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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.

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.

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.

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.

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

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

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.

This research introduces the combined use of partial least squares–structural equation modeling (PLS-SEM) and necessary condition analysis (NCA) that enables researchers to explore and validate hypotheses following a sufficiency logic, as well as hypotheses drawing on a necessity logic. The authors’ objective is to encourage the practice of combining PLS-SEM and NCA as complementary views of causality and data analysis.

The authors present guidelines describing how to combine PLS-SEM and NCA. These relate to the specification of the research objective and the theoretical background, the preparation and evaluation of the data set, running the analyses, the evaluation of measurements, the evaluation of the (structural) model and relationships and the interpretation of findings. In addition, the authors present an empirical illustration in the field of technology acceptance.

The use of PLS-SEM and NCA enables researchers to identify the must-have factors required for an outcome in accordance with the necessity logic. At the same time, this approach shows the should-have factors following the additive sufficiency logic. The combination of both logics enables researchers to support their theoretical considerations and offers new avenues to test theoretical alternatives for established models.

The authors provide insights into the logic, assessment, challenges and benefits of NCA for researchers familiar with PLS-SEM. This novel approach enables researchers to substantiate and improve their theories and helps practitioners disclose the must-have and should-have factors relevant to their decision-making.

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.

The purpose of this paper is to assess the appropriateness of equal weights estimation (sumscores) and the application of the composite equivalence index (CEI) vis-à-vis differentiated indicator weights produced by partial least squares structural equation modeling (PLS-SEM).

The authors rely on prior literature as well as empirical illustrations and a simulation study to assess the efficacy of equal weights estimation and the CEI.

The results show that the CEI lacks discriminatory power, and its use can lead to major differences in structural model estimates, conceals measurement model issues and almost always leads to inferior out-of-sample predictive accuracy compared to differentiated weights produced by PLS-SEM.

In light of its manifold conceptual and empirical limitations, the authors advise against the use of the CEI. Its adoption and the routine use of equal weights estimation could adversely affect the validity of measurement and structural model results and understate structural model predictive accuracy. Although this study shows that the CEI is an unsuitable metric to decide between equal weights and differentiated weights, it does not propose another means for such a comparison.

The results suggest that researchers and practitioners should prefer differentiated indicator weights such as those produced by PLS-SEM over equal weights.

To the best of the authors’ knowledge, this study is the first to provide a comprehensive assessment of the CEI’s usefulness. The results provide guidance for researchers considering using equal indicator weights instead of PLS-SEM-based weighted indicators.

This chapter reviews the methods available to hospitality and tourism researchers to perform moderation analysis with continuous variables in partial least squares structural equation modeling (PLS-SEM), with the objective of enhancing understanding and encouraging the use of these techniques in future papers. The product term method is presented first, followed by an empirical example/application in the context of hospitality and tourism. Two extensions, namely the two-stage approach that can help cope with formative and higher-order constructs, and the orthogonalizing approach that can help generate more accurate results and overcome multicollinearity among tourism variables in the presence of a continuous moderator variable, are then presented and discussed. The chapter concludes by presenting guidelines and recommendations for improving the use of interaction effects in analyses of tourism variables, as well as highlighting ongoing developments in both the product term method and PLS-SEM software.

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.

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.

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.

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.

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

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.

This research aims to explore the factors influencing the adoption intention of eco-friendly smart home appliances among residents in densely populated urban areas of a developing country.

A quantitative research approach was employed to gather data from 348 respondents through purposive sampling. A comparative analysis strategy was then utilized to investigate the adoption of eco-friendly smart home appliances, combining both linear (PLS-SEM) and non-linear (fsQCA) approaches.

The results obtained from PLS-SEM highlight that performance expectancy, facilitating conditions, hedonic motivation, price value, and environmental knowledge significantly influence the adoption intention of eco-friendly smart home appliances. However, the findings suggest that effort expectancy, social influence, and habit are not significantly associated with customers' intention to adopt eco-friendly smart home appliances. On the other hand, the fsQCA results identified eight configurations of antecedents, offering valuable insights into interpreting the complex combined causal relationships among these factors that can generate (each combination) the adoption intention of eco-friendly smart home appliances among densely populated city dwellers.

