Marko Sarstedt

Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research

Purpose – The authors aim to present partial least squares (PLS) as an evolving approach to structural equation modeling (SEM), highlight its advantages and limitations and provide an overview of recent research on the method across various fields. Design/methodology/approach – In this review article, the authors merge literatures from the marketing, management, and management information systems fields to present the state-of-the art of PLS-SEM research. Furthermore, the authors meta-analyze recent review studies to shed light on popular reasons for PLS-SEM usage. Findings – PLS-SEM has experienced increasing dissemination in a variety of fields in recent years with nonnormal data, small sample sizes and the use of formative indicators being the most prominent reasons for its application. Recent methodological research has extended PLS-SEMs methodological toolbox to accommodate more complex model structures or handle data inadequacies such as heterogeneity. Research limitations/implications – While rese...

Common Beliefs and Reality About PLS Comments on Rönkkö and Evermann (2013)

This article addresses Rönkkö and Evermann’s criticisms of the partial least squares (PLS) approach to structural equation modeling. We contend that the alleged shortcomings of PLS are not due to problems with the technique, but instead to three problems with Rönkkö and Evermann’s study: (a) the adherence to the common factor model, (b) a very limited simulation designs, and (c) overstretched generalizations of their findings. Whereas Rönkkö and Evermann claim to be dispelling myths about PLS, they have in reality created new myths that we, in turn, debunk. By examining their claims, our article contributes to reestablishing a constructive discussion of the PLS method and its properties. We show that PLS does offer advantages for exploratory research and that it is a viable estimator for composite factor models. This can pose an interesting alternative if the common factor model does not hold. Therefore, we can conclude that PLS should continue to be used as an important statistical tool for management and organizational research, as well as other social science disciplines.

A Concise Guide to Market Research

▶ Compact, hands-on and step-by-step introduction to quantitative market research techniques ▶ Presents the most important techniques and shows how to translate theoretical choices into SPSS and how to analyze the output ▶ Range of education elements such as learning objectives, keywords, self-assessment tests, case studies ▶ Innovative supplementary online concept, including mobile tags, sample datasets and additional cases

Common Beliefs and Reality About PLS

This article addresses Rönkkö and Evermann’s criticisms of the partial least squares (PLS) approach to structural equation modeling. We contend that the alleged shortcomings of PLS are not due to problems with the technique, but instead to three problems with Rönkkö and Evermann’s study: (a) the adherence to the common factor model, (b) a very limited simulation designs, and (c) overstretched generalizations of their findings. Whereas Rönkkö and Evermann claim to be dispelling myths about PLS, they have in reality created new myths that we, in turn, debunk. By examining their claims, our article contributes to reestablishing a constructive discussion of the PLS method and its properties. We show that PLS does offer advantages for exploratory research and that it is a viable estimator for composite factor models. This can pose an interesting alternative if the common factor model does not hold. Therefore, we can conclude that PLS should continue to be used as an important statistical tool for management and organizational research, as well as other social science disciplines.

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

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.

Editor's comments: a critical look at the use of PLS-SEM in MIS quarterly

Wold’s (1974; 1982) partial least squares structural equation modeling (PLS-SEM) ap-proach and the advanced PLS-SEM algorithms by Lohmoller (Lohmoller 1989) have enjoyed steady popularity as a key multivariate analysis methods in management infor-mation systems (MIS) research (Gefen et al. 2011). Chin’s (1998b) scholarly work and technology acceptance model (TAM) applications (e.g., Gefen and Straub 1997) are milestones that helped to reify PLS-SEM in MIS research. In light of the proliferation of SEM techniques, Gefen et al. (2011), updating Gefen et al. (2000), presented a compre-hensive, organized, and contemporary summary of the minimum reporting requirements for SEM applications. Such guidelines are of crucial importance for advancing research for several reasons. First, researchers wishing to apply findings from prior studies or wanting to contribute to original research must comprehend other researchers’ decisions in order to under-stand the robustness of their findings. Likewise, when studies arrive at significantly different results, the natural course is to attempt explaining the differences in terms of the theory or concept employed, the empirical data used, and how the research method was applied. A lack of clarity on these issues, including the methodological applications, contradicts the goals of such studies (Jackson et al. 2009). Even worse, the misapplication of a technique may result in misinterpretations of empirical outcomes and, hence, false conclusions. Against this background, rigorous research has a long-standing tradition of critically reviewing prior practices of reporting standards and research method use (e.g., Boudreau et al. 2001). While the use of covariance-based SEM (CB-SEM) techniques has been well documented across disciplines (e.g., Medsker et al. 1994; Shook et al. 2004; Steenkamp and Baumgartner 2000), few reviews to date have investigated usage practices specific to PLS-SEM (see, however, Gefen et al. 2000). Previous reviews of such research practices were restricted to strategic management (Hulland 1999) and, more recently, marketing (Hair et al. 2012; Henseler et al. 2009), and accounting (Lee et al. 2011). The question arises as to how authors publishing in top IS journals such as MIS Quarterly have used PLS-SEM thus far, given the SEM recommendations of Gefen et al. (2011). By relating Gefen et al.’s (2011) reporting guidelines to actual practice, we attempt to identify potential problematic areas in PLS-SEM use, problems which may explain some of the criticism of how it has been applied (e.g., Marcoulides et al. 2009; Marcoulides and Saunders 2006). By reviewing previous PLS-SEM research in MIS Quarterly, we can hopefully increase awareness of established reporting standards. The results allow researchers to further improve the already good reporting practices that have been established in MIS Quarterly and other top journals and thus could become blueprints for conducting PLS-SEM analysis in other disciplines such as strategic management and marketing.

