Organizing for well-being in the era of digital labor platforms: Examining job demands, resources, and personal contingencies of macro crowdworkers

As digital labor platforms (DLPs) reshape global labor markets, understanding their impact on worker well-being has become increasingly important. Drawing on the integrated Job Demands-Resources (JD-R) x Heavy Work Investment (HWI) framework, this study investigates the associations between job demands (quantitative demands, digital surveillance), job resources (autonomy, boundary control), and well-being among macro crowdworkers, which is an understudied population compared to micro-task and on-demand workers. Survey data from 421 macro crowdworkers reveal that autonomy and boundary control are associated with lower technostress and exhaustion, and with better work-life balance, while digital surveillance and quantitative demands increase negative outcomes. Notably, personal contingencies create asymmetric moderation of the resource-exhaustion pathway. Autonomy's protective effect against exhaustion is amplified for compulsive workers but absent among income-dependent workers, while boundary control's protective effect against exhaustion remains robust regardless of worker profile. Digital surveillance is associated with technostress and exhaustion but not work-life balance, identifying a boundary condition that differentiates asynchronous macro crowdwork from synchronous on-demand platforms. These findings advance JD-R and HWI research by demonstrating that resource efficacy depends on personal contingencies, with boundary control emerging as a foundational resource. The study offers evidence-based design principles for platforms and regulatory guidance for policymakers to protect worker well-being alongside productivity.