The determinants of online matching platforms for freight services

Abstract

With the development of information technology and the success of E-commerce, online freight exchange platforms have expanded to trucking freight services by matching truckers with shippers directly. Despite the endeavors of online freight platforms to emulate the success of other platform services, they face difficulties in facilitating successful matches. By analyzing the transactional data of an existing freight service platform, we seek to identify the salient factors that improve platform matching rates using a linear regression with splines for freight rate, capturing non-linearities, and a RUM discrete outcome model for matching. Both models are estimated jointly (simultaneously) using full information maximum likelihood (FIML). The empirical findings indicate that successful matching is more likely if the online freight platforms allow making substantial price adjustments once a job has been posted, as well as allowing shippers to have a longer lead time for loading their shipments, particularly in cases where finding a suitable trucker is challenging. Furthermore, allowing customers to revise shipment information provides greater flexibility in accommodating customer needs and ultimately enhances the likelihood of successful matching. Our analysis provides actionable and pragmatic suggestions to improve matching rates for online platforms of freight services.