Algorithms and their Affordances: How Crowdworkers Manage Algorithmic Scores in Online Labour Markets

Abstract On online labour platforms, algorithmic scores are used as indicators of freelancers' work quality and future performance. Recent studies underscore that, to achieve good scores and secure their presence on platforms, freelancers respond to algorithmic control in different ways. However, we argue, to fully understand how freelancers deal with algorithmic scores, we first need to investigate how they interpret scores and, more specifically, what scores can do for them, i.e., perceived algorithmic affordances and constraints. Our interviews and other qualitative data collected with knowledge intensive gig workers on a major platform allow us to explain how the perceived affordances of algorithms (i.e., barrier, individual visibility, self‐extension, rule of the game) act as mechanisms that explain different behavioural and emotional responses over time. Our work contributes to the current debate on the positive and negative consequences of algorithmic work by portraying the fundamental role paid by the individual interpretation of algorithmic scores and by integrating the affordance perspective into our understanding of algorithmic work.