Problems, Common Beliefs and Procedures on the Use of Partial Least Squares Structural Equation Modeling in Business Research (original) (raw)

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

European Business Review, 2014

Purpose: We 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 we merge literatures from the marketing, management, and management information systems fields to present the state-of-the art of PLS-SEM research. Furthermore, we 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 non-normal data, small sample sizes and the use of formative indicators being the most prominent reasons for its application. Recent methodological research has extended PLS-SEM’s methodological toolbox to accommodate more complex model structures or handle data inadequacies such as heterogeneity. Research limitations/implications: While research on the PLS...

Partial least squares structural equation modeling (PLS-SEM)

European Business Review, 2014

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-SEM's methodological toolbox to accommodate more complex model structures or handle data inadequacies such as heterogeneity. Research limitation...

An assessment of the use of partial least squares structural equation modeling in marketing research

Journal of The Academy of Marketing Science, 2012

Most methodological fields undertake regular critical reflections to ensure rigorous research and publication practices, and, consequently, acceptance in their domain. Interestingly, relatively little attention has been paid to assessing the use of partial least squares structural equation modeling (PLS-SEM) in marketing research—despite its increasing popularity in recent years. To fill this gap, we conducted an extensive search in the 30 top ranked marketing journals that allowed us to identify 204 PLS-SEM applications published in a 30-year period (1981 to 2010). A critical analysis of these articles addresses, amongst others, the following key methodological issues: reasons for using PLS-SEM, data and model characteristics, outer and inner model evaluations, and reporting. We also give an overview of the interdependencies between researchers’ choices, identify potential problem areas, and discuss their implications. On the basis of our findings, we provide comprehensive guidelines to aid researchers in avoiding common pitfalls in PLS-SEM use. This study is important for researchers and practitioners, as PLS-SEM requires several critical choices that, if not made correctly, can lead to improper findings, interpretations, and conclusions.

Partial Least Squares Structural Equation Modeling (PLS-SEM) Techniques Using SmartPLS

SmartPLS is one of the prominent software applications for Partial Least Squares Structural Equation Modeling (PLS-SEM). It was developed by Ringle, . The software has gained popularity since its launch in 2005 not only because it is freely available to academics and researchers, but also because it has a friendly user interface and advanced reporting features. Although an extensive number of journal articles have been published on the topic of PLS modeling, the amount of instructional materials available for this software is limited. This paper is written to address this knowledge gap and help beginners to understand how PLS-SEM can be used in marketing research.

Structural equation modeling and regression: Guidelines for research …

The growing interest in Structured Equation Modeling (SEM) techniques and recognition of their importance in IS research suggests the need to compare and contrast different types of SEM techniques so that research designs can be appropriately selected. After assessing the extent to which these techniques are currently being used in IS research, the article presents a running example which analyzes the same dataset via three very different statistical techniques. It then compares two classes of SEM: covariance-based SEM and partial-least-squaresbased SEM. Finally, the article discusses linear regression models and offers guidelines as to when SEM techniques and when regression techniques should be used. The article concludes with heuristics and rule of thumb thresholds to guide practice, and a discussion of the extent to which practice is in accord with these guidelines. Note: The paper is written in such a way that readers with basic knowledge of multivariate statistics can follow the logic and examples. It does not assume the reader is already conversant with LISREL, PLS, or other SEM tools. This tutorial contains:

Structural equation modeling-A second-generation multivariate analysis

The application of Structural Equation Modeling (SEM) in the business research is growing. The second-generation multivariate data analysis technique, SEM is easy to use and provides a high quality statistical analysis. Many visual SEM software programs help in a quick design of the theoretical model and to modify them graphically using simple drawing tools. Further it can estimate the model’s fit, make any modifications and arrive at a final valid model. It is essential to understand how to design the research process appropriate to the SEM analysis. This article describes the steps in SEM analysis, the conventions used in presenting a model, the elements of the SEM technique, the interpretation of the SEM results. The rule of thumb that evaluates the results and the other issues in SEM reporting are discussed.

(CHAPTER 1)REVIEW ON PARTIAL LEAST SQUARE STRUCTURAL EQUATION MODELING (PLS-SEM) USING SMARTPLS 2.0

Structural Equation Modeling (SEM) are complex models allowing us to study the real world complexity by taking into account a whole number of causal relationships among latent concept (i.e. the Latent variables, LVs), each measured by several observed indicators usually defined as Manifest Variables (Esposito Vinzi, 2014) Covariance-based/ Factor based Methods: 1. The aim is to reproduce the sample covariance matrix of the manifest variables by means of the model parameters:  The implied covariance matrix of the manifest variable is a function of the model parameters  It is a confirmatory approach aiming at the validating a model (theory building)  The latent variables are equivalent to common factors (Theoretical and random variables) Variance based/ Composite based / Component based Method 2. The aim is to provide latent variable scores (proxy, composites, factor scores) that are the most correlated to each other as possible and the most representative of their own block of manifest variables  It focuses on latent variable scores computation  It focuses on explaining variances  It is more an explanatory approach than a confirmatory one (operational model strategy)  The latent variables are defined as components or weighted sums of the manifest variables (Fixed variables: linear composites, scores) This leads to different parameters to estimate for latent variables  Factor means and variances in covariance based methods  Weight in component based approaches 1.2 THE CONCEPT OF PLS-SEM AND HOW IT WORKS Today, Partial Least Square Structural Equation Modeling (PLS-SEM) has gaining interesting in statistical inferential to help the researchers construct the complex model CB-SEM (Covariance based SEM) -This method is aimed to reproduced the theoretical covariance matrix, without focusing on explained variance as implemented in AMOS package.

AMOS Covariance-Based Structural Equation Modeling (CB-SEM): Guidelines on its Application as a Marketing Research Tool

Revista Brasileira de Marketing, 2014

Structural equation modeling (SEM) is increasingly a method of choice for concept and theory development in the social sciences, particularly the marketing discipline. In marketing research there increasingly is a need to assess complex multiple latent constructs and relationships. Second-order constructs can be modeled providing an improved theoretical understanding of relationships as well as parsimony. SEM in particular is well suited to investigating complex relationships among multiple constructs. The two most prevalent SEM based analytical methods are covariance-based SEM (CB-SEM) and variance-based SEM (PLS-SEM). While each technique has advantages and limitations, in this article we focus on CB-SEM with AMOS to illustrate its application in examining the relationships between customer orientation, employee orientation, and firm performance. We also demonstrate how higher-order constructs are useful in modeling both responsive and proactive components of customer and employee orientation.