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Open Access
Article
Publication date: 2 December 2022

Francesca Magno, Fabio Cassia and Christian M.M. Ringle

Partial least squares structural equation modeling (PLS-SEM) has become an established social sciences multivariate analysis technique. Since quality management…

Abstract

Purpose

Partial least squares structural equation modeling (PLS-SEM) has become an established social sciences multivariate analysis technique. Since quality management researchers also increasingly using PLS-SEM, this growing interest calls for guidance.

Design/methodology/approach

Based on established guidelines for applying PLS-SEM and evaluating the results, this research reviews 107 articles applying the method and published in eight leading quality management journals.

Findings

The use of PLS-SEM in quality management often only draws on limited information and analysis results. The discipline would benefit from the method's more comprehensive use by following established guidelines. Specifically, the use of predictive model assessment and more advanced PLS-SEM analyses harbors the potential to provide more detailed findings and conclusions when applying the method.

Research limitations/implications

This research provides first insights into PLS-SEM's use in quality management. Future research should identify the key areas and the core quality management models that best support the method's capabilities and researchers' goals.

Practical implications

The results of this analysis guide researchers who use the PLS-SEM method for their quality management studies.

Originality/value

This is the first article to systematically review the use of PLS-SEM in the quality management discipline.

Details

The TQM Journal, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1754-2731

Keywords

Book part
Publication date: 13 August 2012

Mehmet Mehmetoglu

Tourism research contains a large share of consumer behavior-orientated studies using multidimensional constructs (exogenous/endogenous). Accordingly, scholars have mainly…

Abstract

Tourism research contains a large share of consumer behavior-orientated studies using multidimensional constructs (exogenous/endogenous). Accordingly, scholars have mainly made use of a two-step approach that can be referred to as PCA-MLR (principal component analysis and then ordinary least squares multiple linear regression analysis) to examine the relationships among exogenous and endogenous constructs in a statistical model. Although this two-step approach has contributed to the advancement of tourism research, it still suffers from a number of drawbacks which can readily be overcome by a so-called second-generation statistical tool, namely, partial least squares approach to structural equation modeling (PLS-SEM). The current chapter explains and illustrates (with an application to tourism data) the advantages (e.g., several layers of estimations, suiting small sample sizes, robustness to multicollinearity, model-based clustering, etc.) of PLS-SEM both from a statistical and practical point of view. Finally, an elucidation is also provided for suggesting PLS-SEM as an alternative to PCA-MLR instead of COV-SEM (covariance-based structural equation modeling). The chapter concludes by proposing that PLS-SEM is a reliable and flexible statistical approach that is of high value, in particular, for applied research.

Details

Advances in Hospitality and Leisure
Type: Book
ISBN: 978-1-78052-936-3

Keywords

Book part
Publication date: 24 November 2010

Edward E. Rigdon, Christian M. Ringle and Marko Sarstedt

Alongside structural equation modeling (SEM), the complementary technique of partial least squares (PLS) path modeling helps researchers understand relations among sets of…

Abstract

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.

Details

Review of Marketing Research
Type: Book
ISBN: 978-0-85724-475-8

Book part
Publication date: 6 March 2009

Jörg Henseler, Christian M. Ringle and Rudolf R. Sinkovics

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…

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.

Details

New Challenges to International Marketing
Type: Book
ISBN: 978-1-84855-469-6

Article
Publication date: 25 August 2022

Jan-Michael Becker, Jun-Hwa Cheah, Rasoul Gholamzade, Christian M. Ringle and Marko Sarstedt

Partial least squares structural equation modeling (PLS-SEM) has attracted much attention from both methodological and applied researchers in various disciplines – also in…

1383

Abstract

Purpose

Partial least squares structural equation modeling (PLS-SEM) has attracted much attention from both methodological and applied researchers in various disciplines – also in hospitality management research. As PLS-SEM is relatively new compared to other multivariate analysis techniques, there are still numerous open questions and uncertainties in its application. This study aims to address this important issue by offering guidance regarding its use in contexts with which researchers struggle.

