Algorithmic Goal Recommendations and Labor Supply Responses among Gig Economy Workers
Keywords:
algorithmic management, gig economy, labor supply, goal setting, behavioral nudges, platform governance, worker autonomyAbstract
The proliferation of digital labor platforms has fundamentally restructured the relationship between work, worker autonomy, and algorithmic management. Among the most pervasive yet underexamined features of these platforms is the algorithmic goal recommendation system, which suggests personalized productivity targets to gig economy workers. This paper presents a comprehensive systems-level analysis of how such algorithmic goal recommendations shape labor supply decisions, focusing on the structural, behavioral, and governance implications of these socio-technical interventions. We argue that goal recommendation algorithms function as a form of soft paternalistic control, leveraging behavioral nudges to influence worker effort without explicit coercion. Drawing on a synthesis of empirical evidence from field experiments, platform data analyses, and behavioral labor economics, we examine the mechanisms through which goal recommendations affect worker output, scheduling, and retention. The analysis reveals a fundamental tension: while goal recommendations can increase short-term productivity and help workers self-regulate, they also introduce new vulnerabilities including goal manipulation, effort distortion, and the erosion of intrinsic motivation. We further explore the architectural design choices that determine the fairness and robustness of these systems, including the role of reference group selection, dynamic adjustment rates, and feedback loops between platform governance and worker behavior. The paper contextualizes these findings within broader debates on algorithmic management, digital Taylorism, and the future of work. We conclude by proposing a set of design principles for transparent, equitable, and sustainable goal recommendation systems that balance platform efficiency with worker welfare. This research contributes to the growing interdisciplinary literature on algorithmic governance in labor markets and offers actionable insights for platform designers, policymakers, and labor advocates.
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