Collaborations in innovation activities of rural SMEs: a configurational analysis
Bibliographic record
Abstract
Collaboration and innovation are closely linked in innovation ecosystems (IEs). Yet management research that adopts the IE approach has paid little attention to the combinations of collaborations that foster innovation among rural SMEs. We therefore seek to determine what configurations of collaborations between rural SMEs and actors in their IE most enhance their innovationperformance. The study uses fuzzy set Qualitative Comparative Analysis to explore this question in 64 rural SMEs in Canada. The results confirm that at least one collaboration configuration between SMEs and customers and suppliers is needed to enhance SMEs’ innovation performance. Financial institutions (FIs) are present in configurations where collaborations with education and research institutions (ERIs) are absent. Collaborations with economic development organizations seem less effectual. The main contribution is to expand the current analysis on collaboration configurations in IE to SMEs in rural contexts by using a configurational analysis. The specific contributions are: 1) Balanced collaboration of rural SMEs with their IE actors, stimulates the innovation performance, 2) substitutability and complementarity between horizontal and vertical collaborations facilitate this performance, 3) the conjunction of collaborations with ERIs and FIs can be problematic. Actors of innovation support in rural areas can strengthen their strategies by considering the findings.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".