Between Compliance and Defiance: A Grounded Theory of How Gig Workers Adapt to, Pacify, Manipulate, and Resist Algorithmic Management
Keywords:
Algorithmic management, gig economy, platform work, worker resistance, algorithm pacification, Constructivist grounded theory, delivery workers, New York CityAbstract
Algorithmic management plays an increasing role in the digital labour platforms' methods of task allocation, productivity tracking, payment, ratings, and sanctions. This study focuses on the changing patterns of accommodating and resisting reactions that restaurant delivery workers in New York City have to delivery systems over time. The research is conducted with semi-structured critical-incident interviews of full-time and part-time workers on various platforms to develop a grounded theory using a constructivist perspective that fits the interpretive paradigm. Differences in platform dependence, intensity of work, multi-platform involvement, experience, and warning or deactivation exposure are captured in maximum-variation purposive sampling and theoretical sampling. Initial coding, focused coding, constant comparison, writing memos, theoretical coding and conceptual diagramming in NVivo are used to analyse data. The study introduces a conception of worker responses as a repertoire that is dynamic, ranging from compliance, to tactical adaptation, algorithm pacification, manipulation, voice, collective resistance, withdrawal, to exit. Transitions between these responses are dependent on the interpretations of the workers in relation to algorithmic encounters, income dependence, perceived sanction risk, alternative job options, professional identity, and social validation. The study challenges the dichotomy of compliance and resistance and advances a recursive process model to describe how algorithmic encounters, worker sensemaking, response selection, consequences, and reactions by platforms are interlinked; it also provides implications for transparency, appeals, worker voice, and regulation. It thus contributes to learning about the agency of workers in changing, opaque, and consequential algorithmic control systems.
References
Anteby, M., & Chan, C. K. (2018). A self-fulfilling cycle of coercive surveillance: Workers’ invisibility practices and managerial justification. Organization Science, 29(2), 247–263. https://doi.org/10.1287/orsc.2017.1175
Anwar, M. A., & Graham, M. (2020). Hidden transcripts of the gig economy: Labour agency and the new art of resistance among African gig workers. Environment and Planning A: Economy and Space, 52(7), 1269–1291. https://doi.org/10.1177/0308518X19894584
Bonini, T., Treré, E., Yu, Z., Singh, S., Cargnelutti, D., & López-Ferrández, F. J. (2024). Cooperative affordances: How instant messaging apps afford learning, resistance and solidarity among food delivery workers. Convergence: The International Journal of Research into New Media Technologies, 30(1), 554–571. https://doi.org/10.1177/13548565231153505
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
Bucher, T. (2017). The algorithmic imaginary: Exploring the ordinary affects of Facebook algorithms. Information, Communication & Society, 20(1), 30–44. https://doi.org/10.1080/1369118X.2016.1154086
Bulut, E., & Yeşilyurt, A. (2024). Delivery workers’ visibility struggles: Weapons of the gig, (extra)ordinary social media, and strikes. Convergence: The International Journal of Research into New Media Technologies, 30(1), 450–470. https://doi.org/10.1177/13548565231188415
Cameron, L. D. (2024). The making of the “good bad” job: How algorithmic management manufactures consent through constant and confined choices. Administrative Science Quarterly, 69(2), 458–514. https://doi.org/10.1177/00018392241236163
Cameron, L. D., & Rahman, H. A. (2022). Expanding the locus of resistance: Understanding the co-constitution of control and resistance in the gig economy. Organization Science, 33(1), 38–58. https://doi.org/10.1287/orsc.2021.1557
Charmaz, K. (2014). Constructing grounded theory (2nd ed.). SAGE.
Charmaz, K., & Thornberg, R. (2021). The pursuit of quality in grounded theory. Qualitative Research in Psychology, 18(3), 305–327. https://doi.org/10.1080/14780887.2020.1780357
Chun Tie, Y., Birks, M., & Francis, K. (2019). Grounded theory research: A design framework for novice researchers. SAGE Open Medicine, 7, Article 2050312118822927. https://doi.org/10.1177/2050312118822927
Curchod, C., Patriotta, G., Cohen, L., & Neysen, N. (2020). Working for an algorithm: Power asymmetries and agency in online work settings. Administrative Science Quarterly, 65(3), 644–676. https://doi.org/10.1177/0001839219867024
Cutolo, D., & Kenney, M. (2021). Platform-dependent entrepreneurs: Power asymmetries, risks, and strategies in the platform economy. Academy of Management Perspectives, 35(4), 584–605. https://doi.org/10.5465/amp.2019.0103
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
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. https://doi.org/10.1111/1748-8583.12258
Ferrari, F., & Graham, M. (2021). Fissures in algorithmic power: Platforms, code, and contestation. Cultural Studies, 35(4–5), 814–832. https://doi.org/10.1080/09502386.2021.1895250
Galière, S. (2020). When food-delivery platform workers consent to algorithmic management: A Foucauldian perspective. New Technology, Work and Employment, 35(3), 357–370. https://doi.org/10.1111/ntwe.12177
Gandini, A. (2019). Labour process theory and the gig economy. Human Relations, 72(6), 1039–1056. https://doi.org/10.1177/0018726718790002
Griesbach, K., Reich, A., Elliott-Negri, L., & Milkman, R. (2019). Algorithmic control in platform food delivery work. Socius: Sociological Research for a Dynamic World, 5, 1–15. https://doi.org/10.1177/2378023119870041
Hennink, M. M., & Kaiser, B. N. (2022). Sample sizes for saturation in qualitative research: A systematic review of empirical tests. Social Science & Medicine, 292, Article 114523. https://doi.org/10.1016/j.socscimed.2021.114523
