Do They Know Something We Don't? Endorsements from Foreign <scp>MNCs</scp> and Domestic Network Advantages for Start‐Ups
Bibliographic record
Abstract
Plain language summary This article examines the effects of alliances with foreign multinational corporations (MNCs) on a local start‐up's attractiveness as a partner in its domestic research networks. We argue that such international strategic alliances enhance a start‐up's subsequent alliance activity and its status in its domestic R&D network. The analysis shows that, indeed, alliances with foreign MNCs significantly enhance the start‐up's attractiveness and its future alliance activity, especially when the start‐up is young (up to the age of five). Furthermore, alliances with foreign MNCs from a variety of different countries of origin (e.g., U.K., Germany, and France) have stronger effects on a start‐up's subsequent alliance activity, supporting the argument that even in the age of globalization, location still matters. Technical summary This article examines the effects of endorsements from foreign multinational corporations (MNCs) on the centrality of biotech start‐ups within their domestic research networks. We argue that international strategic alliances enhance a start‐up's subsequent movement toward a more central position in its domestic R&D network. Analyzing U.S. biotech start‐ups over time, our findings show that endorsements from foreign MNCs significantly enhance the subsequent network centrality of U.S. biotech start‐ups. This endorsement effect is magnified in the early stages of the start‐up's life cycle. Furthermore, endorsements by foreign MNCs from a variety of different countries of origin have stronger effects on a start‐up's subsequent network centrality, supporting the contention that even in the age of globalization, location still matters. Copyright © 2016 Strategic Management Society.
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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 | 0.003 | 0.013 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".