Generating Cooperative Gain in a Vertical Partnership: A Supplier's Perspective
Bibliographic record
Abstract
Abstract The author examines the sources of cooperative gain for automotive suppliers in the context of vertical partnership. A statistical survey conducted in the automobile industry examines the supplier's perspective. The conceptual framework of the article combines the three traditional theories frequently used for interorganizational studies—industrial organization (IO), transaction cost economy (TCE), resource‐based view (RBV)—with a new fourth approach, the relational view (RV) (Dyer & Singh, 1998). Findings suggest that the relational view and industrial organization hypotheses are entirely supported, while RBV hypotheses are only partially validated and the TCE hypothesis is not supported at all. Finally, statistical results confirm the assumption that suppliers must carefully manage their vertical partnerships in order to leverage cooperative gains. Résumé L'auteur analyse les sources des gains coopératifs pour les fournisseurs partenaires de leurs clients. Une enquête statistique conduite dans l'industrie automobile permet d'analyser le point de vue des entreprises fournisseurs. Le cadre théorique de la recherche combine les trois approches traditionnellement utilisées dans les etudes sur les relations entre les organisations—économie industrielle (IO), économie des coûts de transaction (TCE), et approche par les ressources (RBV)—avec une quatrième et nouvelle approche: l'approche relationnelle (RV) (Dyer & Singh, 1998). Les résultats statistiques corroborent les hypothèses issues de l'approche relationnelle et de l'économie industrielle. Les hypothèses issues de l'approche par les ressources ne sont que partiellement validées et celles issues des propositions de la théorie des coûts de transaction ne trouvent aucun support statistique. L'analyse confirme l'idée que les fournisseurs doivent gérer prudemment leurs partenariats verticaux s'ils veulent en obtenir des gains coopératifs.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".