Organizing NPD networks for high innovation performance: the case of Dutch medical devices SMEs
Bibliographic record
Abstract
This research examines which combination of network characteristics (the network configuration) leads to high innovation performance for small and medium sized companies (SMEs). Even though research has paid significant attention to the relation between the external network and the innovation performance of SMEs, research has not yet clearly demonstrated which configurations most affect innovation in particular contexts. The context of the research is the Dutch medical devices sector. This sector is selected because collaboration with external partners for new product development means becomes increasingly important due to the complexity of the products and the fragmentation of the market. In addition the sector is characterized by very strict regulations. These regulations are the cause of the time and cost consuming product development process. In triangulation with quantitative survey data (N=60), qualitative data was gathered through semi-structured interviews in these same companies (N=50), which resulted in a response rate of 61,9%. The systems approach was used to construct the successful network configuration that is related to high innovation performance. By using this approach we are able to simultaneously address multiple network characteristics. Correlation statistics between the Innovation Performance and the Euclidean Distance showed that the more a companies’ network configuration differed from the successful network configuration, the lower the Innovation Performance of that company. Contrary to what we hypothesized from literature, the results of the social systems approach indicate that the network configuration that is related to high innovation performance includes high levels of resource complementarity and goal alignment, and low levels of trust and network position strength. Instead of the social way of networking, both our quantitative and qualitative findings show that a “businesslike” approach which is focused and consistent is related to high innovation performance.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".