Measuring workload of food delivery riders under algorithmic management

Abstract

With the global expansion of digital platforms, algorithmic management now structures food delivery work through time-based milestones that prioritize speed and efficiency, often shifting risks onto riders. Despite the workforce’s growing importance, evidence on its physical and psychological impacts remains largely survey-based, with little experimental research. This study proposes a conceptual framework linking algorithmic management (time control, task arrangement, performance evaluation, and interaction) to rider behaviours (delivery, riding, and order grabbing), governed by four behavioural rules: faster delivery, completing on time, faster riding, and grabbing more orders. These rules manifest as physical and psychological workload, moderated by demographic factors. To operationalize this framework, a laboratory-based food delivery simulation was developed. Twenty participants completed a 60-minute simulation using a Python-based rider interface and interactive riding videos under time pressure. Physical workload was measured via Heart Rate Reserve (HRR) using a wearable device, and psychological workload via NASA-TLX. Physical exertion was incorporated through stair and elevator navigation while carrying weighted loads to simulate last-mile delivery. Results showed that HRR effectively distinguished delivery stages, while temporal demand, mental demand, and effort were the dominant psychological workload dimensions. Linear regression models with delivery and riding performance as predictors moderately explained both physical and psychological workload. This study establishes a comprehensive framework for measuring food delivery riders’ workload, introduces an innovative laboratory simulation paradigm to assess gig-worker well-being under algorithmic management, and provides empirical evidence to inform better platform design and labour protections.

Keywords: Food Delivery Rider, Workload, Measurement, NASA TLX, Algorithmic Management

DOI: 10.54941/ahfe1007933