Pacifying the algorithm – Anticipatory compliance in the face of algorithmic management in the gig economy

TLDR

Algorithmic management governs digital work platforms like Upwork and Fiverr, yet its opaque and rapidly changing decisions force freelancers to continually adapt, a dynamic largely overlooked in existing research that focuses on worker experience rather than agency. This study, grounded in sociomateriality, investigates the compliance practices freelancers develop to navigate the presumed mechanisms of algorithmic management on such platforms. Using a systematic content analysis of 12,294 comments from an online freelancer community, we identified direct and indirect anticipatory compliance strategies—such as undervaluing work, staying under the radar, limiting client outreach, and regulating emotions—that freelancers employ to maintain platform participation, treating the platform as a shadow employer. Our findings reveal that freelancers pacify the platform algorithm through these practices, engage in additional work, and co‑construct algorithmic power, thereby enriching the literature on algorithmic management.

Abstract

Algorithmic management is used to govern digital work platforms such as Upwork or Fiverr. However, algorithmic decision-making is often non-transparent and rapidly evolving, forcing workers to constantly adapt their behavior. Extant research focuses on how workers experience algorithmic management, while often disregarding the agency that workers exert in dealing with algorithmic management. Following a sociomateriality perspective, we investigate the practices that workers develop to comply with (assumed) mechanisms of algorithmic management on digital work platforms. Based on a systematic content analysis of 12,294 scraped comments from an online community of digital freelancers, we show how workers adopt direct and indirect “anticipatory compliance practices”, such as undervaluing their own work, staying under the radar, curtailing their outreach to clients and keeping emotions in check, in order to ensure their continued participation on the platform, which takes on the role of a shadow employer. Our study contributes to research on algorithmic management by (1) showing how workers adopt practices aimed at “pacifying” the platform algorithm; (2) outlining how workers engage in extra work; (3) showing how workers co-construct the power of algorithms through their anticipatory compliance practices.