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Item type:Publication, Access status: without files , A primer on partial least squares structural equation modeling (PLS-SEM)The third edition of A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) guides readers through learning and mastering the techniques of this approach in clear language. Authors Joseph H. Hair, Jr., G. Tomas M. Hult, Christian Ringle, and Marko Sarstedt use their years of conducting and teaching research to communicate the fundamentals of PLS-SEM in straightforward language to explain the details of this method, with limited emphasis on equations and symbols. A running case study on corporate reputation follows the different steps in this technique so readers can better understand the research applications. Learning objectives, review and critical thinking questions, and key terms help readers cement their knowledge. This edition has been thoroughly updated, featuring the latest version of the popular software package SmartPLS 3. New topics have been added throughout the text, including a thoroughly revised and extended chapter on mediation, recent research on the foundations of PLS-SEM, detailed descriptions of research summarizing the advantages as well as limitations of PLS-SEM, and extended coverage of advanced concepts and methods, such as out-of-sample versus in-sample prediction metrics, higher-order constructs, multigroup analysis, necessary condition analysis, and endogeneity.Publicationtype: Book4981 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Prediction: coveted, yet forsaken? introducing a cross‐validated predictive ability test in partial least squares path modeling(Wiley-Blackwell, 2021-04); ; ; ; Management researchers often develop theories and policies that are forward‐looking. The prospective outlook of predictive modeling, where a model predicts unseen or new data, can complement the retrospective nature of causal‐explanatory modeling that dominates the field. Partial least squares (PLS) path modeling is an excellent tool for building theories that offer both explanation and prediction. A limitation of PLS, however, is the lack of a statistical test to assess whether a proposed or alternative theoretical model offers significantly better out‐of‐sample predictive power than a benchmark or an established model. Such an assessment of predictive power is essential for theory development and validation, and for selecting a model on which to base managerial and policy decisions. We introduce the cross‐validated predictive ability test (CVPAT) to conduct a pairwise comparison of predictive power of competing models, and substantiate its performance via multiple Monte Carlo studies. We propose a stepwise predictive model comparison procedure to guide researchers, and demonstrate CVPAT's practical utility using the well‐known American Customer Satisfaction Index (ACSI) model.Publicationtype: Journal Article157 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Partial least squares structural equation modeling (PLS-SEM) using R : a workbook(Springer, 2021); ; ; ; Publicationtype: Book353 6359 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Manual de partial least squares structural equation modeling (PLS-SEM)(OmniaScience Scholar, 2019-07); ; ; ; Publicationtype: Book665 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Addressing endogeneity in international marketing applications of partial least squares structural equation modelingPartial least squares structural equation modeling (PLS-SEM) has become a key method in international marketing research. Users of PLS-SEM have, however, largely overlooked the issue of endogeneity, which has become an integral component of regression analysis applications. This lack of attention is surprising because the PLS-SEM method is grounded in regression analysis, for which numerous approaches for handling endogeneity have been proposed. To identify and treat endogeneity, and create awareness of how to deal with this issue, this study introduces a systematic procedure that translates control variables, instrumental variables, and Gaussian copulas into a PLS-SEM framework. We illustrate the procedure's efficacy by means of empirical data and offer recommendations to guide international marketing researchers on how to effectively address endogeneity concerns in their PLS-SEM analyses.Publicationtype: Journal Article195 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Mirror, mirror on the wall: a comparative evaluation of composite-based structural equation modeling methods(Springer, 2017-02-16); ; ; ; Composite-based structural equation modeling (SEM), and especially partial least squares path modeling (PLS), has gained increasing dissemination in marketing. To fully exploit the potential of these methods, researchers must know about their relative performance and the settings that