Innovation cycles impacting network effects on R&D cooperation project’s value creation
Bibliographic record
Abstract
Within the field of innovation in business network several authors raised the need for more research into the forces of innovation and efficiency. These divergent and convergent forces run together (Håkansson and Waluszewski, 2002) and can have often opposing effects on innovation outcomes (Waluszewski, 2011a, Hoholm and Olsen, 2012). Therefore, in this paper we will analyze the divergent and convergent network effects taking place in R&D cooperation projects. We investigated the combined effects of technology development, resource heterogeneity, complementarity, and actor jointness on value creation in cooperative R&D projects. Our study showed in the first place the different and opposing network effects on value creation. Secondly, it showed that the network effects are influenced by cycle of technolgy development that is controlled by the policy makers selection of projects. Finally, it showed that efficiency and innovation forces are build up from multiple network effects that can not be assigned to one of the ARA layers.
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".