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

Pasquale Dolce (), Vincenzo Esposito Vinzi () and Carlo Lauro ()
Additional contact information
Pasquale Dolce: Oniris, StatSC
Vincenzo Esposito Vinzi: ESSEC Business School
Carlo Lauro: University of Naples “Federico II”

Chapter Chapter 7 in Partial Least Squares Path Modeling, 2017, pp 153-172 from Springer

Abstract: 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.

Date: 2017
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-64069-3_7

Ordering information: This item can be ordered from
http://www.springer.com/9783319640693

DOI: 10.1007/978-3-319-64069-3_7

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().