The Economics of Algorithmic Management: The Impact of AI Surveillance on the Marginal Productivity of Gig Workers
Keywords:
Algorithmic Management, Algorithmic Fatigue, Marginal Productivity, Gig Economy, Red Queen Dynamics.
Abstract
This research investigates the microeconomic impact of AI-driven surveillance on the marginal productivity of gig workers, addressing a critical gap in neoclassical labor models. While conventional paradigms assume that algorithmic monitoring linearly enhances output, this study introduces the "Algorithmic Fatigue" construct as an endogenous frictional variable that artificially engineers premature diminishing returns. Utilizing a high-powered sample of 1,200 ride-hailing drivers in Jabodetabek, the study employs a hybrid methodological approach integrating Partial Least Squares Structural Equation Modeling (PLS-SEM) and Non-linear Least Squares (NLS) regression. The empirical results reveal a paradoxical architecture: while AI intensity provides a weak direct productivity impetus (β = 0.118), it simultaneously triggers a massive negative indirect effect mediated by algorithmic fatigue (β = -0.415). Econometric simulations demonstrate that the labor duration viability threshold (L*) collapses precipitously from 14.8 hours in low-surveillance environments to a mere 3.3 hours under aggressive algorithmic regimes. Beyond this critical inflection point, workers enter a deficit zone of negative marginal productivity (MPL < 0) characterized by "Red Queen Dynamics" and maladaptive resistance behaviors, such as GPS spoofing and cognitive paralysis. The study concludes that algorithmic management ultimately cannibalizes its own efficiency by destroying the neurocognitive infrastructure of human capital. These findings necessitate an urgent paradigm shift toward cognitive-ecological justice and the implementation of specific labor protections, including the "right to disconnect" and algorithmic transparency mandates.
References
Aisyah, M., Al Arif, M. N. R., Rahmawati, Y., & Ihsan, D. N. (2024). How collaborative consumption effect customer satisfaction and the ride-hailing drivers’ psychological well-being in Indonesia. Asia Pacific Management Review, 29(4), 441–450. https://doi.org/10.1016/j.apmrv.2024.09.003
Arief, F. (2024). Rekonstruksi Agency Theory Berbasis Nilai Al-Adl untuk Mengatasi Moral Hazard. Jurnal Akuntansi Dan Keuangan Syariah (Jurnal Akunsyah), 4(2), 13–21. https://doi.org/10.30863/akunsyah.v4i2.5726
Asfarian, A., Ramadhan, D. A., Sukmasetya, P., Byputra, F., Siregar, R. S., Ridha, A., & Kusumah, F. S. F. (2024). A Survey of Digital Nudges for Fostering Responsible Consumption Behavior in Indonesia Digital Services. IOP Conference Series: Earth and Environmental Science, 1359(1), 012012. https://doi.org/10.1088/1755-1315/1359/1/012012
Ashkrof, P., Correia, G. H. de A., Cats, O., & van Arem, B. (2022). Ride acceptance behaviour of ride-sourcing drivers. Transportation Research Part C: Emerging Technologies, 142, 103783. https://doi.org/10.1016/j.trc.2022.103783
Asrori, S., Isma’il, M., & Gamalinda, E. F. (2025). The flexibility illusion: Algorithmic control and precarity in Indonesia’s gig economy. Simulacra, 8(2), 187–205. https://doi.org/10.21107/sml.v8i2.30214
Balaskas, S., Konstantakopoulou, M., Yfantidou, I., & Komis, K. (2025). Algorithmic Burnout and Digital Well-Being: Modelling Young Adults’ Resistance to Personalized Digital Persuasion. Societies, 15(8), 232. https://doi.org/10.3390/soc15080232
Bari, M. H., & Ara, A. (2025). The Impact Of Machine Learning On Prescriptive Analytics For Optimized Business Decision-Making. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.5050060
Bowdler, M., Lahti, H., Jelenko, M., Buresti, G., & Valtonen, T. (2026). Algorithmic management and psychosocial risks at work: An emerging occupational safety and health challenge. Scandinavian Journal of Work, Environment & Health, 52(1), 1–5. https://doi.org/10.5271/sjweh.4270
