Algorithmic Anxiety: Gig Workers, Platform Algorithms, and Employee Resistance in India

Algorithmic management, gig economy, platform work, employee resistance, India, Zomato, Swiggy, Ola, transparency

Platform firms operating in India — e.g., Ola (ride-hailing), Zomato and Swiggy (food delivery) — coordinate large, dispersed workforces through algorithmic decision systems that allocate tasks, determine incentives, schedule work and adjudicate performance. While such systems enable scale and operational efficiency, they also create conditions that scholars and practitioners increasingly label algorithmic management — automated governance with real effects on workers’ autonomy, earnings and voice. In India, a sequence of media-reported strikes, coordinated mobilisations, and regulatory interventions between 2018–2026 shows how algorithmic opacity, perceived unfairness, and limited contestation channels have produced algorithmic anxiety among gig workers. This case synthesizes peer-reviewed research (Web of Science / Scopus indexed) and contemporaneous news reporting to (a) document the problem, (b) analyze it using work-design, institutional and algorithmic-management theory, and (c) pose managerial and policy options. The case is strictly secondary-source based (no internal company confidential data) and intended for graduate/upper-level courses in strategy, HRM, technology & society, and public policy.