Food Delivery Service and Restaurant: Friend or Foe?
Why this work is in the frame
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Bibliographic record
Abstract
With food delivery services, customers can hire delivery workers to pick up food on their behalf. To investigate the long-term impact of food delivery services on the restaurant industry, we model a restaurant serving food to customers as a stylized single-server queue with two streams of customers. One stream consists of tech-savvy customers who have access to a food delivery service platform. The other stream consists of traditional customers who are not able to use a food delivery service and only walk in by themselves. We study a Stackelberg game, in which the restaurant first sets the food price; the food delivery platform then sets the delivery fee; and, last, rational customers decide whether to walk in, balk, or use a food delivery service if they have access to one. If the restaurant has a sufficiently large established base of traditional customers, we show that the food delivery platform does not necessarily increase demand but may just change the composition of customers, as the segment of tech-savvy customers grows. Hence, paying the platform for bringing in customers may hurt the restaurant’s profitability. We demonstrate that either a one-way revenue-sharing contract with a price ceiling or a two-way revenue-sharing contract can coordinate the system and create a win-win situation. Furthermore, under conditions of no coordination between the restaurant and the platform, we show, somewhat surprisingly, that more customers having access to a food delivery service may hurt the platform itself and the society, when the food delivery service is sufficiently convenient, and the delivery-worker pool is large enough. This is because the restaurant can become a delivery-only kitchen and raise its food price by focusing on food-delivery customers only, leaving little surplus for the platform. This implies that limiting the number of delivery workers can provide a simple yet effective means for the platform to improve its own profitability while benefiting social welfare. This paper was accepted by Charles Corbett, operations management.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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