Determinants of innovation co-operation for manufacturing SMEs: evidence from a systematic review of the literature (1992-2015)
Bibliographic record
Abstract
This paper summarises a comprehensive and systematic review of 29 quantitative studies from peer-reviewed journals published in ranked publications between 1992 to 2015 on the determinants of innovation cooperation for manufacturing SMEs.Applying a documented methodology, it crystallises acquired knowledge by identifying, synthesising and discussing 220 unique determinants stemming from a vast and heterogeneous body of literature.The article introduces an analytical framework integrating different perspectives to approach this concept presenting a holistic and integrated view of the topic.It provides a typology that sorts the determinants into six categories: 1) environmental characteristics; 2) industrial characteristics; 3) organisational characteristics; 4) individual characteristics; 5) partnership characteristics; 6) project characteristics.This systematic review also identifies current gaps in the literature.The provided research perspectives will allow researchers and policymakers to better foster innovation and guide researchers addressing this phenomenon in the future.It clearly lays a foundation for future research on the topic, organising and building upon the literature that has been published so far.
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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 | 0.013 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.017 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".