The Strategic Role of Third‐Party Marketplaces in Retailing
Bibliographic record
Abstract
Retailers are increasingly adopting a dual‐format model. In addition to acting as traditional merchants (buying and reselling goods), these retailers provide a platform for third‐party (3P) sellers to access and compete for the same customers. We investigate the strategic rationale for a retailer to introduce a 3P marketplace. Our analysis provides insights into the growing prevalence of 3P marketplaces. We show that by committing to having an active 3P marketplace, the retailer creates an “outside option” that improves its bargaining position in negotiations with the manufacturer. This can explain the increasing prevalence of such marketplaces. On the other hand, the manufacturer would prefer to eliminate the retailer's outside option and should seek to limit or prevent sales through 3P marketplaces. This is consistent with actions that several manufacturers have taken to limit such sales. Interestingly, if the manufacturer fails to eliminate sales of competing products through the 3P marketplace, then the best strategy for the manufacturer is to allow the retailer to dictate the terms of their contract. This is because a powerful retailer will rely less on its outside option in generating profit, and therefore it will increase the fees charged to 3P sellers and soften the competition between 3P sellers and the manufacturer. The decrease in competition will lead to an increase in the value of outside option of the manufacturer and improve its profit. Additionally, we find that the presence of a 3P marketplace benefits consumers, but this benefit diminishes as the retailer becomes more powerful.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".