Workload and Customer Rating Pressure as Antecedents of Burnout: Examining the Mediating Role of Job Stress in the Ride-Hailing Context
Abstract
This study examines the effects of workload and customer rating pressure on burnout, with job stress as a mediating variable, in the ride-hailing context. Using a quantitative approach, data were collected from 160 ride-hailing drivers in Makassar, Indonesia, and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings reveal that customer rating pressure has a stronger impact on job stress compared to workload, highlighting the critical role of algorithmic management in shaping drivers’ psychological conditions. Job stress is found to significantly influence burnout and serves as a key mediating mechanism. While workload directly and indirectly affects burnout (partial mediation), customer rating pressure influences burnout only indirectly through job stress (full mediation). These results suggest that burnout among gig workers is not merely driven by physical job demands but is primarily shaped by psychological pressures embedded in platform-based evaluation systems. This study contributes to the literature by demonstrating that not all job demands operate through the same pathways, emphasizing the dominance of algorithmic pressure in the gig economy. The findings offer practical implications for platform providers and policymakers to design more sustainable and worker-friendly systems.
References
Almazrouei, H., Alvarez-Torres, F. J., Schiuma, G., & Lopez-Torres, G. C. (2025). The digital creative organisation: are work–life balance, remote working and management support impacting in the creativity? Measuring Business Excellence, ahead-of-print(ahead-of-print). https://doi.org/10.1108/MBE-10-2024-0177
Bakker, A. B., & Demerouti, E. (2017). Job demands–resources theory: Taking stock and looking forward. Journal of Occupational Health Psychology, 22(3), 273–285. https://doi.org/10.1037/ocp0000056
Bakker, A. B., Demerouti, E., & Sanz-Vergel, A. (2023). Job Demands–Resources Theory: Ten Years Later. Annual Review of Organizational Psychology and Organizational Behavior, 10(Volume 10, 2023), 25–53. https://doi.org/https://doi.org/10.1146/annurev-orgpsych-120920-053933
Bokányi, E., & Hannák, A. (2020). Understanding Inequalities in Ride-Hailing Services Through Simulations. Scientific Reports, 10(1), 6500. https://doi.org/10.1038/s41598-020-63171-9
Chen, X., Bai, S., Wei, Y., & Jiang, H. (2023). How income satisfaction impacts driver engagement dynamics in ride-hailing services. Transportation Research Part C: Emerging Technologies, 157, 104418. https://doi.org/10.1016/j.trc.2023.104418
Cheng, Z. (Aaron), Pang, M.-S., & Pavlou, P. A. (2020). Mitigating Traffic Congestion: The Role of Intelligent Transportation Systems. Information Systems Research, 31(3), 653–674. https://doi.org/10.1287/isre.2019.0894
Cramer, J., & Krueger, A. B. (2016). Disruptive Change in the Taxi Business: The Case of Uber. American Economic Review, 106(5), 177–182. https://doi.org/10.1257/aer.p20161002
Fornel, C., & Larcker, D. F. (1981). Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.2307/3151312
Ganster, Daniel C, & Rosen, Christopher C. (2013). Work Stress and Employee Health: A Multidisciplinary Review. Journal of Management, 39(5), 1085–1122. https://doi.org/10.1177/0149206313475815
Ghozali, I. (2021). Partial Least Square: Konsep, Teknik dan Aplikasi Menggunakan Smart PLS 3.2.9 (3rd ed.). Universitas Diponegoro.
Grandey, A. A. (2000). Emotional regulation in the workplace: A new way to conceptualize emotional labor. Journal of Occupational Health Psychology, 5(1), 95–110. https://doi.org/10.1037/1076-8998.5.1.95
HAFEEZ, S., GUPTA, C., & SPRAJCER, M. (2023). Stress and the gig economy: it’s not all shifts and giggles. Industrial Health, 61(2), 140–150. https://doi.org/10.2486/indhealth.2021-0217
Hair, J. F., Howard, M. C., & Nitzl, C. (2021). Review of Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R: A Workbook. In Structural Equation Modeling: A Multidisciplinary Journal (Vol. 30, Issue 1). https://doi.org/10.1080/10705511.2022.2108813
Hair, J. F., Page, M., & Brunsveld, N. (2022). Essentials of Business Research Methods (14th ed.). Routledge. https://doi.org/10.4324/9780367330743
Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/EBR-11-2018-0203
Halbesleben, J. R. B. (2006). Sources of social support and burnout: A meta-analytic test of the conservation of resources model. Journal of Applied Psychology, 91(5), 1134–1145. https://doi.org/10.1037/0021-9010.91.5.1134
Halik, J. B., Rantererung, C. L., Sutomo, D. A., Rasinan, D., Daud, M., & Todingbua, M. A. (2024). Era Disruptif (J. B. Halik (ed.); 1st ed.). CV. Adanu Abimata. https://books.google.co.id/books?hl=en&lr=&id=kKUDEQAAQBAJ&oi=fnd&pg=PA179&ots=GVM0GTeahE&sig=ixF8ruUsRR4KmAtdE_GYwrlvO4k&redir_esc=y#v=onepage&q&f=false
Hall, Jonathan V, & Krueger, Alan B. (2018). An Analysis of the Labor Market for Uber’s Driver-Partners in the United States. ILR Review, 71(3), 705–732. https://doi.org/10.1177/0019793917717222
Haryono, S. (2017). Metode SEM untuk penelitian manajemen dengan AMOS LISREL PLS. Luxima Metro Media, 450.
