I work hard for the algorithm: job demands, resources and strain in (and beyond) the gig economy

Purpose This study examines psychological strain in gig work by analyzing how algorithmic management (AM), framed through the Job Demands–Resources (JD-R) model and Self-Determination Theory (SDT), shapes strain outcomes. We compare gig and non-gig workers to isolate the influence of AM. Design/methodology/approach Using LIWC, we analyze 6,505 Glassdoor job reviews to compare psychological strain among drivers in gig versus non-gig roles. Findings Counterintuitively, compensation and work–life balance – typically strain-buffering resources – are associated with increased strain for gig workers, suggesting that algorithmic control alters how resources are experienced. Research limitations/implications The findings suggest that within the ‘digital cage' of the gig economy, the traditional JD-R resource-to-strain pathway is reconfigured. This highlights the need for research that investigates how the delivery mechanism (AM) of a resource can neutralize its buffering potential. Practical implications Platform organizations must recognize that simply increasing pay or flexibility within need-thwarting structures may inadvertently worsen worker strain. Practitioners should prioritize autonomy-supportive algorithmic designs – moving away from gamified, opaque incentives toward transparent systems that restore operational control to the worker. Originality/value This research provides evidence that AM does not merely add demands but fundamentally reshapes the relationship between job resources and strain within the gig economy. It problematizes the “autonomy paradox” in gig work to explain why the JD-R resource pathway breaks down.