Cogs in the code: Applying labor process theory in algorithmic management of platform-mediated gig work

Muhammed Ali KT; R. Chitra Sivasubramanian

doi:10.31893/multirev.2024225

Keywords:

virtual-organization of work

technology-mediated surveillance

algorithmic control

algorithmic rating and evaluation

Abstract

This article examines the evolving work landscape in the context of technological advancements, addressing a significant gap in understanding the foundational principles of algorithmic management. Employing labor process theory (LPT), this study focuses on power dynamics and control mechanisms within algorithmic management systems in platform-mediated work settings. By exploring managerial aspects such as task assignment, work organization, monitoring, surveillance, and performance evaluation under algorithmic management, this research utilizes LPT to meticulously explore the social relations of production, technological deskilling, division of labor, potential alienation and exploitation, and complex dynamics of control and resistance in the gig economy. By emphasizing the pivotal role of algorithms, this study reveals their influence on shaping the structural aspects of the gig economy, highlighting the intricate interplay between technological advancements and fundamental labor processes. This work also contributes to a deeper understanding of contemporary work dynamics by offering valuable insights into the evolving intersection of technology and labor in the modern workplace.
References
1. Allen-Robertson, J. (2017).The Uber Game: Exploring Algorithmic Management and Resistance - Research Repository. http://repository.essex.ac.uk/id/eprint/20603
2. Anicich, E. M. (2022). Flexing and floundering in the on-demand economy: Narrative identity construction under algorithmic management. Organizational Behavior and Human Decision Processes, 169, 104138. https://doi.org/10.1016/j.obhdp.2022.104138
3. Anwar, M. A., & Graham, M. (2020). Between a rock and a hard place: Freedom, flexibility, precarity and vulnerability in the gig economy in Africa. Competition & Change, 25(2), 237–258. https://doi.org/10.1177/1024529420914473
4. Basukie, J., Wang, Y., & Li, S. (2020). Big data governance and algorithmic management in sharing economy platforms: A case of ridesharing in emerging markets. Technological Forecasting & Social Change/Technological Forecasting and Social Change, 161, 120310. https://doi.org/10.1016/j.techfore.2020.120310
5. Batistič, S., & Van Der Laken, P. (2019). History, Evolution and Future of Big Data and Analytics: A Bibliometric Analysis of Its Relationship to Performance in Organizations. British Journal of Management, 30(2), 229–251. https://doi.org/10.1111/1467-8551.12340
6. Bucher, E. L., Schou, P. K., & Waldkirch, M. (2020). 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
7. Cant, C., & Woodcock, J. (2020). Fast Food Shutdown: From disorganisation to action in the service sector. Capital & Class, 44(4), 513–521. https://doi.org/10.1177/0309816820906357
8. Cheng, M. M., & Hackett, R. D. (2021). A critical review of algorithms in HRM: Definition, theory, and practice. Human Resource Management Review, 31(1), 100698. https://doi.org/10.1016/j.hrmr.2019.100698
9. Cram, W. A., & Wiener, M. (2020). Technology-mediated Control: Case Examples and Research Directions for the Future of Organizational Control. Communications of the Association for Information Systems, 70–91. https://doi.org/10.17705/1cais.04604
10. Cram, W. A., Wiener, M., Tarafdar, M., & Benlian, A. (2020). Algorithmic controls and their implications for gig worker well-being and behavior. In ICIS
11. Danaher, J., Hogan, M. J., Noone, C., Kennedy, R., Behan, A., De Paor, A., Felzmann, H., Haklay, M., Khoo, S. M., Morison, J., Murphy, M. H., O’Brolchain, N., Schafer, B., & Shankar, K. (2017). Algorithmic governance: Developing a research agenda through the power of collective intelligence. Big Data & Society, 4(2), 205395171772655. https://doi.org/10.1177/2053951717726554
12. De Ruyter, A., Brown, M., & Burgess, J. (2018). Gig work and the fourth industrial revolution. Journal of International Affairs, 72(1), 37–50.
