Explore the Optimal Node Degree of Interfirm Network for Efficient Knowledge Sharing
Bibliographic record
Abstract
Background: Network structure is a critical issue for efficient interfirm knowledge sharing. The optimal node degree turns out to be decisive because it is generally regarded as a core proxy of network structural characteristics. This paper is to examine what is the optimal node degree for an efficient network structure. Methods: Based on an interaction rule combining the barter rule and the gift rule, we first describe and then build a knowledge diffusion process. Then using four factors, namely network size, network randomness, knowledge endowment of network, and knowledge stock of each firm, we examine the factors that influence the optimal node degree for efficient knowledge sharing. Results: The simulation results show that the optimal node degree can be determined along the change in outer factors. Furthermore, changing the network randomness and network size has little impact on node degree. Instead, knowledge endowment of network and knowledge stock of each firm both have significant impact on the node degree. Conclusion: We find that an optimal node degree can always be found in any condition, which confirms the existence of a balanced state. Thus, policymakers can determine the appropriate number of links to avoid redundancy and thus reduce cost in interfirm networks. We also examine how different factors influence the size of the optimal node degree, and as a result, policymakers can set an appropriate number of links under different situations.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".