SMEs’ Degree of Openness: The Case of Manufacturing Industries
Why this work is in the frame
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Bibliographic record
Abstract
This paper clusters SMEs based on their degree of openness. In addition, it explores both the internal and external determinants of the different clusters obtained. Based on a survey of 1214 firms in manufacturing industries and using both the dimensions of openness, breadth and depth, we find that SMEs could be clustered in four classes, depending on their degree of openness. We find that SMEs could adopt a closed, an open, an interactive or a user approach to innovation. With respect to the determinants of different classes of SMEs, the results of the logistic regression model, developed in this study, show variables such as national and regional proximities that account for explaining the likelihood that SMEs will be in a more open cluster rather than in a low open cluster. Also, this quantitative study shows that external obstacles to innovation may lead these SMEs from a closed approach to innovation to an interactive, user, or open approach to innovation. Finally, we find that the age of the firm is important in explaining the likelihood that SMEs will be in an open cluster rather than in a closed cluster.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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