13. 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
14. Duggan, J., Sherman, U., Carbery, R., & McDonnell, A. (2021). Boundaryless careers and algorithmic constraints in the gig economy. International Journal of Human Resource Management, 33(22), 4468–4498. https://doi.org/10.1080/09585192.2021.1953565
15. Dunn, M. (2020). Making gigs work: digital platforms, job quality and worker motivations. New Technology, Work and Employment, 35(2), 232–249. https://doi.org/10.1111/ntwe.12167
16. Edward, W. (2020). The Uberisation of work: the challenge of regulating platform capitalism. A commentary. International Review of Applied Economics, 34(4), 512–521. https://doi.org/10.1080/02692171.2020.1773647
17. Fleming, P. (2017). The Human Capital Hoax: Work, Debt and Insecurity in the Era of Uberization. Organization Studies, 38(5), 691–709. https://doi.org/10.1177/0170840616686129
18. Gal, U., Jensen, T. B., & Stein, M. K. (2020). Breaking the vicious cycle of algorithmic management: A virtue ethics approach to people analytics. Information and Organization, 30(2), 100301. https://doi.org/10.1016/j.infoandorg.2020.100301
19. Galiere, 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
20. Gandini, A. (2018). Labour process theory and the gig economy. Human Relations, 72(6), 1039–1056. https://doi.org/10.1177/0018726718790002
21. Gao, Y., Rong, H., & Huang, J. Z. (2005). Adaptive grid job scheduling with genetic algorithms. Future Generation Computer Systems, 21(1), 151–161. https://doi.org/10.1016/j.future.2004.09.033
22. Glavin, P., Bierman, A., & Schieman, S. (2021). Über-Alienated: Powerless and Alone in the Gig Economy. Work and Occupations, 48(4), 399–431. https://doi.org/10.1177/07308884211024711
23. Heiland, H. (2021). Neither timeless, nor placeless: Control of food delivery gig work via place-based working time regimes. Human Relations, 75(9), 1824–1848. https://doi.org/10.1177/00187267211025283
24. Huang, H. (2022). Algorithmic management in food‐delivery platform economy in China. New Technology, Work and Employment, 38(2), 185–205. https://doi.org/10.1111/ntwe.12228
25. Huws, N. U. (2016). Logged labour: a new paradigm of work organisation? Work Organisation, Labour and Globalisation/Work Organisation, Labour & Globalisation, 10(1). https://doi.org/10.13169/workorgalaboglob.10.1.0007
26. Issar, S., & Aneesh, A. (2021). What is algorithmic governance? Sociology Compass, 16(1). https://doi.org/10.1111/soc4.12955
27. 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), 205395172110203. https://doi.org/10.1177/20539517211020332
28. Jarrahi, M. H., Sutherland, W., Nelson, S. B., & Sawyer, S. (2019). Platformic Management, Boundary Resources for Gig Work, and Worker Autonomy. Computer Supported Cooperative Work (CSCW)/Computer Supported Cooperative Work, 29(1–2), 153–189. https://doi.org/10.1007/s10606-019-09368-7
29. Kaine, S., & Josserand, E. (2019). The organisation and experience of work in the gig economy. Journal of Industrial Relations, 61(4), 479–501. https://doi.org/10.1177/0022185619865480
30. Kellogg, K. C., Valentine, M. A., & Christin, A. (2020). Algorithms at Work: The New Contested Terrain of Control. the Academy of Management Annals, 14(1), 366–410. https://doi.org/10.5465/annals.2018.0174
31. Kim, S., & Christensen, A. L. (2017). The Dark and Bright Sides of Personal Use of Technology at Work: A Job Demands–Resources Model. Human Resource Development Review, 16(4), 425–447. https://doi.org/10.1177/1534484317725438
32. Kuhn, K. M., & Maleki, A. (2017). Micro-entrepreneurs, Dependent Contractors, and Instaserfs: Understanding Online Labor Platform Workforces. the Academy of Management Perspectives/Academy of Management Perspectives, 31(3), 183–200. https://doi.org/10.5465/amp.2015.0111
33. Lee, M. K., Kusbit, D., Metsky, E., & Dabbish, L. (2015). Working with Machines. https://doi.org/10.1145/2702123.2702548
34. Li, A. K. (2021). Beyond algorithmic control: flexibility, intermediaries, and paradox in the on-demand economy. Information, Communication & Society, 25(14), 2012–2027. https://doi.org/10.1080/1369118x.2021.1924225
