Partial Least Squares Structural Equation Modeling: Rigorous Applications, Better Results and Higher Acceptance
About: This article is published in Long Range Planning.The article was published on 2013-02-01. It has received 2166 citations till now. The article focuses on the topics: Partial least squares regression & Structural equation modeling.
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TL;DR: A comprehensive overview of the considerations and metrics required for partial least squares structural equation modeling (PLS-SEM) analysis and result reporting can be found in this paper, where the authors provide an overview of previously and recently proposed metrics as well as rules of thumb for evaluating the research results based on the application of PLSSEM.
Abstract: The purpose of this paper is to provide a comprehensive, yet concise, overview of the considerations and metrics required for partial least squares structural equation modeling (PLS-SEM) analysis and result reporting. Preliminary considerations are summarized first, including reasons for choosing PLS-SEM, recommended sample size in selected contexts, distributional assumptions, use of secondary data, statistical power and the need for goodness-of-fit testing. Next, the metrics as well as the rules of thumb that should be applied to assess the PLS-SEM results are covered. Besides presenting established PLS-SEM evaluation criteria, the overview includes the following new guidelines: PLSpredict (i.e., a novel approach for assessing a model’s out-of-sample prediction), metrics for model comparisons, and several complementary methods for checking the results’ robustness.,This paper provides an overview of previously and recently proposed metrics as well as rules of thumb for evaluating the research results based on the application of PLS-SEM.,Most of the previously applied metrics for evaluating PLS-SEM results are still relevant. Nevertheless, scholars need to be knowledgeable about recently proposed metrics (e.g. model comparison criteria) and methods (e.g. endogeneity assessment, latent class analysis and PLSpredict), and when and how to apply them to extend their analyses.,Methodological developments associated with PLS-SEM are rapidly emerging. The metrics reported in this paper are useful for current applications, but must always be up to date with the latest developments in the PLS-SEM method.,In light of more recent research and methodological developments in the PLS-SEM domain, guidelines for the method’s use need to be continuously extended and updated. This paper is the most current and comprehensive summary of the PLS-SEM method and the metrics applied to assess its solutions.
6,220 citations
TL;DR: Partial least squares (PLS) is an evolving approach to structural equation modeling (SEM), highlighting its advantages and limitations and providing an overview of recent research on the method across various fields as discussed by the authors.
Abstract: 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 limitations/implications – While rese
5,191 citations
TL;DR: In this article, the authors address Ronkko and Evermann's criticisms of the Partial Least Squares (PLS) approach to structural equation modeling and 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.
Abstract: This article addresses Ronkko 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 Ronkko 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 Ronkko 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.
1,906 citations
TL;DR: Partial least squares structural equation modeling (PLS-SEM) has become a popular method for estimating path models with latent variables and their relationships as discussed by the authors, and a common goal of PLSSEM analyses is to identify key success factors and sources of competitive advantage for important target constructs such as customer satisfaction, customer loyalty, behavioral intentions, and user behavior.
Abstract: Partial least squares structural equation modeling (PLS-SEM) has become a popular method for estimating path models with latent variables and their relationships. A common goal of PLS-SEM analyses is to identify key success factors and sources of competitive advantage for important target constructs such as customer satisfaction, customer loyalty, behavioral intentions, and user behavior. Building on an introduction of the fundamentals of measurement and structural theory, this chapter explains how to specify and estimate path models using PLS-SEM. Complementing the introduction of the PLS-SEM method and the description of how to evaluate analysis results, the chapter also offers an overview of complementary analytical techniques. A PLS-SEM application of the widely recognized corporate reputation model illustrates the method.
1,842 citations
TL;DR: This study illustrates the state-of-the-art use of mediation analysis in the context of PLS-structural equation modeling (SEM) by challenging the conventional approach to mediation analysis and providing more accurate alternatives.
