Partial Least Squares Structural Equation Modeling Applications Research Articles

Using necessary condition analysis to complement multigroup analysis in partial least squares structural equation modeling

With the growing importance of partial least squares structural equation modeling (PLS-SEM) in marketing and consumer research, the use of Multigroup Analysis (MGA) for discovering observed heterogeneity (i.e., differences in relationships between variables for subgroups of the population under investigation) and deriving relevant operational results has become of great interest. However, these analyses are based exclusively on an additive sufficiency logic and do not permit researchers to test and validate hypotheses drawing on a necessity logic, the latter having been the focus of recent significant developments. Addressing this concern, the present paper offers guidelines for combining the use of Necessary Condition Analysis (NCA) and MGA performed with PLS-SEM. Taken together, these analyses can explore and improve knowledge about predefined subgroups of interest, enhance the understanding of relationships, refine the role of specific key antecedents by discovering meaningful necessary conditions, and therewith, contribute to theorizing. An empirical illustration drawing on the relationship between corporate social responsibility and customer loyalty is developed in a step-by-step fashion to provide marketing researchers with the guidelines to conduct the MGA and NCA, and finally report and interpret the results in accordance with both the sufficiency and the necessity logics. This integrative procedure contributes to the advancement of PLS-SEM applications. By delivering a better understanding of the group-specific results of a PLS-SEM–based MGA in a necessity logic, it promotes the complementary usage of sufficiency and necessity logics and therefore helps researchers to uncover novel theoretical and practical results when evaluating the data.

Examining the crucial societal incentive approaches and their impact mechanisms to enhance the purchase intention of carbon-labeled products: A conjoined application of PLS-SEM and NCA methodologies

Enhancing consumers’ purchase intention for carbon-labeled products is crucial for the sustainable development of the market economy. This study aims to address a key challenge by exploring the often-overlooked impact of external incentive mechanisms in the existing literature. Utilizing partial least squares structural equation modeling (PLS-SEM) and necessary condition analysis (NCA), the research dissects survey data from 730 Chinese consumers. It reveals the complex influence of four social incentives (i.e., monetary incentives, price incentives, spiritual incentives, and information incentives) on purchasing intentions from the perspective of perceived value. The results indicate that consumers’ awareness and trust in carbon labels are highly important but show modest performance. To be specific, among the four incentive measures, information incentives significantly promote purchasing intentions, followed by spiritual incentives. Interestingly, although the price incentives are a key precondition for increasing purchasing intentions, they do not show a direct and statistically significant effect. Besides, monetary incentives specifically facilitate the formation of purchasing intentions by enhancing consumers’ perception of the efficacy of carbon-labeled products, while information incentives promote intentions by influencing consumers’ desire for value expression. Benefiting from these four incentive measures, this study provides valuable insights to help develop effective strategies for promoting carbon labeling and green products, refine the design of environmentally friendly choice architectures for public policy, and enhance our understanding of consumer behavior in contemporary culture.

Robustness checks in PLS-SEM: A review of recent practices and recommendations for future applications in business research

For the last decade, scholars have employed partial least squares structural equation modeling (PLS-SEM) extensively in business research. However, when applying PLS-SEM, researchers need to perform various robustness checks before and after model estimation. This study showcases the findings of a review of PLS‐SEM use in business research, by examining papers published between 2016 and 2021 in business journals. The study explores the extent to which researchers have performed robustness checks regarding nonnormality, endogeneity, unobserved heterogeneity, nonlinearity, and heteroskedasticity. The findings highlight that statistical rigor remains a serious problem in business-related studies employing PLS-SEM. Despite some encouraging improvements in the last few years, the vast majority of recent business-related studies using PLS-SEM have systematically overlooked robustness checks. This study calls for continued emphasis on the importance of robustness checks and the correct application of appropriate techniques, providing recommendations and guidelines for future PLS-SEM applications.

