The effects of marketing intensity on high growth firms' internationalisation
Bibliographic record
Abstract
This paper examines how the marketing intensity of high growth firms (HGFs) affects their pace of growth, and in particular entry into international markets. We use a multicase study approach to empirically examine how marketing intensity influences the actual growth and internationalisation of four high growth firms in Canada. The findings show that for HGFs to attain and sustain their high growth, they need to internationalize in order to increase revenue and profit margin, which are necessary for their sustenance in Canada, a medium-sized economy which may be too small to support the growth aspirations of HGFs. We also discovered that these firms are part of broader value chain system and their economic viability depends on the rest of the value chain. This study contributes to the study of international entrepreneurship by uncovering the impact of marketing intensity of HGFs, yielding new theoretic insights and practical recommendations for entrepreneurs aspiring to create a venture that experiences high growth.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".