Quality competition between national and store brands
Bibliographic record
Abstract
It is commonly assumed in private label literature that store brands are of lower quality than competing national brands. In this paper, we contest this notion by studying quality competition between a national- brand manufacturer and a store-brand retailer. The manufacturer sells its national-brand products through the retailer who produces a competing store brand at the same time. The two parties first invest in their brand qualities, after which the manufacture determines the wholesale price for the national brand and the retailer decides the retail prices for both brands. With a general quality-dependent cost structure, we explicitly characterize the equilibrium in both price and quality levels under various channel power structures. The results suggest that the store brand could possibly be of higher quality than the national brand even in absence of cost disparity; however, the store brand will charge a lower retail price whether its quality is superior to the national brand or not. Further, price competition and quality competition bear opposite implications on equilibrium solutions as well as profitability levels. Surprisingly, the manufacturer may benefit from a more costly production or quality investment scenario, while both the retailer and the supply chain will suffer from the same. The paper highlights the importance of accounting for quality decisions in the study of private label products.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".