35. Milkman, R., Elliott-Negri, L., Griesbach, K., & Reich, A. (2020). Gender, Class, and the Gig Economy: The Case of Platform-Based Food Delivery. Critical Sociology, 47(3), 357–372. https://doi.org/10.1177/0896920520949631
36. Newlands, G. (2020). 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
37. Nickell, D. (2019). Using Data Analytics in the Management of Employees: Digital Means of Tracking, Monitoring, and Surveilling Worker Activities Social Sciences, Sociology, Management and complex organizations. Psychosociological Issues in Human Resource Management, 7(2), 43. https://doi.org/10.22381/pihrm7120197
38. Norlander, P., Jukic, N., Varma, A., & Nestorov, S. (2021). The effects of technological supervision on gig workers: organizational control and motivation of Uber, taxi, and limousine drivers. International Journal of Human Resource Management, 32(19), 4053–4077. https://doi.org/10.1080/09585192.2020.1867614
39. 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
40. Parth, S., & Bathini, D. R. (2021). Microtargeting control: Explicating algorithmic control and nudges in platform‐mediated cab driving in India. New Technology, Work and Employment, 36(1), 74–93. https://doi.org/10.1111/ntwe.12188
41. Pignot, E. (2021). Who is pulling the strings in the platform economy? Accounting for the dark and unexpected sides of algorithmic control. Organization, 30(1), 140–167. https://doi.org/10.1177/1350508420974523
42. Popescu, G. H., Petrescu, I. E., & Sabie, O. M. (2018). Algorithmic labor in the platform economy: digital infrastructures, job quality, and workplace surveillance. Economics, Management and Financial Markets, 13(3), 74-79.
43. Pregenzer, Michael; Remus, Ulrich; and Wiener, Martin,. (2021). AIS Electronic Library (AISeL); “Algorithms in the Driver’s Seat: Explaining Workers’ Reactions to Algorithmic Control”. ECIS 2021 Research Papers. 83. https://aisel.aisnet.org/ecis2021_rp/83
44. Rachmawati, R., Safitri, N., Zakia, L., Lupita, A., & De Ruyter, A. (2021). Urban gig workers in Indonesia during COVID-19: The experience of online ‘ojek’ drivers. Work Organisation, Labour and Globalisation/Work Organisation, Labour & Globalisation, 15(1). https://doi.org/10.13169/workorgalaboglob.15.1.0031
45. Rani, U., & Furrer, M. (2020). Digital labour platforms and new forms of flexible work in developing countries: Algorithmic management of work and workers. Competition & Change, 25(2), 212–236. https://doi.org/10.1177/1024529420905187
46. Ravenelle, A. J. (2019). “We’re not uber:” control, autonomy, and entrepreneurship in the gig economy. Journal of Managerial Psychology, 34(4), 269–285. https://doi.org/10.1108/jmp-06-2018-0256
47. Rosenblat, A., & Stark, L. (2015). Uber’s Drivers: Information Asymmetries and Control in Dynamic Work. Social Science Research Network. https://doi.org/10.2139/ssrn.2686227
48. Schildt, H. (2016). Big data and organizational design – the brave new world of algorithmic management and computer augmented transparency. Innovation, 19(1), 23–30. https://doi.org/10.1080/14479338.2016.1252043
49. Stark, D., & Pais, I. (2021). , Algorithmic Management in the Platform Economy. Èkonomičeskaâ Sociologiâ, 22(3), 71–103. https://doi.org/10.17323/1726-3247-2021-3-71-103
50. Surie, A. (2018). Are Ola and Uber drivers entrepreneurs or exploited workers. Economic and Political Weekly, 53(24), 1-7.
51. Timko, P., & Van Melik, R. (2021). Being a Deliveroo Rider: Practices of Platform Labor in Nijmegen and Berlin. Journal of Contemporary Ethnography, 50(4), 497–523. https://doi.org/10.1177/0891241621994670
52. Veen, A., Barratt, T., & Goods, C. (2019). 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
53. Wiener, M., Cram, W. A., & Benlian, A. (2021). 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
54. Wood, A., & Lehdonvirta, V. (2021). Platform Precarity: surviving algorithmic insecurity in the gig economy. Social Science Research Network. https://doi.org/10.2139/ssrn.3795375
55. Wood, A., Graham, M., Lehdonvirta, V., & Hjorth, I. (2018). 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

