The role of technology transfer offices in growing new entrepreneurial ecosystems around mid-sized universities
Bibliographic record
Abstract
The role of universities has evolved from its traditional focus on education and research to active participation in regional economic development. Technology transfer offices (TTOs) were created at Canadian universities to help regulate and monetise the transfer of knowledge created by the university researchers to the marketplace. In this paper, we examined the role TTOs can play in developing a new entrepreneurial ecosystem around mid-sized research universities, based on the perceptions and expectations of the key stakeholders, involved in the technology transfer process, about the role of TTOs, and if their role can help in developing entrepreneurial culture in their universities. We found that the ability of TTOs to effectively support the commercialisation of university research results is related to the existence of an entrepreneurial culture in the university. If the culture and an entrepreneurial ecosystem do not exist, the role TTOs can play is more limited to its well-established facilitation role. Our findings confirm that TTOs can play a critical role in coordinating different bottom-up initiatives to promote entrepreneurship, and in attracting and integrating new external resources to the university.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.007 |
| 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".