Goal Escalation, Risk Preferences, and Algorithmic Management in Gig Labor Markets
Keywords:
gig economy, algorithmic management, goal escalation, risk preferences, platform labor, behavioral design, labor policyAbstract
The rapid expansion of gig labor markets has placed algorithmic management systems at the center of work coordination, performance evaluation, and incentive design. These systems increasingly employ dynamic goal-setting mechanisms—such as surge multipliers, quest bonuses, and streak rewards—that interact with workers’ heterogeneous risk preferences and can trigger upward goal escalation. This paper provides an interdisciplinary analysis of the structural interplay between goal escalation, risk attitudes, and algorithmic management architectures in platform-mediated work. Drawing on behavioral economics, organization studies, and human-computer interaction research, we examine the technical infrastructure that enables automated goal construction, the psychological dynamics of reference-point shifting and escalation of commitment, and the differential responses shaped by workers’ risk profiles. We identify critical system-level trade-offs among efficiency, fairness, worker autonomy, and safety, and highlight how opaque algorithmic governance can redistribute risk from platforms to workers while intensifying effort. Through cross-domain comparisons and policy analysis, the paper scrutinizes the regulatory challenges of mandating transparency, limiting exploitative goal design, and protecting labor standards without undermining the flexibility that many gig workers value. We propose a research and policy agenda centered on algorithmic accountability, participatory goal governance, and the development of sustainable incentive architectures that align platform objectives with long-term worker welfare.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
