IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-3-319-64069-3_7.html
   My bibliography  Save this book chapter

Predictive Path Modeling Through PLS and Other Component-Based Approaches: Methodological Issues and Performance Evaluation

In: Partial Least Squares Path Modeling

Author

Listed:
  • Pasquale Dolce

    (Oniris, StatSC)

  • Vincenzo Esposito Vinzi

    (ESSEC Business School)

  • Carlo Lauro

    (University of Naples “Federico II”)

Abstract

This chapter deals with the predictive use of PLS-PM and related component-based methods in an attempt to contribute to the recent debates on the suitability of PLS-PM for predictive purposes. Appropriate measures and evaluation criteria for the assessment of models in terms of predictive ability are more and more desirable in PLS-PM. The performance of the models can be improved by choosing the appropriate parameter estimation procedure among the different existing ones or by making developments and modifications of the latter. A recent example of this type of work is the non-symmetrical approach for component-based path modeling, which leads to a new method, called non-symmetrical composite-based path modeling. In the composites construction stage, this new method explicitly takes into account the directions of the relationships in the inner model. Results are promising for this new method, especially in terms of predictive relevance.

Suggested Citation

  • Pasquale Dolce & Vincenzo Esposito Vinzi & Carlo Lauro, 2017. "Predictive Path Modeling Through PLS and Other Component-Based Approaches: Methodological Issues and Performance Evaluation," Springer Books, in: Hengky Latan & Richard Noonan (ed.), Partial Least Squares Path Modeling, chapter 0, pages 153-172, Springer.
  • Handle: RePEc:spr:sprchp:978-3-319-64069-3_7
    DOI: 10.1007/978-3-319-64069-3_7
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    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:spr:sprchp:978-3-319-64069-3_7. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.