B2B growth and sales · 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
Zhuoxin Li, Gang Wang (2024). On-Demand Delivery Platforms and Restaurant Sales. Management Science. https://doi.org/10.1287/mnsc.2021.01010.
Plain-English take
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: Use it as a B2B mechanism or sales-process design reference. Validate the relationship in your deal size, buying committee and sales cycle.
How much weight to give this
3.5/5 Moderate evidence for a specific setting
The 1–5 evidence-check score is a practical reading aid, not a formal peer review or a probability that a conclusion is true. It combines design strength (2 points), sample reporting (1), comparison/control clarity (1), and how much of the paper could be checked (1). Access affects how confidently this directory can assess a study, not the study's underlying quality. Blinding is reported separately because it is not relevant to every 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.
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.
Based on the publisher-described design and comparison; see the article for complete analysis.
Publisher record was fetched directly; full text was not assumed open.
Double-blind? Blinding was not established from the retrieved publisher record.
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.
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.
This is a paraphrase of the source. Check the original paper for definitions, estimates, statistical uncertainty and analysis details.
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.
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.
Evidence access
Publisher abstract/metadata retrieved; consult the linked paper for full methods and tables.
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.
If only an abstract or metadata was available, the profile is limited to claims supported there. A paper's paywall does not change its evidentiary status, but it limits what we can substantiate from accessible text.