First-Come, First-Serve? Gig Worker Performance and Task Allocation in an On-demand Platform
Year: 2023
Type: conference-paper
Abstract: On-demand platforms operating on a gig-contractor model rely on self-scheduling gig workers to meet realtime demand, while maintaining desired levels of productivity and service quality.Such platforms usually allocate gig workers to tasks on a first-come-first-serve basis, which is often inefficient.This paper seeks to investigate whether allocation of workers to tasks based on prior experience and task characteristics could act as an alternative to the FCFS method.Since on-demand platforms frequently expand to new geographies where workers might not have prior experience, it is unlikely that allocating tasks to workers based on experience alone would significantly improve performance.We investigate whether recent experience can positively impact gig workers' task performance, and thus be utilized as a ranking parameter in task allocations.Utilizing data from an on-demand grocery platform, we first developed an econometric model to analyze gig workers' task performance based on their same-day experience, while accounting for sample selection and endogeneity.We find that as same-day experience increases, task productivity and service quality improves, even after controlling for worker and task characteristics.We developed two task allocation algorithms to allocate tasks based on workers' rank, and task characteristics.We calculate predicted improvements in performance from the new allocations.
Author: Reeju Guha
Institution: IE University
Language: English
Subfield: Sociology and Political Science
Field: Social Sciences
Domain: Social Sciences
Open Access status: bronze
OpenAlex ID: W4388025152