Algorithmic Control, Work Precarity and Physical Well-being among Platform-based Gig Workers in India: A Sociological Study of Delivery Workers and Ride-sharing Drivers

Abstract

The emergence of platform-based employment has transformed how work is organised and how people experience their daily work in India, particularly in food delivery and app-based transportation services. This study examines the relationship between algorithmic control, work precarity, and physical well-being among platform-based gig workers from a sociological perspective. This research uses primary data from snowball sampling of 200 gig economy workers—100 food delivery personnel and 100 app-based taxi drivers—engaged with platform-based services in Faridabad, Gurugram, and Delhi. The participants are linked to platforms including Zomato, Swiggy, Ola, and Uber.  We collected data through a structured interview schedule covering socio-demographic characteristics, working hours, work intensity, income stability, platform-mediated control, occupational conditions, and physical well-being. We used descriptive statistics, cross-tabulation, chi-square analysis, and correlation analysis to examine relationships between working conditions and physical well-being. The findings indicate substantial work intensity: 38% of respondents reported 8–10 working hours per day, 31% reported more than 10 hours, and 59% reported working seven days a week. Income instability was reported by 73% of respondents. Algorithmic control was prominent, with 77% reporting platform-determined task allocation and 83% experiencing rating pressure. Fatigue was reported by 74% of delivery workers and 52% of cab drivers, while back pain was reported by 68% and 48%, respectively. We found a significant association between work type and fatigue (χ² = 10.42, p < .05), and working hours were positively associated with fatigue (r = .64). The findings highlight the sociological and physical consequences of intensive, digitally mediated platform work.