Internationalization, environmental engagement and innovation: insights from machine learning methods
Bibliographic record
Abstract
Purpose This study aims to explore the factors influencing the intention of entrepreneurs in small- and medium-sized enterprises (SMEs) to expand their businesses internationally. Addressing the fragmented understanding of SME internationalization drivers, this research tackles the problem of lacking an integrated framework that considers demographic characteristics, entrepreneurial experience, innovation, financial needs and environmental engagement. Design/methodology/approach In 2022, the authors conducted a survey of 1,160 entrepreneurs in Quebec, Canada. Among them, 380 entrepreneurs whose businesses were not operating abroad shared their intentions regarding whether they planned to expand internationally. The data were analyzed using supervised machine learning methods, particularly the Decision Tree algorithm, to identify the most influential variables. This study shifts the focus to the intention, offering a more inclusive perspective on the early-stage motivations and conditions that precede global expansion. Findings The results reveal that immigrant status, entrepreneurial experience (both in terms of business tenure and number of ventures created), and innovation activities are key predictors. Needs such as subsidies, along with environmental engagement, also emerged as significant factors. Originality/value Theoretically, this study contributes to a deeper understanding of internationalization intentions by offering a multidimensional and behavioral perspective. It highlights the central role of innovation as a strategic lever, the value of diverse entrepreneurial profiles and the complexity of the relationship between environmental engagement and global expansion. These findings can help public policy and entrepreneurial support programs, while also serving as a comparative basis for future research in other geographic contexts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".