Dual Branding by National Brand Manufacturers: Drivers and Outcomes
Bibliographic record
Abstract
This article is the first generalizable empirical analysis of dual branding, that is, the supply of private labels (PLs) by national brand (NB) manufacturers. The authors compile a unique data set combining the identity of PL suppliers in over 260 packaged goods categories with multiple years of scanner data in the Spanish grocery market to offer several contributions. First, they provide new descriptive insights on the prevalence of dual branding in categories where the manufacturer does and does not have NBs, the longevity of PL supply arrangements, and the differences in PL sourcing across retailers. Second, they integrate the literature on motivators and dissuaders of dual branding and test the impact of relevant manufacturer, retailer, and dyad characteristics on PL supply in NB and non-NB categories. The results reveal a more nuanced empirical reality than is evident from prior research regarding the role of multicategory scope, fighter brands, NB differentiation, and size and positioning of the retailer's PL. Third, they examine the outcomes of PL supply for the NBs of dual branders and find that starting (terminating) PL supply to a retailer significantly benefits (hurts) the relative distribution depth but not the relative share of the dual brander's NBs at that retailer.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".