Founding Entrepreneurs' Characteristics: Impacts on New Ventures’ Internationalization
Bibliographic record
Abstract
One of the main challenges for scholars studying micro-level international expansion is to identify new proxies for firm-specific advantages (FSAs), in lieu of - or in addition to - strengths in R&D/patents and advertising/brand names, and to predict which firms are most likely to engage earlier than other ones in economic activities abroad. We investigate the propensity of new venture firms to internationalize, thereby becoming international new ventures (INVs). We suggest that particular founding entrepreneurs’ characteristics can function as FSAs supporting early internationalization. We empirically test our new conceptual approach using Kauffman firm-level survey data, thereby including 4,928 U.S.-based new businesses founded in 2004. Our results show that three parameters, namely the education level of INV owners, their status/experience as immigrants, and the number of other businesses they started, are closely linked to early new venture internationalization, and can be usefully interpreted as INV FSAs. Recognizing these new types of FSAs confirms internalization theory as the core theory in international business and entrepreneurship studies.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".