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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

Article
Publication date: 18 May 2021

S. Mostafa Rasoolimanesh, Christian M. Ringle, Marko Sarstedt and Hossein Olya

This study aims to propose guidelines for the joint use of partial least squares structural equation modeling (PLS-SEM) and fuzzy-set qualitative comparative analysis…

1896

Abstract

Purpose

This study aims to propose guidelines for the joint use of partial least squares structural equation modeling (PLS-SEM) and fuzzy-set qualitative comparative analysis (fsQCA) to combine symmetric and asymmetric perspectives in model evaluation, in the hospitality and tourism field.

Design/methodology/approach

This study discusses PLS-SEM as a symmetric approach and fsQCA as an asymmetric approach to analyze structural and configurational models. It presents guidelines to conduct an fsQCA based on latent construct scores drawn from PLS-SEM, to assess how configurations of exogenous constructs produce a specific outcome in an endogenous construct.

Findings

This research highlights the advantages of combining PLS-SEM and fsQCA to analyze the causal effects of antecedents (i.e., exogenous constructs) on outcomes (i.e., endogenous constructs). The construct scores extracted from the PLS-SEM analysis of a nomological network of constructs provide accurate input for performing fsQCA to identify the sufficient configurations required to predict the outcome(s). Complementing the assessment of the model’s explanatory and predictive power, the fsQCA generates more fine-grained insights into variable relationships, thereby offering the means to reach better managerial conclusions.

Originality/value

The application of PLS-SEM and fsQCA as separate prediction-oriented methods has increased notably in recent years. However, in the absence of clear guidelines, studies applied the methods inconsistently, giving researchers little direction on how to best apply PLS-SEM and fsQCA in tandem. To address this concern, this study provides guidelines for the joint use of PLS-SEM and fsQCA.

Article
Publication date: 4 March 2014

Joe F. Hair Jr, Marko Sarstedt, Lucas Hopkins and Volker G. Kuppelwieser

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…

34376

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 research on the PLS-SEM method has gained momentum during the last decade, there are ample research opportunities on subjects such as mediation or multigroup analysis, which warrant further attention.

Originality/value

This article provides an introduction to PLS-SEM for researchers that have not yet been exposed to the method. The article is the first to meta-analyze reasons for PLS-SEM usage across the marketing, management, and management information systems fields. The cross-disciplinary review of recent research on the PLS-SEM method also makes this article useful for researchers interested in advanced concepts.

Details

European Business Review, vol. 26 no. 2
Type: Research Article
ISSN: 0955-534X

Keywords

Open Access
Article
Publication date: 9 May 2016

Nicole Franziska Richter, Rudolf R. Sinkovics, Christian M. Ringle and Christopher Schlägel

Structural equation modeling (SEM) has been widely used to examine complex research models in international business and marketing research. While the covariance-based SEM

18968

Abstract

Purpose

Structural equation modeling (SEM) has been widely used to examine complex research models in international business and marketing research. While the covariance-based SEM (CB-SEM) approach is dominant, the authors argue that the field’s dynamic nature and the sometimes early stage of theory development more often require a partial least squares SEM (PLS-SEM) approach. The purpose of this paper is to critically review the application of SEM techniques in the field.

Design/methodology/approach

The authors searched six journals with an international business (and marketing) focus (Management International Review, Journal of International Business Studies, Journal of International Management, International Marketing Review, Journal of World Business, International Business Review) from 1990 to 2013. The authors reviewed all articles that apply SEM, analyzed their research objectives and methodology choices, and assessed whether the PLS-SEM papers followed the best practices outlined in the past.

Findings

Of the articles, 379 utilized CB-SEM and 45 PLS-SEM. The reasons for using PLS-SEM referred largely to sampling and data measurement issues and did not sufficiently build on the procedure’s benefits that stem from its design for predictive and exploratory purposes. Thus, the procedure’s key benefits, which might be fruitful for the theorizing process, are not being fully exploited. Furthermore, authors need to better follow best practices to truly advance theory building.