Design/methodology/approach

The authors examine the most prominent questions and answers posed in a well-known PLS-SEM discussion forum. The authors do so by using a text analysis technique to identify the most salient topics.

Findings

The data analysis identifies three salient PLS-SEM topics (i.e. bootstrapping and significance testing, higher-order constructs and moderation).

Research limitations/implications

The results allow us to address the PLS-SEM community’s main methodological issues. The authors discuss each area separately and provide explanations and guidelines.

Practical implications

The guidelines on the most important PLS-SEM topics provide decision-making and application aids. In this way, the authors make a decisive contribution to clarifying ambiguities when applying the PLS-SEM method in hospitality management research and other disciplines.

Originality/value

There has as yet been no systematic analysis of this kind in the field of PLS-SEM; the authors, therefore, present the first research results. The findings and recommendations provide guidance for PLS-SEM applications in hospitality research and practice.

Details

International Journal of Contemporary Hospitality Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0959-6119

Keywords

Abstract

Details

Applying Partial Least Squares in Tourism and Hospitality Research
Type: Book
ISBN: 978-1-78756-700-9

Article
Publication date: 16 August 2022

John W. Cadogan and Nick Lee

This study aims to correct errors in, and comment on the claims made in the comment papers of Rigdon (2022) and Henseler and Schuberth (2022), and to tidy up any…

Abstract

Purpose

This study aims to correct errors in, and comment on the claims made in the comment papers of Rigdon (2022) and Henseler and Schuberth (2022), and to tidy up any substantive oversights made in Cadogan and Lee (2022).

Design/methodology/approach

The study discusses and clarifies the gap between Rigdon’s notion of scientific realism and the metaphysical, semantic and epistemological commitments that are broadly agreed to be key principles of scientific realism. The study also examines the ontological status of the variables that Henseler and Schuberth claim are emergent using emergence logic grounded in the notion that variables are only truly emergent if they demonstrate a failure of generative atomism.

Findings

In scientific realism, hypothetical causal contact between the unobserved and the observed is a key foundational stance, and as such, Rigdon’s concept proxy framework (CPF) is inherently anti-realist in nature. Furthermore, Henseler and Schuberth’s suggestion that composite-creating statistical packages [such as partial least squares (PLS)] can model emergent variables should be treated with skepticism by realists.

Research limitations/implications

Claims made by Rigdon regarding the realism of CPF are unfounded, and claims by Henseler and Schuberth regarding the universal suitability of partial least squares (PLS) as a tool for use by researchers of all ontological stripes (see their Table 5) do not appear to be well-grounded.

Practical implications

Those aspiring to do science according to the precepts of scientific realism need to be careful in assessing claims in the literature. For instance, despite Rigdon’s assertion that CPF is a realist framework, we show that it is not. Consequently, some of Rigdon’s core criticisms of the common factor logic make no sense for the realist. Likewise, if the variables resulting from composite creating statistical packages (like PLS) are not really emergent (contrary to Henseler and Schuberth) and so are not real, their utility as tools for scientific realist inquiry are called into question.

Originality/value

This study assesses PLS using the Eleatic Principle and examines H&S’s version of emergent variables from an ontological perspective.

Open Access
Article
Publication date: 17 August 2022

Jörg Henseler and Florian Schuberth

In their paper titled “A Miracle of Measurement or Accidental Constructivism? How PLS Subverts the Realist Search for Truth,” Cadogan and Lee (2022) cast serious doubt on…

Abstract

Purpose

In their paper titled “A Miracle of Measurement or Accidental Constructivism? How PLS Subverts the Realist Search for Truth,” Cadogan and Lee (2022) cast serious doubt on PLS’s suitability for scientific studies. The purpose of this commentary is to discuss the claims of Cadogan and Lee, correct some inaccuracies, and derive recommendations for researchers using structural equation models.

