Managed by the algorithm: platform discourse, entrepreneurial subjectivity, and precarity among Indonesian gig workers

Abdul Wahid

Authors

Abdul Wahid UIN Madura Author

Keywords:

algorithmic management, entrepreneurial subjectivity, gig workers, precarity

Abstract

Algorithmic management has expanded across Indonesia’s gig economy, where platform narratives of flexibility, partnership, and opportunity coexist with informal employment, unstable protection, and digitally mediated control. This study examines how official platform discourse constructs entrepreneurial subjectivity and organises precarity among Indonesian gig workers. A qualitative critical discourse design was applied to twenty-one public documents comprising platform terms, recruitment pages, driver communications, corporate releases, verified reporting, scholarly materials, and policy documents, analysed through discourse framing, subject-positioning, and precarity-and-control matrices. Findings show that welfare, flexibility, and recognition legitimise platform participation by presenting conditional benefits and operational choice as evidence of partnership. Workers are simultaneously positioned as autonomous, calculative, self-investing, resilient, and responsible for converting platform opportunities into sustainable livelihoods. However, tariff setting, performance metrics, benefit eligibility, account governance, risk transfer, information asymmetry, and protection gaps delimit this autonomy and distribute uncertainty disproportionately towards workers. This study contributes a relational account of platform labour by conceptualising conditional entrepreneurial autonomy as a discursive and institutional arrangement through which limited operational discretion coexists with concentrated infrastructural authority, thereby integrating language, subject formation, and algorithmic governance within one analytical framework in a Global South setting often examined mainly through employment relations, technical systems, or workers’ experiences.

References

Alacovska, A., Bucher, E., & Fieseler, C. (2022). A relational work perspective on the gig economy: Doing creative work on digital labour platforms. Work, Employment and Society, 38(1), 161–179. https://doi.org/10.1177/09500170221103146

Alacovska, A., Bucher, E., & Fieseler, C. (2024). Algorithmic paranoia: Gig workers’ affective experience of abusive algorithmic management. New Technology, Work and Employment, 40. https://doi.org/10.1111/ntwe.12317

Anwar, I. A., Garcia, P., & Hui, J. (2024). Entangled independence: From labor rights to gig “empowerment” under the algorithmic gaze. Proceedings of the ACM on Human-Computer Interaction, 8, 1–26. https://doi.org/10.1145/3686916

Benlian, A., Wiener, M., Cram, W., 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

BPS-Statistics Indonesia. (2025). Labor force situation in Indonesia February 2025.

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

Chan, N. K. (2022). Algorithmic precarity and metric power: Managing the affective measures and customers in the gig economy. Big Data & Society, 9(2). https://doi.org/10.1177/20539517221133779

Cini, L. (2022). Resisting algorithmic control: Understanding the rise and variety of platform worker mobilisations. New Technology, Work and Employment. https://doi.org/10.1111/ntwe.12257

Crayne, M. P., & Newlin, A. B. (2023). Driven to succeed, or to leave? The variable impact of self-leadership in rideshare gig work. The International Journal of Human Resource Management, 35(1), 98–120. https://doi.org/10.1080/09585192.2023.2211712

Dedema, M., & Rosenbaum, H. S. (2024). Socio-technical issues in the platform-mediated gig economy: A systematic literature review. Journal of the Association for Information Science and Technology, 75(4), 344–374. https://doi.org/10.1002/asi.24868

Dorschel, R. (2022). A new middle-class fraction with a distinct subjectivity: Tech workers and the transformation of the entrepreneurial self. The Sociological Review, 70(6), 1302–1320. https://doi.org/10.1177/00258172221103015

Duggan, J., Carbery, R., McDonnell, A., & Sherman, U. (2023). Algorithmic HRM control in the gig economy: The app-worker perspective. Human Resource Management. https://doi.org/10.1002/hrm.22168

Duggan, J. R., Sherman, U., Carbery, R., & McDonnell, A. (2021). Boundaryless careers and algorithmic constraints in the gig economy. The International Journal of Human Resource Management, 33(22), 4468–4498. https://doi.org/10.1080/09585192.2021.1953565

