Examining Algorithmic Management and Traffic Exposure on Food Delivery Courier Well-Being in Jakarta's Gig Economy

Abstract

This study examines Indonesia's online food-delivery sector, which embeds thousands of couriers (ojol) into algorithmic management systems while they navigate Jakarta's chronic traffic congestion and environmental hazards. Drawing on the Job Demands–Control–Support framework, it tests whether algorithmic management amplifies the negative impact of traffic exposure on courier well-being. A cross-sectional survey of GoFood, GrabFood, and ShopeeFood couriers in Jakarta was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). Due to measurement model screening, Algorithmic Management and Courier Well-Being were retained as single-indicator constructs, explaining R^2=0.096 of variance in courier well-being. Traffic exposure demonstrated a statistically significant negative effect on well-being (β=-0.295,p=0.001), indicating that congestion and accident risk are the strongest correlates of lower well-being. Conversely, the direct path from algorithmic management (β=-0.081,p=0.405) and the interaction term (β=0.014,p=0.898) were not statistically significant, indicating these stressors operate independently in this sample. These findings reposition traffic exposure as the strongest correlate of lower well-being among Jakarta couriers, while highlighting measurement scale limitations in platform labor research. The study provides transparent empirical evidence to inform Indonesian platform regulation and gig-work reforms.