Algorithmic HRM control in the gig economy: The app-worker perspective

Abstract

Work in the gig economy is championed by platform organizations as affording individuals the flexibility to decide when, where, and how much they wish to work. The reality is more complex. In app-based gig work, we propose the concept of “algorithmic HRM control,” which acts as an omnipresent and distinctive control system that differs from traditional forms of control in two significant ways: first, the reliance upon, and pervasiveness of, algorithmic technologies in its enactment; and second, the substantial direct and indirect influence of non-organizational parties in controlling workers. Through a qualitative research design, this article delineates the scope of algorithmic HRM control in allocating and coordinating tasks, managing performance and rewards, and aligning the actions of workers with organizational objectives. Our analysis also unpacks the rigidity and complexities of the control system, as experienced by workers, and the influential role of non-organizational parties in exerting unique, distinct forms of control. In so doing, we build upon emerging research on the duality of algorithmic HRM by revealing the inherent flaws or challenges from the perspective of the most central party—the gig worker. While output-oriented control is pervasive, process and normative control elements are also found to exist in some scenarios, creating significant concerns for workers.