Assimilation Patterns in the Use of Advanced Manufacturing Technologies in SMEs: Exploring their Effects on Product Innovation Performance
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Bibliographic record
Abstract
Manufacturing small and medium-sized enterprises (SMEs) are more and more adopting advanced manufacturing technologies (AMT) aimed at fostering product innovation process, improving product quality, streamlining the production process, and gaining productivity. In this study, we analyze the relationship between AMT proficiency levels in manufacturing SMEs and product innovation performance. Using data from 616 manufacturing SMEs, and considering a wide range of various AMT (20 different types of AMT grouped into 5 categories), we derived three AMT assimilation patterns through a cluster analysis procedure combining hierarchical and non-hierarchical clustering algorithms. The analysis of the relationship between AMT assimilation patterns and product innovation performance shows a rather unexpected picture: in spite of the existence of clearly distinct patterns of AMT assimilation, we find no significant relationship between any pattern and product innovation performance. Instead, we find the organizational and environmental context of SMEs to be more determinant for product innovation performance than any of the AMT assimilation patterns. From a practical point of view, this study indicates that manufacturing SMEs managers interested in fostering their innovation capabilities through AMT assimilation need to be aware of the contingency effects of their organizational size, age, and sector of activity.
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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 | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| 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