This study offers crucial marketing insights for various stakeholders, including homeowners, technology developers and manufacturers, smart home service providers, real estate developers, and government entities. The findings provide guidance on how these stakeholders can effectively encourage customers to adopt eco-friendly smart home appliances, aligning with future environmental sustainability demands. The research implications underscore the significance of exploring the antecedents that influence customers' adoption intention of eco-friendly technologies, contributing to the attainment of future sustainability goals.

The environmental sustainability of smart homes, particularly in densely populated city settings in developing countries, has received limited attention in previous studies. Therefore, this study aims to address the pressing issue of global warming and make a meaningful contribution to future sustainability goals related to smart housing technologies. Therefore, this study employs a comprehensive approach, combining both PLS-SEM (linear) and fsQCA (non-linear) techniques to provide a more thorough examination of the factors influencing the adoption of environmentally sustainable smart home appliances.

Although user stickiness has been studied for several years in the field of live e-commerce, little attention has been paid to the effects of streamer attributes on user stickiness in this field. Rooted in the stimulus-organism-response (S-O-R) theory, this study investigated how streamer attributes influence user stickiness.

The authors obtained 496 valid samples from Chinese live e-commerce users and explored the formation of user stickiness using partial least squares-structural equation modeling (PLS-SEM). Artificial neural network (ANN) was used to capture linear and non-linear relationships and analyze the normalized importance ranking of significant variables, supplementing the PLS-SEM results.

The authors found that attractiveness and similarity positively impacted parasocial interaction (PSI). Expertise and trustworthiness positively impacted perceived information quality. Moreover, streamer-brand preference mediated the relationship between PSI and user stickiness, as well as the relationship between perceived information quality and user stickiness. Compared to PLS-SEM, the predictive ability of ANN was more robust. Further, the results of PLS-SEM and ANN both showed that attractiveness was the strongest predictor of user stickiness.

This study explained how streamer attributes affect user stickiness and provided a reference value for future research on user behavior in live e-commerce. The exploration of the linear and non-linear relationships between variables based on ANN supplements existing research. Moreover, the results of this study have implications for practitioners on how to improve user stickiness and contribute to the development of the livestreaming industry.

Prior research in tourism and hospitality domain calls for closer attention to model specification when using partial least square-structural equation modeling (PLS-SEM), including the choice of software and algorithm for PLS model estimation. This paper aims to illustrate the significance of choosing appropriate algorithms for testing the nature of relationships by comparing findings using two different PLS-SEM software packages.

Using a field experiment, relationships between visual servicescape aesthetics and affective responses are conceptualized based on literature in environmental psychology and marketing domains. With photographic surrogates as stimuli in two different hospitality service contexts – spa and upscale restaurant – data are collected from 350 respondents.

By comparing results of SmartPLS 3.2 and WarpPLS 5.0 software and theoretical understanding from environmental psychology literature, it is illustrated that the results and their interpretations may not be in line with theory if model specifications are not correctly implemented and are not addressed through usage of software with a relevant algorithm to test them.

The study highlights the implications for model specification issues such as type of variables and nature of relationships that tourism and hospitality researchers often face and also how use of appropriate algorithms can overcome limitations of model testing for complex models and provide empirical rigor to support theory.

本论文使用两种不同的PLS-SEM处理软件来测试理论模型。通过解析模型设定参数问题, 特别是通过结构关系本性分析, 本论文指出选择合适的软件测试模型在酒店旅游领域的PLS研究中是非常关键的。

本论文借助图像拍摄手段采用实验的采样方式, 在两个不同的酒店服务场所—按摩和高档餐厅—搜集350份数据。本论文采用Smart PLS 3.2 和Warp PLS 5.0 软件来测试PLS-SEM。 这两款软件支持线性和非线性理论关系的比较。

通过Smart PLS 3.2 和Warp PLS 5.0 软件得出的报告结果分析, 不同软件处理PLS得出的结果可能有偏差, 而且会不符合理论设定。如果模型设定参数不正确, 通过使用合适的PLS-SEM软件和相关的数据分析加以辅助, 可能会解决参数不正确的问题。

本论文的比较分析结果可能会帮助到酒店和旅游领域的研究者们, 在做对有关PLS-SEM软件选择的时候, 哪些软件可以更加合适的测试模型有着参考意义。

本论文重点指出了模型设定参数的相关问题, 比如旅游酒店领域常见的变量种类和关系属性等。本论文还研究了如何选择合适的数据分析方法来克服测试复杂模型时的限制, 并且提供实践结果来支撑理论。