University-SME Collaboration and Open Innovation: Intellectual-Property Management Tools and the Roles of Intermediaries
Bibliographic record
Abstract
In 2009, the Conseil de la science et de la technologie du Québec (CST) made 13 recommendations to the Government of Quebec in order to shift innovative actors towards open-innovation practices adapted to the province's context: diversified economic sectors, a majority of small and medium-sized enterprises (SMEs), public universities, etc. Among these recommendations are: i) to set up flexible mechanisms to promote research collaboration between public-private sectors such as universities and SMEs, and ii) to optimize intermediation bodies’ contribution to establish open-innovation practices. Furthermore, the lack of adequate understanding and tools for the management of intellectual property (IP) was identified as a major inhibitor of open-innovation practices, to which actors should pay specific attention. In this article, we present results and recommendations from a field study focused on two groups of actors: i) companies involved in collaborative innovation and ii) intermediary agents enabling innovation and technology transfer. Our first goal was to shed some light on factors that facilitate open innovation through improved university-enterprise collaborations and, more importantly, that attempt to overcome the irritants related to IP management. Our second goal was to analyze the roles of diverse intermediaries in the fostering of successful collaborations between universities and SMEs. Our study yielded three findings: i) SMEs do not care about understanding and improving their capabilities about IP and are not equipped with adequate tools and best practices for managing IP and for managing the overall collaborative mechanisms in general; ii) this gap in preparation for open innovation is persistent, since even the intermediaries, whose role is to guide SMEs in university-enterprise collaborations, suffer themselves from the lack of appropriate IP transfer and sharing tools, and do not perceive the need to offer better support in this regard; and iii) overall, current IP-transfer and collaboration-management tools are not sophisticated enough to provide appropriate support for the implementation of open innovation, by which we mean more open and collaborative innovation in the context of university-enterprise collaborations.
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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 | 0.024 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.024 | 0.020 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".