Brand positioning and consumer taste information
Bibliographic record
Abstract
In this paper, we study how a retailer can benefit from acquiring consumer taste information in the presence of competition between the retailers store brand (SB) and a manufacturers national brand (NB). In our model, there is ex-ante uncertainty about consumer preferences for distinct product features, and the retailer has an advantage in resolving this uncertainty because of his close proximity to consumers. Our focus is on the impact of the retailers information acquisition and disclosure strategy on the positioning of the brands. Our analysis reveals that acquiring taste information allows the retailer to make better SB positioning decisions. Information disclosure, however, enables the manufacturer to make better NB positioning decisions - which in return may benefit or hurt the retailer. For instance, if a particular product feature is quite popular, then it is beneficial for the retailer to incorporate that feature into the SB, and inform the manufacturer so that the NB also includes this feature. Information sharing, in these circumstances, benefits both the retailer and the manufacturer, even though it increases the intensity of competition between the brands. But, there are situations in which the retailer refrains from information sharing so that a potentially poor positioning decision by the NB makes the SB the only provider of the popular feature. The retailer always benefits from acquiring information. However, it is beneficial to the manufacturer only if the retailer does not introduce an SB due to the associated high fixed cost.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".