The Use of Partial Least Squares Path Modeling in International Marketing

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The Use of Partial Least Squares Path Modeling in International Marketing

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The Use of Partial Least Squares Path Modeling in International Marketing

Jörg Henseler¹, Jörg Henseler², Christian M. Ringle³, Rudolf R. Sinkovics⁴•Institutions (4)

Universidade Nova de Lisboa¹, Radboud University Nijmegen², University of Newcastle³, University of Auckland⁴

01 Jan 2009-Social Science Research Network-

TL;DR: An evaluation of double-blind reviewed journals through important academic publishing databases 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.

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Abstract: Purpose: This paper discusses partial least squares path modeling (PLS), a powerful structural equation modeling technique for research on international marketing. While a significant body of research provides guidance for the use of covariance-based structural equation modeling (CBSEM) in international marketing, there are no subject-specific guidelines for the use of PLS so far.Methodology/approach: A literature review of the use of PLS in international marketing reveals the increasing application of this methodology.Findings: This paper reveals the strengths and weaknesses of PLS in the context of research on international marketing, and provides guidance for multi-group analysis.Originality/value of paper: The paper assists researchers in making well-grounded decisions regarding the application of PLS in certain research situations and provides specific implications for an appropriate application of the methodology.

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Journal Article•DOI•

A new criterion for assessing discriminant validity in variance-based structural equation modeling

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Jörg Henseler¹, Christian M. Ringle², Marko Sarstedt³, Marko Sarstedt⁴•Institutions (4)

University of Twente¹, Hamburg University of Technology², University of Newcastle³, Otto-von-Guericke University Magdeburg⁴

01 Jan 2015-Journal of the Academy of Marketing Science

TL;DR: In this paper, the heterotrait-monotrait ratio of correlations is used to assess discriminant validity in variance-based structural equation modeling. But it does not reliably detect the lack of validity in common research situations.

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Abstract: Discriminant validity assessment has become a generally accepted prerequisite for analyzing relationships between latent variables. For variance-based structural equation modeling, such as partial least squares, the Fornell-Larcker criterion and the examination of cross-loadings are the dominant approaches for evaluating discriminant validity. By means of a simulation study, we show that these approaches do not reliably detect the lack of discriminant validity in common research situations. We therefore propose an alternative approach, based on the multitrait-multimethod matrix, to assess discriminant validity: the heterotrait-monotrait ratio of correlations. We demonstrate its superior performance by means of a Monte Carlo simulation study, in which we compare the new approach to the Fornell-Larcker criterion and the assessment of (partial) cross-loadings. Finally, we provide guidelines on how to handle discriminant validity issues in variance-based structural equation modeling.

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12,855 citations

Cites background from "The Use of Partial Least Squares Pa..."

...Variance-based structural equation modeling (SEM) is growing in popularity, which the plethora of recent developments and discussions (e.g., Henseler et al. 2014; Hwang et al. 2010; Lu et al. 2011; Rigdon 2014; Tenenhaus and Tenenhaus 2011), as well as its frequent application across different disciplines, demonstrate (e.g., Hair et al. 2012a, b; Lee et al. 2011; Peng and Lai 2012; Ringle et al. 2012)....
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...J. Henseler Faculty of Engineering Technology, University of Twente, Enschede, Netherlands e-mail: j.henseler@utwente.nl J. Henseler ISEGI, Universidade Nova de Lisboa, Lisbon, Portugal C. M. Ringle Hamburg University of Technology (TUHH), Hamburg, Germany e-mail: c.ringle@tuhh.de C. M. Ringle University of Newcastle, Newcastle, Australia M. Sarstedt Otto-von-Guericke-University Magdeburg, Magdeburg, Germany M. Sarstedt (*) University of Newcastle, Newcastle, Australia e-mail: marko.sarstedt@ovgu.de In this paper, we focus on examining discriminant validity as one of the key building blocks of model evaluation (e.g.,Bagozzi and Phillips 1982; Hair et al. 2010)....
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...It is important to note that the elimination of items purely on statistical grounds can have adverse consequences for the construct measures’ content validity (e.g., Hair et al. 2014)....
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...Reviews of PLS use suggest that these recommendations have been widely applied in published research in the fields of management information systems (Ringle et al. 2012), marketing (Hair et al. 2012a), and strategic management (Hair et al. 2012b)....
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...Discriminant validity ensures that a construct measure is empirically unique and represents phenomena of interest that other measures in a structural equation model do not capture (Hair et al. 2010)....
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Journal Article•DOI•

PLS-SEM: Indeed a Silver Bullet

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Joseph F. Hair¹, Christian M. Ringle², Marko Sarstedt³•Institutions (3)

Kennesaw State University¹, University of Technology, Sydney², Ludwig Maximilian University of Munich³

01 Apr 2011-The Journal of Marketing Theory and Practice

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.

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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.

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11,624 citations

Cites background or methods from "The Use of Partial Least Squares Pa..."

