ALGORITHMIC CONTROL AND DIGNITY AMONG GIG WORKERS
Creators: name: Huang, Xuemei
Publisher: name: Washington State University
Year: 2026
Subjects: Gig workers
Dignity
algorithmic control

description: Human dignity in the workplace is critical for individuals and organizations, as emphasized in humanistic management. The rise of digitalization challenges dignity, as it risks reducing individuals to mere cogs in a technological machine. This is especially true of gig workers under control of algorithmic management by gig platforms. In light of these challenges, this study investigates how algorithmic control affects gig workers’ human dignity through perceived objectification and the subsequent impact on psychological well-being and continuance intention. Drawn from the CARE (Claims, Affronts, Responses, Equilibrium) theory, algorithmic control literature, and human dignity studies, this study develops a middle-range theory that proposes the impact of seven algorithmic control mechanisms and their interaction with algorithmic control transparency on perceived objectification and the subsequent impact on gig workers’ psychological well-being and continuance intention. I collected 328 responses from gig workers providing local, low-skilled services through a two-stage survey administered on Prolific. The findings of this study support the middle-range theory, suggesting that algorithmic control mechanisms falling under coercive control, with the exception of algorithmic rating, are associated with increased perceived objectification, indicating that gig workers feel being treated as an object, which affronts their inherent dignity. These coercive control mechanisms include algorithmic restricting, requiring, monitoring, and sanctioning. In contrast, cooperative control mechanisms (algorithmic recommending and rewarding) and algorithmic control transparency reduced perceived objectification, indicating that it can claim inherent dignity. This study also indicates that perceived objectification negatively impacted psychological well-being, which positively impacts continuance intention. As one of the early studies in IS that centers on inherent dignity, this study provides an empirically supported middle-range theory of inherent dignity. This study also provides a scale for assessing perceived objectification that indicate four possible ways of inherent dignity affronts, which broaden the current understanding of inherent dignity beyond its constituent components explicitly or implicitly identified in prior studies, such as autonomy and fairness. This expanded understanding is critical in recognizing the significance of human dignity, providing a comprehensive view of its antecedents and consequences, especially those overlooked in prior studies due to their relatively narrow focus. This study enriches control literature by identifying additional features to expand possible control styles that focus on how control is implemented. These features include whether the control is implemented by providing support and autonomy, as evidenced by differences between coercive and cooperative control. This study also contributes to algorithmic control literature by examining its comprehensive characteristics through the seven-control-mechanism framework, contrasting previous previously overgeneralized approaches. This study also contributes to understanding the impact of algorithmic control on well-being, a crucial yet underexplored area. The objectification scale extends objectification into IS literature and enriches its understanding beyond representing symbols as concrete objects in systems design. This scale also addresses the underestimated wrongfulness of six other objectification features overlooked in the current instrumentality-centric approach.
description: Practically, this study suggest gig workers and other gig platform stakeholders should prioritize cooperative algorithmic control mechanisms with transparent algorithm use for enhancing gig workers’ inherent dignity and psychological well-being and potentially gig platforms’ service quality and retention rate.