Testing measurement invariance of composites using partial least squares

Purpose – Research on international marketing usually involves comparing different groups of respondents. When using structural equation modeling (SEM), group comparisons can be misleading unless researchers establish the invariance of their measures. While methods have been proposed to analyze measurement invariance in common factor models, research lacks an approach in respect of composite models. The purpose of this paper is to present a novel three-step procedure to analyze the measurement invariance of composite models (MICOM) when using variance-based SEM, such as partial least squares (PLS) path modeling. Design/methodology/approach – A simulation study allows us to assess the suitability of the MICOM procedure to analyze the measurement invariance in PLS applications. Findings – The MICOM procedure appropriately identifies no, partial, and full measurement invariance. Research limitations/implications – The statistical power of the proposed tests requires further research, and researchers using the MICOM procedure should take potential type-II errors into account. Originality/value – The research presents a novel procedure to assess the measurement invariance in the context of composite models. Researchers in international marketing and other disciplines need to conduct this kind of assessment before undertaking multigroup analyses. They can use MICOM procedure as a standard means to assess the measurement invariance.

Structural Modeling of Heterogeneous Data with Partial Least Squares

Alongside structural equation modeling (SEM), the complementary technique of partial least squares (PLS) path modeling helps researchers understand relations among sets of observed variables. Like SEM, PLS began with an assumption of homogeneity – one population and one model – but has developed techniques for modeling data from heterogeneous populations, consistent with a marketing emphasis on segmentation. Heterogeneity can be expressed through interactions and nonlinear terms. Additionally, researchers can use multiple group analysis and latent class methods. This chapter reviews these techniques for modeling heterogeneous data in PLS, and illustrates key developments in finite mixture modeling in PLS using the SmartPLS 2.0 package.

A review of recent approaches for capturing heterogeneity in partial least squares path modelling

– The purpose of this paper is to critically review the latest approaches for capturing and explaining heterogeneity in partial least squares (PLS) path modelling and to classify these into a methodological taxonomy. Furthermore, several areas for future research effort are introduced in order to stimulate ongoing development in this important research field., – Different approaches to treat heterogeneity in PLS path models are introduced, critically evaluated and classified into a methodological taxonomy. Future research directions are derived from a comparison of benefits and limitations of the procedures., – The review reveals that finite mixture‐PLS can be regarded as the most comprehensive and commonly used procedure for capturing heterogeneity within a PLS path modelling framework. However, further research is necessary to explore the capabilities and limitations of the approach., – Directions for additional research, common to most latent class detection procedures include the verification and comparison of available approaches, the handling of large data sets, the allowance of varying structures of path models, the profiling of segments and the problem of model selection., – Whereas modelling heterogeneity in covariance structure analysis has been studied for several years, research interest has only recently been devoted to the question of clustering in PLS path modelling. This is the first contribution which critically consolidates available approaches, discloses problematic aspects and addresses significant areas for future research.

Uncovering and Treating Unobserved Heterogeneity with Fimix-Pls: Which Model Selection Criterion Provides an Appropriate Number of Segments?

Since its first introduction in the Schmalenbach Business Review, Hahn et al.’s (2002) finite mixture partial least squares (FIMIX-PLS) approach to response-based segmentation in variance-based structural equation modeling has received much attention from the marketing and management disciplines. When applying FIMIX-PLS to uncover unobserved heterogeneity, the actual number of segments is usually unknown. As in any clustering procedure, retaining a suitable number of segments is crucial, since many managerial decisions are based on this result. In empirical research, applications of FIMIX-PLS rely on information and classification criteria to select an appropriate number of segments to retain from the data. However, the performance and robustness of these criteria in determining an adequate number of segments has not yet been investigated scientifically in the context of FIMIX-PLS. By conducting computational experiments, this study provides an evaluation of several model selection criteria’s performance and of different data characteristics’ influence on the robustness of the criteria. The results engender key recommendations and identify appropriate model selection criteria for FIMIX-PLS. The study’s findings enhance the applicability of FIMIX-PLS in both theory and practice.