Human-AI Decision Dynamics: How Risk Propensity and Trust Impact Choices Through Decision Fatigue, Conditional on AI Understanding

This research investigates how trust in AI, risk-taking propensity, decision fatigue, and knowledge of AI interact to shape human-AI decision-making processes in organizational settings. With AI systems now central to decision-making, it is vital to understand the psychological and cognitive underpinnings behind their adoption and performance. This study seeks to examine these interplays and emphasize how these variables combine to determine decision results. Quantitative research design was used, which gathered data from 244 workers from different organizations. Structured questionnaires with previously validated measures were used. ADANCO software was utilized to analyze the data, where Structural Equation Modeling (SEM) was applied to examine the hypothesized associations between variables. The findings substantiated all six hypothesized paths. Decision making was positively affected by trust in AI and risk propensity, while decision fatigue negatively affected it. Decision fatigue mediated and AI understanding moderated many paths, affirming its key position within decision dynamics. The model provided strong explanatory power for AI-integrated decision contexts. The research has theoretical contribution by synthesizing psychological concepts with AI interaction scholarship. At a practical level, it provides tactical guidance for managers to develop AI decision systems to fit human cognitive traits and behavioral inclinations.

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