Control or Empowerment: Toward a Research Agenda on the Duality of Algorithmic Management

Abstract

Online labor platforms employ intelligent algorithmic technologies for work processes and rules, enabling algorithmic management of gig workers. Despite a growing body of research on algorithmic management, the field lacks tools to capture worker experiences, hindering empirical investigations into its subsequent impact mechanisms. This study introduces the Perceived Algorithmic Management Questionnaire, developed and validated through deep interviews and grounded theoretical analysis, to confirm the concept of algorithmic management duality. Drawing on the job demands–resources theory, we argue that the four dimensions of algorithm management both constrain and enable worker work engagement, resulting from workers' efforts to maintain autonomy and cope with deprivation caused by algorithm management. Overall, these findings shed light on the dual nature of algorithmic management, setting the stage for further exploration of its effects on worker behavior.