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to cite

KT, M. A., & Sivasubramanian, R. C. (2024). Cogs in the code: Applying labor process theory in algorithmic management of platform-mediated gig work. Multidisciplinary Reviews, 7(10), 2024225. https://doi.org/10.31893/multirev.2024225

[1] Allen-Robertson, J. (2017).The Uber Game: Exploring Algorithmic Management and Resistance - Research Repository. http://repository.essex.ac.uk/id/eprint/20603

[2] Anicich, E. M. (2022). Flexing and floundering in the on-demand economy: Narrative identity construction under algorithmic management. Organizational Behavior and Human Decision Processes, 169, 104138. https://doi.org/10.1016/j.obhdp.2022.104138

[3] Anwar, M. A., & Graham, M. (2020). Between a rock and a hard place: Freedom, flexibility, precarity and vulnerability in the gig economy in Africa. Competition & Change, 25(2), 237–258. https://doi.org/10.1177/1024529420914473

[4] Basukie, J., Wang, Y., & Li, S. (2020). Big data governance and algorithmic management in sharing economy platforms: A case of ridesharing in emerging markets. Technological Forecasting & Social Change/Technological Forecasting and Social Change, 161, 120310. https://doi.org/10.1016/j.techfore.2020.120310

[5] Batistič, S., & Van Der Laken, P. (2019). History, Evolution and Future of Big Data and Analytics: A Bibliometric Analysis of Its Relationship to Performance in Organizations. British Journal of Management, 30(2), 229–251. https://doi.org/10.1111/1467-8551.12340

[6] Bucher, E. L., Schou, P. K., & Waldkirch, M. (2020). 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

[7] Cant, C., & Woodcock, J. (2020). Fast Food Shutdown: From disorganisation to action in the service sector. Capital & Class, 44(4), 513–521. https://doi.org/10.1177/0309816820906357

[8] Cheng, M. M., & Hackett, R. D. (2021). A critical review of algorithms in HRM: Definition, theory, and practice. Human Resource Management Review, 31(1), 100698. https://doi.org/10.1016/j.hrmr.2019.100698

[9] Cram, W. A., & Wiener, M. (2020). Technology-mediated Control: Case Examples and Research Directions for the Future of Organizational Control. Communications of the Association for Information Systems, 70–91. https://doi.org/10.17705/1cais.04604

[10] Cram, W. A., Wiener, M., Tarafdar, M., & Benlian, A. (2020). Algorithmic controls and their implications for gig worker well-being and behavior. In ICIS

[11] Danaher, J., Hogan, M. J., Noone, C., Kennedy, R., Behan, A., De Paor, A., Felzmann, H., Haklay, M., Khoo, S. M., Morison, J., Murphy, M. H., O’Brolchain, N., Schafer, B., & Shankar, K. (2017). Algorithmic governance: Developing a research agenda through the power of collective intelligence. Big Data & Society, 4(2), 205395171772655. https://doi.org/10.1177/2053951717726554

[12] De Ruyter, A., Brown, M., & Burgess, J. (2018). Gig work and the fourth industrial revolution. Journal of International Affairs, 72(1), 37–50.

