IDEAS home Printed from https://ideas.repec.org/a/igg/jkss00/v7y2016i3p1-18.html
   My bibliography  Save this article

AHP-Driven Knowledge Leakage Risk Assessment Model: A Construct-Apply-Control Cycle Approach

Author

Listed:
  • Haley Wing Chi Tsang

    (Knowledge Management and Innovation Research Centre, The Hong Kong Polytechnic University, Hong Kong)

  • Wing Bun Lee

    (Knowledge Management and Innovation Research Centre, The Hong Kong Polytechnic University, Hong Kong)

  • Eric Tsui

    (Knowledge Management and Innovation Research Centre, The Hong Kong Polytechnic University, Hong Kong)

Abstract

Intellectual Capital (IC) is becoming more widely understood by the academic and business communities, especially its important role in value creation of an organization. However, few people are aware that IC, if not managed properly, may also pose threats, sometime serious, to an organization. Knowledge leakage from an organization, for example, may come about when an experienced employee leaves for another job. Knowledge leakage is pervasive throughout an organization but is seldom noticed until the consequence is felt. This intellectual capital risk has to be systematically and effectively identified, assessed and controlled in the whole value chain of an organization. An AHP (Analytic Hierarchy Process) based multi-dimensional decision making and assessment model is developed to determine knowledge leakage risk in an organization.

Suggested Citation

  • Haley Wing Chi Tsang & Wing Bun Lee & Eric Tsui, 2016. "AHP-Driven Knowledge Leakage Risk Assessment Model: A Construct-Apply-Control Cycle Approach," International Journal of Knowledge and Systems Science (IJKSS), IGI Global, vol. 7(3), pages 1-18, July.
  • Handle: RePEc:igg:jkss00:v:7:y:2016:i:3:p:1-18
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJKSS.2016070101
    Download Restriction: no
    ---><---

    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:igg:jkss00:v:7:y:2016:i:3:p:1-18. 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: Journal Editor (email available below). General contact details of provider: https://www.igi-global.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.