Transaction cost implication of private branding and empirical evidence
Bibliographic record
Abstract
Abstract Branding and transaction cost economics represent two research streams that rarely cross paths in the literature. In this study, I explore the transaction cost implication of private branding, a practice whereby products supplied by unaffiliated manufacturers are sold under private brands owned by retailers. The main thesis is that private branding can preempt a special case of asset specificity called brand specificity, where retailers also invest in the marketing of an outsourced product, but subsequent reputation effects (positive or negative) are specific to the manufacturer who brands the product. Retailers, thus, will not be fully motivated to optimize their investment in product marketing unless they take over the branding right. With potential barriers to private branding being controlled, data obtained from a national chain reveal that the retailer deploys its marketing resources according to the branding status of a product, implying that private branding can deflect the transaction cost of solving the brand specificity problem. The results offer new theoretical insights into branding and transaction cost analysis. This efficiency‐based approach to private branding also provides practitioners with useful guidelines for crafting a branding strategy that will facilitate cooperation between manufacturers and retailers. Copyright © 2009 John Wiley & Sons, Ltd.
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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 | 0.008 | 0.110 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.031 | 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".