18 University Entrepreneurial Ecosystems: Exploring Digitalization in University Incubators
Bibliographic record
Abstract
Universities collaborate with governments (local, regional, provincial, and federal) and industrial partners to help their students and faculty members to develop new ventures and commercialize innovations via various entrepreneurship programs. Within university entrepreneurial ecosystems (UEEs), many entities promote entrepreneurial activities. Digitalization has facilitated collaboration within the different entities of UEEs and helped entrepreneurs to access critical resources. University incubators (UIs) are a key driver of UEEs to help entrepreneurial activities led by students and faculty members to be successful. In this study, first we explore the UEEs by highlighting the different entities associated with the UIs. Next, using the business model canvas (BMC), we present a university incubator BMC (UIBMC) that provides a comprehensive map of the value creation activities for customers, partners, and other stakeholders. The UIBMC can help UI managers refine and optimize their processes and unique services to customers (e.g., incubatees). Finally, we provide examples of the role of digital technologies in key activities of UIs. Incubator managers can strategically use these technologies to not only improve their internal operations but also to connect seamlessly with UEEs to enhance their roles as regional innovation hubs. We use a multiple case study approach and provide in-depth analyses and supporting data collected from Canadian UIs. We provide theoretical and practical insights to enrich the UEE literature and integrate it with the literature on digital 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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".