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Identifying and treating unobserved heterogeneity with FIMIX-PLS: part I – method

Joe F. Hair, Jr. (Kennesaw State University, Kennesaw, Georgia, USA)
Marko Sarstedt (Faculty of Economics and Management, Otto-von-Guericke-University Magdeburg, Magdeburg, Germany and Faculty of Business and Law, University of Newcastle, Newcastle, Australia)
Lucy M Matthews (Department of Marketing, Middle Tennessee State University, Murfreesboro, Tennessee, 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: 11 January 2016

6940

Abstract

Purpose

The purpose of this paper is to provide an overview of unobserved heterogeneity in the context of partial least squares structural equation modeling (PLS-SEM), its prevalence and challenges for social science researchers. Part II – in the next issue (European Business Review, Vol. 28 No. 2) – presents a case study, which illustrates how to identify and treat unobserved heterogeneity in PLS-SEM using the finite mixture PLS (FIMIX-PLS) module in the SmartPLS 3 software.

Design/methodology/approach

The paper merges literatures from various disciplines, such as management information systems, marketing and statistics, to present a state-of-the-art review of FIMIX-PLS. Based on this review, the paper offers guidelines on how to apply the technique to specific research problems.

Findings

FIMIX-PLS offers a means to identify and treat unobserved heterogeneity in PLS-SEM and is particularly useful for determining the number of segments to extract from the data. In the latter respect, prior applications of FIMIX-PLS restricted their focus to a very limited set of criteria, but future studies should broaden the scope by considering information criteria, theory and logic.

Research limitations/implications

Since the introduction of FIMIX-PLS, a range of alternative latent class techniques have emerged to address some of the limitations of the approach relating, for example, to the technique’s inability to handle heterogeneity in the measurement models and its distributional assumptions. The second part of this article (Part II) discusses alternative latent class techniques in greater detail and calls for the joint use of FIMIX-PLS and PLS prediction-oriented segmentation.

Originality/value

This paper 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 in Part I, Part II follows up by offering a step-by-step tutorial on how to use FIMIX-PLS in SmartPLS 3.

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

Hair, Jr., J.F., Sarstedt, M., Matthews, L.M. and Ringle, C.M. (2016), "Identifying and treating unobserved heterogeneity with FIMIX-PLS: part I – method", European Business Review, Vol. 28 No. 1, pp. 63-76. https://doi.org/10.1108/EBR-09-2015-0094

Publisher

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Emerald Group Publishing Limited

Copyright © 2016, Emerald Group Publishing Limited

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