Abstract: Indirect or mediated effects constitute a type of relationship between constructs that often occurs in partial least squares (PLS) path modeling. Over the past few years, the methods for testing mediation have become more sophisticated. However, many researchers continue to use outdated methods to test mediating effects in PLS, which can lead to erroneous results. One reason for the use of outdated methods or even the lack of their use altogether is that no systematic tutorials on PLS exist that draw on the newest statistical findings. The paper aims to discuss these issues.,This study illustrates the state-of-the-art use of mediation analysis in the context of PLS-structural equation modeling (SEM).,This study facilitates the adoption of modern procedures in PLS-SEM by challenging the conventional approach to mediation analysis and providing more accurate alternatives. In addition, the authors propose a decision tree and classification of mediation effects.,The recommended approach offers a wide range of testing options (e.g. multiple mediators) that go beyond simple mediation analysis alternatives, helping researchers discuss their studies in a more accurate way.
1,146 citations
References
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Book•
01 Jan 2014
TL;DR: The Second Edition of this practical guide to partial least squares structural equation modeling is designed to be easily understood by those with limited statistical and mathematical training who want to pursue research opportunities in new ways.
Abstract: With applications using SmartPLS (www.smartpls.com)—the primary software used in partial least squares structural equation modeling (PLS-SEM)—this practical guide provides concise instructions on how to use this evolving statistical technique to conduct research and obtain solutions. Featuring the latest research, new examples, and expanded discussions throughout, the Second Edition is designed to be easily understood by those with limited statistical and mathematical training who want to pursue research opportunities in new ways.
13,621 citations
TL;DR: The authors conclude that PLS-SEM path modeling, if appropriately applied, is indeed a "silver bullet" for estimating causal models in many theoretical models and empirical data situations.
Abstract: Structural equation modeling (SEM) has become a quasi-standard in marketing and management research when it comes to analyzing the cause-effect relations between latent constructs. For most researchers, SEM is equivalent to carrying out covariance-based SEM (CB-SEM). While marketing researchers have a basic understanding of CB-SEM, most of them are only barely familiar with the other useful approach to SEM-partial least squares SEM (PLS-SEM). The current paper reviews PLS-SEM and its algorithm, and provides an overview of when it can be most appropriately applied, indicating its potential and limitations for future research. The authors conclude that PLS-SEM path modeling, if appropriately applied, is indeed a "silver bullet" for estimating causal models in many theoretical models and empirical data situations.
11,624 citations
TL;DR: The current paper reviews four recent studies in the strategic management area which use PLS and notes that the technique has been applied inconsistently, and at times inappropriately, and suggests standards for evaluating future PLS applications.
Abstract: Advances in causal modeling techniques have made it possible for researchers to simultaneously examine theory and measures. However, researchers must use these new techniques appropriately. In addition to dealing with the methodological concerns associated with more traditional methods of analysis, researchers using causal modeling approaches must understand their underlying assumptions and limitations.
Most researchers are well equipped with a basic understanding of LISREL-type models. In contrast, current familiarity with PLS in the strategic management area is low. The current paper reviews four recent studies in the strategic management area which use PLS. The review notes that the technique has been applied inconsistently, and at times inappropriately, and suggests standards for evaluating future PLS applications. Copyright © 1999 John Wiley & Sons, Ltd.
6,205 citations
TL;DR: An extensive search in the 30 top ranked marketing journals allowed us to identify 204 PLS-SEM applications published in a 30-year period (1981 to 2010), and a critical analysis of these articles addresses the following key methodological issues: reasons for using PLS, data and model characteristics, outer and inner model evaluations, and reporting.
Abstract: 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.
5,328 citations
06 Mar 2009
TL;DR: In this article, the authors conducted an exhaustive literature review to determine the status quo of PLS path modeling in international marketing research and found that more than 30 academic articles in the domain of international marketing used PLS as a means of statistical analysis.
Abstract: In order to determine the status quo of PLS path modeling in international marketing research, we conducted an exhaustive literature review. An evaluation of double-blind reviewed journals through important academic publishing databases (e.g., ABI/Inform, Elsevier ScienceDirect, Emerald Insight, Google Scholar, PsycINFO, Swetswise) revealed that more than 30 academic articles in the domain of international marketing (in a broad sense) used PLS path modeling as means of statistical analysis. We assessed what the main motivation for the use of PLS was in respect of each article. Moreover, we checked for applications of PLS in combination with one or more additional methods, and whether the main reason for conducting any additional method(s) was mentioned.
5,282 citations