Traditional and digital entrepreneurial ecosystems: a framework of differences and similarities
Bibliographic record
Abstract
Abstract Entrepreneurial ecosystems in various geographical areas of the world are often compared in the context of entrepreneurship research. There are far fewer comparative studies on different types of ecosystems. In this study, a traditional entrepreneurial ecosystem based in Canada is compared with a digital entrepreneurial ecosystem specializing in life sciences, in Switzerland, bridging the gap between both and yielding previously unknown insights. The aim is twofold: to decipher both the differences and the similarities between the two models and to describe the predominant type of entrepreneurship in each case. The method consisted of a quantitative study of socio-economic data in combination with the administration of a qualitative analysis of interviews with—industry, government, and university—experts with links to one or the other ecosystem. The main findings showed that the traditional ecosystem had varied entrepreneurial support, public financial support, and collaborative networks between SMEs and start-ups, whereas in the specialized digital ecosystem, business support tended to be sector-specific with private financial support and networks emerging between multinationals and start-ups. Our study contributes to entrepreneurship research by showing that high-tech industries such as biotechnology and medical technology manage to go beyond a purely digital approach in digital ecosystems. The generic nature of the high-tech industries within the traditional ecosystem was the main driver of traditional entrepreneurship, while the sector-specific approaches of the industries within the specialized digital ecosystems were shown to drive innovative entrepreneurship.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.003 | 0.025 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.001 | 0.009 |
| 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".