IDEAS home Printed from https://ideas.repec.org/a/hin/jnlmpe/8072941.html
   My bibliography  Save this article

The Relationship between Social Entrepreneurship Capability of SOM Neural Network Algorithm and New Enterprise Performance

Author

Listed:
  • Tianhua Li
  • Wei Liu

Abstract

As an important factor in the creation and growth of new enterprises, social entrepreneurship and new enterprise performance have attracted more and more attention from scholars in recent years. This research analyzes the network relationship of new companies on the basis of existing entrepreneurial network research. This paper proposes social entrepreneurial capabilities and new company performance based on the SOM neural network algorithm to solve these problems and then establishes entrepreneurial networks, organizational learning, and new business performance models. The method of this paper is to study the SOM neural network algorithm and then establish the entrepreneurial ability and enterprise performance evaluation system. The function of these methods is to put forward the meaning and research of venture capital. It also defines the meaning and research of innovation capabilities based on innovation theory, ensuring the scientific nature of the evaluation indicators, evaluation standards, and evaluation processes of innovative enterprises. In this survey, this paper conducted a field survey in Shanxi Province, China, and analyzed the internal impact of the network of social entrepreneurship and new companies on corporate performance. The survey results show that the value of the correlation β between entrepreneurial orientation and entrepreneurial environment dynamics is 0.167 (P

Suggested Citation

  • Tianhua Li & Wei Liu, 2022. "The Relationship between Social Entrepreneurship Capability of SOM Neural Network Algorithm and New Enterprise Performance," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-12, August.
  • Handle: RePEc:hin:jnlmpe:8072941
    DOI: 10.1155/2022/8072941
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/mpe/2022/8072941.pdf
    Download Restriction: no

    File URL: http://downloads.hindawi.com/journals/mpe/2022/8072941.xml
    Download Restriction: no

    File URL: https://libkey.io/10.1155/2022/8072941?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
    ---><---

    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:jnlmpe:8072941. 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.