JOURNAL ARTICLE
Food Ordering and Delivery: How Platforms and Restaurants Should Split the Pie.
Published In: Management Science (INFORMS), 2026, v. 72, n. 3. P. 1748 1 of 3
Database: Business Source Ultimate 2 of 3
Authored By: Oh, Jaelynn; Glaeser, Chloe Kim; Su, Xuanming 3 of 3
Abstract
This article analyzes the commission-based contracting practices between online food delivery platforms and partner restaurants, focusing on how these contracts affect pricing, restaurant display, and delivery costs. It identifies two key inefficiencies in current decentralized systems: (1) platforms feature distant restaurants that overbid on commissions without internalizing delivery costs, leading to higher overall delivery expenses, and (2) platforms bear full delivery costs but receive only a fraction of food revenues, resulting in high delivery fees and limited service coverage. Using a game-theoretic model, the authors propose a coordinating contract where platforms and restaurants share both food revenues and delivery costs proportionally, aligning incentives and achieving the first-best outcome that maximizes total profits. Numerical analysis based on data from a representative U.S. city shows that this contract reduces commission rates and delivery fees, increases restaurant profits, and improves total system welfare, though platform profits increase only in certain markets, particularly those farther from restaurants. The study also extends the model to multiple markets and continuous demand, discussing practical implementation challenges and suggesting that sharing delivery costs alongside food revenues can better balance interests in food delivery marketplaces.
Additional Information
- Source:Management Science (INFORMS). 2026/03, Vol. 72, Issue 3, p1748
- Document Type:Article
- Subject Area:Business and Management
- Publication Date:2026
- ISSN:0025-1909
- DOI:10.1287/mnsc.2023.00435
- Accession Number:192085209
- Copyright Statement:Copyright of Management Science (INFORMS) is the property of INFORMS: Institute for Operations Research & the Management Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Looking to go deeper into this topic? Look for more articles on EBSCOhost.