Research limitations/implications

The authors examined a subset of journals in the field and did not include general management journals that publish international business and marketing-related studies. Fur-thermore, the authors found only limited use of PLS-SEM in the journals the authors considered relevant to the study.

Originality/value

The study contributes to the literature by providing researchers seeking to adopt SEM as an analytical method with practical guidelines for making better choices concerning an appropriate SEM approach. Furthermore, based on a systematic review of current practices in the international business and marketing literature, the authors identify critical challenges in the selection and use of SEM procedures and offer concrete recommendations for better practice.

Details

International Marketing Review, vol. 33 no. 3
Type: Research Article
ISSN: 0265-1335

Keywords

Article
Publication date: 18 December 2018

Gabriel Cepeda-Carrion, Juan-Gabriel Cegarra-Navarro and Valentina Cillo

Structural equation modelling (SEM) has been defined as the combination of latent variables and structural relationships. The partial least squares SEM (PLS-SEM) is used…

3791

Abstract

Purpose

Structural equation modelling (SEM) has been defined as the combination of latent variables and structural relationships. The partial least squares SEM (PLS-SEM) is used to estimate complex cause-effect relationship models with latent variables as the most salient research methods across a variety of disciplines, including knowledge management (KM). Following the path initiated by different domains in business research, this paper aims to examine how PLS-SEM has been applied in KM research, also providing some new guidelines how to improve PLS-SEM report analysis.

Design/methodology/approach

To ensure an objective way to analyse relevant works in the field of KM, this study conducted a systematic literature review of 63 publications in three SSCI-indexed and specific KM journals between 2015 and 2017.

Findings

Our results show that over the past three years, a significant amount of KM works has empirically used PLS-SEM. The findings also suggest that in light of recent developments of PLS-SEM reporting, some common misconceptions among KM researchers occurred mainly related to the reasons for using PLS-SEM, the purposes of PLS-SEM analysis, data characteristics, model characteristics and the evaluation of the structural models.

Originality/value

This study contributes to that vast KM literature by documenting the PLS-SEM-related problems and misconceptions. Therefore, it will shed light for better reports in PLS-SEM studies in the KM field.

Details

Journal of Knowledge Management, vol. 23 no. 1
Type: Research Article
ISSN: 1367-3270

Keywords

Article
Publication date: 27 March 2019

Joseph F. Hair, Marko Sarstedt and Christian M. Ringle

Partial least squares structural equation modeling (PLS-SEM) is an important statistical technique in the toolbox of methods that researchers in marketing and other social…

3407

Abstract

Purpose

Partial least squares structural equation modeling (PLS-SEM) is an important statistical technique in the toolbox of methods that researchers in marketing and other social sciences disciplines frequently use in their empirical analyses. The purpose of this paper is to shed light on several misconceptions that have emerged as a result of the proposed “new guidelines” for PLS-SEM. The authors discuss various aspects related to current debates on when or when not to use PLS-SEM, and which model evaluation metrics to apply. In addition, this paper summarizes several important methodological extensions of PLS-SEM researchers can use to improve the quality of their analyses, results and findings.

Design/methodology/approach

The paper merges literature from various disciplines, including marketing, strategic management, information systems, accounting and statistics, to present a state-of-the-art review of PLS-SEM. Based on these findings, the paper offers a point of orientation on how to consider and apply these latest developments when executing or assessing PLS-SEM-based research.

Findings

This paper offers guidance regarding situations that favor the use of PLS-SEM and discusses the need to consider certain model evaluation metrics. It also summarizes how to deal with endogeneity in PLS-SEM, and critically comments on the recent proposal to adjust PLS-SEM estimates to mimic common factor models that are the foundation of covariance-based SEM. Finally, this paper opposes characterizing common concepts and practices of PLS-SEM as “out-of-date” without providing well-substantiated alternatives and solutions.

