An Empirical Analysis of Algorithmic Control and Worker Perceptions in the Gig Economy

Abstract

The gig economy has redefined labour structures through platform-mediated work, where customer ratings, algorithmic scoring, and digital surveillance significantly shape workers’ earnings, autonomy, and job stability. This study examines gig workers’ perceptions across five critical dimensions—Job Security and Stability, Fairness and Transparency of Algorithms, Work Autonomy and Flexibility, Psychological and Social Well-being, and Access to Support and Redress Mechanisms. Using primary data from 102 gig workers in Chennai, non-parametric analyses such as the Kruskal–Wallis test and Chi-square tests reveal statistically significant differences in perceptions based on educational qualification, work experience, income levels, and motivations for entering gig work. Results show that higher educational levels and longer work experience are associated with heightened concerns regarding fairness, stability, and support mechanisms. Motivational factors also critically influence perception, with workers seeking independence reporting more favourable views than those who join due to limited job alternatives. The findings highlight the need for transparent algorithmic governance, strengthened grievance systems, and policy reforms to ensure equitable and sustainable gig work environments.