Internationalization and Innovation: The Effects of a Strategy Mix on the Economic Performance of French SMEs
Bibliographic record
Abstract
A growing body of literature has studied innovation and internationalization as essential competitiveness strategies. Small and medium-sized enterprises (SMEs) can achieve growth by launching new products or reaching new customers. A mix of both of these strategies represents a challenging opportunity for businesses. Most studies explore export and research activities as joint explanatory variables. This article contributes to the literature by considering the joint dynamics of internationalization and innovation strategies and measuring the impact of this strategy mix on the financial performance of French SMEs. Both strategies consumehuman, technical, financial, commercial, and organizational resources. The antagonism and the complementarity of international development and innovation activities are explored in this paper. Based on a sample of 335 French SMEs, the results confirm a strong interdependence between technological appropriation in internationalized SMEs and their business growth. Indeed, structuring and engaging in research and development activities for exporting SMEs will contribute to an increase in activity volume. The results also indicate technological resources as a common driver of both innovation and internationalization activities. The combination of product development and geographical market expansion is the most valuable combined strategy that is positively related to research and development intensity.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".