EMPLOYMENT CONDITIONS AND QUALITY OF LIFE OF GIG WORKERS: A STUDY OF FOOD DELIVERY WORKERS IN AHMEDABAD
DOI:
https://doi.org/10.69980/hsnsmq83Keywords:
algorithmic management, food delivery workers, gig economy, income instability, worker well-beingAbstract
Digital labour platforms have revolutionised service delivery and work relations in terms of coordinating workers via data-driven systems. In this study, the association between three factors—Earning instability, Algorithmic Management, Regulatory Protection—with the autonomy, job satisfaction, quality of life, and overall well-being of the app-based food delivery workers of Ahmedabad, India was examined. The research designed was quantitative cross section with descriptive and explanatory. A structured questionnaire that used five point Likert scale measures was used to collect data from 174 food delivery workers. The test of internal consistency was carried out using Cronbach's alpha and the test of the hypothesis was carried out using linear regression. Measurement scales were found to have acceptable to good reliability ranging between .76 and .85. Results indicated that there was a significant and negative correlation between income instability and quality of life. Algorithmic management was also negatively and significantly related to worker autonomy and job satisfaction. However, there was perceived regulatory protection, and this was positively and significantly related to worker well-being. The results suggest that the employment access and scheduling flexibility should not be the only measures used to assess platform work. Employment quality is also about the predictability of earnings, transparency of automated employment decisions, meaningful worker discretion and access to institutional safeguards. The study draws on Job Demands-Resources Theory (JD-R) and Labour Process Theory, which are theories that have focused on physically mediated work, and introduces two new structural job demands, "income uncertainty" and "algorithmic control," to apply them to digitally mediated work. It also provides concrete implications for the platform managers and policymakers in terms of transparency in compensation, grievance systems, social protection systems, and responsible algorithmic governance.
References
1.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
2.Brayfield, A. H., & Rothe, H. F. (1951). An index of job satisfaction. Journal of Applied Psychology, 35(5), 307–311. https://doi.org/10.1037/h0055617
3.Brislin, R. W. (1970). Back-translation for cross-cultural research. Journal of Cross-Cultural Psychology, 1(3), 185–216. https://doi.org/10.1177/135910457000100301
4.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
5.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
6.Fairwork. (2024). Fairwork India ratings 2024: Labour standards in the platform economy. https://fair.work/en/fw/publications/fairwork-india-ratings-2024-labour-standards-in-the-platform-economy/
7.Faul, F., Erdfelder, E., Buchner, A., & Lang, A.-G. (2009). Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. Behavior Research Methods, 41(4), 1149–1160. https://doi.org/10.3758/BRM.41.4.1149
8.Felix, B., Dourado, D., & Nossa, V. (2023). Algorithmic management, preferences for autonomy/security and gig-workers’ wellbeing: A matter of fit? Frontiers in Psychology, 14, Article 1088183. https://doi.org/10.3389/fpsyg.2023.1088183
9.Gandini, A. (2019). Labour process theory and the gig economy. Human Relations, 72(6), 1039–1056. https://doi.org/10.1177/0018726718790002
10.Ghorpade, Y., Rahman, A. A., & Jasmin, A. (2024). Social insurance for gig workers: Insights from a discrete choice experiment in Malaysia. International Social Security Review, 77(3), 3–30. https://doi.org/10.1111/issr.12365
11.Goods, C., Veen, A., & Barratt, T. (2019). “Is your gig any good?” Analysing job quality in the Australian platform-based food-delivery sector. Journal of Industrial Relations, 61(4), 502–527. https://doi.org/10.1177/0022185618817069
12.Hussain, M. S. (2024). Algorithmic management of workers through food delivery platforms in India. Journal of Extreme Anthropology, 8(2), 74–106. https://doi.org/10.5617/jea.11784
13.International Labour Organization. (2021). World employment and social outlook 2021: The role of digital labour platforms in transforming the world of work. https://www.ilo.org/publications/flagship-reports/role-digital-labour-platforms-transforming-world-work
14.International Labour Organization. (2024). Expansion of the gig and platform economy in India: Opportunities for employer and business member organizations. https://www.ilo.org/publications/expansion-gig-and-platform-economy-india-opportunities-employer-and
15.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
16.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
17.Li, S., & Jiang, L. (2022). New forms of labor time control and imaginary freedom: A study of the labor process of food delivery workers. The Journal of Chinese Sociology, 9, Article 8. https://doi.org/10.1186/s40711-022-00163-4
18.Lin, P. M. C., Peng, K.-L., Au, W. C., & Baum, T. (2023). Food-delivery workers in the sharing economy: Supply-side human resource transformation. International Journal of Hospitality & Tourism Administration, 24(4), 491–516. https://doi.org/10.1080/15256480.2021.2015042
19.Liu, R., & Yin, H. (2024). How algorithmic management influences gig workers’ job crafting. Behavioral Sciences, 14(10), Article 952. https://doi.org/10.3390/bs14100952
20.Meijerink, J., & Bondarouk, T. (2023). The duality of algorithmic management: Toward a research agenda on HRM algorithms, autonomy and value creation. Human Resource Management Review, 33(1), Article 100876. https://doi.org/10.1016/j.hrmr.2021.100876
21.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
22.Morgeson, F. P., & Humphrey, S. E. (2006). The Work Design Questionnaire: Developing and validating a comprehensive measure for assessing job design and the nature of work. Journal of Applied Psychology, 91(6), 1321–1339. https://doi.org/10.1037/0021-9010.91.6.1321
23.NITI Aayog. (2022). India’s booming gig and platform economy: Perspectives and recommendations on the future of work. https://www.niti.gov.in/node/375
24.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
25.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
26.Tavakol, M., & Dennick, R. (2011). Making sense of Cronbach’s alpha. International Journal of Medical Education, 2, 53–55. https://doi.org/10.5116/ijme.4dfb.8dfd
27.The WHOQOL Group. (1998). Development of the World Health Organization WHOQOL-BREF quality of life assessment. Psychological Medicine, 28(3), 551–558. https://doi.org/10.1017/S0033291798006667
28.Umer, H. (2021). Illusory freedom of physical platform workers: Insights from Uber Eats in Japan. The Economic and Labour Relations Review, 32(3), 437–452. https://doi.org/10.1177/1035304621992466
29.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
30.Wang, S., Li, L. Z., & Coutts, A. (2022). National survey of mental health and life satisfaction of gig workers: The role of loneliness and financial precarity. BMJ Open, 12(12), Article e066389. https://doi.org/10.1136/bmjopen-2022-066389
31.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
32.Wu, P. F., Zheng, R., Zhao, Y., & Li, Y. (2022). Happy riders are all alike? Ambivalent subjective experience and mental well-being of food-delivery platform workers in China. New Technology, Work and Employment, 37(3), 425–444. https://doi.org/10.1111/ntwe.12243
33.Zhao, S., & Liu, Y. (2024). Job demands-resources on digital gig platforms and counterproductive work behavior. Frontiers in Psychology, 15, Article 1378247. https://doi.org/10.3389/fpsyg.2024.1378247
