The JV Dilemma: Cooperating and Competing in Joint Ventures
Bibliographic record
Abstract
Abstract This paper investigates how joint venture parents interact in light of the JV dilemma, the need to act for joint gains while protecting one's own interests. Partial least squares (PLS) analysis is used to assess the proposed model which contends that parent compatibility is related to levels of cooperation and competition, which in turn affect venture performance. The key findings were that: (a) compatibility was positively linked with cooperation, which was associated with better JV performance, and (b) competitive behaviours were not significantly linked to performance. Résumé Cette dissertation a pour sujet une enquěte sur l'interaction des maisons mères des entreprises en vue du dilemme des entreprises en commun (JV Dilemma), le besoin d'une action dirigée envers les gains communs tout en protegeant une zone d'intérět. L'analyse Partial Least Squares (PLS) est utilitsée pour l'évaluation du modèle proposé qui soutient que la compatibilité des maisons mères est reliée au niveau de la coopération et la compétition qui à son tour affecte la perfomance de l'entreprise. Les cléfs de ces constatations sont (a) que la compatibilité était positivement liée 4aG la cooperation qui était associées avec une meilleure performance JV (entreprises en commun), et (b) les comportements com‐pétitifs n 'étaient pas liés significativement à la performance.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".