Abstract
Background: Network structure is a critical issue for efficient inter-firm knowledge sharing. The optimal node degree plays a major role because it is generally regarded as a core proxy of network structural characteristics. This paper aims 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, this study first describes and then builds a knowledge diffusion process. Then using four factors, namely network size, network randomness, knowledge endowment of network, and knowledge stock of each firm, we examined 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 with the change in external factors. Furthermore, changing the network randomness and network size has a little impact on the node degree. Instead, both knowledge endowment of network and knowledge stock of each firm have a significant impact on the node degree. Conclusion: It has been found 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 examined 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.Keywords: Knowledge diffusion, knowledge sharing, modeling and simulation, network structure, node degree, social network.
Graphical Abstract
Related Journals
International Journal of Sensors, Wireless Communications and Control
Related Books
Challenges and Opportunities for Deep Learning Applications in Industry 4.0
Recent Advances in IoT and Blockchain Technology
Augmented Intelligence: Deep Learning, Machine Learning, Cognitive Computing, Educational Data Mining
Human-Computer Interaction and Beyond: Advances Towards Smart and Interconnected Environments Part II
Machine Learning and Its Application: A Quick Guide for Beginners
Computational Intelligence For Data Analysis
Applied Machine Learning and Multi-Criteria Decision-Making in Healthcare
Advanced Computing Techniques: Implementation, Informatics and Emerging Technologies
How to Design Optimization Algorithms by Applying Natural Behavioral Patterns
Human-Computer Interaction and Beyond: Advances Towards Smart and Interconnected Environments (Part I)
3