Budiono, M. F. (2023). Measuring the Job Precariousness Experienced by Gojek Drivers in Indonesia. AKSELERASI: Jurnal Ilmiah Nasional, 5(2), 56–64. https://doi.org/10.54783/jin.v5i2.709
Cruz, M. D. (2023). Labor Productivity, Real Wages, and Employment in OECD Economies. Structural Change and Economic Dynamics, 66, 367–382. https://doi.org/10.1016/j.strueco.2023.05.007
Curran, D. (2023). Surveillance capitalism and systemic digital risk: The imperative to collect and connect and the risks of interconnectedness. Big Data & Society, 10(1). https://doi.org/10.1177/20539517231177621
Dold, L., & Speck, C. (2021). Resolving the productivity paradox of digitalised production. International Journal of Production Management and Engineering, 9(2), 65. https://doi.org/10.4995/ijpme.2021.15058
Fakhri, M. R., Novikasari, N., & Sitorus, R. J. (2025). Determinan Kejadian Kelelahan Kerja Kronis (burnout) pada Pekerja BPJS Kesehatan Cabang Batam. SENTRI: Jurnal Riset Ilmiah, 4(12), 4613–4625. https://doi.org/10.55681/sentri.v4i12.5518
Gong, T. (2025). Algorithmic management and gig workers: engagement, exhaustion and citizenship behavior. Management Decision, 1–22. https://doi.org/10.1108/MD-01-2025-0111
Hajiheydari, N., & Delgosha, M. S. (2024). Investigating engagement and burnout of gig-workers in the age of algorithms: an empirical study in digital labor platforms. Information Technology & People, 37(7), 2489–2522. https://doi.org/10.1108/ITP-11-2022-0873
Harliantara, H., & Ozturk, B. (2026). The transition from terrestrial radio to audio-on-demand: analyzing audience autonomy and emotional connection among urban youth in Surabaya. Frontiers in Communication, 11. https://doi.org/10.3389/fcomm.2026.1803100
Hasugian, Y. E. (2026). Analisis dampak Artificial Intelligence (AI) terhadap Sektor Tenaga Kerja di Indonesia. Majelis: Jurnal Hukum Indonesia, 3(1), 84–101. https://doi.org/10.62383/majelis.v3i1.1501
Izzati, N. R., & Kwang, E. N. (2025). Regulating Platform Work in Indonesia: Exploring Legal Options for Workers’ Protection. Jurnal Ketenagakerjaan, 20(3). https://doi.org/10.47198/jnaker.v20i3.622
Lin, Q., Sun, R., & Zhu, Q. (2025). Perceived algorithmic control and gig workers’ work engagement: assessing the mediating role of psychological empowerment and the moderating effect of deep acting. BMC Psychology, 13(1), 1237. https://doi.org/10.1186/s40359-025-03570-7
Majumdar, P. (2026). The Economics of Distrust: Systemic Risks of Digital Surveillance and Corporate Insecurity in the Modern Enterprise. Journal of Human Resource Management, 14(1), 12–21. https://doi.org/10.11648/j.jhrm.20261401.12
Mufti, H., Fauzi, D. U., & Putra, T. R. (2026). Algorithmic Management in the Future of Work A Systematic Literature Review in the Context of Gig Workers. Bisman (Bisnis Dan Manajemen): The Journal of Business and Management, 9(1), 36–53. https://doi.org/10.36815/bisman.v9i1.4589
Muhyiddin, M., Annazah, N. S., Henriko Tobing, H., Habibi, N., Fauziyah, F., & Harsiwie, R. I. P. (2024). The Ambiguity of Employment Relationship in Indonesia’s Gig Economy: A Study of Online Motorcycle Taxi Drivers. Jurnal Ketenagakerjaan, 19(3), 262–280. https://doi.org/10.47198/jnaker.v19i3.416
Niebler, V., Pirina, G., Secchi, M., & Tomassoni, F. (2023). Towards ‘bogus employment?’ The contradictory outcomes of ride-hailing regulation in Berlin, Lisbon and Paris. Cambridge Journal of Regions, Economy and Society, 16(2), 289–301. https://doi.org/10.1093/cjres/rsad007
Nu, Y., Belavina, E., & Girotra, K. (2024). Using Artificial Intelligence To Reduce Food Waste. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4826777
Nuryanti, S., & Burhan, O. K. (2025). Flexible work arrangements in the eyes of Indonesian. Jurnal Psikologi, 24(1), 101–112. https://doi.org/10.14710/jp.24.1.101-112
Putri, T. E., Darmawan, P., & Heeks, R. (2023). What is fair? The experience of Indonesian gig workers. Digital Geography and Society, 5, 100072. https://doi.org/10.1016/j.diggeo.2023.100072
