As artificial intelligence (AI) becomes increasingly integrated into organizational leadership, it is critical to understand how algorithmic decision-making affects employee well-being. This study investigates how varying levels of AI involvement in leadership – ranging from fully human to hybrid (human-AI collaboration) to fully automated – influence employees’ emotional responses at work. It also examines whether the emotional impact of leader type depends on the outcome of a managerial decision (positive vs. negative). To investigate these questions, we conducted a vignette-based online experiment using a 3x2 between-subjects design. Participants (N=153 workers) were randomly assigned to one of six short, standardized leadership scenarios that varied by leader type (human, hybrid, or AI) and decision outcome (positive or negative). The vignettes described a realistic workplace situation in which a leader communicates a decision about a project’s continuation. Subsequently, emotional responses were measured using validated affective scales. The results showed that higher AI involvement led to lower positive affect, particularly following favorable decisions, while negative affect remained largely unaffected. These results suggest that, while AI leadership is not emotionally harmful, it also fails to generate positive engagement. Positive affect was strongest when positive decisions were delivered by a human leader and weakest when delivered by an AI. These findings contribute to leadership and human-AI interaction research by highlighting an emotional asymmetry in AI-led leadership. Practically speaking, these results imply that while AI offers efficiency, it lacks the interpersonal resonance necessary for emotionally meaningful interactions. Therefore, organizations should consider maintaining human involvement in contexts where recognition, trust, or relational sensitivity are important.     «

As artificial intelligence (AI) becomes increasingly integrated into organizational leadership, it is critical to understand how algorithmic decision-making affects employee well-being. This study investigates how varying levels of AI involvement in leadership – ranging from fully human to hybrid (human-AI collaboration) to fully automated – influence employees’ emotional responses at work. It also examines whether the emotional impact of leader type depends on the outcome of a managerial decisi...     »