Artificial Intelligence (AI) has been a subject of great interest for its potential to enhance human intelligence. However, existing findings suggest that user opinions about AI are mixed, with some embracing it while others express deep concern and try to avoid it. Here, we conducted a comprehensive review of relevant research to identify potential antecedents to AI aversion. Based on the theory of effective use and the adaptive structuration theory, we collapsed the precursors into four dimensions to develop a concise research model that holistically explains users’ AI aversion. We then conducted online experiments to test the hypotheses empirically. The results indicate that perceived AI bias and perceived social influence are strong predictors of AI aversion. Additionally, a significant difference was found between the simple and complex task groups. These findings provide insights into the factors that contribute to AI aversion and have implications for designing and developing AI systems.
Rahman, Md Jabir; Liang, Huigang; and Xue, Yajiong, "AI Aversion: A Task Dependent Multigroup Analysis" (2023). PACIS 2023 Proceedings. 86.
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