Knowledge Ecosystems, Universities, and Innovation in Small and Medium-Sized Enterprises: Establishing a Knowledge Infrastructure Governance Theoretical Framework and Conditions for Success
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".