Competitive Price-Matching Guarantees in an Uncertain Demand Environment
Bibliographic record
Abstract
Price-matching-guarantees (PMGs) are offers by firms whereby they assure customers that they will match any lower price offered by the competition for an identical product. Extant economics/marketing literature assumes that a simple proof of the competitor’s lower price is sufficient to grant the price match. Under this form of PMG and deterministic customer demand, it has been shown that PMGs may actually lead to tacit collusion such that all retailers set their price and order levels like a monopolist (in a competitive setting). However, firms nowadays reserve the right to verify the availability of the product at the competitor location and decline to match the lower price if the product is not available there. This option brings stocking decisions of the firms, and hence demand uncertainty, into play. Our study focuses on these elements. We model two identical newsvendor retailers that offer PMGs in a stochastic, price-sensitive demand environment. The retailers compete by simultaneously selecting prices and stocking quantities. We present a comprehensive characterization of the equilibrium prices and stocking levels for the case when the firms verify availability before matching the price, and the case when they do not. We also investigate how retail prices and order quantities are influenced by PMGs in an uncertain demand environment, as well as how uncertainty in demand affects structural properties related to “deterministic” PMGs. Our results demonstrate that the tacit collusion outcome of deterministic models extends to a stochastic demand environment if a simple proof is deemed sufficient for a price match. However, we also show that demand uncertainty enables the retailer to price discriminate by verifying the availability of the product at competing stores before granting any price-match request. In such a case, one firm increases its price and quantity even beyond monopoly levels. By comparing the equilibria, we identify product/market characteristics under which verifying the availability is desirable to the retailers. We find that the opportunity cost of not verifying the availability increases with the uncertainty in the market. From a managerial perspective, our results indicate that verifying availability can be a significant profit-enhancing mechanism and show how the benefits vary with the product/market characteristics. We find that verifying the availability is most profitable for highly anticipated, relatively more costly, innovative products which face relatively more uncertain demand. Geographical proximity between competing retailers is another factor that makes price discrimination and verification of availability a valuable retail pricing strategy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".