favor each method’s use. While numerous simulation studies have aimed to evaluate the performance of composite-based SEM methods, practically all of them defined populations using common factor models, thereby assessing the methods on erroneous grounds. This study is the first to offer a comprehensive assessment of composite-based SEM techniques on the basis of composite model data, considering a broad range of model constellations. Results of a large-scale simulation study substantiate that PLS and generalized structured component analysis are consistent estimators when the underlying population is composite model-based. While both methods outperform sum scores regression in terms of parameter recovery, PLS achieves slightly greater statistical power.Publicationtype: Journal Article134 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM)With applications using SmartPLS (www.smartpls.com) - the primary software used in partial least squares structural equation modeling (PLS-SEM) - this practical guide provides concise instructions on how to use this evolving statistical technique to conduct research and obtain solutions. Featuring the latest research, new examples, and expanded discussions throughout, the Second Edition is designed to be easily understood by those with limited statistical and mathematical training who want to pursue research opportunities in new ways.Publicationtype: Book2332 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Partial Least Squares Strukturgleichungsmodellierung (PLS-SEM): Eine anwendungsorientierte Einführung(Vahlen, 2017); ; ; ; Die Partial Least Squares Strukturgleichungsmodellierung (PLS-SEM) hat sich in der wirtschafts- und sozialwissenschaftlichen Forschung als geeignetes Verfahren zur Schätzung von Kausalmodellen behauptet. Dank der Anwenderfreundlichkeit des Verfahrens und der vorhandenen Software ist es inzwischen auch in der Praxis etabliert. Dieses Buch liefert eine anwendungsorientierte Einführung in die PLS-SEM. Der Fokus liegt auf den Grundlagen des Verfahrens und deren praktischer Umsetzung mit Hilfe der SmartPLS-Software. Das Konzept des Buches setzt dabei auf einfache Erläuterungen statistischer Ansätze und die anschauliche Darstellung zahlreicher Anwendungsbeispiele anhand einer einheitlichen Fallstudie. Viele Grafiken, Tabellen und Illustrationen erleichtern das Verständnis der PLS-SEM. Zudem werden dem Leser herunterladbare Datensätze, Aufgaben und weitere Fachartikel zur Vertiefung angeboten. Damit eignet sich das Buch hervorragend für Studierende, Forscher und Praktiker, die die PLS-SEM zur Gewinnung von Ergebnissen mit den eigenen Daten und Modellen nutzen möchten. SmartPLS ist das führende Softwareprogramm zur Schätzung von PLS-basierten Strukturgleichungsmodellen. Die Erläuterungen und die im Buch vorgeschlagenen Vorgehensweisen spiegeln den aktuellen Stand der Forschung wider.Publicationtype: Book548 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Common beliefs and reality about PLS : comments on Rönkkö and Evermann (2013)(Sage Publications, 2014-04); ; ; ; This article addresses Ro¨nkko¨ and Evermann’s criticisms of the partial least squares (PLS) approach to structural equation modeling. We contend that the alleged shortcomings of PLS are not due to problems with the technique, but instead to three problems with Ro¨nkko¨ and Evermann’s study: (a) the adherence to the common factor model, (b) a very limited simulation designs, and (c) overstretched generalizations of their findings. Whereas Ro¨nkko¨ and Evermann claim to be dispelling myths about PLS, they have in reality created new myths that we, in turn, debunk. By examining their claims, our article contributes to reestablishing a constructive discussion of the PLS method and its properties. We show that PLS does offer advantages for exploratory research and that it is a viable estimator for composite factor models. This can pose an interesting alternative if the common factor model does not hold. Therefore, we can conclude that PLS should continue to be used as an important statistical tool for management and organizational research, as well as other social science disciplines.Publicationtype: Journal ArticleScopus© Citations 2291 384 328 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , A Primer on Partial Least Squares Structural Equation ModelingWith applications using SmartPLS (www.smartpls.com) - the primary software used in partial least squares structural equation modeling (PLS-SEM) - this practical guide provides concise instructions on how to use this evolving statistical technique to conduct research and obtain solutions. Featuring the latest research, new examples, and expanded discussions throughout, the Second Edition is designed to be easily understood by those with limited statistical and mathematical training who want to pursue research opportunities in new ways.Publicationtype: Book1322