[13] 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

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

[15] Dunn, M. (2020). Making gigs work: digital platforms, job quality and worker motivations. New Technology, Work and Employment, 35(2), 232–249. https://doi.org/10.1111/ntwe.12167

[16] Edward, W. (2020). The Uberisation of work: the challenge of regulating platform capitalism. A commentary. International Review of Applied Economics, 34(4), 512–521. https://doi.org/10.1080/02692171.2020.1773647

[17] Fleming, P. (2017). The Human Capital Hoax: Work, Debt and Insecurity in the Era of Uberization. Organization Studies, 38(5), 691–709. https://doi.org/10.1177/0170840616686129

[18] Gal, U., Jensen, T. B., & Stein, M. K. (2020). Breaking the vicious cycle of algorithmic management: A virtue ethics approach to people analytics. Information and Organization, 30(2), 100301. https://doi.org/10.1016/j.infoandorg.2020.100301

[19] Galiere, 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

[20] Gandini, A. (2018). Labour process theory and the gig economy. Human Relations, 72(6), 1039–1056. https://doi.org/10.1177/0018726718790002

[21] Gao, Y., Rong, H., & Huang, J. Z. (2005). Adaptive grid job scheduling with genetic algorithms. Future Generation Computer Systems, 21(1), 151–161. https://doi.org/10.1016/j.future.2004.09.033

[22] Glavin, P., Bierman, A., & Schieman, S. (2021). Über-Alienated: Powerless and Alone in the Gig Economy. Work and Occupations, 48(4), 399–431. https://doi.org/10.1177/07308884211024711

[23] Heiland, H. (2021). Neither timeless, nor placeless: Control of food delivery gig work via place-based working time regimes. Human Relations, 75(9), 1824–1848. https://doi.org/10.1177/00187267211025283

[24] Huang, H. (2022). Algorithmic management in food‐delivery platform economy in China. New Technology, Work and Employment, 38(2), 185–205. https://doi.org/10.1111/ntwe.12228

[25] Huws, N. U. (2016). Logged labour: a new paradigm of work organisation? Work Organisation, Labour and Globalisation/Work Organisation, Labour & Globalisation, 10(1). https://doi.org/10.13169/workorgalaboglob.10.1.0007

[26] Issar, S., & Aneesh, A. (2021). What is algorithmic governance? Sociology Compass, 16(1). https://doi.org/10.1111/soc4.12955

[27] 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), 205395172110203. https://doi.org/10.1177/20539517211020332

[28] Jarrahi, M. H., Sutherland, W., Nelson, S. B., & Sawyer, S. (2019). Platformic Management, Boundary Resources for Gig Work, and Worker Autonomy. Computer Supported Cooperative Work (CSCW)/Computer Supported Cooperative Work, 29(1–2), 153–189. https://doi.org/10.1007/s10606-019-09368-7

[29] Kaine, S., & Josserand, E. (2019). The organisation and experience of work in the gig economy. Journal of Industrial Relations, 61(4), 479–501. https://doi.org/10.1177/0022185619865480

[30] Kellogg, K. C., Valentine, M. A., & Christin, A. (2020). Algorithms at Work: The New Contested Terrain of Control. the Academy of Management Annals, 14(1), 366–410. https://doi.org/10.5465/annals.2018.0174

[31] Kim, S., & Christensen, A. L. (2017). The Dark and Bright Sides of Personal Use of Technology at Work: A Job Demands–Resources Model. Human Resource Development Review, 16(4), 425–447. https://doi.org/10.1177/1534484317725438

[32] Kuhn, K. M., & Maleki, A. (2017). Micro-entrepreneurs, Dependent Contractors, and Instaserfs: Understanding Online Labor Platform Workforces. the Academy of Management Perspectives/Academy of Management Perspectives, 31(3), 183–200. https://doi.org/10.5465/amp.2015.0111

[33] Lee, M. K., Kusbit, D., Metsky, E., & Dabbish, L. (2015). Working with Machines. https://doi.org/10.1145/2702123.2702548

[34] Li, A. K. (2021). Beyond algorithmic control: flexibility, intermediaries, and paradox in the on-demand economy. Information, Communication & Society, 25(14), 2012–2027. https://doi.org/10.1080/1369118x.2021.1924225

[35] Milkman, R., Elliott-Negri, L., Griesbach, K., & Reich, A. (2020). Gender, Class, and the Gig Economy: The Case of Platform-Based Food Delivery. Critical Sociology, 47(3), 357–372. https://doi.org/10.1177/0896920520949631

