The Evolution of Followership in AI-Mediated Leadership and How Employee Emotional Responses Change When Leaders Use AI for People Management Decisions

Abstract

The increasing involvement of artificial intelligence (AI) in people management is reshaping leadership practices and changing how employees experience their roles as followers. Organizations increasingly use AI-supported systems in performance evaluation, scheduling, task allocation, employee development, and career-related decisions. Although these systems may improve consistency and analytical efficiency, their use can also affect the relational and emotional foundations of leadership. This study examines how AI-mediated leadership practices relate to relational followership quality, employee affective states, and job satisfaction. Relational followership quality is examined through leader–member exchange quality and perceived social exchange quality. Drawing on Leader–Member Exchange Theory, Social Exchange Theory, and Affective Events Theory, the study employed a quantitative, cross-sectional survey design. Data were collected from 54 full-time employees with varying levels of exposure to AI-supported people management practices. Pearson correlation and multiple regression analyses were conducted to examine the proposed relationships. The findings showed that employees’ perceptions of fair, transparent, and responsive AI-mediated leadership practices were positively associated with leader–member exchange quality, perceived social exchange quality, favorable affective states, and job satisfaction. AI-mediated leadership practices and perceived social exchange quality jointly explained 36.1% of the variance in employee affective states. Furthermore, AI-mediated leadership practices and employee affective states explained 51.6% of the variance in job satisfaction, with employee affective states emerging as the stronger predictor. The findings suggest that employees respond not only to the presence of AI but also to the fairness, transparency, accountability, and relational context surrounding its use. The study contributes to the literature by presenting employees as active interpreters of AI-mediated leadership rather than passive recipients of algorithmically supported decisions.