Joint venture evolution: extending the real options approach
Bibliographic record
Abstract
Abstract Real options theory has emerged as a promising avenue to study joint venture (JV) evolution as a strategic response to managing uncertainty. We extend the real options approach by integrating it with game theory. Such a combined method enriches the valuation functions of each partnering firm and helps to identify the optimal decisions for exercising options in a JV. In our model, each firm's synergy from the joint operation and its knowledge acquisition capability (KAC) can significantly influence the competitive dynamics between partners, potentially affecting how each firm decides to acquire, divest, or dissolve. We employ a new solution technique in real options theory to capture the stochastic process of three factors, and use computer simulation to test the model under varying conditions. The results are stated in five testable propositions, providing a better understanding of the dynamics of a JV. We find that symmetries between partners in synergy or KAC contribute to stability or dissolution of the JV, whereas asymmetries in synergy or KAC make acquisition of the JV assets by one partner desirable. Copyright © 2008 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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".