Resource differences between Born Global and Born Regional firms : Evidence from Canadian Small and Medium-Sized Manufacturers 1997-2004
Bibliographic record
Abstract
While international business research has intensively observed the determinants and outcomes of International New Ventures (INV), we only have a limited understanding on why some INVs pursue regional focused internationalization (so called Born Regional Firms), while others decide for a globally dispersed approach (so called Born Global firms). This study draws on resource-based theory and applies logistic regression on a longitudinal sample of 604 Canadian small and medium-sized exporting manufactures to investigate how initial internal resources differ between Born Global and Born Regional firms. We find that, compared to Born Regionals, Born Globals have significantly greater foreign market knowledge, and have significantly higher initial performance. We advance research about the internationalization-performance link by means of a reverse causality regarding this relation in which initial performance influences the internationalization approach of INVs. This provides additional support for the regionalization hypothesis and the assumption that a global expansion does not necessarily lead to higher performance, but demands a greater amount of resource input.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".