The Effect of Algorithmic Management on Psychological Well-Being of Gig Workers: Moderating Role of Perceived Autonomy
DOI:
https://doi.org/10.65080/ajbmss.v2i2.CM2622101031Keywords:
Algorithmic management, gig workers, psychological well-being, autonomy perception, job demands-resources modelAbstract
Introduction: Algorithmic Management (AM) is a novel form of work organisation that has emerged in recent times as a tool for the growing gig economy, which can be found in the ride-sharing and online freelancing sectors. This study analysed the perceived level of algorithmic management practices and their association with the psychological well-being of gig workers, specifically perceived autonomy.
Methodology: A quantitative research design was used to gather data from 342 active gig workers from different digital labour platforms in Pakistan. Data were collected online from gig worker community groups and social media over six weeks in Pakistan. A structural equation model (SEM) was used to test the hypothesised relationships using SmartPLS version 4.0.
Results: The results showed a statistically significant negative association between algorithmic management and PWB. However, this negative association was moderated by perceived autonomy, meaning that when perceived autonomy was higher, the negative association between algorithmic management and PWB was weaker, thus protecting workers from psychological strain. Based on the Job Demands-Resources (JD-R) theory, these results were interpreted by conceptualising algorithmic management as a job characteristic that generates or intensifies specific job demands, such as surveillance, performance evaluation, and reduced discretion.
Conclusion: Perceived autonomy is considered an important job resource in the digital labour environment. The study provides contextual evidence using a multi-platform sample and perceived level of algorithmic management, and calls on algorithmic management platform designers and policymakers to prioritise well-being in algorithmic management by engaging mechanisms that encourage worker autonomy.
Downloads
References
Bakker, A. B., & Demerouti, E. (2024). Job demands–resources theory: Frequently asked questions. Journal of Occupational Health Psychology, 29(3), 188–200. https://psycnet.apa.org/doi/10.1037/ocp0000376
Bakker, A. B., Demerouti, E., Sanz-Vergel, A. I., & Rodríguez-Muñoz, A. (2023). La Teoría de las Demandas y Recursos Laborales: Nuevos Desarrollos en la Última Década. Journal of Work and Organizational Psychology, 39(3), 157–167. https://doi.org/10.5093/jwop2023a17
Benlian, A., Wiener, M., Cram, W. A., Krasnova, H., Maedche, A., Möhlmann, M., Recker, J., & Remus, U. (2022). Algorithmic management. Business & Information Systems Engineering, 64, 825–839.
https://doi.org/10.1007/s12599-022-00764-w
Bucher, E. L., Schou, P. K., & Waldkirch, M. (2021). Pacifying the algorithm: Anticipatory compliance in the face of algorithmic management in the gig economy. Organization, 28(1), 44–67. https://doi.org/10.1177/1350508420961531
Caza, B. B., Reid, E. M., Ashford, S. J., & Granger, S. (2022). Working on my own: Measuring the challenges of gig work. Human Relations, 75(11), 2122–2159. https://doi.org/10.1177/00187267211030098
Crisan, G.-A., Popescu, M. E., Militaru, E., & Cristescu, A. (2023). EU diversity in terms of digitalization on the labor market in the post-COVID-19 context. Economies, 11(12), 293. https://doi.org/10.3390/economies11120293
Curchod, C., Giamporcaro, S., & Kuk, G. (2021). Consent and dissent among platform-based workers: How symbolism steers Uber drivers’ motivation. Academy of Management Proceedings, 2021(1), 14528. https://doi.org/10.5465/AMBPP.2021.14528abstract
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates. Available from: https://search.worldcat.org/title/statistical-power-analysis-for-the-behavioral-sciences/oclc/469384708
De Stefano, V., & Aloisi, A. (2019). Fundamental labour rights, platform work and human-rights protection of non-standard workers. In J. R. Bellace & B. ter Haar (Eds.), Labour, business and human rights law (pp. 359–379). Edward Elgar Publishing. https://doi.org/10.2139/ssrn.3125866
De Stefano, V., Durri, I., Stylogiannis, C., & Wouters, M. (2021). Platform work and the employment relationship. International Labour Organization. Available from: https://www.ilo.org/publications/platform-work-and-employment-relationship
Duggan, J., Carbery, R., McDonnell, A., & Sherman, U. (2023). Algorithmic HRM control in the gig economy: The app-worker perspective. Human Resource Management, 62(6), 883–899. https://doi.org/10.1002/hrm.22168
Félix, B., Dourado, D., & Nossa, V. (2023). Algorithmic management, preferences for autonomy/security and gig-workers' wellbeing: A matter of fit? Frontiers in Psychology, 14, 1088183. https://doi.org/10.3389/fpsyg.2023.1088183
Gandini, A. (2019). Labour process theory and the gig economy. Human Relations, 72(6), 1039–1056. https://doi.org/10.1177/0018726718790002
Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2022). A primer on partial least squares structural equation modeling (PLS-SEM) (3rd ed.). SAGE. Available from: https://www.sagepub.com/shop/buy-a-book/a-primer-on-partial-least-squares-structural-equation-modeling-pls-sem-4-293282
Konjen, H. (2025). Algorithmic management and the future of human work: Implications for autonomy, collaboration, and innovation [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2511.14231
Liu, M., Lan, Y., Liu, Z., Liu, M., & Xia, Y. (2024). Navigating the maze: The effects of algorithmic management on employee performance. Humanities and Social Sciences Communications, 11, 965. https://doi.org/10.1057/s41599-024-03453-z
Liu, R., & Yin, H. (2024). How algorithmic management influences gig workers’ job crafting. Behavioral Sciences, 14(10), 952. https://doi.org/10.3390/bs14100952
Möhlmann, M., Zalmanson, L., Henfridsson, O., & Gregory, R. W. (2021). Algorithmic management of work on online labor platforms: When matching meets control. MIS Quarterly, 45(4), 1999–2022. https://doi.org/10.25300/MISQ/2021/15333
Neufeld, A. (2025). Applying SDT and JD-R: A practical guide to systemic wellness reforms in medical education. MedEdPublish, 15(18), 18. https://doi.org/10.12688/mep.20886.1
Neufeld, A. (2026). Flourishing by design: Applying self-determination theory and the job demands-resources model to systems-level wellness in medical education. MedEdPublish, 15, 18. https://doi.org/10.12688/mep.20886.3
Noponen, N., Feshchenko, P., Auvinen, T., Luoma-aho, V., & Abrahamsson, P. (2024). Taylorism on steroids or enabling autonomy? A systematic review of algorithmic management. Management Review Quarterly, 74, 1695–1721. https://doi.org/10.1007/s11301-023-00345-5
Parent-Rocheleau, X., & Parker, S. K. (2022). Algorithms as work designers: How algorithmic management influences the design of jobs. Human Resource Management Review, 32(3), 100838. https://doi.org/10.1016/j.hrmr.2021.100838
Parent-Rocheleau, X., Parker, S. K., Bujold, A., & Gaudet, M. C. (2024). Creation of the algorithmic management questionnaire: A six-phase scale development process. Human Resource Management, 63(1), 25–44.
https://doi.org/10.1002/hrm.22185
Pilatti, G. R., Pinheiro, F. L., & Montini, A. A. (2024). Systematic literature review on gig economy: Power dynamics, worker autonomy, and the role of social networks. Administrative Sciences, 14(10), 267. https://doi.org/10.3390/admsci14100267
Ringle, C. M., Sarstedt, M., & Straub, D. W. (2012). Editor's comments: A critical look at the use of PLS-SEM in “MIS Quarterly.” MIS Quarterly, 36(1), iii–xiv. https://doi.org/10.2307/41410402
Ryan, R. M., & Deci, E. L. (2019). Brick by brick: The origins, development, and future of self-determination theory. In A. J. Elliot (Ed.), Advances in motivation science (Vol. 6, pp. 111–156). Elsevier. https://doi.org/10.1016/bs.adms.2019.01.001
Spreitzer, G. M., Cameron, L., & Garrett, L. E. (2017). Alternative work arrangements: Two images of the new world of work. Annual Review of Organizational Psychology and Organizational Behavior, 4, 473–499.
https://doi.org/10.1146/annurev-orgpsych-032516-113332
Veen, A., Barratt, T., & Goods, C. (2020). Platform-capital’s ‘app-etite’ for control: A labour process analysis of food-delivery work in Australia. Work, Employment and Society, 34(3), 388–406. https://doi.org/10.1177/0950017019836911
Wan, W., Liu, W., & Hu, H. (2024a). The career development of food delivery riders in China: A qualitative investigation. [Preprint]. Research Square. https://doi.org/10.21203/rs.3.rs-4935308/v1
Wan, Z., Zhang, L., Wang, L., & Zhang, F. (2024b). Navigating autonomy: Unraveling the dual influence of job autonomy on workplace well-being in the gig economy. Frontiers in Psychology, 15, 1369495. https://doi.org/10.3389/fpsyg.2024.1369495
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. https://doi.org/10.1177/0950017018785616
Zhang, M. M., Cooke, F. L., Ahlstrom, D., & McNeil, N. (2025). The rise of algorithmic management and implications for work and organisations. New Technology, Work and Employment, 40(3), 659–671.
https://doi.org/10.1111/ntwe.12343
Zha, X., Hu, E., Shan, H., Huang, L., & Han, M. (2026). Algorithmic management and gig worker well-being: Unpacking the roles of job autonomy, precarity and union instrumentality. Personnel Review, 1–25.
https://doi.org/10.1108/PR-03-2024-0230
Zhao, S., & Liu, Y. (2024). Job demands-resources on digital gig platforms and counterproductive work behavior. Frontiers in Psychology, 15, 1378247. https://doi.org/10.3389/fpsyg.2024.1378247
Zayid, H., Alzubi, A., Berberoğlu, A., & Khadem, A. (2024). How do algorithmic management practices affect workforce well-being? A parallel moderated mediation model. Behavioral Sciences, 14(12), 1123.
https://doi.org/10.3390/bs14121123
Zhu, J., Zhang, B., & Wang, H. (2024). The double-edged sword effects of perceived algorithmic control on platform workers’ service performance. Humanities and Social Sciences Communications, 11, 316.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Muhammad Qasim (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
