Network embeddedness in the internationalization of biotechnology entrepreneurs
Bibliographic record
Abstract
This study investigates how entrepreneurs of biotech enterprises embed in domestic and international networks so as to internationalize. We advance a contextual framework of embeddedness of internationalizing entrepreneurs, providing a contribution (i) by synthesizing and applying existing conceptual insights from the networking literature to provide a more culturally sensitive view of getting embedded for international entrepreneurship in the biotech industry and (ii) by adding insights into the practices and (micro)processes of how and in what ways embeddedness integrates with the internationalization of biotech entrepreneurs. Our study involves six entrepreneurs from Canada, Finland, and New Zealand. Context-specific embeddedness was studied by exploring the (i) type, (ii) strength, (iii) locality, and (iv) importance of the international and national network ties among internationalizing entrepreneurs. We found differences in relation to the locality of universities and research institutes, role and type of financiers, and customer focus in internationalization. For instance, while customers were central to the embeddedness of Canadian and New Zealand entrepreneurs, Finnish entrepreneurs had no focus on their customers, but acted solely through sales channels and partners. The customer focus of New Zealand entrepreneurs was mainly international, whereas it was domestic in the case of Canadian entrepreneurs.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.000 | 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".