...We must distinguish between reflective and formative measurement models to evaluate them (Henseler, Ringle, and Sinkovics 2009)....
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...…complementary in that the nonparametric and variance-based PLS-SEM approach’s advantages are the parametric and covariance-based SEM approach’s disadvantages and vice versa (Fornell and Bookstein 1982; Henseler, Ringle, and Sinkovics 2009; Jöreskog and Wold 1982; Lohmöller 1989; Schneeweiß 1991)....
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...The bootstrap sample enables the estimated coefficients in PLS-SEM to be tested for their significance (Henseler, Ringle, and Sinkovics 2009)....
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...The desire to test complete theories and concepts is one of the major reasons authors conducting business research— particularly marketing—have embraced SEM (Henseler, Ringle, and Sinkovics 2009; Steenkamp and Baumgartner 2000)....
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...While far less popular than CB-SEM, PLS-SEM has been increasingly applied in marketing and other business disciplines (e.g., Henseler, Ringle, and Sinkovics 2009), with more than 100 published studies featuring PLS-SEM in the top 20 marketing journals (for the marketing journal ranking, see Hult,…...
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Journal Article•DOI•

When to use and how to report the results of PLS-SEM

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Joseph F. Hair, Jeffrey J. Risher, Marko Sarstedt, Christian M. Ringle

14 Jan 2019-European Business Review

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.

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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.

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6,220 citations

Journal Article•DOI•

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

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Joseph F. Hair¹, Marko Sarstedt², Marko Sarstedt³, Christian M. Ringle³, Christian M. Ringle⁴, Jeannette A. Mena⁵ - Show less +2 more•Institutions (5)

Kennesaw State University¹, Ludwig Maximilian University of Munich², University of Newcastle³, Hamburg University of Technology⁴, University of South Florida⁵

01 May 2012-Journal of the Academy of Marketing Science

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.

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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.

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5,328 citations

Cites background or methods or result from "The Use of Partial Least Squares Pa..."

...More recently, Henseler et al. (2009) and Reinartz et al. (2009) assessed PLS-SEM use in (international) marketing research but focused only on the reasons for choosing this method....
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...Recently, PLS-SEM application has expanded in marketing research and practice with the recognition that PLS-SEM’s distinctive methodological features make it a possible alternative to the more popular CB-SEM approaches (Henseler et al. 2009)....
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...The selection of initial values for outer weights may impose changes in the outer models and/or the inner model estimates (e.g., Henseler et al. 2009 )....
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...Sign change option Use individual sign changes Henseler et al. 2009...
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...Weighting scheme Use path weighting scheme Henseler 2010; Henseler et al. 2009 ...
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Journal Article•DOI•

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

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Joseph F. Hair¹, Marko Sarstedt², Lucas Hopkins³, Volker G. Kuppelwieser⁴•Institutions (4)

Kennesaw State University¹, Otto-von-Guericke University Magdeburg², Georgia State University³, NEOMA Business School⁴

23 Jul 2014-European Business Review

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.

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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

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5,191 citations

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Abstract: A general formula (α) of which a special case is the Kuder-Richardson coefficient of equivalence is shown to be the mean of all split-half coefficients resulting from different splittings of a test. α is therefore an estimate of the correlation between two random samples of items from a universe of items like those in the test. α is found to be an appropriate index of equivalence and, except for very short tests, of the first-factor concentration in the test. Tests divisible into distinct subtests should be so divided before using the formula. The index $$\bar r_{ij} $$ , derived from α, is shown to be an index of inter-item homogeneity. Comparison is made to the Guttman and Loevinger approaches. Parallel split coefficients are shown to be unnecessary for tests of common types. In designing tests, maximum interpretability of scores is obtained by increasing the first-factor concentration in any separately-scored subtest and avoiding substantial group-factor clusters within a subtest. Scalability is not a requisite.

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37,235 citations

"The Use of Partial Least Squares Pa..." refers background in this paper

...Five issues are covered: Sections 3.1 and 3.2 provide an overview of the use of PLS path modeling for the assessment of reflective and formative measurement models, respectively....
[...]

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TL;DR: This article presents bootstrap methods for estimation, using simple arguments, with Minitab macros for implementing these methods, as well as some examples of how these methods could be used for estimation purposes.

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Abstract: This article presents bootstrap methods for estimation, using simple arguments. Minitab macros for implementing these methods are given.

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Northwestern University¹, Portland State University²

01 Jan 1988-Psychological Bulletin

TL;DR: In this paper, the authors provide guidance for substantive researchers on the use of structural equation modeling in practice for theory testing and development, and present a comprehensive, two-step modeling approach that employs a series of nested models and sequential chi-square difference tests.

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Abstract: In this article, we provide guidance for substantive researchers on the use of structural equation modeling in practice for theory testing and development. We present a comprehensive, two-step modeling approach that employs a series of nested models and sequential chi-square difference tests. We discuss the comparative advantages of this approach over a one-step approach. Considerations in specification, assessment of fit, and respecification of measurement models using confirmatory factor analysis are reviewed. As background to the two-step approach, the distinction between exploratory and confirmatory analysis, the distinction between complementary approaches for theory testing versus predictive application, and some developments in estimation methods also are discussed.

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34,720 citations

"The Use of Partial Least Squares Pa..." refers methods in this paper

...Moreover, hypothesis building and the assessment of CBSEM results through global goodness-of-fit Partial Least Squares Path Modeling in International Marketing 297 criteria further emphasizes the theory-testing, rather than theory-building, character of this methodology (Anderson & Gerbing, 1988)....
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