Create a Win-Win Situation between the Knowledge Diffusion and the Benefits by Placing the Cost at the Threshold

Abstract:

Collaboration on knowledge is an essential channel for fostering the production and integration of knowledge. Knowledge collaboration user interactions can evolve into a network for knowledge collaboration. The "resources" variable has a significant effect on knowledge diffusion in the actual world. This paper examines the impact of resource production and consumption processes on the knowledge diffusion. We construct the knowledge diffusion model and determine the threshold for knowledge diffusion's propagation. We analyze the existing collaboration network dataset, Erdos Collaboration Network (ERDOS), and demonstrate that it exhibits clustering and small-world behavior. Using ERDOS data, we investigate the effect of resource generation and consumption processes on knowledge diffusion, as well as the role of self-learning and review mechanisms in this process. In addition, we find that the steady-state density of informed users is insensitive to both the benchmark knowledge diffusion rate and the maximum resource-mediated knowledge diffusion rate. In the actual world, managers can set the cost at the threshold, creating a win-win situation between the degree of knowledge diffusion and the benefits.

• Periodical: Advances in Science and Technology (Volume 144)
• Pages: 39-46
• DOI: https://doi.org/10.4028/p-n3MXvc
• Online since: March 2024
• Authors: Ya Nan Wang*, Jun Wang
• Keywords: Knowledge Collaboration Networks, Knowledge Diffusion Model, Mean-Field Method, Resource Constraints

References

[1] H. Huang, Y. Ma, Modeling the competitive diffusions of rumor and knowledge and the impacts on epidemic spreading, Appl. Math. Comput. 388 (2021) 125536.

DOI: 10.1016/j.amc.2020.125536

[2] L. Xu, R. Ding, L. Wang, How to facilitate knowledge diffusion in collaborative innovation projects by adjusting network density and project roles, Scientometrics 127 (2022) 1353–1379.

DOI: 10.1007/s11192-021-04255-9

[3] J. Jang, X. Ju, U. Ryu, H. Om, Coevolutionary characteristics of knowledge diffusion and knowledge network structures: A GA-ABM model, JASSS 22 (2019) 3.

DOI: 10.18564/jasss.4037

[4] Z. Yue, H. Xu, G. Yuan, Y. Qi, Modeling knowledge diffusion in the disciplinary citation network based on differential dynamics, Scientometrics 127 (2022) 7593–7613.

DOI: 10.1007/s11192-022-04491-7

[5] C.M. Tsai, The knowledge diffusion model associated with innovative knowledge, Expert Syst. Appl. 36 (2009) 11323-11331.

DOI: 10.1016/j.eswa.2009.03.044

[6] H. Wang, J. Wang, M. Small, Knowledge transmission model with differing initial transmission and retransmission process, Physica A 507 (2018) 478-488.

DOI: 10.1016/j.physa.2018.05.041

[7] H. Zhu, J. Ma, Knowledge diffusion in complex networks by considering time-varying information channels, Physica A 494 (2018) 225-235.

DOI: 10.1016/j.physa.2017.12.046

[8] Y. Zhang, X. Li, M.A. Aziz-alaoui, C. Bertelle, J. Guan, S. Zhou, Knowledge diffusion in complex networks, Concurr. Comp-Pract E. 29 (2016) 1-13.

[9] X. Gao, J. Guan, Network model of knowledge diffusion, Scientometrics 90 (2011) 749-762.

DOI: 10.1007/s11192-011-0554-z

[10] R. Cowan, Network models of innovation and knowledge diffusion, General Information 16 (2004) 155-174.

[11] R. Cowan, N. Jonard, Network structure and the diffusion of knowledge, J. Econ. Dyn. Control 28 (2004) 1557–1575.

[12] D.J. Watts, S.H. Strogatz, Collective dynamics of small-world networks, Nature (London) 393 (1998) 440–442.

DOI: 10.1038/30918

[13] X. Gao, L. Tian, W. Li, Coupling interaction impairs knowledge and green behavior diffusion in complex networks, J. Clean Prod. 249 (2020) 119419.

DOI: 10.1016/j.jclepro.2019.119419

[14] Y. Wang, J. Wang, R. Zhang, O. Liu, Enhanced by mobility? Effect of users' mobility on information diffusion in coupled online social networks, Physica A 607 (2022) 128201.

DOI: 10.1016/j.physa.2022.128201

[15] Y. Wang, J. Wang, H. Wang, R. Zhang, O. Liu, Users' mobility enhances information diffusion in online social networks, Inform. Sciences 546 (2021) 329-348.

DOI: 10.1016/j.ins.2020.07.061