Key Resource Sharing and Sustainable Innovation in Innovation Consortium: A Multiagent Collaboration and Multihelix Perspective
Bibliographic record
Abstract
ABSTRACT Faced with the intensification of international competition, the innovation consortium, as an important open innovation paradigm to promote the deep integration of industrial chain and innovation chain, has become a key path for the breakthrough of industrial core technology. However, there are some practical challenges in forming an innovation consortium, such as prominent sharing barriers of key resources and insufficient collaborative efficiency, which make it challenging to meet the needs of sustainable innovation. Based on this, this paper breaks through the traditional linear collaboration analysis framework. It constructs a game model of “leading enterprise‐cooperative organization‐government” to describe the interaction mechanism of multiagent key resource sharing decision‐making in an information asymmetric environment from the perspective of multiagent collaboration and multihelix. Vensim‐PLE simulation software is used to simulate and analyze the influencing factors of multihelix of key resource sharing. The results show that the degree of participation of various agents in sharing key resources is closely related to the sharing ability, cost, benefit, and coordination mechanism. In addition, improving the effectiveness of sharing key resources, enhancing the willingness and scope of sharing, and optimizing resource potential differentials can significantly promote the effect of multihelix of key resources. Therefore, this paper puts forward relevant management suggestions on improving innovation consortium's incentive and constraint mechanism, dynamic selection of partners, and construction of pricing and compensation mechanisms to enhance the innovation consortium's sustainable innovation. To sum up, this study not only expands the research paradigm of open innovation and enriches the connotation dimension of collaborative innovation theory but also provides new ideas for innovation consortiums under competitive environment to solve the dilemma of “resource island” and achieve sustainable collaborative innovation, which has significant practical value for guiding the breakthrough of industrial core technologies.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".