PLS-SEM - skriveni dragulj u metodologiji naučnog istraživanja turizma

Purpose - The main objective of this paper is to provide a well-organized guide for the application of partial least squares structural equation modeling (PLS-SEM) in tourism research. In this way, the paper strives to encourage future tourism studies to use PLS-SEM and contribute to methodological advances in the field. Methodology-This paper systematically examines the application of PLS-SEM with a particular focus on the application of hierarchical constructs in tourism research and carefully analyzes and classifies the existing literature on PLS-SEM. Results-Specific steps for evaluating and interpreting the hierarchical latent variables of the PLS model are presented and explained. Implications-This paper contributes to advancing the application of PLS-SEM in tourism research by providing researchers with a valuable tool to improve both the rigor of empirical investigation and theoretical development in the field. The insights gained from this paper can guide subsequent research to investigate specific tourism-related scenarios, potentially leading to new transformative discoveries and paradigm shifts in our understanding of tourism dynamics.

The value of self-determination theory in marketing studies: Insights from the application of PLS-SEM and NCA to anti-food waste apps

Self-determination theory (SDT) is a theory of human motivation that highlights the distinction between intrinsic and extrinsic motivations. Recent research has advocated its wider use in marketing studies, suggesting that it has strong predictive accuracy for consumer behaviors, and has proposed arguments about the necessity of both intrinsic and extrinsic motivations for marketing outcomes. However, these statements have not been empirically substantiated. We address this gap by studying the motivations for attitude and intention to use anti-food waste apps. Data from 141 users and 227 non-users of the app “Too Good To Go” are analyzed using partial least squares structural equation modeling (PLS-SEM) and several of its methodological extensions (e.g., multigroup analysis and the cross-validated predictive ability test), and necessary condition analysis (NCA). The findings support the argument that SDT accurately predicts consumer attitudes and behaviors, while NCA provides a nuanced view of the necessity of intrinsic and extrinsic motivations.

Elevating students’ lives through immersive learning experiences in a safe metaverse

Metaverse platforms devoted to educational purposes are becoming increasingly popular among educators worldwide due to the benefits they offer in elevating students’ lives. This study explores how immersive platform features, blockchain features, a safe learning environment and new learning models contribute to the educational outcomes. Emerging literature on educational Metaverse platforms has been used to develop a genuine conceptual model. The model was tested using data collected from 100 educators located in different countries from worldwide. The data were analyzed using partial least squares structural equation modeling (PLS-SEM), Random Forest and other different feature importance methods, fuzzy-set qualitative comparative analysis (fsQCA), thematic content analysis and Latent Dirichlet Allocation (LDA). The application of PLS-SEM outlines that immersive platform features and safe learning environments positively affects educational outcomes. Random Forest and feature importance methods investigate the predictive power of items included in the model, fsQCA reveals the combinations of these factors that lead to high educational outcomes, while the qualitative insights reveal the key themes emerged from educators’ perspectives on the use of Metaverse platforms. This study contributes to Metaverse literature, which is relatively understudied in the educational sector.

The impact of female representation and ethnic diversity in committees on environmental, social and governance performance in Malaysia

Purpose This study aims to explore how females on committees (FOC) and committee ethnic diversity (CED) impact environmental, social and governance performance (ESGP). Design/methodology/approach This study examines 126 listed firms under the coverage of FTSE ESG Ratings in Bursa Malaysia between 2017 and 2019. This study applies partial least squares structural equation modeling (PLS-SEM) to examine the hypotheses. While the risk of common method variance is minimised using multiple data sources for the analysis, instrumental variable-free approach, i.e. Gaussian copula method which is implemented in SmartPLS 4.0 has been used to address the potential endogeneity of the model. Findings Empirical evidence demonstrates significant positive direct relationships between FOC and ESGP, as well as CED and ESGP. The argument of resource dependence theory and positive empirical results on the two direct relationships hold firm despite several committees being aggregated as one construct with the aim of providing different insights into the literature. Practical implications This study provides implications for firm leadership to consider reviewing the composition of committees by increasing female representation while striking a balance in the appointment of committee members of different ethnicities to enhance firm ESGP. Originality/value To the best of the author’s knowledge, this study adopts a holistic approach by capturing, for the first time, the female representation of audit, nomination, remuneration and risk management committees. These dimensions are further developed into a single quantifiable variable, presented as FOC. Similarly, the ethnic diversity of the respective committees is aggregated and developed into a single quantifiable construct: the CED. Unlike most existing studies that commonly use econometric software, the application of PLS-SEM in this study contributes to the limited body of corporate governance and ESG studies that use PLS-SEM.

