A competitive marketplace or an unfair competitor? An analysis of Amazon and its best sellers ranks
Bibliographic record
Abstract
Abstract To assess the performance of third‐party sellers relative to Amazon, this study estimates the effect of different sales strategies on Amazon's reported best sellers rank (BSR) of ground coffee in the USA and Canada and red wine in the United Kingdom using a fixed‐effects model. The products are either ‘sold and shipped by Amazon’ (Amazon), ‘sold by the third‐party seller and fulfilled by Amazon’ (FBA), or ‘sold and fulfilled by a third‐party merchant’ (FBM). For each of the grocery products and in all empirical specifications, FBM increases the BSR, reducing the relative sales performance of the product in its category. Specifically, FBM increases the BSR of grocery products by 60% relative to Amazon whereas the effect of FBA on BSR is mostly indistinguishable from the effect of Amazon on BSR. These results suggest that Amazon and FBA mostly perform equivalently, but both sales strategies outperform FBM. However, whether the relatively poor performance of the third‐party (FBM) shippers and sellers is due to unfair competition by Amazon remains an open question.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".