Bibliographic record
Abstract
In this article, the authors study the role of a store brand in building store loyalty through a game theoretic analysis. In a market in which a segment of consumers is sensitive to product quality and consumers' brand choice in low-involvement packaged goods categories is characterized by inertia, the authors show that quality store brands can be an instrument for retailers to generate store differentiation, store loyalty, and store profitability, even when the store brand does not have a margin advantage over the national brand. In addition, this loyalty argument does not apply for the “cheap and nasty” private label strategy. Such a private label policy, on the contrary, reinforces rather than reduces price competition among stores. Indeed, the quality of the store brand must be above a threshold level to create this opportunity. It also follows that quality store brands, when carried by competing retailers, can be an implicit coordination mechanism that enables all the retailers to become more profitable. Finally, a quality store brand policy is profitable only if a significant portion of shoppers buys the national brand. This surprising result establishes the complementary roles of store brands and national brands. The former create store differentiation and loyalty, whereas the latter enable the retailer to raise prices and increase store profitability. The authors provide empirical support for their thesis by using evidence from Europe and household-level scanner panel data from the United States and Canada.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.008 | 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".