Alliance Structure and the Scope of Knowledge Transfer: Evidence from U.S.-Japan Agreements
Bibliographic record
Abstract
Prior research suggests that equity joint ventures (JVs) are particularly effective vehicles for accessing complex technology. Different schools of thought have emphasized different reasons why joint ventures might support greater knowledge transfer than “bare” license agreements: incentive alignment, organizational embeddedness, and enhanced administrative controls. We probe and refine these theoretical perspectives, drawing out implications of the different theories for the extent and speed of alliance-related knowledge transfer, as well as for knowledge “leakage” in areas not directly related to alliance activities. Using a proprietary data set derived from regulatory filings with the Japanese government we test these implications in our empirical analysis of U.S.-Japan agreements. The picture that emerges from the analysis is one of particularly intense but contained knowledge transfer in equity joint ventures, relative to bare license agreements: knowledge transfers directly related to the alliance activity are enhanced in the JV, and the speed of integration into the Japanese firm's subsequent innovations also increases. In marked contrast, leakage of unrelated technology is significantly reduced. These findings suggest that administrative structures that reduce technology leakage are a key feature of the equity joint venture, a result that is inconsistent with a “pure” knowledge-based perspective on alliances.
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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 | 0.007 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".