Qodriyah, I. S. L., & Wungu, T. D. K. (2025). Utilizing Stacking Ensemble Algorithm for Employee Productivity Prediction (Case Study of PT PLN (Persero) Unit Induk Distribusi Sulawesi Selatan, Sulawesi Tenggara dan Sulawesi Barat). Jurnal Locus Penelitian Dan Pengabdian, 4(10), 9432–9447. https://doi.org/10.58344/locus.v4i10.4292
Singh, K., & Kuldeep. (2024). Algorithmic Exhaustion: A Cyber Psychological Investigation into the Nexus of Social Media Algorithms, Cognitive Fatigue, and Digital Addiction. International Journal of Multidisciplinary Research and Growth Evaluation, 5(4), 1440–1443. https://doi.org/10.54660/.IJMRGE.2024.5.4.1440-1443
Sun, S., Lu, L. X., Lu, S. F., & Gu, W. (2024). When Peers Matter More: The Dominance of Social Influence Over Algorithm Accuracy in Patient Decision Making. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4966406
Susanti, V., Sumarwan, U., Simanjuntak, M., & Yusuf, E. Z. (2020). The Rational Factors of Perceived Quality and Perceived Value as the Drivers of Customer Satisfaction and Brand Loyalty. Bisnis & Birokrasi Journal, 26(3). https://doi.org/10.20476/jbb.v26i3.11121
van Zoonen, W., ter Hoeven, C., & Morgan, R. (2023). Finding meaning in crowdwork: An analysis of algorithmic management, work characteristics, and meaningfulness. The Information Society, 39(5), 322–336. https://doi.org/10.1080/01972243.2023.2243262
Wijiono, A. K., Vitianingsih, A. V., Kacung, S., Maukar, A. L., & Pamudi, P. (2024). Deteksi Notifikasi Suspend pada Aplikasi Ojek Online Menggunakan Metode MOORA. Jurnal Teknologi Sistem Informasi Dan Aplikasi, 7(3), 1351–1362. https://doi.org/10.32493/jtsi.v7i3.42159
Wusono, M., Yona, M., Ariyati, Y., Hasibuan, R., Siregar, H., Nasution, H., Simanjuntak, W., Barus, P., & Yolanda, S. (2025). Algorithmic Management and Employee Autonomy: Impacts on Creativity in Remote First Companies. Journal of Economics and Management Scienties, 315–321. https://doi.org/10.37034/jems.v8i1.288
Yang, H., Li, D., & Hu, P. (2024). Decoding algorithm fatigue: The role of algorithmic literacy, information cocoons, and algorithmic opacity. Technology in Society, 79, 102749. https://doi.org/10.1016/j.techsoc.2024.102749
Yolusever, A. (2026). The Red Queen in the Dashboard: Co-Evolutionary Dynamics of Algorithmic Control and Worker Resistance. Ekonomi Politika ve Finans Arastirmalari Dergisi, 11(1), 140–159. https://doi.org/10.30784/epfad.1829094
Yuliana, Y., Aggraeni, P., & Bondan, A. (2025). Algoritmic Leadership: Literature Review Of Opportunities And Challenges In The Digital Age. Al-Abshar: Journal of Islamic Education Management, 4(1), 33–53. https://doi.org/10.58223/al-abshar.v4i1.466
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
Zhang, L., Ling, X., & Yang, C. (2026). Looking at both sides of algorithmic control and employee well-being: a job demands−resources model. Frontiers in Psychology, 17. https://doi.org/10.3389/fpsyg.2026.1736349
Arief, F. (2024). Rekonstruksi Agency Theory Berbasis Nilai Al-Adl untuk Mengatasi Moral Hazard. Jurnal Akuntansi Dan Keuangan Syariah (Jurnal Akunsyah), 4(2), 13–21. https://doi.org/10.30863/akunsyah.v4i2.5726
Asfarian, A., Ramadhan, D. A., Sukmasetya, P., Byputra, F., Siregar, R. S., Ridha, A., & Kusumah, F. S. F. (2024). A Survey of Digital Nudges for Fostering Responsible Consumption Behavior in Indonesia Digital Services. IOP Conference Series: Earth and Environmental Science, 1359(1), 012012. https://doi.org/10.1088/1755-1315/1359/1/012012
Ashkrof, P., Correia, G. H. de A., Cats, O., & van Arem, B. (2022). Ride acceptance behaviour of ride-sourcing drivers. Transportation Research Part C: Emerging Technologies, 142, 103783. https://doi.org/10.1016/j.trc.2022.103783
Asrori, S., Isma’il, M., & Gamalinda, E. F. (2025). The flexibility illusion: Algorithmic control and precarity in Indonesia’s gig economy. Simulacra, 8(2), 187–205. https://doi.org/10.21107/sml.v8i2.30214
