Unlocking Value Co-Creation in Entrepreneurial Ecosystems: The Vital Role of Institutions
Bibliographic record
Abstract
The entrepreneurial ecosystem is quite complicated because of the presence of numerous stakeholders and the inclusion of multicultural and social elements in diverse communities. The role of entrepreneurship education in developing entrepreneurial skills and aptitude has evolved. The collaboration between universities, companies, and organizations in the collaborative online international learning (COIL) approach plays an important role in the entrepreneurial ecosystem to enhance value co-creation. To extend the limited literature on value creation through entrepreneurship education among stakeholders and analyze the entrepreneurial ecosystem from a micro perspective, this study investigated why companies and organizations support universities at the individual, organizational, and institutional levels to foster entrepreneurial ecosystems. Following a global career course using the COIL approach, semi-structured interviews were conducted in person or via Zoom with four representatives of the Embassy of Canada to Japan, Ernst & Young, and Manulife from April to May 2022. The modified grounded theory approach was used to analyze the responses from three institutions. The results showed that students were provided with the opportunity to solve actual issues that the three institutions faced and the students’ perspectives were considered to identify and develop high-quality proposals at the individual, organizational, and institutional levels. The institutional philosophy, organizational engagement and development, and personal development of the representatives of these institutions effectively create values within universities while also forming entrepreneurial ecosystems at Japanese and Canadian companies, organizations, and universities to help build the next generation of leaders. This study has important implications through its contribution to society and the development of an entrepreneurial ecosystem in collaboration with the academic, industrial, and public sectors.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.023 |
| Scholarly communication | 0.019 | 0.015 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".