AI-Mediated Leadership and Leader–Member Exchange Quality: A Social Exchange Perspective on Fairness and Transparency

Abstract

The growing use of artificial intelligence in people management is changing how employees experience leadership and evaluate their relationships with managers and organizations. AI-supported systems increasingly contribute to scheduling, work allocation, employee development, recruitment, and performance monitoring. This study examines the relationships between AI-mediated leadership practices, leader–member exchange quality, and perceived social exchange quality. Fairness and transparency are employed as interpretive lenses for understanding these relationships rather than as independently measured variables. Drawing on Leader–Member Exchange Theory and Social Exchange Theory, the study adopted a quantitative, cross-sectional survey design. Data were collected from 54 employees representing different organizational functions and levels of exposure to AI-supported people management. Pearson correlation and simple linear regression analyses were conducted to test the proposed relationships. AI-mediated leadership practices were positively associated with leader–member exchange quality ((r=.512,\ p<.001)) and perceived social exchange quality ((r=.478,\ p<.001)). The regression results indicated that AI-mediated leadership practices explained 26.2% of the variance in leader–member exchange quality and 22.9% of the variance in perceived social exchange quality. These findings provide exploratory evidence that employees’ experiences of AI-supported management are meaningfully associated with relational outcomes. However, they do not demonstrate that increased automation causes stronger workplace relationships. The study highlights the continued importance of human accountability, meaningful explanation, employee voice, and relational leadership when AI informs people management decisions. It contributes to the literature by distinguishing employees’ relationships with immediate leaders from their broader social exchange relationships with organizations. Future research should directly measure perceived fairness and transparency and examine their roles using larger, longitudinal, and multi-organizational samples.