Identifying and Treating Unobserved Heterogeneity with FIMIX-PLS: Part 2 - Case Study
Marketing & Professional Sales
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.
European Business Review
Digital Object Identifier (DOI)
Lucy M Matthews , Marko Sarstedt , Joseph F. Hair , Christian M. Ringle , (2016) "Identifying and treating unobserved heterogeneity with FIMIX-PLS: Part II – A case study", European Business Review, Vol. 28 Iss: 2, pp.208 - 224