AI ‐Enabled Performance Feedback Design: The Effects of Anthropomorphic AI Feedback Source, Feedback Valence and Emotional Arousal on Employee Advice‐Taking Behaviour
Qinglan Chen, Yuehua Chen, Yuhong Tu, Zan Zhong · Accounting and Finance · 2026
ABSTRACT Companies are increasingly using artificial intelligence (AI) to provide employees with performance feedback, but its efficacy is inconsistent. Drawing upon the theories of mind perception and feedback intervention, we examine the effects of anthropomorphic AI feedback source and feedback valence in conveying performance feedback on employees' advice‐taking behaviours. We conducted a 2 × 2 experiment in which we manipulated anthropomorphic AI feedback source (more versus less) and examined how feedback valence (positive versus negative) affects subsequent advice‐taking behaviour. Using an electrodermal activity (EDA) device in a production task in which employees received feedback from either a more or a less anthropomorphic AI, we predict and find that receiving positive feedback from a more anthropomorphic AI instead of a less anthropomorphic counterpart results in higher emotional arousal, which leads to more subsequent advice‐taking behaviour. However, under a negative feedback valence, employees' emotional arousal and subsequent advice‐taking behaviour do not differ between a more anthropomorphic AI and less anthropomorphic counterpart. Accordingly, we offer theoretical insights into the role of emotional language in performance feedback using anthropomorphic AI systems.