Research limitations/implications

The paper paves the way for future discussions and suggests a way forward to reach consensus regarding situations that favor PLS-SEM use and its application.

Practical implications

This paper offers guidance on how to consider the latest methodological developments when executing or assessing PLS-SEM-based research.

Originality/value

This paper complements recently proposed “new guidelines” with the aim of offering a counter perspective on some strong claims made in the latest literature on PLS-SEM. It also clarifies some misconceptions regarding the application of PLS-SEM.

Details

European Journal of Marketing, vol. 53 no. 4
Type: Research Article
ISSN: 0309-0566

Keywords

Article
Publication date: 8 January 2018

Faizan Ali, Woo Gon Kim, Jun (Justin) Li and Cihan Cobanoglu

Structural equation modelling (SEM) has increasingly been used by hospitality and tourism researchers to examine complex relationships. This paper aims to highlight the…

1579

Abstract

Purpose

Structural equation modelling (SEM) has increasingly been used by hospitality and tourism researchers to examine complex relationships. This paper aims to highlight the benefits and limitations of SEM for hospitality and tourism research and compare its two main approaches, i.e. covariance-based SEM (CB-SEM) and partial least squares-SEM (PLS-SEM).

Design/methodology/approach

By using a comparative approach, this study parallels SEM’s two main approaches, i.e. CB-SEM and PLS-SEM, using three different examples from hospitality and tourism industry. Both the approaches are compared side by side in terms of assumptions, validity and reliability of measurement models, item retention and loadings, strength and significance of path relationships and coefficient of determinations.

Findings

The findings show that even though both methods analyse measurement theory and structural path models, there are relatively higher advantages for hospitality and tourism researchers in applying PLS-SEM.

Research limitations/implications

Because of the limitations of only using three examples, the results and trends generated in this study may not be generalized to all research in hospitality and tourism discipline. Moreover, the Likert scale has been used to measure the constructs in both the studies, which may have biased the results.

Originality value

This study is the first to compare the usage of both the SEM approaches in hospitality and tourism research. The findings of this study provide significant implications and directions for hospitality and tourism researchers to apply PLS-SEM in the future.

Details

International Journal of Contemporary Hospitality Management, vol. 30 no. 1
Type: Research Article
ISSN: 0959-6119

Keywords

Article
Publication date: 9 October 2018

Ahmet Usakli and Kemal Gurkan Kucukergin

The purpose of this study is to review the use of partial least squares-structural equation modeling (PLS-SEM) in the field of hospitality and tourism and thereby to…

2282

Abstract

Purpose

The purpose of this study is to review the use of partial least squares-structural equation modeling (PLS-SEM) in the field of hospitality and tourism and thereby to assess whether the PLS-SEM-based papers followed the recommended application guidelines and to investigate whether a comparison of journal types (hospitality vs tourism) and journal qualities (top-tier vs other leading) reveal significant differences in PLS-SEM use.

Design/methodology/approach

A total of 206 PLS-SEM based papers published between 2000 and April 2017 in the 19 SSCI-indexed hospitality and tourism journals were critically analyzed using a wide range of guidelines for the following aspects of PLS-SEM: the rationale of using the method, the data characteristics, the model characteristics, the model assessment and reporting the technical issues.

Findings

The results reveal that some aspects of PLS-SEM are correctly applied by researchers, but there are still some misapplications, especially regarding data characteristics, formative measurement model evaluation and structural model assessment. Furthermore, few significant differences were found on the use of PLS-SEM between the two fields (hospitality and tourism) and between the journal tiers (top-tier and other leading).

Practical implications

To enhance the quality of research in hospitality and tourism, the present study provides recommendations for improving the future use of PLS-SEM.

