The Effects of Online Review Platforms on Restaurant Revenue, Consumer Learning, and Welfare
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper quantifies the effects of online review platforms on restaurant revenue and consumer welfare. Using a novel data set containing revenues and information from major online review platforms in Texas, I show that online review platforms help consumers learn about restaurant quality more quickly. The effects on learning show up in restaurant revenues. Specifically, doubling the review activity increases the revenue of a high-quality independent restaurant by 5%–19% and decreases that of a low-quality restaurant by a similar amount. These effects vary widely across restaurants’ locations. Restaurants around highway exits are affected twice as much as those in nonhighway areas, implying that reviews are more useful to travelers and tourists than locals. The effects also decline as restaurants age, consistent with the diminishing value of information in learning. In contrast, chain restaurants are affected to a much lesser degree than independent restaurants. Building on this evidence, I develop a structural demand model with aggregate social learning. Counterfactual analyses indicate that online review platforms raise consumer welfare much more for tourists than for locals. By encouraging consumers to eat out more often at high-quality independent restaurants, online review platforms increased the total industry revenue by 3.0% over the period from 2011–2015. This paper was accepted by Matthew Shum, marketing. Supplemental Material: Data and the online appendix are available at https://doi.org/10.1287/mnsc.2021.4279 .
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it