Factors Influencing Collaborative Innovation Project Performance: The Case of China
Bibliographic record
Abstract
This study conducted a comprehensive and systematic investigation of the influencing factors for collaborative innovation project (CIP) performance. First, a theoretical framework model was constructed, and then a structural equation model (SEM) was used for an empirical analysis of 199 CIPs. Furthermore, we divided the factors into tangible and intangible categories and considered the impact mechanism of nine typical factors on project performance. The results are as follows: (1) All nine factors had a significant positive impact on the performance of collaborative innovation projects, among which benefit distribution and collaborative innovation capability were the most important. (2) Benefit distribution, resource dependence, organizational climate, and collaborative innovation affected project performance, both directly and indirectly. (3) Effective communication, leadership support, knowledge sharing, and collaborative innovation ability only had a direct influence, while the incentive mechanism played only an indirect role. Finally, three suggestions were put forward on the idea of high-quality, sustainable development.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".