Improving PLS-SEM use for business marketing research

A review of studies published in Industrial Marketing Management over the past two decades and more shows that these studies not only used partial least squares structural equation modeling (PLS-SEM) widely to estimate and empirically substantiate theoretically established models with constructs, but did so increasingly. In line with their study goals, researchers provided reasons for using PLS-SEM (e.g., model complexity, limited sample size, and prediction). These reasons are frequently not fully convincing, requiring further clarification. Additionally, our review reveals that researchers' assessment and reporting of their measurement and structural models are insufficient. Certain tests and thresholds that they use are also inappropriate. Finally, researchers seldom apply more advanced PLS-SEM analytic techniques, although these can support the results' robustness and may create new insights. This paper addresses the issues by reviewing business marketing studies to clarify PLS-SEM's appropriate use. Furthermore, the paper provides researchers and practitioners in the business marketing field with a best practice orientation and describes new opportunities for using PLS-SEM. To this end, the paper offers guidelines and checklists to support future PLS-SEM applications.

The Use of Partial Least Squares Structural Equation Modeling and Complementary Methods in International Management Research

Research in international business and management (IM) is highly complex, contextual, and spans various subfields and related theoretical lenses. These characteristics pose research challenges that require utilizing sophisticated research designs and methodologies. This paper and our focused issue on ‘The use of partial least squares structural equation modeling (PLS-SEM) and complementary methods in IM research’ further clarify whether and how researchers using PLS-SEM can address (some of) the challenges in IM research. We explain how researchers can benefit from the (advanced) capabilities that PLS-SEM offers; either as a stand-alone method or in triangulation efforts that leverage complementary approaches. In addition, we review the IM literature for PLS-SEM applications and evaluate whether and how researchers are already using these approaches. We identify some room for improvement when it comes to the application of both more advanced PLS-SEM capabilities and the triangulation of PLS-SEM with qualitative data analyses, and techniques such as fuzzy set qualitative comparative analyses and necessary condition analyses. For better guidance, we refer to PLS-SEM application examples in which the methodological advances are used.

Impact of market demand on recurring hallmark sporting event spectators: an empirical study of the Shanghai Masters

PurposeThe current study was designed to (1) identify core and peripheral market demand for a recurring hallmark sporting event, testing their impact on event identification and behavioral intentions; and to (2) explore the effect of core and peripheral market demand on event identification between first-time and repeat spectators.Design/methodology/approachResearch participants (N = 540) were spectators at the Shanghai Masters over a span of seven days. Data were analyzed using partial least squares structural equation modeling (PLS-SEM) and partial least squares multi-group analysis (PLS-MGA).FindingsSignificant, positive relationships were found between core market demand and event identification, and between core market demand and behavioral intentions. In contrast, peripheral market demand only had significant, positive effect on event identification; however, findings revealed that event identification fully mediated the relationships between peripheral market demand and behavioral intentions. Additionally, the effect of peripheral market demand on event identification was greater among first-time spectators than repeat spectators.Originality/valueThis study contributed to the application of PLS-SEM in sport management research by adopting a formative-formative hierarchical component model (HCM) to address the prevailing measurement model misspecification of market demand constructs. The findings highlighted the merits of promoting market demand associated with recurring hallmark sporting events and the importance of enhancing event identification through differential market penetration schemes across different spectator groups.

The combined use of symmetric and asymmetric approaches: partial least squares-structural equation modeling and fuzzy-set qualitative comparative analysis

PurposeThis 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/approachThis 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.FindingsThis 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/valueThe 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.

PLS-SEM STATISTICAL PROGRAMS: A REVIEW

Partial least squares structural equation modeling (PLS-SEM) is one of the most widely used methods of multivariate data analysis. Although previous research has discussed different aspects of PLS-SEM, little has been done to explain the attributes of the various PLS-SEM statistical applications. The objective of this editorial is to discuss the multiple PLS-SEM applications, including SmartPLS, WarpPLS, and ADANCO. It is written based on information received from the developers via emails as well as our ongoing understanding and experience of using these applications. We hope this editorial will serve as a manual for users to understand the unique characteristics of each PLS-SEM application and make informed decisions on the most appropriate application for their research.

