Efficiency and Personalization as Value Creation in Internationalizing High‐Technology SMEs
Bibliographic record
Abstract
Abstract This article investigates the sources of value creation in the exchange relationships between internationalizing Canadian small high‐technology firms and their various stakeholders. The paper reviews the concepts of exchange and value, and applies these concepts to small internationalizing high‐technology businesses. The data are based on semi‐structured interviews with 20 Canadian high‐technology small and medium‐sized enterprises (HTSMEs). The analysis shows that HTSMEs strive to attain internal and external efficiency. Efficiency, however, is a subset of Personalization, which consists of developing close relationships with dedicated partners in order to create sustainable value. This study demonstrates that value creation for HTSMEs is not only a matter of creating products at the cutting edge, but primarily of managing a constellation of relationships within evolving networks. Résumé La présente étude recherche les sources de création de valeur dans les relations d'échange entre les petites entreprises canadiennes à haute technologie en voie d'internationalisation et les différentes parties prenantes. Elle passe en revue les concepts d'échange et de valeur qu'elle applique aux petites entreprises à haute technologie en voie d'internationalisation. Les données sont fondées sur des entretiens semi‐structurées réalisée auprès de 20 petites et moyennes entreprises canadiennes à haute technologie (HTSME). L'analyse montre que les HTSME recherchent laborieusement l'efficacité interne et externe. Mais cette efficacité est une sous‐variante de la Personnalisation qui consiste à développer des relations privilégiées avec des partenaires dévoués, afin de créer une valeur durable. Notre étude démontre que la création de valeur pour les HTSME n'est pas seulement synonyme de création de produit d'avantgarde. Elle signifie aussi et surtout gestion d'une constellation de relations dans des réseaux en évolution.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".