The Internationalization of Small and Medium-Sized Firms
Bibliographic record
Abstract
Internationalization among small- andmedium-sized enterprises (SMEs) has been the focus of numerous studies. Thiswork expands upon the previous research by examining internationalization froman approach that combines both behavioral theory, including organizationallearning, and the new venture theory of internationalization. To test the hypotheses derived from this theoretical framework, a sample of92 firms is selected in 2000 from a database created by the Center ofEntrepreneurship at the Vlerick Leuven Gent Management School in Belgium.Firms included in the sample are independent of large corporations andare owner-managed. In addition, 1999 sales data from a database of theNational Bank of Belgium is utilized. Several factors were measured,including internationalization intent, international learning effort, domesticlearning effort, and entrepreneurial orientation of the firm. The analyses indicate the following findings: seeking and expandingknowledge of foreign markets and the internationalizaiton process may increaseinternationalization by impacting assessments of available opportunities; firmsthat are willing to take bold risks -- i.e., with an entrepreneurial mindset --are more likely to develop a long term international presence; andinternational and domestic learning activities (environmental scanning,intelligence about competitors) are often similarly related to entrepreneurialorientation. However, firms that invest more in domestic rather thaninternationallearning activities are less likely to internationalize,which may impede the firm's long-term accomplishments. (AKP)
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".