Do They Know Something We Don't? Endorsements from Foreign <scp>MNCs</scp> and Domestic Network Advantages for Start‐Ups
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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 ., G ermany, and F rance) 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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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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 it