Control, Collaboration, and Productivity in International Joint Ventures: Theory and Evidence
Bibliographic record
Abstract
This study analyzes the following unresolved questions: In international joint ventures (IJVs) in a developing country, how could different IJV structures address control and collaboration considerations, and what is the likely effect of such different structures on IJV productivity? Theoretically, we suggest that the ambiguity surrounding these questions reflects the tendency of researchers to view control and collaboration as opposing objectives, studying one or the other; in contrast, we provide a more integrative perspective that blends the two objectives, focusing on common underlying issues relating to enhancing partner commitment, ensuring partner knowledge contributions, and reducing partner risks. We address the most salient design consideration for IJV partners, that is, IJV ownership structure, to posit that joint consideration of the control benefit of a higher foreign ownership level in IJVs and the collaboration benefit of a more balanced IJV ownership structure results in an expected inverted U-curve relationship between foreign ownership and IJV productivity. Additionally, we posit and test how three environmental contingencies, by affecting the need for control and collaboration in IJVs, would further influence the specific shape of the inverted U-curve relationship. We find strong support for our theory using an extensive longitudinal dataset of over 5,000 IJVs in China from 1999-2003. We discuss the value of our approach and findings both for researchers and for IJV partners seeking the dual benefits of control and collaboration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".