Algorithmic management of crowdworkers: Implications for workers’ identity, belonging, and meaningfulness of work

Online labor platforms function as meta-organizations, blending elements of an open market and hierarchical structure by algorithmically governing goals and objectives. This study investigates how this algorithmic management approach influences the perceived meaningfulness of work among crowdworkers, with a particular focus on its effects through identity and belonging. Drawing on survey data collected from 1291 crowdworkers, our findings highlight that algorithmic control and algorithmic matching are differently associated with the meaningfulness of work. Algorithmic control, characterized by directive oversight, exhibits a negative association with perceived meaningfulness. In contrast, algorithmic matching, which pairs workers with online tasks, fosters a positive perception of meaningfulness. We demonstrate that these associations are (partially) mediated by identity and belonging mechanisms. Specifically, we demonstrate that crowdworkers tend to self-organize support and social confirmation using online communities, which provides a sense of meaningfulness. This research advances our understanding of the experiences of crowdworkers within online labor platforms, shedding light on the multifaceted implications of algorithmic management. It emphasizes the importance of fostering supportive and communicative environments in work settings characterized by algorithmic governance mechanisms.