An exploratory study on how SMEs are open to external sources of information
Bibliographic record
Abstract
Purpose The purpose of this paper is to draw on recent developments in the open innovation literature to explore whether the openness of SMEs to the four categories of external sources of information (ESI) is complementary, substitute or independent, while assessing the determinants of SMEs’ openness to these ESI. Design/methodology/approach This research is based on data from a survey of 451 manufacturing SMEs in the province of Québec, Canada. Data have been elaborated through a multivariate probit model to empirically show that SMEs are considered to be simultaneously open to different ESI. The results of this study show significant heterogeneity in the determinants of SMEs’ openness to these ESI. Findings The study found that the SMEs’ openness to different ESI seems to be complementary rather than substitute; and not all variables included in the model explain the SMEs’ openness to the different ESI. Practical implications The paper provides practical implications for managers and policy makers including the SMEs’ managers’ role to recognize the consolidation of different ESI jointly instead of separately. Furthermore, managers and policy makers should attempt to provide a fair context to SMEs to manage their openness ecosystem. Originality/value This study is virtually the first to investigate both the complementarity and the determinants of SMEs’ openness to different ESI using a sophisticated econometric model.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 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".