European Business Review· 2015Q1
Identifying and treating unobserved heterogeneity with FIMIX-PLS: part I – method
- 1,510citations
- Q1SCImago
- 2015year
Short summary
FIMIX-PLS offers a method to identify and address unobserved heterogeneity in PLS-SEM, aiding in determining the optimal number of data segments.
AI-generated from the title and abstract; the full text is not read.
Key points
- FIMIX-PLS can identify and treat unobserved heterogeneity in PLS-SEM.
- The method is useful for determining the number of segments to extract from data.
- Future studies should consider information criteria, theory, and logic when using FIMIX-PLS.
- Alternative latent class techniques exist that address FIMIX-PLS limitations.
AI-generated from the title and abstract; the full text is not read.
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.
The authors' abstract, as published at the source. European Business Review, 2015 · DOI ↗
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Field: Organizational Behavior and Human Resource Management
Organizational Behavior and Human Resource ManagementBusiness, Management and Accounting