IDEAS home Printed from https://ideas.repec.org/a/hin/complx/4301489.html
   My bibliography  Save this article

Simulation Analysis of Knowledge Transfer in a Knowledge Alliance Based on a Circular Surface Radiator Model

Author

Listed:
  • Yi Su
  • Tianchi Li

Abstract

Based on the theory of acoustic waves, a circular surface radiator model is introduced as a basis for constructing a knowledge transfer model for a knowledge alliance. The three main variables in the model are chosen to be the number of enterprises in knowledge alliance, the frequency of knowledge transfer, and the relationship distances between the knowledge bodies. The internal mechanism of knowledge transfer in a knowledge alliance is studied, and the direct relationships among the internal influencing factors are explored. The results show that the number of enterprises in knowledge alliance, knowledge transfer frequency, and knowledge transfer effect are positively correlated. The “Rayleigh distance” in the knowledge field is the appropriate relationship distance measure for assessing knowledge transfer within the alliance. The Rayleigh distance is highly correlated with the number of enterprises in knowledge alliance and knowledge transfer frequency. Moreover, the number of enterprises in knowledge alliance and knowledge transfer frequency are interrelated.

Suggested Citation

  • Yi Su & Tianchi Li, 2020. "Simulation Analysis of Knowledge Transfer in a Knowledge Alliance Based on a Circular Surface Radiator Model," Complexity, Hindawi, vol. 2020, pages 1-27, May.
  • Handle: RePEc:hin:complx:4301489
    DOI: 10.1155/2020/4301489
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/8503/2020/4301489.pdf
    Download Restriction: no

    File URL: http://downloads.hindawi.com/journals/8503/2020/4301489.xml
    Download Restriction: no

    File URL: https://libkey.io/10.1155/2020/4301489?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yingying Sun & Lei Wu & Shi Yin, 2020. "Green Innovation Risk Identification of the Manufacturing Industry under Global Value Chain Based on Grounded Theory," Sustainability, MDPI, vol. 12(24), pages 1-26, December.
    2. Wei Sun & Yi Su, 2020. "Analysing Green Forward–Reverse Logistics with NSGA-II," Sustainability, MDPI, vol. 12(15), pages 1-18, July.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hin:complx:4301489. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.