[36] Newlands, G. (2020). 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

[37] Nickell, D. (2019). Using Data Analytics in the Management of Employees: Digital Means of Tracking, Monitoring, and Surveilling Worker Activities Social Sciences, Sociology, Management and complex organizations. Psychosociological Issues in Human Resource Management, 7(2), 43. https://doi.org/10.22381/pihrm7120197

[38] Norlander, P., Jukic, N., Varma, A., & Nestorov, S. (2021). The effects of technological supervision on gig workers: organizational control and motivation of Uber, taxi, and limousine drivers. International Journal of Human Resource Management, 32(19), 4053–4077. https://doi.org/10.1080/09585192.2020.1867614

[39] 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

[40] Parth, S., & Bathini, D. R. (2021). Microtargeting control: Explicating algorithmic control and nudges in platform‐mediated cab driving in India. New Technology, Work and Employment, 36(1), 74–93. https://doi.org/10.1111/ntwe.12188

[41] Pignot, E. (2021). Who is pulling the strings in the platform economy? Accounting for the dark and unexpected sides of algorithmic control. Organization, 30(1), 140–167. https://doi.org/10.1177/1350508420974523

[42] Popescu, G. H., Petrescu, I. E., & Sabie, O. M. (2018). Algorithmic labor in the platform economy: digital infrastructures, job quality, and workplace surveillance. Economics, Management and Financial Markets, 13(3), 74-79.

[43] Pregenzer, Michael; Remus, Ulrich; and Wiener, Martin,. (2021). AIS Electronic Library (AISeL); “Algorithms in the Driver’s Seat: Explaining Workers’ Reactions to Algorithmic Control”. ECIS 2021 Research Papers. 83. https://aisel.aisnet.org/ecis2021_rp/83

[44] Rachmawati, R., Safitri, N., Zakia, L., Lupita, A., & De Ruyter, A. (2021). Urban gig workers in Indonesia during COVID-19: The experience of online ‘ojek’ drivers. Work Organisation, Labour and Globalisation/Work Organisation, Labour & Globalisation, 15(1). https://doi.org/10.13169/workorgalaboglob.15.1.0031

[45] Rani, U., & Furrer, M. (2020). Digital labour platforms and new forms of flexible work in developing countries: Algorithmic management of work and workers. Competition & Change, 25(2), 212–236. https://doi.org/10.1177/1024529420905187

[46] Ravenelle, A. J. (2019). “We’re not uber:” control, autonomy, and entrepreneurship in the gig economy. Journal of Managerial Psychology, 34(4), 269–285. https://doi.org/10.1108/jmp-06-2018-0256

[47] Rosenblat, A., & Stark, L. (2015). Uber’s Drivers: Information Asymmetries and Control in Dynamic Work. Social Science Research Network. https://doi.org/10.2139/ssrn.2686227

[48] Schildt, H. (2016). Big data and organizational design – the brave new world of algorithmic management and computer augmented transparency. Innovation, 19(1), 23–30. https://doi.org/10.1080/14479338.2016.1252043

[49] Stark, D., & Pais, I. (2021). , Algorithmic Management in the Platform Economy. Èkonomičeskaâ Sociologiâ, 22(3), 71–103. https://doi.org/10.17323/1726-3247-2021-3-71-103

[50] Surie, A. (2018). Are Ola and Uber drivers entrepreneurs or exploited workers. Economic and Political Weekly, 53(24), 1-7.

[51] Timko, P., & Van Melik, R. (2021). Being a Deliveroo Rider: Practices of Platform Labor in Nijmegen and Berlin. Journal of Contemporary Ethnography, 50(4), 497–523. https://doi.org/10.1177/0891241621994670

[52] Veen, A., Barratt, T., & Goods, C. (2019). 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

[53] Wiener, M., Cram, W. A., & Benlian, A. (2021). 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

[54] Wood, A., & Lehdonvirta, V. (2021). Platform Precarity: surviving algorithmic insecurity in the gig economy. Social Science Research Network. https://doi.org/10.2139/ssrn.3795375

[55] Wood, A., Graham, M., Lehdonvirta, V., & Hjorth, I. (2018). 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

Abstract

References

How to cite