Existing research has shown that people experience third-party evaluations as a form of control because they try to align their behavior with evaluations’ criteria to secure more favorable resources, recognition, and opportunities from external audiences. Much of this research has focused on evaluations with transparent criteria, but increasingly, algorithmic evaluation systems are not transparent. Drawing on over three years of interviews, archival data, and observations as a registered user on a labor platform, I studied how freelance workers contend with an opaque third-party evaluation algorithm—and with what consequences. My findings show the platform implemented an opaque evaluation algorithm to meaningfully differentiate between freelancers’ rating scores. Freelancers experienced this evaluation as a form of control but could not align their actions with its criteria because they could not clearly identify those criteria. I found freelancers had divergent responses to this situation: some experimented with ways to improve their rating scores, and others constrained their activity on the platform. Their reactivity differed based not only on their general success on the platform—whether they were high or low performers—but also on how much they depended on the platform for work and whether they experienced setbacks in the form of decreased evaluation scores. These workers experienced what I call an “invisible cage”: a form of control in which the criteria for success and changes to those criteria are unpredictable. For gig workers who rely on labor platforms, this form of control increasingly determines their access to clients and projects while undermining their ability to understand and respond to factors that determine their success.

Existing literature examines control and resistance in the context of service organizations that rely on both managers and customers to control workers during the execution of work. Digital platform companies, however, eschew managers in favor of algorithmically mediated customer control—that is, customers rate workers, and algorithms tally and track these ratings to control workers’ future platform-based opportunities. How has this shift in the distribution of control among platforms, customers, and workers affected the relationship between control and resistance? Drawing on workers’ experiences from a comparative ethnography of two of the largest platform companies, we find that platform use of algorithmically mediated customer control has expanded the service encounter such that organizational control and workers’ resistance extend well beyond the execution of work. We find that workers have the most latitude to deploy resistance early in the labor process but must adjust their resistance tactics because their ability to resist decreases in each subsequent stage of the labor process. Our paper, thus, develops understanding of resistance by examining the relationship between control and resistance before, during, and after a task, providing insight into how control and resistance function in the gig economy. We also demonstrate the limitations of platforms’ reliance on algorithmically mediated customer control by illuminating how workers’ everyday interactions with customers can influence and manipulate algorithms in ways that platforms cannot always observe.

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This paper develops a new understanding about how “client managers”—those using platform labor markets to hire and manage workers—attempt to maintain control when managing skilled contractors. We conducted an inductive field study analyzing interactions between client managers and contractors in software development “gigs” mediated by a platform labor market. The platform provided multiple tools client managers could use for control, including in response to unexpected events. We found that, when managers used the tools to exert coercive control over contractors acting unexpectedly, it backfired and contributed to uncompleted project outcomes. In contrast, when they refrained from using the tools for coercive control in such circumstances and instead engaged in what we call collaborative repair, their actions contributed to completed project outcomes. Collaborative repair refers to interactions that surface misaligned interpretations of a situation and help parties negotiate new, reciprocal expectations that restore trust and willingness to continue an exchange. Client managers’ attempts at collaborative repair yielded fuller understanding of project-related breakdowns and shared investment in new expectations, facilitating effective control and completed projects. This study extends prior theories of control by characterizing the new client manager role created by platforms and demonstrating how initiating repair is integral for managers’ capacity to accomplish control in these comparatively brittle work relationships.

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