Early internationalization and performance of small high‐tech “born‐globals”
Bibliographic record
Abstract
Purpose The purpose of this paper is to investigate the early internationalization and the performance of small firms in technology‐intensive industries. Design/methodology/approach Using a sample of 278 small US firms in technology‐intensive industries, this paper employs quantitative methodologies to test hypotheses. Findings The findings indicate that such organizational variables as firm size and international experience have a non‐linear, inverted U‐shaped relationship with these firms’ early internationalization. Some strategic variables, such as R&D intensity, have significant impacts, whereas others, such as advertising intensity and strategic alliances, have none. However, the interactions between these strategic variables have a more significant influence upon these firms’ early internationalization than do the individual strategic variables in isolation. Moreover, early internationalization has significant and positive impacts on the performance of these firms. Practical implications The paper’s findings have important managerial implications. The paper identifies the driving forces for the early globalization of small firms and provides useful guidelines for managers to manage these factors in their efforts to maximize firm performance. Originality/value The paper differentiates organizational factors from strategic factors against the background of small “born globals” in technology industries and investigates the interactions among these internal factors and external factors, i.e. the environments of technology industries. Findings of non‐linear relationships among these factors shed light on the strategy determinants of a unique group of small to medium‐sized enterprises and their performance.
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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 | 0.003 |
| 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 | 0.000 |
| 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".