Stochastic evolution of interaction-coupled value co-creation behavior of enterprises in high-technology industrial clusters under the perspective of multiple networks
Bibliographic record
Abstract
Collaboration and innovation among entities within high-tech industrial clusters are fundamental for establishing and augmenting their competitiveness, as well as ensuring the resilience and security of their supply chains. The article examines the adaptive evolution of value co-creation behavior among cluster enterprises by considering the interplay of various network attributes. It develops a stochastic evolution game model to analyze the interactive value co-creation behavior and performs a dynamic evolution simulation analysis. The study's findings indicate a critical threshold for the enhancement of knowledge complementarity and specialization superposition, which fosters the interactive coupling value co-creation behavior among cluster enterprises. This phenomenon exhibits an “inverted U-shaped” evolutionary pattern, initially promoting and subsequently suppressing such behavior. Notably, the critical threshold for specialization superposition is significantly lower than that for knowledge complementarity. Additionally, an increase in relationship strength can enhance the high interactive coupling value co-creation behavior of cluster enterprises. The enhancement of relational strength can result in significant interactive coupling and value co-creation among cluster enterprises; however, environmental random interference factors will not influence the final outcomes but will complicate the interactive coupling process among these enterprises. The government, in formulating adaptive policies for cluster innovation, should emphasize the enhancement of knowledge ecological niches within cluster enterprises, bolster specialization in industry segments, facilitate the establishment of interconnected industrial value chains, and foster trusting networks among cluster enterprises. This approach aims to mitigate the impact of environmental random interference factors, thereby promoting efficient interactive coupling and value co-creation among cluster enterprises. The interactive coupling value of co-creative activity among cluster firms can be effectively and continually developed.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".