Gerber, C. (2022). Gender and precarity in platform work: Old inequalities in the new world of work. New Technology, Work and Employment. https://doi.org/10.1111/ntwe.12233

Grohmann, R., Pereira, G., Guerra, A., Abílio, L., Moreschi, B., & Jurno, A. (2021). Platform scams: Brazilian workers’ experiences of dishonest and uncertain algorithmic management. New Media & Society, 24(7), 1611–1631. https://doi.org/10.1177/14614448221099225

Huang, H.-C. (2021). Riders on the storm: Amplified platform precarity and the impact of COVID-19 on online food-delivery drivers in China. Journal of Contemporary China, 31(135), 351–365. https://doi.org/10.1080/10670564.2021.1966895

Jarrahi, M. H., Newlands, G., Lee, M. K., Wolf, C. T., Kinder, E., & Sutherland, W. (2021). Algorithmic management in a work context. Big Data & Society, 8(2). https://doi.org/10.1177/20539517211020332

Kadolkar, I., Kepes, S., & Subramony, M. (2024). Algorithmic management in the gig economy: A systematic review and research integration. Journal of Organizational Behavior. https://doi.org/10.1002/job.2831

Ķešāne, I., & Spuriņa, M. (2024). Sociological types of precarity among gig workers: Lived experiences of food-delivery workers in Riga. Social Inclusion. https://doi.org/10.17645/si.7696

Maury, O. (2023). The fragmented labor power composition of gig workers: Entrepreneurial tendency and the heterogeneous production of difference. Critical Sociology, 50(7–8), 1167–1182. https://doi.org/10.1177/08969205231216418

Meijerink, J., & Bondarouk, T. V. (2021). The duality of algorithmic management: Toward a research agenda on HRM algorithms, autonomy and value creation. Human Resource Management Review. https://doi.org/10.1016/j.hrmr.2021.100876

Muldoon, J., & Raekstad, P. (2022). Algorithmic domination in the gig economy. European Journal of Political Theory, 22(4), 587–607. https://doi.org/10.1177/14748851221082078

Novianto, A. (2024). Gamification from below as a form of resistance: Algorithmic control, precarity, and resistance dynamics among Indonesian gig workers. New Technology, Work and Employment, 40. https://doi.org/10.1111/ntwe.12324

Öborn, D. R., MacKenzie, R., Örnebring, H., & Van Couvering, E. (2024). Bypassing the limitations of algorithmic management via out-of-app activities and the emergence of opportunistic agency in the Swedish gig economy. New Technology, Work and Employment, 40. https://doi.org/10.1111/ntwe.12323

Popan, C. (2021). Embodied precariat and digital control in the “gig economy”: The mobile labor of food-delivery workers. Journal of Urban Technology, 31(1), 109–128. https://doi.org/10.1080/10630732.2021.2001714

Qadri, R., & D’Ignazio, C. (2022). Seeing like a driver: How workers repair, resist, and reinforce the platform’s algorithmic visions. Big Data & Society, 9(2). https://doi.org/10.1177/20539517221133780

Sun, P., Chen, J. Y., & Rani, U. (2021). From flexible labour to “sticky labour”: A tracking study of workers in the food-delivery platform economy of China. Work, Employment and Society, 37(2), 412–431. https://doi.org/10.1177/09500170211021570

Vieira, T. (2023). Platform couriers’ self-exploitation: The case study of Glovo. New Technology, Work and Employment. https://doi.org/10.1111/ntwe.12272

Walker, M., Fleming, P., & Berti, M. (2021). “You can’t pick up a phone and talk to someone”: How algorithms function as biopower in the gig economy. Organization, 28(1), 26–43. https://doi.org/10.1177/1350508420978831

World Bank. (2021). Beyond unicorns: Harnessing digital technologies for inclusion in Indonesia.

World Bank. (2024). Informal workers in Indonesia: Characteristics and implications for retirement savings coverage. World Bank.

Yasih, D. W. P. (2022). Normalizing and resisting the new precarity: A case study of the Indonesian gig economy. Critical Sociology, 49(6), 847–863. https://doi.org/10.1177/08969205221087130