Balaskas, S., Konstantakopoulou, M., Yfantidou, I., & Komis, K. (2025). Algorithmic Burnout and Digital Well-Being: Modelling Young Adults’ Resistance to Personalized Digital Persuasion. Societies, 15(8), 232. https://doi.org/10.3390/soc15080232
Bari, M. H., & Ara, A. (2025). The Impact Of Machine Learning On Prescriptive Analytics For Optimized Business Decision-Making. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.5050060
Bowdler, M., Lahti, H., Jelenko, M., Buresti, G., & Valtonen, T. (2026). Algorithmic management and psychosocial risks at work: An emerging occupational safety and health challenge. Scandinavian Journal of Work, Environment & Health, 52(1), 1–5. https://doi.org/10.5271/sjweh.4270
Budiono, M. F. (2023). Measuring the Job Precariousness Experienced by Gojek Drivers in Indonesia. AKSELERASI: Jurnal Ilmiah Nasional, 5(2), 56–64. https://doi.org/10.54783/jin.v5i2.709
Cruz, M. D. (2023). Labor Productivity, Real Wages, and Employment in OECD Economies. Structural Change and Economic Dynamics, 66, 367–382. https://doi.org/10.1016/j.strueco.2023.05.007
Curran, D. (2023). Surveillance capitalism and systemic digital risk: The imperative to collect and connect and the risks of interconnectedness. Big Data & Society, 10(1). https://doi.org/10.1177/20539517231177621
Dold, L., & Speck, C. (2021). Resolving the productivity paradox of digitalised production. International Journal of Production Management and Engineering, 9(2), 65. https://doi.org/10.4995/ijpme.2021.15058
Fakhri, M. R., Novikasari, N., & Sitorus, R. J. (2025). Determinan Kejadian Kelelahan Kerja Kronis (burnout) pada Pekerja BPJS Kesehatan Cabang Batam. SENTRI: Jurnal Riset Ilmiah, 4(12), 4613–4625. https://doi.org/10.55681/sentri.v4i12.5518
Gong, T. (2025). Algorithmic management and gig workers: engagement, exhaustion and citizenship behavior. Management Decision, 1–22. https://doi.org/10.1108/MD-01-2025-0111
Hajiheydari, N., & Delgosha, M. S. (2024). Investigating engagement and burnout of gig-workers in the age of algorithms: an empirical study in digital labor platforms. Information Technology & People, 37(7), 2489–2522. https://doi.org/10.1108/ITP-11-2022-0873
Harliantara, H., & Ozturk, B. (2026). The transition from terrestrial radio to audio-on-demand: analyzing audience autonomy and emotional connection among urban youth in Surabaya. Frontiers in Communication, 11. https://doi.org/10.3389/fcomm.2026.1803100
Hasugian, Y. E. (2026). Analisis dampak Artificial Intelligence (AI) terhadap Sektor Tenaga Kerja di Indonesia. Majelis: Jurnal Hukum Indonesia, 3(1), 84–101. https://doi.org/10.62383/majelis.v3i1.1501
Izzati, N. R., & Kwang, E. N. (2025). Regulating Platform Work in Indonesia: Exploring Legal Options for Workers’ Protection. Jurnal Ketenagakerjaan, 20(3). https://doi.org/10.47198/jnaker.v20i3.622
Lin, Q., Sun, R., & Zhu, Q. (2025). Perceived algorithmic control and gig workers’ work engagement: assessing the mediating role of psychological empowerment and the moderating effect of deep acting. BMC Psychology, 13(1), 1237. https://doi.org/10.1186/s40359-025-03570-7
Majumdar, P. (2026). The Economics of Distrust: Systemic Risks of Digital Surveillance and Corporate Insecurity in the Modern Enterprise. Journal of Human Resource Management, 14(1), 12–21. https://doi.org/10.11648/j.jhrm.20261401.12
Mufti, H., Fauzi, D. U., & Putra, T. R. (2026). Algorithmic Management in the Future of Work A Systematic Literature Review in the Context of Gig Workers. Bisman (Bisnis Dan Manajemen): The Journal of Business and Management, 9(1), 36–53. https://doi.org/10.36815/bisman.v9i1.4589
Muhyiddin, M., Annazah, N. S., Henriko Tobing, H., Habibi, N., Fauziyah, F., & Harsiwie, R. I. P. (2024). The Ambiguity of Employment Relationship in Indonesia’s Gig Economy: A Study of Online Motorcycle Taxi Drivers. Jurnal Ketenagakerjaan, 19(3), 262–280. https://doi.org/10.47198/jnaker.v19i3.416
