Goal-Setting AI Agents and Labor Supply Dynamics in Gig Platforms: A Behavioral Operations Perspective
Keywords:
goal-setting AI, gig platforms, labor supply, behavioral operations, algorithmic management, platform governance, fairness, sustainabilityAbstract
The integration of artificial intelligence agents that design and communicate performance goals is reshaping labor supply dynamics on digital gig platforms. Moving beyond static incentive schemes, platforms now deploy personalized goal-setting agents that nudge workers toward target earnings, task counts, or activity durations. This paper examines the behaviorally informed operations of such goal-setting AI agents from an interdisciplinary systems perspective. Drawing on behavioral economics, operations management, and human-computer interaction literatures, we analyze how goal design choices interact with bounded rationality, reference dependence, and motivational heterogeneity to produce emergent labor supply equilibria at scale. The discussion centers on the architectural trade-offs inherent in building goal-setting agents that balance platform efficiency, worker autonomy, and long-term workforce sustainability. We unpack governance dilemmas around algorithmic transparency, fairness across worker segments, and the robustness of these agents to strategic gaming. Further, we examine deployment infrastructure requirements, feedback-loop stability, and the ethical implications of embedding behavioral nudges into algorithmic management architectures. Rather than focusing narrowly on individual-level experiments, the paper treats the AI-mediated goal-setting system as a socio-technical infrastructure whose design parameters can reshape the market structure of gig work. Policy recommendations emphasize the need for auditability, participatory goal-setting architectures, and regulatory frameworks that constrain exploitative optimization trajectories. The analysis reveals that goal-setting AI agents, while capable of improving short-run service throughput, introduce systemic risks related to workforce churn, algorithmic discrimination, and motivational crowding, demanding new governance principles that align platform objectives with worker well-being and societal expectations.
References
1. Locke, E. A., & Latham, G. P. (2002). Building a practically useful theory of goal setting and task motivation: A 35-year odyssey. American Psychologist, 57(9), 705–717.
2. Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263–291.
3. Bendoly, E., Donohue, K., & Schultz, K. L. (2006). Behavior in operations management: Assessing recent findings and revisiting old assumptions. Journal of Operations Management, 24(6), 737–752.
4. Chen, M. K., Chevalier, J. A., Rossi, P. E., & Oehlsen, E. (2019). The value of flexible work: Evidence from Uber drivers. Journal of Political Economy, 127(6), 2735–2794.
5. Kellogg, K. C., Valentine, M. A., & Christin, A. (2020). Algorithms at work: The new contested terrain of control. Academy of Management Annals, 14(1), 366–410.
6. Wood, A. J., Graham, M., Lehdonvirta, V., & Hjorth, I. (2019). Good gig, bad gig: Autonomy and algorithmic control in the global gig economy. Work, Employment and Society, 33(1), 56–75.
7. Rosenblat, A., & Stark, L. (2016). Algorithmic labor and information asymmetries: A case study of Uber’s drivers. International Journal of Communication, 10, 3758–3784.
8. Burtch, G., Carnahan, S., & Greenwood, B. N. (2018). Can you gig it? An empirical examination of the gig economy and entrepreneurial activity. Management Science, 64(12), 5497–5520.
9. Yeung, K. (2018). Algorithmic regulation: A critical interrogation. Regulation & Governance, 12(4), 505–523.
10. Selbst, A. D., Boyd, D., Friedler, S. A., Venkatasubramanian, S., & Vertesi, J. (2019). Fairness and abstraction in sociotechnical systems. In Proceedings of the Conference on Fairness, Accountability, and Transparency (pp. 59–68).
11. Barocas, S., & Selbst, A. D. (2016). Big data’s disparate impact. California Law Review, 104(3), 671–732.
12. Acquisti, A., Brandimarte, L., & Loewenstein, G. (2015). Privacy and human behavior in the age of information. Science, 347(6221), 509–514.
13. Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608.
14. Gino, F., & Pisano, G. (2008). Toward a theory of behavioral operations. Manufacturing & Service Operations Management, 10(4), 676–691.
15. Croson, R., & Donohue, K. (2006). Behavioral causes of the bullwhip effect and the observed value of inventory information. Management Science, 52(3), 323–336.
16. Lehdonvirta, V. (2018). Flexibility in the gig economy: Managing time on three online piecework platforms. New Technology, Work and Employment, 33(1), 13–29.
17. Schor, J. B. (2020). After the gig: How the sharing economy got hijacked and how to win it back. University of California Press.
18. Min, X., Chi, W., Hu, X., & Ye, Q. (2024). Set a goal for yourself? A model and field experiment with gig workers. Production and Operations Management, 33(1), 205-224.
19. Zuboff, S. (2015). Big other: Surveillance capitalism and the prospects of an information civilization. Journal of Information Technology, 30(1), 75–89.
20. Huang, N., Burtch, G., Hong, Y., & Pavlou, P. A. (2020). Unemployment and worker participation in the gig economy: Evidence from an online labor market. Information Systems Research, 31(2), 431–448.
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