Profitability of private brands of e‐commerce platforms offering competing national brands under agency selling
Bibliographic record
Abstract
Abstract This paper investigates the impact of a private brand (PB) introduction by an e‐commerce platform. Contrary to previous research, the platform allows competing manufacturers to sell their national brands (NBs) directly to consumers for an agency fee. Our game‐theoretic analysis allows us to derive the following key insights. The levels of competition between NBs, and NBs and the PB, as well as the agency fees manufacturers pay to the platform are critical in determining the profitability of introducing PBs. Introducing a PB may not benefit the platform, especially when the PB and the NBs are asymmetric and are competing closely. However, when the platform can profit by introducing a PB, it is at the expense of NB manufacturers as they are pressured to reduce their prices and also experience a decline in sales. Finally, introducing PBs enhances consumer welfare by reducing NB prices and expanding consumer demand in the product category.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".