Towards an integrated understanding of university research commercialisation: a university spin-off perspective
Bibliographic record
Abstract
University spin-offs (USOs) are increasingly recognised as a prime mechanism to generate technological, economic and societal impact on regional and national levels. Yet, despite favourable institutional and policy arrangements fostering academic entrepreneurship, many USOs face major issues to overcome the liability of newness and smallness in the initial venturing phases. In result, existing research commercialisation practices remain ineffective with the majority of USOs failing to reach the expected objectives. While this problem has garnered attention both in research and practice, there is a lack of comprehensive understanding of USOs at the early-stage of development, hindering more impactful research commercialisation. To address this problem and to develop new actionable insights, this dissertation employs robust multi-disciplinary, mixed-method techniques. First, this dissertation consolidates and synthesises the existing knowledge on university-industry collaborations and academic entrepreneurship, and presents these concepts as interconnected, multi-layered ecosystems at the individual, organisational and institutional levels. Second, this dissertation examines how early-stage USO characteristics need to be shaped to overcome the initial venturing phases and acquire funding as a leverage for long-term survival. Third, by employing robust text mining and unsupervised machine learning techniques, this dissertation presents a novel USO typology with different venture development trajectories in relation to exploitative and explorative technology development and technology commercialisation activities. The findings of this dissertation foster a comprehensive understanding of university-industry collaborations and academic entrepreneurship research field. Additionally, this dissertation presents new actionable insights for academic entrepreneurs with regards to key determinants of USO development, and stimulates a development of effective government-based support mechanisms of research commercialisation.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.007 | 0.018 |
| Scholarly communication | 0.041 | 0.038 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.022 | 0.002 |
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".