Algorithmic Management and Employee Resistance: The Mediating Role of Perceived Loss of Autonomy

Abstract

In the present study, algorithmic management was shown to influence employee resistance and it was investigated whether perceived loss of autonomy was a mediating variable. The study was based on the organizational control theory and self-determination theory, which suggests that greater algorithmic management can raise an employee's sense of loss of control or sense of discretion in their work. Such perceptions can then lead to resistance intentions. A scenario-based experimental design was used to investigate the proposed causal relationships. High and low levels of algorithmic management were created for the workplace scenarios, and employees were randomly allocated to one of these conditions. Participants then read the scenario and filled out questionnaires about their sense of loss of autonomy and intentions to resist. Independent-samples t-tests and analysis of variance were employed to compare the experimental conditions, and mediation analysis to test the mediation role of perceived loss of autonomy. The study was supposed to show that higher exposure to algorithmic management would lead to higher reports of loss of autonomy and higher intention to resist organizational practices among the employees. The results can help to inform research on algorithmic management, as they reveal autonomy as a key psychological mechanism by which technological control may shape employee reactions. Additionally, the study offers practical guidance to organizations, highlighting the importance of trying to balance the need for technological efficiency and monitoring with the need for discretion, participation and control for employees.