IDEAS home Printed from https://ideas.repec.org/a/eee/tefoso/v79y2012i8p1537-1547.html
   My bibliography  Save this article

Nonlinear influence on R&D project performance

Author

Listed:
  • Chen, Yu-Shan
  • Chang, Ke-Chiun
  • Chang, Ching-Hsun

Abstract

This study applies artificial neural network (ANN) to explore the relationships between the performance of R&D projects and its determinants. The results indicate that the quality of project environment has an inverse U-shaped effect on the performance of R&D projects, and both project managers' skills and the effectiveness of teamwork have monotonic positive influences on it. Besides, this study utilizes self-organizing map (SOM) to classify the Taiwanese information and electronics companies into three groups and further provides some suggestions. In addition, this paper uses an in-depth interview of qualitative research to explore why the quality of project environment has an inverse U-shaped effect on the R&D project performance, and finds out the main reason. There are two managerial implications in this study. First, the relationships between the performance of R&D projects and its determinants are not always linear in the complex and uncertain environment nowadays. Second, companies must care about the inverse U-shaped effect of the quality of project environment on the performance of R&D projects, although they can enhance the extent of project managers' skills and the effectiveness of their teamwork as much as possible.

Suggested Citation

  • Chen, Yu-Shan & Chang, Ke-Chiun & Chang, Ching-Hsun, 2012. "Nonlinear influence on R&D project performance," Technological Forecasting and Social Change, Elsevier, vol. 79(8), pages 1537-1547.
    • Handle: RePEc:eee:tefoso:v:79:y:2012:i:8:p:1537-1547
    DOI: 10.1016/j.techfore.2012.04.007
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0040162512000820
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.techfore.2012.04.007?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

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


    Cited by:

    1. María Jesús Rodríguez-Gulías & David Rodeiro-Pazos & Sara Fernández-López & Manuel Ángel Nogueira-Moreiras, 2021. "The effect of regional resources on innovation: a firm-centered approach," The Journal of Technology Transfer, Springer, vol. 46(3), pages 760-791, June.
    2. Lee, Changyong & Kwon, Ohjin & Kim, Myeongjung & Kwon, Daeil, 2018. "Early identification of emerging technologies: A machine learning approach using multiple patent indicators," Technological Forecasting and Social Change, Elsevier, vol. 127(C), pages 291-303.

    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:eee:tefoso:v:79:y:2012:i:8:p:1537-1547. 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: Catherine Liu (email available below). General contact details of provider: http://www.sciencedirect.com/science/journal/00401625 .

    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.