Prevalence and Longitudinal Trends of Early Internationalisation Patterns among Canadian SMEs
Bibliographic record
Abstract
Purpose: Recently, studies call for a more nuanced perspective on different internationalization patterns pursued by early internationalizers. These studies argue that most Born Global firms turn out to be Born Regional and that the proportion of true Born Global firms would be overestimated. Moreover, literature claims that the proportion of Born Global firms increases over time due to macroeconomic trends. We investigate these assumptions by providing a dynamic perspective on the prevalence of different types of internationalization patterns among Canadian small and medium-sized exporters (SMEs).\nDesign/methodology/approach: To empirically examine the ideas above, we constructed a unique large-scale longitudinal (1997–2004) dataset. A multinomial logit model is employed to estimate a firm’s predicted probability, ceteris paribus, of choosing different internationalization patterns: Born Global, Born Regional, and Gradual Internationalization.\n Findings: We find that Born Global firms indeed account for a smaller proportion than Born Regional firms (16% vs. 27%). However, we find evidence that Born Globals and Born Regionals are increasingly established over time and that macroeconomic factors seem to account for this development at least partially.\nOriginality/value\nCombining a rigorous empirical analysis with a unique large scale longitudinal dataset, we address two fundamental research questions in the international entrepreneurship (IE) literature a) which internationalization pattern prevails and b) if the Born Global pattern is increasingly established over time. We therewith theoretically contribute by comparing the predictive value of different internationalization frameworks international new venture (INV) framework, stage-models and regionalization hypothesis), toward which there is considerable current debate.
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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 | 0.004 |
| 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) | 0.004 | 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".