Algorithmic Management of Workers through Food Delivery Platforms in India - Mohammad Sajjad Hussain (Journal of Extreme Anthropology)
This study explores the role of algorithmic management in India’s food delivery industry, specifically focusing on platforms like Swiggy and Zomato, which act more like employers than mere intermediaries. By harnessing extensive data, these platforms automate decision-making processes and influence work dynamics through surveillance, control, and ongoing performance assessments. Based on 25 months of field research in Hyderabad, including six months working as a delivery worker and conducting interviews with platform employees, this research reveals often overlooked elements of algorithmic governance. It examines how fleet managers, geo-fencing, food preparation times, and monitoring practices are utilized to optimize labour and exert control. The findings also indicate that real-time identity checks, adherence to dress codes, and location tracking contribute to a form of surveillance that is both remote and continuous. By placing food delivery platforms in the context of broader discussions around digital labour, control, and automation, this research illustrates how algorithmic management not only automates management functions but also heightens job insecurity through the reintroduction of piece-rate pay. It challenges the perception of these platforms as neutral facilitators, highlighting their direct involvement in shaping and regulating the labour process.