Clique Structure and Enterprise Innovation: an Empirical Research on China's High-end Equipment Manufacturing Industry
Bibliographic record
Abstract
Clique problem has been fully researched in the social relations network of sociology，But the cliques in inter-enterprise networks have attracted people's attention in recent years. Based on the alliance data of the high-end equipment manufacturing industry in China in 2000-2013, we construct innovation network，and use negative binomial regression models to analyze the impact of the clique structure on enterprise innovation. The results show that the more the clique numbers in the alliance innovation network, the stronger the enterprise innovation capability. Whether the enterprises listed have a negative moderating effect on the impact of clique numbers on enterprise innovation performance. That is, for the listed enterprises, more clique numbers cannot significantly promote enterprise innovation, but for non-listed enterprises, more clique numbers are conducive to enterprise innovation. Innovation accumulation has no significant positive moderating effect on the impact of clique numbers on enterprise innovation performance. The impact of the coreness values on the enterprise innovative output in an inverted-U curvilinear way, and the coreness values also moderates the effect of innovation accumulation on enterprise innovation capability in an inverted-U curvilinear way. The conclusions of the research can provide the basis for the enterprise to embed the network cliques and for the relevant government departments to formulate the alliance policy.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".