Bibliographic record
Abstract
In different industries, such as automobiles, chemicals, or retailing, competitors are joining forces in establishing electronic marketplaces to reduce inefficiencies in the purchasing process and cut costs by combining their buying power. Joining such an alliance leads to reduced costs, including those of possible rivals, because members share the development and operating costs. A company that joins an alliance agrees to share its suppliers with others, which may lead to more intense competition among the increased number of suppliers, and it may further benefit an alliance member at the expense of companies left outside the alliance. Natural questions that could arise, then, are when would a firm prefer to take part in an electronic marketplace joint venture; when would it prefer that other firms, possibly rivals, join the venture; and what are the financial consequences of either joining an alliance or remaining independent? In an attempt to gain a better understanding of the issues, we have developed a model of three retailers whose products may have a certain degree of substitutability. We provide some conditions, in terms of product substitutability and compatibility of retailers, that would lead to the formation of a three-member alliance, or a two-member alliance, or no alliance at all. We also study the effect of alliance structure and compatibility of retailers on the profit of a company.
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".