Kadolkar, I., Kepes, S., & Subramony, M. (2025). Algorithmic management in the gig economy: A systematic review and research integration. Journal of Organizational Behavior, 46(7), 1057–1080. https://doi.org/10.1002/job.2831
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. https://doi.org/10.5465/annals.2018.0174
Le Breton, C., & Galière, S. (2023). The role of organizational settings in social learning: An ethnographic focus on food-delivery platform work. Human Relations, 76(7), 990–1016. https://doi.org/10.1177/00187267221081295
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). Association for Computing Machinery. https://doi.org/10.1145/2702123.2702548
Lei, Y.-W. (2021). Delivering solidarity: Platform architecture and collective contention in China’s platform economy. American Sociological Review, 86(2), 279–309. https://doi.org/10.1177/0003122420979980
Malterud, K., Siersma, V. D., & Guassora, A. D. (2016). Sample size in qualitative interview studies: Guided by information power. Qualitative Health Research, 26(13), 1753–1760. https://doi.org/10.1177/1049732315617444
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
Newlands, G. (2021). Algorithmic surveillance in the gig economy: The organization of work through Lefebvrian conceived space. Organization Studies, 42(5), 719–737. https://doi.org/10.1177/0170840620937900
New York City Department of Consumer and Worker Protection. (n.d.-a). Delivery worker laws: Frequently asked questions. Retrieved July 26, 2026, from https://www.nyc.gov/site/dca/workers/workersrights/food-delivery-worker-laws-faqs.page
New York City Department of Consumer and Worker Protection. (n.d.-b). Minimum pay rate for delivery workers. Retrieved July 26, 2026, from https://www.nyc.gov/site/dca/workers/Delivery-Worker-Public-Hearing-Minimum-Pay-Rate.page
New York City Department of Consumer and Worker Protection. (2022). A minimum pay rate for app-based restaurant delivery workers in New York City. https://www.nyc.gov/assets/dca/downloads/pdf/workers/Delivery-Worker-Study-November-2022.pdf
New York City Department of Consumer and Worker Protection. (2026, January 26). Major victory for NYC delivery workers: Landmark protections take effect today. https://www.nyc.gov/site/dca/news/009-26/major-victory-nyc-delivery-workers-landmark-protections-take-effect-today
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), Article 100838. https://doi.org/10.1016/j.hrmr.2021.100838
Rahman, H. A. (2021). The invisible cage: Workers’ reactivity to opaque algorithmic evaluations. Administrative Science Quarterly, 66(4), 945–988. https://doi.org/10.1177/00018392211010118
Reyes, A. (2024). The porous boundaries of public and private messages: Solidarity networks of Latin American food delivery workers in NYC. Convergence: The International Journal of Research into New Media Technologies, 30(1), 471–490. https://doi.org/10.1177/13548565231210984
Rosenblat, A., & Stark, L. (2016). Algorithmic labor and information asymmetries: A case study of Uber’s drivers. International Journal of Communication, 10, 3758–3784.
Schor, J. B., Attwood-Charles, W., Cansoy, M., Ladegaard, I., & Wengronowitz, R. (2020). Dependence and precarity in the platform economy. Theory and Society, 49(5–6), 833–861. https://doi.org/10.1007/s11186-020-09408-y
Shapiro, A. (2018). Between autonomy and control: Strategies of arbitrage in the “on-demand” economy. New Media & Society, 20(8), 2954–2971. https://doi.org/10.1177/1461444817738236
Stark, D., & Vanden Broeck, P. (2024). Principles of algorithmic management. Organization Theory, 5(2), 1–24. https://doi.org/10.1177/26317877241257213
Tassinari, A., & Maccarrone, V. (2020). Riders on the storm: Workplace solidarity among gig economy couriers in Italy and the UK. Work, Employment and Society, 34(1), 35–54. https://doi.org/10.1177/0950017019862954
Temple, B., & Young, A. (2004). Qualitative research and translation dilemmas. Qualitative Research, 4(2), 161–178. https://doi.org/10.1177/1468794104044430
Tong, A., Sainsbury, P., & Craig, J. (2007). Consolidated criteria for reporting qualitative research: A 32-item checklist for interviews and focus groups. International Journal for Quality in Health Care, 19(6), 349–357. https://doi.org/10.1093/intqhc/mzm042
Tuomi, A., Jianu, B., Hua, M., Roelofsen, M., & Passos Ascenção, M. (2024). Strategies for communicating and mitigating algorithmic control on delivery platforms. Convergence: The International Journal of Research into New Media Technologies, 30(5), 1685–1709. https://doi.org/10.1177/13548565241270908
Vallas, S., & Schor, J. B. (2020). What do platforms do? Understanding the gig economy. Annual Review of Sociology, 46, 273–294. https://doi.org/10.1146/annurev-soc-121919-054857
van Doorn, N., & Chen, J. Y. (2021). Odds stacked against workers: Datafied gamification on Chinese and American food-delivery platforms. Socio-Economic Review, 19(4), 1345–1367. https://doi.org/10.1093/ser/mwab028
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
Wiener, M., Cram, W. A., & Benlian, A. (2023). Algorithmic control and gig workers: A legitimacy perspective of Uber drivers. European Journal of Information Systems, 32(3), 485–507. https://doi.org/10.1080/0960085X.2021.1977729
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
Wood, A. J., & Lehdonvirta, V. (2021). Antagonism beyond employment: How the “subordinated agency” of labour platforms generates conflict in the remote gig economy. Socio-Economic Review, 19(4), 1369–1396. https://doi.org/10.1093/ser/mwab016
Yu, Z., Treré, E., & Bonini, T. (2022). The emergence of algorithmic solidarity: Unveiling mutual aid practices and resistance among Chinese delivery workers. Media International Australia, 183(1), 107–123. https://doi.org/10.1177/1329878X221074793
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
