A dynamic management capabilities view of small to medium-sized enterprise export readiness: a Canadian perspective
Bibliographic record
Abstract
Increased trade liberalisation and advancements in technology have established the foundation for global expansion of small and medium-sized enterprises (SME). However, data demonstrates that most SMEs continue to focus almost exclusively on their domestic market. In this study, we leverage resource orchestration (RO) and dynamic capabilities (DC) to explore the managerial and firm level resources critical to supporting SME export expansion. This includes conducting multi-staged qualitative research to define these resources (N = 28). This research identifies company age, operational resources, financial capacity, and employee knowledge and skills. In addition, we isolate dynamic managerial capabilities (DMCs) related to cognition (e.g., managerial experiences and decision-making), social capital (e.g., developing strong internal and external networks) and human capital (e.g., ability to maximise the value of people) as critical to export expansion. Lastly, we use our findings to develop a conceptual model and associated instrumentation of SME export readiness to guide future empirical research.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".