WAL-MART AS A LEADING RETAILER IN THE SUPPLY CHAIN
Bibliographic record
Abstract
The Wal-Mart effect has a dramatic impact on upstream manufacturers in a supply chain. This study applies a game-theoretic approach to analyze the effects of the leading retailer in a supply chain. We propose three models relating to the interactions between upstream duopolistic manufacturers and a downstream retailer: The first model represents that both manufacturers react simultaneously and independently to the retailer‟s price decision. The second model describes both manufacturers reacting to the retailer‟s decision in a leader-follower price competitive condition. The third model is a traditional upstream-dominating situation, which will be employed to contrast with the first two downstream-dominating models. By changing the degree of substitutability of the two products made by these two manufacturers, there are some findings: (i) As a downstream leader in the supply chain, the retailer profit is more than the sum of the two duopolistic manufacturers. (ii) If the duopolistic manufacturers also play the leader-follower game, the leader manufacturer‟s profit is greater than the follower manufacturer‟s profit. (iii) When comparing to the manufacturer-dominating model, the retailer-dominating models have the lower retail price and an increase in sale quantities. (iv) Compared to the manufacturer-dominating model, the retailer-dominating models‟ producer surplus, consumer surplus, and social welfare are improved.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".