Knowledge Ecosystems, Universities, and Innovation in Small and Medium-Sized Enterprises: Establishing a Knowledge Infrastructure Governance Theoretical Framework and Conditions for Success
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Scholars underscore the advantages of knowledge ecosystems, where local universities play a central role in advancing innovation within the system. Nonetheless, to date, no research has elucidated what knowledge ecosystems factors drive innovation success in small and medium enterprises (SMEs). Further, the elements modelling effective SME and university relationships and knowledge infrastructure governance are still a blur. Utilizing the meta-synthesis approach, this study provides a thematic review of existing evidence relating to knowledge ecosystems, university-firm collaboration, and innovation success in SMEs. An SME innovation and knowledge infrastructure governance framework, including 16 factors classified under 3 actor layers (SME [knowledge & learning processes], embedded university, and integrated knowledge community), was obtained. The framework, coupled with activity examples, will allow universities, SMEs, policy-makers, and scholars to obtain a clearer understanding of how to leverage university–firm collaborations to create successful knowledge communities fostering innovation success in SMEs. Further research could explore and provide criteria and measures to assess the impact and direction of the relationships and governance factors outlined in the framework.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it