Socio-cultural barriers to developing a regional entrepreneurial ecosystem
Bibliographic record
Abstract
Purpose The purpose of this paper is to provide a contextual analysis that helps explain how socio-cultural factors are negatively impacting the evolution of the entrepreneurial ecosystem in a struggling regional economy. Design/methodology/approach A case study method is used to provide a detailed contextual analysis triangulating primary and secondary data. Findings This paper provides insight into a region impeded from embracing the benefits of innovation-driven entrepreneurship in fostering economic development. The authors show that socio-cultural factors may be inhibiting the region from having a functional entrepreneurial ecosystem that can support innovation. Specific aspects of culture and social capital weaknesses are identified and insight into the potential causes of these impediments were offered. As well, the paper shows how the fundamental nature of culture may be affecting other elements of the entrepreneurial ecosystem from maturing. Originality/value This paper adds to a small, but growing, body of literature that is illustrating the evolutionary nature of entrepreneurial ecosystems and the significant impact of socio-cultural attributes to that evolution. This paper responds to calls to investigate contexts in which innovation does not thrive and where economic challenges prevail. The value of this research paper is to provide conceptual contributions in a contextual analysis from which other researchers can draw comparisons, insights and inspiration for similar approaches. Despite the abundance of research discussing the importance of culture, there are very few actual case studies showing concrete examples of culture and its influence on a region’s entrepreneurial ecosystem.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.005 |
| 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".