Options
Treating unobserved heterogeneity in PLS path modeling: A comparison of FIMIX-PLS with different data analysis strategies
Publikationstyp
Journal Article
Date Issued
2010-08-20
Sprache
English
Author(s)
TORE-URI
Journal
Volume
37
Issue
8
Start Page
1299
End Page
1318
Citation
Journal of Applied Statistics 37 (8): 1299-1318 (2010-08-20)
Publisher DOI
In the social science disciplines, the assumption that the data stem from a single homogeneous population is often unrealistic in respect of empirical research. When applying a causal modeling approach, such as partial least squares path modeling, segmentation is a key issue in coping with the problem of heterogeneity in the estimated cause-effect relationships. This article uses the novel finite-mixture partial least squares (FIMIX-PLS) method to uncover unobserved heterogeneity in a complex path modeling example in the field of marketing. An evaluation of the results includes a comparison with the outcomes of several data analysis strategies based on a priori information or k-means cluster analysis. The results of this article underpin the effectiveness and the advantageous capabilities of FIMIX-PLS in general PLS path model set-ups by means of empirical data and formative as well as reflective measurement models. Consequently, this research substantiates the general applicability of FIMIX-PLS to path modeling as a standard means of evaluating PLS results by addressing the problem of unobserved heterogeneity.
Subjects
Corporate reputation
Finite mixture
Heterogeneity
Latent class
Market segmentation
Partial least square (pls)
Path modeling
DDC Class
000: Allgemeines, Wissenschaft