Significant factors impacting export decisions of small- and medium-sized softwood sawmill firms in North America
Bibliographic record
Abstract
An augmented internationalization process (AIP) model is developed to explain important factors influencing decisions of small- and medium-sized softwood sawmill firms in the United States (US) and Canada. The decision to participate in exporting (i.e., export orientation) and the decision to intensify exporting activities (i.e., export involvement) are analyzed using ordinal probit hurdle regression model. Production capacity, geographical location, and the degree of differentiation strategies are factors playing important roles in determining the level of internationalization of the firm (i.e., export orientation + export involvement). Larger US firms are more likely to participate in exporting activities, whereas Canadian firms of all sizes are exporters. Also, firms in the US South are unlikely to participate in international business activities unless they adopt a product differentiation strategy, and even then they are more likely to use intermediary firms rather than undertake export activities directly. Firms adopting a differentiation strategy rather than a cost-leadership strategy are more likely to have a higher degree of internationalization. A major conclusion of the analysis is that developing a product differentiation strategy is a key to participation in international markets.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".