Leveraging Data Analytics for Enhanced Workforce Management in the Gig Economy

Abstract

Purpose: In the ever-shifting and ever-demanding life on a platform-based labor yard, data analysis stood tall as the major unifying factor among gig workers, essentially in the food delivery business This study aims to investigate how Zomato delivery partners perceive algorithmic management's impact on their work experiences, particularly in the food delivery industry.

Methodology: To better understand how gig workers see algorithmic management systems in platform-based work contexts like food delivery and ride-hailing services, this study used a quantitative research approach. A properly designed self-administered questionnaire that was based on a comprehensive analysis of the literature and theoretical frameworks pertinent to algorithmic decision-making and worker experiences was used to gather data. The study included 300 gig workers in total, which provided a strong sample size suitable for multivariate statistical analyses, guaranteeing adequate statistical power and representativeness within the targeted population.

Result: This study argues that fair and transparent algorithmic governance can create long-lasting, mutually beneficial relationships between gig workers and their platforms.