Niebler, V., Pirina, G., Secchi, M., & Tomassoni, F. (2023). Towards ‘bogus employment?’ The contradictory outcomes of ride-hailing regulation in Berlin, Lisbon and Paris. Cambridge Journal of Regions, Economy and Society, 16(2), 289–301. https://doi.org/10.1093/cjres/rsad007
Nu, Y., Belavina, E., & Girotra, K. (2024). Using Artificial Intelligence To Reduce Food Waste. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4826777
Nuryanti, S., & Burhan, O. K. (2025). Flexible work arrangements in the eyes of Indonesian. Jurnal Psikologi, 24(1), 101–112. https://doi.org/10.14710/jp.24.1.101-112
Putri, T. E., Darmawan, P., & Heeks, R. (2023). What is fair? The experience of Indonesian gig workers. Digital Geography and Society, 5, 100072. https://doi.org/10.1016/j.diggeo.2023.100072
Qodriyah, I. S. L., & Wungu, T. D. K. (2025). Utilizing Stacking Ensemble Algorithm for Employee Productivity Prediction (Case Study of PT PLN (Persero) Unit Induk Distribusi Sulawesi Selatan, Sulawesi Tenggara dan Sulawesi Barat). Jurnal Locus Penelitian Dan Pengabdian, 4(10), 9432–9447. https://doi.org/10.58344/locus.v4i10.4292
Singh, K., & Kuldeep. (2024). Algorithmic Exhaustion: A Cyber Psychological Investigation into the Nexus of Social Media Algorithms, Cognitive Fatigue, and Digital Addiction. International Journal of Multidisciplinary Research and Growth Evaluation, 5(4), 1440–1443. https://doi.org/10.54660/.IJMRGE.2024.5.4.1440-1443
Sun, S., Lu, L. X., Lu, S. F., & Gu, W. (2024). When Peers Matter More: The Dominance of Social Influence Over Algorithm Accuracy in Patient Decision Making. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4966406
Susanti, V., Sumarwan, U., Simanjuntak, M., & Yusuf, E. Z. (2020). The Rational Factors of Perceived Quality and Perceived Value as the Drivers of Customer Satisfaction and Brand Loyalty. Bisnis & Birokrasi Journal, 26(3). https://doi.org/10.20476/jbb.v26i3.11121
van Zoonen, W., ter Hoeven, C., & Morgan, R. (2023). Finding meaning in crowdwork: An analysis of algorithmic management, work characteristics, and meaningfulness. The Information Society, 39(5), 322–336. https://doi.org/10.1080/01972243.2023.2243262
Wijiono, A. K., Vitianingsih, A. V., Kacung, S., Maukar, A. L., & Pamudi, P. (2024). Deteksi Notifikasi Suspend pada Aplikasi Ojek Online Menggunakan Metode MOORA. Jurnal Teknologi Sistem Informasi Dan Aplikasi, 7(3), 1351–1362. https://doi.org/10.32493/jtsi.v7i3.42159
Wusono, M., Yona, M., Ariyati, Y., Hasibuan, R., Siregar, H., Nasution, H., Simanjuntak, W., Barus, P., & Yolanda, S. (2025). Algorithmic Management and Employee Autonomy: Impacts on Creativity in Remote First Companies. Journal of Economics and Management Scienties, 315–321. https://doi.org/10.37034/jems.v8i1.288
Yang, H., Li, D., & Hu, P. (2024). Decoding algorithm fatigue: The role of algorithmic literacy, information cocoons, and algorithmic opacity. Technology in Society, 79, 102749. https://doi.org/10.1016/j.techsoc.2024.102749
Yolusever, A. (2026). The Red Queen in the Dashboard: Co-Evolutionary Dynamics of Algorithmic Control and Worker Resistance. Ekonomi Politika ve Finans Arastirmalari Dergisi, 11(1), 140–159. https://doi.org/10.30784/epfad.1829094
Yuliana, Y., Aggraeni, P., & Bondan, A. (2025). Algoritmic Leadership: Literature Review Of Opportunities And Challenges In The Digital Age. Al-Abshar: Journal of Islamic Education Management, 4(1), 33–53. https://doi.org/10.58223/al-abshar.v4i1.466
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
Zhang, L., Ling, X., & Yang, C. (2026). Looking at both sides of algorithmic control and employee well-being: a job demands−resources model. Frontiers in Psychology, 17. https://doi.org/10.3389/fpsyg.2026.1736349
Published
2026-05-23
How to Cite
Sudrajat, D. (2026). The Economics of Algorithmic Management: The Impact of AI Surveillance on the Marginal Productivity of Gig Workers. International Journal of Economics, Business and Innovation Research, 5(03), 2630-2650. https://doi.org/10.63922/ijebir.v5i03.3664
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