Executing and interpreting applications of PLS-SEM: Updates for family business researchers

The use of partial least squares structural equation modeling (PLS-SEM) has been gaining momentum in family business research. Since the publication of a PLS-SEM guidelines article in the Journal of Family Business Strategy’s special issue on “Innovative and Established Research Methods in Family Business” in 2014, methodological research has developed new model evaluation methods and metrics and sharpened our understanding of the method’s strengths and limitations. In light of these developments, we extend prior guidelines on PLS-SEM applications by discussing new model evaluation procedures (e.g., model selection) and metrics (e.g., PLSpredict). In addition, we highlight the usefulness of methodological extensions for discrete choice modeling and endogeneity assessment that considerably extend the scope of the PLS-SEM method, and emerging opportunities for the application of PLS-SEM with archival (secondary) data. PLS-SEM remains a valuable method in the context of family business research, especially when it comes to gaining a more sophisticated understanding of the drivers of family business behavior. Because of its properties, the PLS-SEM approach proves particularly valuable when the aim is to predict target variables (e.g., family firm performance) in the context of a causal model.

This fast car can move faster: a review of PLS-SEM application in higher education research

The relevance and prominence of the partial least squares structural equation modeling (PLS-SEM) method has recently increased in higher education research, especially in explanatory and predictive studies. We therefore first aim to assess previous PLS-SEM applications by providing a systematic review; second, we aim to highlight and summarize important guidelines for conducting a rigorous PLS-SEM analysis of the current state of results reporting in higher education journals. Specifically, this study focuses on empirical PLS-SEM applications in 14 major higher education journals indexed in the Thomson Reuters Web of Science and in the Elsevier-Scopus databases between 1999 and 2018. We initially identified 49 relevant papers published in 10 higher education journals. Based on these papers’ generally followed guidelines, we thereafter identified various issues related to data screening, model characteristics, measurement model evaluation, structural model evaluation, and the application of state-of-the-art PLS-SEM advanced methods requiring particular attention. Furthermore, we recommend recent guidelines to improve PLS-SEM applications and practices, besides providing specific suggestions regarding utilizing the method’s strength in terms of relevant higher education research questions. Our findings remind researchers, reviewers, and journal editors to remain vigilant, should help them avoid inaccuracies in future publications, and ensure rigor.

Social dimension activates the usage and academic impact of Open Access publications in Andean countries: a structural modeling-based approach

Most of the altmetric research in developing countries is descriptive and has not tested models exploring relationships among article-level factors. Therefore, there is a knowledge gap on this issue. The research question for this study was the following: Social dimension is the factor that activates the usage and citation of Open Access publications in Andean countries. DOIs of Andean countries publications in PLoS journals were downloaded using (n = 3,186) using rplos, an Application Programming Interface (API) developed for R: Bolivia (n = 121), Chile (n = 1,180), Colombia (n = 826), Ecuador (n = 266), Peru (n = 622), and Venezuela (n = 171). Data modeling was carried out with Partial Least Squares – Structural Equation Modeling (PLS-SEM). The hypothesized structural model accounted for at least 41% of the explained variance of citations (R2 = 0.406). Among the two associated factors, the usage made the highest contribution (at least 0.639), while the social media showed a negative contribution. The application of PLS-SEM for modeling the information-seeking behavior, at the document level, highlights the positive evidence for the path social → usage → citation, but a negative and nonsignificant association between the social dimension and citation.

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

PurposeThe 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.Design/methodology/approachThis 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.FindingsMost 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.Research limitations/implicationsMethodological 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.Originality/valueIn 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.

Explaining Systemic Risk in Money Market Funds

For the first time, this study evaluates the contributions to systemic risk in the context of U.S. institutional prime money market funds (MMFs) from different sources using partial least squares structural equation modeling (PLS-SEM). The primary motivation behind this study is to trace systemic risk to its underlying sources and measure which types of relationships provide significant explanation using PLS-SEM. I illustrate the application of PLS-SEM and interpretation of results in a step-by-step manner to empower those new to PLS-SEM, and undertake robustness testing. Findings indicate that through crisis years, macroprudential indicators contribute to potential systemic risk more than prudential indicators. This suggests that macroprudential indicators that can be traced to individual MMFs market positions are more important in understanding systemic risk during crises, and further underlines the interconnectedness of markets. PLS-SEM can be used to test the explanatory power of new indicators as they emerge in an exploratory environment.

