Algorithmic Management in Limbo: Task-Driven Interweaving of Hierarchy and Market Management

The growing use of algorithmic management (AM) in human resource (HR) activities has attracted growing attention from HR scholars, as organizations increasingly rely on digital labor platforms to leverage external workers. This study examines how these platforms apply AM in human resource management (HRM) and how these algorithmic systems embed both market and hierarchy management principles for shaping worker control and autonomy. Specifically, we seek to examine how AM manifests in two distinct ways across these platforms: one involving hierarchy and control, and another involving matching and autonomy. Using an inductive qualitative design, we analyzed 33 semi-structured interviews with platform workers and documentary data from 23 digital labor platforms. Whereas prior research often frames AM in binary terms—that is, market/autonomy versus hierarchy/control—we explore how task characteristics influence the joint application of both market and hierarchy principles in AM for HRM activities of digital labor platforms. Our findings show how platforms dynamically calibrate market and hierarchy approaches to AM in response to task demands, balancing flexibility, oversight, discretion, and incentives. For HR scholars, this study highlights the flexible and conditional nature of AM systems that blend autonomy and control in nuanced ways. By moving beyond the dominant autonomy-versus-control dichotomy, we show how AM is configured to align with diverse forms of work across platforms, enhancing efficiency while sustaining worker engagement amid evolving task demands.