The Effect of Environmental Turbulence and Leader Characteristics on International Performance: Are Knowledge‐Based Firms Different?
Bibliographic record
Abstract
Abstract The aim of this paper is to study the effect of environmental turbulence and leader characteristics on international performance. It is suggested that these phenomena explain the differences between knowledge‐intensive companies and traditional industrial enterprises in the internationalization process. The empirical part of the study is based on a large cross‐industrial survey of Finnish small and medium‐sized enterprises. Our results indicate that knowledge‐intensive firms have experienced more intensive international growth than other firms. They are also operating in an environment in which technological turbulence is significantly higher, and their leaders put more emphasis on internationalization. Generally, environmental turbulence is a better indicator of international performance in knowledge‐intensive firms than in others. Résumé Dans le présent article, nous étudions l'impact de la turbulence environnementale et des caractéristiques des leaders sur la performance internationale. On estime que ces phénomènes rendent compte des différences qui existent, dans le processus d'internationalisation, entre les entreprises à forte concentration de savoir et les entreprises industrielles traditionnelles. La partie empirique de l'étude s'appuie sur une grande enquête trans‐industrielle de petites et moyennes entreprises finnoises. Nos résultats indiquent que les entreprises à forte concentration de savoir connaissent une croissance internationale plus grande que les autres entreprises. L'étude montre aussi que les entreprises à forte concentration de savoir opèrent dans un environnement marqué par une plus grande turbulence technologique. Par ailleurs, leurs leaders mettent plus l'accent sur l'internationalisation. D'une façon générale, la turbulence environnementale permet de mieux apprécier la performance internationale dans les entreprises à forte concentration de savoir que dans d'autres entreprises.
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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 | 0.002 | 0.009 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".