AI-Driven Productivity Nudges and Task Completion Behavior on Digital Labor Platforms

Authors

  • Chetan A. Agarwal Department of Computer Science, Colorado State University, Fort Collins, CO, USA.
  • Aarav D. Iyer Department of Electrical Engineering and Computer Science, University of Kansas, Lawrence, KS, USA.
  • Timothy Watson Department of Computer Science, University of Houston, Houston, TX, USA.
  • Yash M. Srinivasan Department of Computer Science, University of North Texas, Denton, TX, USA.

Keywords:

artificial intelligence, productivity nudges, digital labor platforms, algorithmic management, task completion behavior, socio-technical systems, platform governance

Abstract

Digital labor platforms have fundamentally restructured the relationship between workers, tasks, and algorithmic management systems. Central to this transformation is the deployment of artificial intelligence-driven productivity nudges, which are subtle, often personalized interventions designed to influence worker behavior without imposing explicit mandates. This paper presents a comprehensive analysis of how such nudges affect task completion behavior on digital labor platforms, examining the interplay between system architecture, worker autonomy, and platform governance. We develop a conceptual framework that situates productivity nudges within the broader context of socio-technical systems, emphasizing the structural trade-offs between algorithmic efficiency and human agency. Drawing on empirical evidence from field experiments and observational studies, we analyze how nudge design parameters, including timing, frequency, framing, and personalization, interact with individual worker characteristics to produce heterogeneous behavioral responses. The paper further investigates the infrastructural dependencies underlying nudge deployment, including real-time data pipelines, feedback loops, and recommendation engines, and examines how these technical components introduce latent biases and fairness concerns. We discuss the sustainability of nudge-based interventions over time, considering habituation effects, worker resistance, and the potential for algorithmic fatigue. Governance implications are explored through the lens of platform accountability, transparency obligations, and the ethical boundaries of behavioral modification. The analysis reveals that while AI-driven nudges can enhance short-term productivity metrics, their long-term efficacy is contingent upon careful calibration of system architecture, respect for worker autonomy, and robust institutional oversight. We conclude by proposing design principles for responsible nudge systems that balance platform objectives with worker well-being, and we identify critical directions for future interdisciplinary research at the intersection of artificial intelligence, labor economics, and human-computer interaction.

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Published

2025-03-15

How to Cite

Chetan A. Agarwal, Aarav D. Iyer, Timothy Watson, & Yash M. Srinivasan. (2025). AI-Driven Productivity Nudges and Task Completion Behavior on Digital Labor Platforms. Computer Science and Engineering Transactions, 3(1). Retrieved from https://csetx.org/index.php/cset/article/view/159

Issue

Vol. 3 No. 1 (2025): Computer Science and Engineering Transactions

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Articles

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