IDEAS home Printed from https://ideas.repec.org/a/hin/jnddns/4602870.html
   My bibliography  Save this article

Construction of an Early Risk Warning Model of Organizational Resilience: An Empirical Study Based on Samples of R&D Teams

Author

Listed:
  • Si-hua Chen

Abstract

Facing fierce competition, it is critical for organizations to keep advantages either actively or passively. Organizational resilience is the ability of an organization to anticipate, prepare for, respond to, and adapt to incremental change and sudden disruptions in order to survive and prosper. It is of particular importance for enterprises to apprehend the intensity of organizational resilience and thereby judge their abilities to withstand pressure. By conducting an exploratory factor analysis and a confirmatory factor analysis, this paper clarifies a five-factor model for organizational resilience of R&D teams. Moreover, based on it, this paper applies fuzzy integrated evaluation method to build an early risk warning model for organizational resilience of R&D teams. The application of the model to a company shows that the model can adequately evaluate the intensity of organizational resilience of R&D teams. The results are also supposed to contribute to applied early risk warning theory.

Suggested Citation

  • Si-hua Chen, 2016. "Construction of an Early Risk Warning Model of Organizational Resilience: An Empirical Study Based on Samples of R&D Teams," Discrete Dynamics in Nature and Society, Hindawi, vol. 2016, pages 1-9, May.
    • Handle: RePEc:hin:jnddns:4602870
    DOI: 10.1155/2016/4602870
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/DDNS/2016/4602870.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/DDNS/2016/4602870.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2016/4602870?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:jnddns:4602870. 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.