A Pricing-Error Rule on Share Distribution in Equity Joint Ventures: The Bayesian Approach
Bibliographic record
Abstract
Equity joint ventures (EJVs) are a popular mode of inter-firm cooperation, where share distribution is critical to the success of the co-ownership relationship. The prevalence of EJVs in the business world has attracted substantial research attention, but the literature still lacks a clear rule for the partners to follow in splitting the equity shares of an EJV. In this study, we fill this research gap by adopting the Bayesian approach to propose a pricing- error rule on share distribution in EJVs. Essentially, equity participation in an EJV and subsequent profit sharing serve for the partners to correct for the errors that they may make in pricing the inputs that they contribute to the EJV. For profit sharing to fully nullify their pricing errors, the shares of the EJV must be split between them in a percentage combination that reflects the relative sizes of their pricing errors. Since pricing errors are unobservable beforehand, share distribution in EJVs is similar to a Bayesian process, where the partners keep updating their estimates of pricing errors in adjusting the ownership structure of the EJV. Our pricing-error rule bridges a critical gap in EJV literature and points out several promising directions for future research. It also offers strong implications for managers to boost the power of EJVs as a governance mode of inter-firm cooperation.
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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 | 0.029 | 0.124 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".