Treating unobserved heterogeneity in PLS path modeling: a comparison of FIMIX-PLS with different data analysis strategies

Sarstedt, Marko und Ringle, Christian M. (2010): Treating unobserved heterogeneity in PLS path modeling: a comparison of FIMIX-PLS with different data analysis strategies. In: Journal of Applied Statistics, Bd. 37, Nr. 8: S. 1299-1318

Abstract

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.

• Dokumententyp: Zeitschriftenartikel
• Publikationsform: Publisher's Version
• Fakultät: Betriebswirtschaft > Institut für Marktorientierte Unternehmensführung
• Themengebiete: 300 Sozialwissenschaften > 330 Wirtschaft
• ISSN: 0266-4763
• Sprache: Englisch