Algorithmic management and the spatiotemporality of workarounds

Existing literature on algorithmic management has explored how workers respond to algorithmic control using workarounds. However, the role of spatiotemporal factors in shaping these workarounds is largely overlooked. This study therefore examines how workers use spatiotemporal workarounds in response to algorithmic management. We integrate Lefebvre’s spatial triad and rhythmanalysis to analyse data from an online community of Amazon delivery drivers. Our findings show how delivery drivers proactively adapt to spatiotemporal constraints imposed by algorithms by modifying their routes, adjusting the pace and rhythm of their work and using workarounds to proactively influence future algorithmic decisions. Our study contributes to the growing literature on the spatiotemporality of algorithmic work, highlights the role of humans as proactive agents in shaping algorithmic decisions and exerting temporal sovereignty and reconceptualises workarounds as dynamic, evolving and future-oriented adaptations rather than immediate resistance tactics.