Intended and unintended termination of international joint ventures
Bibliographic record
Abstract
Abstract This study proposes that international joint ventures (IJVs) are terminated either when the initial purposes of the formation of the IJV have been achieved (intended termination), or when unanticipated contingencies that emerge in the external, internal, or inter‐partner conditions after the establishment of the IJV impede the continuation of its operation (unintended termination). Our study examines the factors that affect intended and unintended termination and the longevity of IJVs. The findings show that approximately 90 percent of all IJV terminations are unintended and 10 percent intended, and that the frequency of intended termination and unintended termination varies noticeably depending on the initial purposes of formation. This suggests that the termination of IJVs is significantly contingent on their formation. The findings also show that the longevity of IJVs varies according to the initial purposes of formation, the initial conditions under which the IJV is formed, and the types of unanticipated contingencies that it encounters. The key theoretical issues and practical implications of the distinction between the intended and unintended termination of IJVs are also discussed. Copyright © 2007 John Wiley & Sons, Ltd.
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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 | 0.009 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".