Breaking Boundaries: A Study on the Innovative Network Strategies of Canadian SMEs Expanding into Unfamiliar Markets
Bibliographic record
Abstract
Small and medium-sized enterprises (SMEs) rely on network ties to facilitate overseas expansion. However, the expansion may be hindered when geographical and institutional distance is great, and boundaries of network connections are reached since the information from those networks may be distorted and lack objectivity. Nevertheless, Canadian SMEs with native-born owners have succeeded in expanding into geographically and institutionally distant markets despite possessing no pre-existing ties. Drawing on insights from institution- and network-based views, we investigate this understudied issue through a generic inductive interview of six Canadian SMEs that have either expanded into or plan expansion into the United Arab Emirates in which they had no pre-existing ties. Results reveal that all firms understood that institutional differences affect the business environment. They embarked on a distinct networking approach, focusing on broadening their existing network in the early stage and making full use of outsourcing networking activities. The research helps pave the way for further clarity and understanding of the dynamic nature of international entrepreneurship when relating to distant yet commercially attractive 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.017 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".