Leading Like Human: Clarifying the Connotation and Validating the Structure of Algorithmic Leadership

Algorithmic leadership refers to the process by which AI algorithms perform task guidance and relationship support traditionally undertaken by human leaders to influence employees in achieving organizational goals. Yet, its connotation, structure, and behavioral content remain unclear in both algorithmic management and leadership research. This study addresses these gaps by integrating leadership behavior theory with existing findings on algorithmic impacts in the workplace. Through theoretical deduction, we propose a two-dimensional structure of algorithmic leadership—algorithmic control and algorithmic norm—and delineate its behavioral components. Following a rigorous six-step scale development procedure and using three samples (N = 1250), we empirically validate this structure, resulting in a reliable and valid 28-item measurement scale. This research clarifies the conceptual domain of algorithmic leadership, provides a robust tool for assessing employee perceptions, and offers a foundation for future theoretical exploration and practical application. Theoretical and managerial implications are discussed.