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8
Hobfoll, S. E. (1989). Conservation of resources: A new attempt at conceptualizing stress. American Psychologist, 44(3), 513–524. https://doi.org/10.1037/0003-066X.44.3.513
Hobfoll, S. E., Halbesleben, J., Neveu, J.-P., & Westman, M. (2018). Conservation of Resources in the Organizational Context: The Reality of Resources and Their Consequences. Annual Review of Organizational Psychology and Organizational Behavior, 5(Volume 5, 2018), 103–128. https://doi.org/10.1146/annurev-orgpsych-032117-104640
Kellogg, K. C., Valentine, M. A., & Christin, A. (2019). 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
Lang, J. J., Yang, L. F., Cheng, C., Cheng, X. Y., & Chen, F. Y. (2023). Are algorithmically controlled gig workers deeply burned out? An empirical study on employee work engagement. BMC Psychology, 11(1), 354. https://doi.org/10.1186/s40359-023-01402-0
Lee, M. K., Kusbit, D., Metsky, E., & Dabbish, L. (2015). Working with Machines: The Impact of Algorithmic and Data-Driven Management on Human Workers. Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, 1603–1612. https://doi.org/10.1145/2702123.2702548
Maslach, C., & Jackson, S. E. (1981). The measurement of experienced burnout. Journal of Organizational Behavior, 2(2), 99–113. https://doi.org/https://doi.org/10.1002/job.4030020205
Memon, M. A., Ramayah, T., Ting, H., & Cheah, J. (2025). PURPOSIVE SAMPLING : A REVIEW AND GUIDELINES FOR QUANTITATIVE RESEARCH. Journal of Applied Structural Equation Modeling, 9(1), 1–23. https://doi.org/10.47263/JASEM.9(1)01
Park, S.-K., Rhee, M.-K., & Lee, S.-W. (2021). The effects of job demands and resources on turnover intention: The mediating roles of emotional exhaustion and depersonalization. PubMed, 70(1), 301–309. https://doi.org/10.3233/WOR-213574
Parker, S. L., Dawson, N., Van den Broeck, A., Sonnentag, S., & Neal, A. (2021). Employee motivation profiles, energy levels, and approaches to sustaining energy: A two-wave latent-profile analysis. Journal of Vocational Behavior, 131, 103659. https://doi.org/https://doi.org/10.1016/j.jvb.2021.103659
Pencavel, J. (2015). The Productivity of Working Hours. The Economic Journal, 125(589), 2052–2076. https://doi.org/10.1111/ecoj.12166
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/https://doi.org/10.1016/j.diggeo.2023.100072
Rosenblat, A., & Stark, L. (2016). Algorithmic Labor and Information Asymmetries : A Case Study of Uber ’ s Drivers. International Journal of Communication, 10(2016), 3758–3784. https://doi.org/10.2139/ssrn.2686227
Santosa, P. I. (2018). Metode Penelitian Kuantitatif: Pengembangan Hipotesis dan Pengujiannya Menggunakan SmartPLS (Giovanny (ed.); 1st ed.). Penerbit ANDI.
Schaufeli, W. B. (2017). Applying the Job Demands-Resources model: A ‘how to’ guide to measuring and tackling work engagement and burnout. Organizational Dynamics, 46(2), 120–132. https://doi.org/https://doi.org/10.1016/j.orgdyn.2017.04.008
Sekaran, U., & Bougie, R. (2017). Research Method for Business (6th ed.). Salemba Empat.
Spector, P. E., & Jex, S. M. (1998). Development of four self-report measures of job stressors and strain: Interpersonal Conflict at Work Scale, Organizational Constraints Scale, Quantitative Workload Inventory, and Physical Symptoms Inventory. Journal of Occupational Health Psychology, 3(4), 356–367. https://doi.org/10.1037/1076-8998.3.4.356
Sugiyono. (2020). Metode Penelitian Kuantitatif, kualitatif, dan R&D (Sutopo (ed.); Edisi ke-2). Alfabeta.
Wood, Alex J, Graham, Mark, Lehdonvirta, Vili, & Hjorth, Isis. (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
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