Modeling the online food delivery pricing and waiting time: Evidence from Davis, Sacramento, and San Francisco

Abstract

The proliferation of delivery services has increased researchers' and practitioners' interest in understanding their potential impacts on urban mobility. While there have been studies focusing on the operations and management of such services, there is a lack of understanding about the quality of the delivery service and their pricing schemes. In this empirical study, we examine the delivery fee and wait time variations for ordering food using four food delivery applications in the U.S.: DoorDash, Grubhub, Postmates, and Uber Eats. We collected delivery data featuring a variety of attributes of cost, timing, restaurant type, and location. The analyses cover all pricing items comprising total cost across the apps and various study areas. Furthermore, we estimated mixed-effect ordered-logistic regression (ologit) models to identify the variables and assess their association with food delivery fees and wait times. Results revealed that delivery fee variation across time of day or day of the week is negligible; however, delivery distance significantly influences delivery fees, and this effect varies considerably across apps. The observed effect of requester location reveals potential zone-based delivery pricing schemes by apps. Delivery wait time is shorter when demand and supply are high. Platforms with more established networks of users and couriers feature shorter wait times. The results of this study provide insight into user experiences with food delivery services and the choices available, which can help inform the development of pricing schemes for food delivery; the development of better optimization and operations management strategies by incorporating knowledge of key variables.