Platforms · Observational restaurant-panel study using linear regressions with fixed effects and matched treatment/control samples; the publisher page also reports instrumental-variables/two-stage least-squares analyses, a platform policy-change analysis, count-
On-Demand Delivery Platforms and Restaurant Sales
Management Science · 2024
Summary
Restaurants overall benefit: delivery platforms increase total takeout sales and create positive spillovers to dine-in visits. Effects are heterogeneous: the overall positive effect for fast-food chains is four times that for independent restaurants; platforms substitute for independent restaurants' own takeout but complement chain takeout, while increasing dine-in visits for both. The paper also reports evidence that platform price comparison intensifies competition and reduces independent restaurants' ability to differentiate through premium service/dine-in experience.
For your marketing
Possible founder application
Use it as a B2B mechanism or sales-process design reference. Validate the relationship in your deal size, buying committee and sales cycle.
Reported result
What the study found
Restaurants overall benefit: delivery platforms increase total takeout sales and create positive spillovers to dine-in visits. Effects are heterogeneous: the overall positive effect for fast-food chains is four times that for independent restaurants; platforms substitute for independent restaurants' own takeout but complement chain takeout, while increasing dine-in visits for both. The paper also reports evidence that platform price comparison intensifies competition and reduces independent restaurants' ability to differentiate through premium service/dine-in experience.
Research question
What the authors investigated
Do on-demand delivery platforms substitute for or complement restaurants' own takeout and dine-in channels, and what is their net effect on restaurant demand and revenue?
Design
Observational restaurant-panel study using linear regressions with fixed effects and matched treatment/control samples; the publisher page also reports instrumental-variables/two-stage least-squares analyses, a platform policy-change analysis, count-model robustness checks, and alternative difference-in-differences specifications.
Sample and setting
Chicago metropolitan area (17 counties across Illinois, Indiana, and Wisconsin): 19,117 restaurants, about 48% on at least one of DoorDash, Grubhub, and Uber Eats by the end of 2019. The panel combines Yelp/YellowPages restaurant characteristics, platform-partnership data, SafeGraph weekly foot-traffic data, and anonymized aggregate bank-card transactions; reported main specifications include 603,244 observations.
Scope and transfer
Limitations to keep in view
The publisher explicitly says further research could use consumer-level data to study channel choice, examine promotional effects across channels, study platform design/governance, and investigate ghost/cloud kitchens; it also notes the study focuses on traditional restaurants operating multiple channels. These are carefully stated scope/future-research limitations, not claims that the authors label as bias.
Research sources
The publisher abstract reports that empirical analyses find increased total takeout sales and positive dine-in spillovers, with effects depending on independent versus chain restaurants and takeout versus dine-in channels; the article's data section identifies the Chicago panel and its multiple data sources. Direct publisher-page retrieval was recorded on 2026-10-09; profile summarizes the accessible record and does not claim independent review of inaccessible full text.