Algorithmic Leadership and Employee Well-Being: A Multidisciplinary Qualitative Study of AI-Driven Decision-Making in Modern Organizations

Abstract

The quick adoption of artificial intelligence (AI) in the decision-making process of organizations has created a new type of leadership that is also known as algorithmic leadership. Contrary to the traditional human-centered approaches to leadership, algorithmic leadership bases its services on data-driven systems to influence, assess, and in some cases, substitute managerial decision-making procedures. Although these systems have no doubt brought efficiency, consistency and objectivity, the implications on the well-being of employees are under explored and debatable. This is a theoretical qualitative research paper that investigates the impact of algorithmic leadership on employee well-being on the psychological, emotional, and social levels. The thematic syntheses of the study are based on the literature of interdisciplinary fields, including management studies, organizational psychology, information systems, and the AI ethics, and use the thematic qualitative syntheses to uncover common patterns, tensions, and perceptions related to AI-driven leadership practices. The result shows that there were four super-themes, namely; perceived loss of autonomy, algorithmic opaque and uncertain, reconfiguration of trust and fairness, and ambivalent well-being outcomes. Although algorithmic leadership has the capacity to minimize the influence of human bias and increase procedural consistency, it can also increase stress, decrease the perceived control, and minimize people-focused elements of leadership that play a significant role in employee well-being. The present research will be relevant to the developing body of research on the topic of algorithmic management as it will provide a well-being-focused conceptual framework and emphasize the socio-technical relationships that influence employee experience in the workplaces mediated by AI. They are applied to practical implications on organizational leaders, system designers, and policymakers through the need to understand AI leadership systems should be transparent, participatory, and ethically informed.

Keywords: algorithmic leadership, employee well-being, artificial intelligence, qualitative study, AI-driven decision-making, organizational ethics