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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.

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.

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.

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.

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.

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.

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.

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.

Partial least squares (PLS) path modeling is a variance-based structural equation modeling (SEM) technique that is widely applied in business and social sciences. Its ability to model composites and factors makes it a formidable statistical tool for new technology research. Recent reviews, discussions, and developments have led to substantial changes in the understanding and use of PLS. The paper aims to discuss these issues.

This paper aggregates new insights and offers a fresh look at PLS path modeling. It presents new developments, such as consistent PLS, confirmatory composite analysis, and the heterotrait-monotrait ratio of correlations.

PLS path modeling is the method of choice if a SEM contains both factors and composites. Novel tests of exact fit make a confirmatory use of PLS path modeling possible.

This paper provides updated guidelines of how to use PLS and how to report and interpret its results.

In recent years, the use of structural equation modeling (SEM) has become widespread in tourism and hospitality research. Because there are two different approaches to SEM (i.e., covariance-based SEM and variance-based, partial least squares SEM), this brings challenges for researchers about which SEM to use and what to report in each SEM approach. Therefore, the purpose of this chapter is to discuss the differences between CB-SEM and PLS-SEM and to provide comprehensive guidelines for researchers on how to apply each SEM. Within this context, the authors first briefly summarize the fundamentals and advantages of using SEM. Then, the authors explain in detail the major issues that should be considered when selecting between CB-SEM and PLS-SEM. Finally, to ensure rigorous research practices, the authors provide step-by-step guidelines for the application of both CB-SEM and PLS-SEM.

Partial least squares structural equation modeling (PLS-SEM) has become popular in the information systems (IS) field for modeling structural relationships between latent variables as measured by manifest variables. However, while researchers using PLS-SEM routinely stress the causal-predictive nature of their analyses, the model evaluation assessment relies exclusively on criteria designed to assess the path model's explanatory power. To take full advantage of the purpose of causal prediction in PLS-SEM, it is imperative for researchers to comprehend the efficacy of various quality criteria, such as traditional PLS-SEM criteria, model fit, PLSpredict, cross-validated predictive ability test (CVPAT) and model selection criteria.

A systematic review was conducted to understand empirical studies employing the use of the causal prediction criteria available for PLS-SEM in the database of Industrial Management and Data Systems (IMDS) and Management Information Systems Quarterly (MISQ). Furthermore, this study discusses the details of each of the procedures for the causal prediction criteria available for PLS-SEM, as well as how these criteria should be interpreted. While the focus of the paper is on demystifying the role of causal prediction modeling in PLS-SEM, the overarching aim is to compare the performance of different quality criteria and to select the appropriate causal-predictive model from a cohort of competing models in the IS field.

The study found that the traditional PLS-SEM criteria (goodness of fit (GoF) by Tenenhaus, R2 and Q2) and model fit have difficulty determining the appropriate causal-predictive model. In contrast, PLSpredict, CVPAT and model selection criteria (i.e. Bayesian information criterion (BIC), BIC weight, Geweke–Meese criterion (GM), GM weight, HQ and HQC) were found to outperform the traditional criteria in determining the appropriate causal-predictive model, because these criteria provided both in-sample and out-of-sample predictions in PLS-SEM.

This research substantiates the use of the PLSpredict, CVPAT and the model selection criteria (i.e. BIC, BIC weight, GM, GM weight, HQ and HQC). It provides IS researchers and practitioners with the knowledge they need to properly assess, report on and interpret PLS-SEM results when the goal is only causal prediction, thereby contributing to safeguarding the goal of using PLS-SEM in IS studies.

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.

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.

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).

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

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).

As technology in tourism and hospitality (TTH) develops technical artifacts according to visitors’ demands, it must deal with both behavioral and design constructs in the context of structural equation modeling (SEM). While behavioral constructs are typically modeled as common factors, the study at hand introduces the composite into TTH to model artifacts. To deal with both kinds of constructs, this paper aims to exploit partial least squares path modeling (PLS-PM) as a confirmatory approach to estimate models containing common factors and composites.

The study at hand presents PLS-PM in its current form, i.e. as a full-fledged approach for confirmatory purposes. By introducing the composite to model artifacts, TTH scholars can use PLS-PM to answer research questions of the type “Is artifact xyz useful?”, contributing to a further understanding of TTH. To demonstrate the composite model, an empirical example is used.

PLS-PM is a promising approach when the model contains both common factors and composites. By applying the test for overall model fit, empirical evidence can be obtained for latent variables and artifacts. In doing so, researchers can statistically test whether a developed artifact is useful.

To the best of the authors’ knowledge, this is the first study to discuss the practical application of composite and common factor models in TTH research. Besides introducing the composite to model artifacts, the study at hand also guides scholars in the assessment of PLS-PM results.

因为旅游酒店科技（TTH）根据游客需求而定制科技产品, TTH必须在结构方程模型（SEM）下结合游客行为和设计等变量。一般行为变量在模型中是常见因子, 本研究将这些变量编入TTH结构成为模块。本研究采用PLS-PM方法来预估含有隐性变量和模块的模型。.

本研究设计PLS-PM模式, 即确定性全变量方法。TTH学者们通过引进结构形成模型模块, 使用PLS-PM研究方法, 以回答研究问题“模块xyz有用吗？”, 因此对TTH进一步理解。为了展示复合模型, 本论文采用实际验证。.

PLS-PM在面对模块内存在常见因子和复合模块的结构时是有力方法。实际验证结果通过整体最佳模型参数, 得到隐性变量和模块。为此, 研究者们能够在统计方法上测量是否开发的模型模块是否有用。.

据作者所知, 本论文是首个研究在TTH领域上应用模块和常见因子模型。本研究引进显性变量在模型模块中, 以指导学者评估PLS-PM结果报告。.

The partial least squares structural equation modeling (PLS-SEM) approach for construction management (CM) scholars has become the preferred approach for its capability of assessing the complex relationship and relaxed normality and sample size assumptions. This paper systematically maps the structure of knowledge about PLS-SEM in CM using bibliometric analysis. Also, the study employs meta-analysis to explore how data and model characteristics, model evaluation and advanced modeling techniques have been utilized in the CM domain.

This study integrated two methods: bibliometric analysis on a sample of 211 articles identified using the PRISMA framework and meta-analysis on 163 articles identified based on the availability of full-length articles and relevant information.

The results revealed the leading knowledge formation entities (countries, institutions, authors, sources and documents). Also, the study employs full content analysis to identify six research themes, and meta-analysis is used to explore the use of PLS-SEM based on the following criteria: (1) reasons for using PLS-SEM in CM, (2) data characteristics, (3) model characteristics and evaluation and (4) use of advanced modeling and analysis techniques. Further, the study uses regression analysis and identifies “advanced modeling and analysis techniques” as the critical feature responsible for the publication in a journal with high scientific prestige. Finally, the study presented the comprehensive guidelines to be used by construction management scholars who wish to use PLS-SEM in their research work.

To the author’s knowledge, it is the first study of this kind to use PLS-SEM in CM research. This study provides an extensive analysis of the Scopus database and an in-depth review of the data characteristics, model characteristics and use of advanced modeling techniques in CM research.