Behavioral Responses to AI-Generated Performance Targets in Flexible Work Environments
Keywords:
artificial intelligence, performance targets, flexible work, algorithmic management, socio-technical systems, behavioral responses, governanceAbstract
The proliferation of artificial intelligence in organizational management has introduced a new paradigm for setting performance targets, particularly within flexible work environments where traditional supervisory mechanisms are attenuated. This paper investigates the behavioral responses of workers to AI-generated performance targets, conceptualizing these targets as emergent properties of complex socio-technical systems rather than as neutral optimization tools. We argue that the deployment of algorithmic target-setting architectures fundamentally alters the psychological contract between workers and organizations, shifting from negotiated, human-mediated goal structures to dynamically computed, data-driven benchmarks. Drawing on a synthesis of systems theory, behavioral economics, and organizational psychology, we develop a framework that examines the structural trade-offs inherent in such systems, including the tension between algorithmic efficiency and worker autonomy, the robustness of target generation models under distributional shift, and the fairness implications of opaque performance metrics. We analyze how flexible work environments amplify these dynamics by decoupling temporal and spatial supervision, thereby increasing reliance on algorithmic proxies for productivity. The paper further explores governance and policy implications, including the need for transparency standards, audit mechanisms for algorithmic bias, and participatory design approaches that incorporate worker voice into target-setting processes. Case illustrations from platform-mediated gig work and remote knowledge work are used to ground the discussion. We conclude by outlining a research agenda focused on the long-term sustainability of AI-driven performance management systems, emphasizing the importance of interdisciplinary approaches to ensure that such systems remain both efficient and equitable.
References
1. 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.
2. 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.
3. Jarrahi, M. H., & Sutherland, W. (2019). Algorithmic management and algorithmic competencies: Understanding and appropriating algorithms in gig work. In Information in Contemporary Society (pp. 578-589). Springer.
4. Choudhury, P., Foroughi, C., & Larson, B. (2021). Work-from-anywhere: The productivity effects of geographic flexibility. Strategic Management Journal, 42(4), 655-683.
5. 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.
6. Lee, M. K., Kusbit, D., Metsky, E., & Dabbish, L. (2015). Working with machines: The impact of algorithmic and data-driven management on human workers. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (pp. 1603-1612).
7. Duggan, J., Sherman, U., Carbery, R., & McDonnell, A. (2020). Algorithmic management and app-work in the gig economy: A research agenda for employment relations and HRM. Human Resource Management Journal, 30(1), 114-132.
8. Rosenblat, A., & Stark, L. (2016). Algorithmic labor and information asymmetries: A case study of Uber's drivers. International Journal of Communication, 10, 3758-3784.
9. Trist, E. L., & Bamforth, K. W. (1951). Some social and psychological consequences of the longwall method of coal-getting. Human Relations, 4(1), 3-38.
10. Pasquale, F. (2015). The black box society: The secret algorithms that control money and information. Harvard University Press.
11. Strathern, M. (1997). 'Improving ratings': Audit in the British university system. European Review, 5(3), 305-321.
12. 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.
13. Bhave, D. P., Teo, L. H., & Dalal, R. S. (2020). Privacy at work: A review and a research agenda for a contested terrain. Journal of Management, 46(1), 127-164.
14. Agrawal, A., Gans, J., & Goldfarb, A. (2018). Prediction machines: The simple economics of artificial intelligence. Harvard Business Review Press.
15. Gagné, M., & Deci, E. L. (2005). Self-determination theory and work motivation. Journal of Organizational Behavior, 26(4), 331-362.
16. Quinonero-Candela, J., Sugiyama, M., Schwaighofer, A., & Lawrence, N. D. (2009). Dataset shift in machine learning. MIT Press.
17. Veale, M., Van Kleek, M., & Binns, R. (2018). Fairness and accountability design needs for algorithmic support in high-stakes public sector decision-making. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (pp. 1-14).
18. Barocas, S., & Selbst, A. D. (2016). Big data's disparate impact. California Law Review, 104(3), 671-732.
19. Colquitt, J. A., Conlon, D. E., Wesson, M. J., Porter, C. O., & Ng, K. Y. (2001). Justice at the millennium: A meta-analytic review of 25 years of organizational justice research. Journal of Applied Psychology, 86(3), 425-445.
20. Caza, A., & Carroll, S. J. (2012). Critical theory and organizational behavior. In The Oxford handbook of organizational psychology (pp. 123-145). Oxford University Press.
21. Bernstein, E. S. (2012). The transparency trap: How visibility can undermine learning and performance. Harvard Business Review, 90(10), 99-105.
22. Anwar, M. A., & Graham, M. (2021). Between a rock and a hard place: The role of data and algorithms in the gig economy. Big Data & Society, 8(1), 1-14.
23. Ticona, J., & Mateescu, A. (2018). Trusted strangers: Carework platforms' cultural entrepreneurship in the on-demand economy. New Media & Society, 20(11), 4384-4404.
24. Kroll, J. A., Huey, J., Barocas, S., Felten, E. W., Reidenberg, J. R., Robinson, D. G., & Yu, H. (2017). Accountable algorithms. University of Pennsylvania Law Review, 165(3), 633-705.
25. Selbst, A. D., & Powles, J. (2017). Meaningful information and the right to explanation. International Data Privacy Law, 7(4), 233-242.
26. Muller, M., & Druin, A. (2012). Participatory design: The third space in HCI. In The human-computer interaction handbook (pp. 1125-1154). CRC Press.
27. Dwork, C., Hardt, M., Pitassi, T., Reingold, O., & Zemel, R. (2012). Fairness through awareness. In Proceedings of the 3rd Innovations in Theoretical Computer Science Conference (pp. 214-226).
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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.
