Tackling the impersonality of algorithmic management: community-building strategies and social well-being on online labor platforms

Online labor platforms (OLPs) like Amazon Mechanical Turk or UpWork have created new income opportunities for independent workers across the globe. Workers connect to clients for short-term tasks of varying complexity, like labelling images or designing animations. These workers operate remotely, with limited opportunities to socialize with each other. Moreover, algorithms monitor their performance through impersonal and often invisible data surveillance. OLPs are hence accused of eliciting social isolation and violating article 15 of the EU’s Platform Work Directive, which guarantees platform workers’ right to social communication. Some OLPs and independent workers have started to address this issue through “community-building strategies” to increase the sense of social belonging and well-being among workers (e.g., creating in-platform chat functions or social media forums). Building on theories of social well-being and research on platform organizations, we investigate an array of community-building strategies developed by OLPs and workers, and their effects on workers’ social well-being (e.g., sense of cohesion, interpersonal acceptance, and social contribution). This project will (1) classify strategies OLPs and workers implement to foster social connectedness, (2) test how these strategies relate to workers’ social well-being to critically assess the popular notion that OLPs foster social isolation and fragment the workforce.