Canadian SME Internationalization to the UAE: A Case Study Through a Network-based Lens
Bibliographic record
Abstract
Canadian small-and-medium enterprises (SMEs) can enhance their chances for success through access to potentially lucrative foreign markets and have commonly relied on network ties to facilitate overseas expansion. However, relying heavily on network ties to enter such markets may hinder expansion when geographical distance is great, and boundaries of network connections are reached since information borne out of those networks may lack objectivity and create distorted images of reality, suggesting that network-based explanations are overstated in the existing literature. The role of network ties is salient with immigrant-owned internationalizing SMEs whereby shortfalls in firm resources are offset by network links to host markets. Nevertheless, Canadian SMEs with native-born owners with no inherent ties have succeeded in expanding into such markets. Therefore, how exactly do such SMEs expand into geographically and culturally distant markets without pre-existing network ties? A case study of 6 Canadian SMEs with owners who have either expanded into or plan expansion into the United Arab Emirates, a culturally and geographically distant market, despite possessing no pre-existing ties. Results reveal that these firms have navigated the uncertainties of expanding into distant markets with little or no pre-existing network ties through early-stage network broadening activities and outsourcing networking activities to domestic market experts.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.020 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".