial. Existing research has largely attributed this resistance to concerns about accuracy, trust, and job displacement, but has overlooked AI-specific phenomena such as hallucinations and ethical concerns. We address this gap by developing a research model based on user resistance literature, which integrates two contextualized perspectives: (1) AI-hallucinations and (2) ethics. Grounded using a quantitative survey with 185 users familiar with AI systems, we found that ethics-related factors, particularly perceived threats and moral obligations, drive user resistance, whereas hallucination-related concerns have limited explanatory power. Contrary to our assumption, users with higher critical thinking show lower user resistance. We discuss these findings, derive implications for user resistance and AI literature, and develop directions for further research.
Paper Number
1012
Recommended Citation
Schiller, Christian A. and Maier, Christian, "User Resistance Towards Artificial Intelligence: A Study Focusing on AI-Hallucinations and Ethics" (2025). AMCIS 2025 Proceedings. 26.
https://aisel.aisnet.org/amcis2025/sigadit/sigadit/26
User Resistance Towards Artificial Intelligence: A Study Focusing on AI-Hallucinations and Ethics
Although artificial intelligence (AI) systems such as ChatGPT offer transformational potential for knowledge work, user resistance to their adoption remains substantial. Existing research has largely attributed this resistance to concerns about accuracy, trust, and job displacement, but has overlooked AI-specific phenomena such as hallucinations and ethical concerns. We address this gap by developing a research model based on user resistance literature, which integrates two contextualized perspectives: (1) AI-hallucinations and (2) ethics. Grounded using a quantitative survey with 185 users familiar with AI systems, we found that ethics-related factors, particularly perceived threats and moral obligations, drive user resistance, whereas hallucination-related concerns have limited explanatory power. Contrary to our assumption, users with higher critical thinking show lower user resistance. We discuss these findings, derive implications for user resistance and AI literature, and develop directions for further research.
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