To read the full version of this content please select one of the options below:

Identifying and treating unobserved heterogeneity with FIMIX-PLS: Part II – A case study

Lucy M. Matthews (Department of Marketing, Middle Tennessee State University, Murfreesboro, Tennessee, USA)
Marko Sarstedt (Faculty of Economics and Management, Otto-von-Guericke-University, Magdeburg, Germany, and Faculty of Business and Law, University of Newcastle, Newcastle, Australia)
Joseph F. Hair (Coles College of Business, Kennesaw State University, Kennesaw, Georgia, USA)
Christian M. Ringle (Institute of Human Resource Management and Organizations, Hamburg University of Technology (TUHH), Hamburg, Germany, and Faculty of Business and Law, University of Newcastle, Newcastle, Australia)

European Business Review

ISSN: 0955-534X

Article publication date: 14 March 2016

Abstract

Purpose

Part I of this article (European Business Review, Volume 28, Issue 1) offered an overview of unobserved heterogeneity in the context of partial least squares structural equation modeling (PLS-SEM), its prevalence and challenges for social sciences researchers. This paper aims to provide an example that explains how to identify and treat unobserved heterogeneity in PLS-SEM by using the finite mixture PLS (FIMIX-PLS) module in the SmartPLS 3 software (Part II).

Design/methodology/approach

This case study illustrates the application of FIMIX-PLS using a popular corporate reputation model.

Findings

The case study demonstrates the capability of FIMIX-PLS to identify whether unobserved heterogeneity significantly affects structural model relationships. Furthermore, it shows that FIMIX-PLS is particularly useful for determining the number of segments to extract from the data.

Research limitations/implications

Since the introduction of FIMIX-PLS, a range of alternative latent class techniques has appeared. These techniques address some of the limitations of the approach relating to, for example, its failure to handle heterogeneity in measurement models, or its distributional assumptions. This research discusses alternative latent class techniques and calls for the joint use of FIMIX-PLS and PLS prediction-oriented segmentation.

Originality/value

This article is the first to offer researchers, who have not been exposed to the method, an introduction to FIMIX-PLS. Based on a state-of-the-art review of the technique, the paper offers a step-by-step tutorial on how to use FIMIX-PLS by using the SmartPLS 3 software.

Keywords

Acknowledgements

This article refers to the FIMIX-PLS module of the SmartPLS 3 software (www.smartpls.com). Christian M. Ringle acknowledges a financial interest in SmartPLS.

Citation

Matthews, L.M., Sarstedt, M., Hair, J.F. and Ringle, C.M. (2016), "Identifying and treating unobserved heterogeneity with FIMIX-PLS: Part II – A case study", European Business Review, Vol. 28 No. 2, pp. 208-224. https://doi.org/10.1108/EBR-09-2015-0095

Publisher

:

Emerald Group Publishing Limited

Copyright © 2016, Emerald Group Publishing Limited