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Record W4413872340 · doi:10.5267/j.ijiec.2025.8.001

Stochastic evolution of interaction-coupled value co-creation behavior of enterprises in high-technology industrial clusters under the perspective of multiple networks

2025· article· en· W4413872340 on OpenAlexvenueno aff
Guangjun Ou, Tianyue Zhang

Bibliographic record

VenueInternational Journal of Industrial Engineering Computations · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCollaboration in agile enterprises
Canadian institutionsnot available
FundersNational Social Science Fund of China
KeywordsPerspective (graphical)Value (mathematics)Industrial engineeringBusinessMaterials scienceBiochemical engineeringComputer scienceEngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.437
Threshold uncertainty score0.486

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.280
Teacher spread0.266 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2025
Admission routes1
Has abstractyes

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