Originality/value

The present study fills a sizeable gap in hospitality and tourism literature and extends the previous assessments on the use of PLS-SEM by providing a wider perspective on the issue (i.e. includes both hospitality and tourism journals rather than the previous reviews that focus on either tourism or hospitality), using a larger sample size of 206 empirical studies, investigating the issue over a longer time period (from 2000 to April, 2017, including the in-press articles), extending the scope of criteria (guidelines) used in the review and comparing the PLS-SEM use between the two allied fields (hospitality and tourism) and between the journal tiers (top-tier and other leading).

Details

International Journal of Contemporary Hospitality Management, vol. 30 no. 11
Type: Research Article
ISSN: 0959-6119

Keywords

Article
Publication date: 5 October 2016

Christian Nitzl

In management accounting research, the capabilities of Partial Least Squares Structural Equation Modelling (PLS-SEM) have only partially been utilized. These yet…

Abstract

In management accounting research, the capabilities of Partial Least Squares Structural Equation Modelling (PLS-SEM) have only partially been utilized. These yet unexploited capabilities of PLS-SEM are a useful tool in the often explorative state of research in management accounting. After reviewing eleven top-ranked management accounting journals through the end of 2013, 37 articles in which PLS-SEM is used are identified. These articles are analysed based on multiple relevant criteria to determine the progress in this research area, including the reasons for using PLS-SEM, the characteristics of the data and the models, and model evaluation and reporting. A special focus is placed on the degree of importance of these analysed criteria for the future development of management accounting research. To ensure continued theoretical development in management accounting, this article also offers recommendations to avoid common pitfalls and provides guidance for the advanced use of PLS-SEM in management accounting research.

Details

Journal of Accounting Literature, vol. 37 no. 1
Type: Research Article
ISSN: 0737-4607

Keywords

Article
Publication date: 25 June 2019

Galit Shmueli, Marko Sarstedt, Joseph F. Hair, Jun-Hwa Cheah, Hiram Ting, Santha Vaithilingam and Christian M. Ringle

Partial least squares (PLS) has been introduced as a “causal-predictive” approach to structural equation modeling (SEM), designed to overcome the apparent dichotomy…

5820

Abstract

Purpose

Partial least squares (PLS) has been introduced as a “causal-predictive” approach to structural equation modeling (SEM), designed to overcome the apparent dichotomy between explanation and prediction. However, while researchers using PLS-SEM routinely stress the predictive nature of their analyses, model evaluation assessment relies exclusively on metrics designed to assess the path model’s explanatory power. Recent research has proposed PLSpredict, a holdout sample-based procedure that generates case-level predictions on an item or a construct level. This paper offers guidelines for applying PLSpredict and explains the key choices researchers need to make using the procedure.

Design/methodology/approach

The authors discuss the need for prediction-oriented model evaluations in PLS-SEM and conceptually explain and further advance the PLSpredict method. In addition, they illustrate the PLSpredict procedure’s use with a tourism marketing model and provide recommendations on how the results should be interpreted. While the focus of the paper is on the PLSpredict procedure, the overarching aim is to encourage the routine prediction-oriented assessment in PLS-SEM analyses.

Findings

The paper advances PLSpredict and offers guidance on how to use this prediction-oriented model evaluation approach. Researchers should routinely consider the assessment of the predictive power of their PLS path models. PLSpredict is a useful and straightforward approach to evaluate the out-of-sample predictive capabilities of PLS path models that researchers can apply in their studies.

Research limitations/implications

Future research should seek to extend PLSpredict’s capabilities, for example, by developing more benchmarks for comparing PLS-SEM results and empirically contrasting the earliest antecedent and the direct antecedent approaches to predictive power assessment.

Practical implications

This paper offers clear guidelines for using PLSpredict, which researchers and practitioners should routinely apply as part of their PLS-SEM analyses.

Originality/value

This research substantiates the use of PLSpredict. It provides marketing researchers and practitioners with the knowledge they need to properly assess, report and interpret PLS-SEM results. Thereby, this research contributes to safeguarding the rigor of marketing studies using PLS-SEM.

Details

European Journal of Marketing, vol. 53 no. 11
Type: Research Article
ISSN: 0309-0566

Keywords

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