Design/methodology/approach

This paper uses scenario analysis to show which estimators are appropriate for reflective measurement models and composite models, and formulates the statistical model that underlies PLS Mode A. It also contrasts two different perspectives: PLS as an estimator for structural equation models vs. PLS-SEM as an overarching framework with a sui generis logic.

Findings

There are different variants of PLS, which include PLS, consistent PLS, PLSe1, PLSe2, proposed ordinal PLS and robust PLS, each of which serves a particular purpose. All of these are appropriate for scientific inquiry if applied properly. It is not PLS that subverts the realist search for truth, but some proponents of a framework called “PLS-SEM.” These proponents redefine the term “reflective measurement,” argue against the assessment of model fit and suggest that researchers could obtain “confirmation” for their model.

Research limitations/implications

Researchers should be more conscious, open and respectful regarding different research paradigms.

Practical implications

Researchers should select a statistical model that adequately represents their theory, not necessarily a common factor model, and formulate their model explicitly. Particularly for instrumentalists, pragmatists and constructivists, the composite model appears promising. Researchers should be concerned about their estimator’s properties, not about whether it is called “PLS.” Further, researchers should critically evaluate their model, not seek confirmation or blindly believe in its value.

Originality/value

This paper critically appraises Cadogan and Lee (2022) and reminds researchers who wish to use structural equation modeling, particularly PLS, for their statistical analysis, of some important scientific principles.

Details

European Journal of Marketing, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0309-0566

Keywords

Open Access
Article
Publication date: 13 April 2022

Florian Schuberth, Manuel E. Rademaker and Jörg Henseler

This study aims to examine the role of an overall model fit assessment in the context of partial least squares path modeling (PLS-PM). In doing so, it will explain when it…

1399

Abstract

Purpose

This study aims to examine the role of an overall model fit assessment in the context of partial least squares path modeling (PLS-PM). In doing so, it will explain when it is important to assess the overall model fit and provides ways of assessing the fit of composite models. Moreover, it will resolve major concerns about model fit assessment that have been raised in the literature on PLS-PM.

Design/methodology/approach

This paper explains when and how to assess the fit of PLS path models. Furthermore, it discusses the concerns raised in the PLS-PM literature about the overall model fit assessment and provides concise guidelines on assessing the overall fit of composite models.

Findings

This study explains that the model fit assessment is as important for composite models as it is for common factor models. To assess the overall fit of composite models, researchers can use a statistical test and several fit indices known through structural equation modeling (SEM) with latent variables.

Research limitations/implications

Researchers who use PLS-PM to assess composite models that aim to understand the mechanism of an underlying population and draw statistical inferences should take the concept of the overall model fit seriously.

Practical implications

To facilitate the overall fit assessment of composite models, this study presents a two-step procedure adopted from the literature on SEM with latent variables.

Originality/value

This paper clarifies that the necessity to assess model fit is not a question of which estimator will be used (PLS-PM, maximum likelihood, etc). but of the purpose of statistical modeling. Whereas, the model fit assessment is paramount in explanatory modeling, it is not imperative in predictive modeling.

Details

European Journal of Marketing, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0309-0566

Keywords

Article
Publication date: 28 March 2022

John W. Cadogan and Nick Lee

This study aims to determine whether partial least squares path modeling (PLS) is fit for purpose for scholars holding scientific realist views.

Abstract

Purpose

This study aims to determine whether partial least squares path modeling (PLS) is fit for purpose for scholars holding scientific realist views.

Design/methodology/approach

The authors present the philosophical foundations of scientific realism and constructivism and examine the extent to which PLS aligns with them.

Findings

PLS does not align with scientific realism but aligns well with constructivism.

Research limitations/implications

Research is needed to assess PLS’s fit with instrumentalism and pragmatism.

Practical implications

PLS has no utility as a realist scientific tool but may be of interest to constructivists.

Originality/value

To the best of the authors’ knowledge, this study is the first to assess PLS’s alignments and mismatches with constructivist and scientific realist perspectives.

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