Internalization theory, entrepreneurship and international new ventures
Bibliographic record
Abstract
Purpose – The aim of the article is to establish robust linkages between internalization theory and the empirical phenomenon of international new ventures (INVs). Here, the focus is on firm-specific advantages (FSAs) critical to early new venture internationalization. Design/methodology/approach – On the conceptual level, we explain how the INV literature can easily be accommodated using an internalization theory lens, and we formulate hypotheses to that effect. On the empirical level, we use the Kauffman Firm Survey (KFS) dataset, which includes a panel of 4,928 US-based new businesses founded in 2004, tracked over their early years of operations. We use logistic regressions building upon pooled cross-sections, and including lagged dependent variables. Findings – INV-type foreign expansion is a special case of international growth, easily and credibly predicted by internalization. No new theory beyond internalization theory is needed to explain this phenomenon. Originality/value – The early stages of the Uppsala model, in terms of requisite resources accumulation and recombination, may have been undertaken at the individual level, by founding entrepreneurs, in the pre-stage of the new venture, and are “invisible” when focusing on organizational experience built up in the new venture. Here, particular founding entrepreneurs’ characteristics function as FSAs.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".