Gain more insight from your PLS-SEM results

Purpose The purpose of this paper is to introduce the importance-performance map analysis (IPMA) and explain how to use it in the context of partial least squares structural equation modeling (PLS-SEM). A case study, drawing on the IPMA module implemented in the SmartPLS 3 software, illustrates the results generation and interpretation. Design/methodology/approach The explications first address the principles of the IPMA and introduce a systematic procedure for its use, followed by a detailed discussion of each step. Finally, a case study on the use of technology shows how to apply the IPMA in empirical PLS-SEM studies. Findings The IPMA gives researchers the opportunity to enrich their PLS-SEM analysis and, thereby, gain additional results and findings. More specifically, instead of only analyzing the path coefficients (i.e. the importance dimension), the IPMA also considers the average value of the latent variables and their indicators (i.e. performance dimension). Research limitations/implications An IPMA is tied to certain requirements, which relate to the measurement scales, variable coding, and indicator weights estimates. Moreover, the IPMA presumes linear relationships. This research does not address the computation and interpretation of non-linear dependencies. Practical implications The IPMA is particularly useful for generating additional findings and conclusions by combining the analysis of the importance and performance dimensions in practical PLS-SEM applications. Thereby, the IPMA allows for prioritizing constructs to improve a certain target construct. Expanding the analysis to the indicator level facilitates identifying the most important areas of specific actions. These results are, for example, particularly important in practical studies identifying the differing impacts that certain construct dimensions have on phenomena such as technology acceptance, corporate reputation, or customer satisfaction. Originality/value This paper is the first to offer researchers a tutorial and annotated example of an IPMA. Based on a state-of-the-art review of the technique and a detailed explanation of the method, this paper introduces a systematic procedure for running an IPMA. A case study illustrates the analysis, using the SmartPLS 3 software.

Bootstrapping and PLS-SEM: A step-by-step guide to get more out of your bootstrap results

Statistical inference, which relies on bootstrapping in partial least squares structural equation modeling (PLS-SEM), lies at the heart of developing practically relevant and academically rigorous theory. Inspection of PLS-SEM applications in European management research reveals that there is still much to be gained in terms of bootstrapping. This paper suggests several bootstrapping best practices and demonstrates how to conduct them for frequently encountered, yet often ignored, PLS-SEM situations such as the assessment of (non) direct effects, the comparison of effects, and the evaluation of the coefficient of determination.

Explaining Systemic Risk in Money Market Funds: An Exploratory Study

Evaluating creation of systemic risk is an ongoing regulatory activity and a difficult task given the dynamic linkages found in the financial system among multiple counterparties. This study evaluates the contributions to systemic risk in U.S. institutional prime money market funds (MMFs) from different sources using partial least squares structural equation modeling (PLS-SEM). The four years (2011-2014) cover the period following major reforms in 2010 implemented by the U.S. Securities and Exchange Commission (SEC), and includes years of the Eurozone sovereign debt crisis (2011-2012), as well as year 2011 marking the U.S. debt ceiling crisis. The primary motivation behind this study is to trace systemic risk in MMFs to its sources and measure which types of relationships provide significant explanation. In a first application of PLS-SEM with MMFs, I illustrate how the method can be used to explain systemic risk in an effort to help regulators, managers of MMFs and investors better monitor the market. This study evaluates the contribution of prudential and macroprudential indicators to systemic risk. Findings indicate that through the crisis years (2011-2012), macroprudential indicators contribute to potential systemic risk more than prudential indicators. However, this relationship is switched during 2013-2014 when the impact of Eurozone sovereign debt and US debt ceiling crises is less noticeable. This observation suggests that macroprudential indicators that can be traced to individual MMFs (i.e., funds’ market positions) are more important in understanding systemic risk during crises, and further underlines the interconnectedness of markets.