2022 - Research.com Social Sciences and Humanities in Germany Leader Award
Marko Sarstedt spends much of his time researching Partial least squares regression, Structural equation modeling, Partial least squares path modeling, Econometrics and Factor analysis. His Partial least squares regression research incorporates elements of Domain, Marketing, Latent variable and Applied mathematics. His Structural equation modeling study combines topics in areas such as Data mining, Management science, Covariance, Software and Popularity.
The study incorporates disciplines such as Contrast, Least squares and Artificial intelligence in addition to Partial least squares path modeling. The concepts of his Econometrics study are interwoven with issues in Test validity, External validity, Omnibus test and Validity. His work on Optimal discriminant analysis, Construct validity, Predictive validity and Criterion validity as part of his general Statistics study is frequently connected to Multitrait-multimethod matrix, thereby bridging the divide between different branches of science.
His primary areas of investigation include Structural equation modeling, Partial least squares regression, Econometrics, Marketing and Partial least squares path modeling. His work deals with themes such as Latent variable, Data mining, Management science, Factor analysis and Data science, which intersect with Structural equation modeling. Marko Sarstedt has included themes like Customer satisfaction, Segmentation, Applied mathematics and Covariance in his Partial least squares regression study.
His Econometrics study incorporates themes from Regression analysis, Statistics, Empirical research, Market segmentation and Construct validity. His Marketing research is multidisciplinary, incorporating perspectives in Sample, Public relations and Reputation. His research ties Artificial intelligence and Partial least squares path modeling together.
Structural equation modeling, Partial least squares regression, Management science, Latent variable and Data science are his primary areas of study. His study with Structural equation modeling involves better knowledge in Statistics. Machine learning covers Marko Sarstedt research in Partial least squares regression.
His Management science research includes themes of Explanatory power, Project management and Originality. His research investigates the link between Latent variable and topics such as Econometrics that cross with problems in Nomological network and Construct validity. His study in Data science is interdisciplinary in nature, drawing from both Visualization and Leverage.
His main research concerns Structural equation modeling, Partial least squares regression, Applied mathematics, Management science and Latent variable. In general Structural equation modeling, his work in Partial least squares path modeling is often linked to Methodological research linking many areas of study. His work carried out in the field of Partial least squares regression brings together such families of science as Predictive power, Key and Data mining.
His Applied mathematics research incorporates themes from Construct and Order. His Management science research focuses on Marketing and how it relates to Factor analysis and Toolbox. His research integrates issues of Graphical user interface, Software, Industrial engineering and Consumer behaviour in his study of Latent variable.
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A primer on partial least squares structural equation modeling (PLS-SEM)
Joseph F. Hair;G. Tomas M. Hult;Christian M. Ringle;Marko Sarstedt.
PLS-SEM: Indeed a Silver Bullet
Joe F. Hair;Christian M. Ringle;Marko Sarstedt.
The Journal of Marketing Theory and Practice (2011)
A new criterion for assessing discriminant validity in variance-based structural equation modeling
Joerg Henseler;Christian M. Ringle;Marko Sarstedt;Marko Sarstedt.
Journal of the Academy of Marketing Science (2015)
An assessment of the use of partial least squares structural equation modeling in marketing research
Joe F. Hair;Marko Sarstedt;Marko Sarstedt;Christian M. Ringle;Christian M. Ringle;Jeannette A. Mena.
Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research
Joseph F. Hair;Marko Sarstedt;Lucas Hopkins;Volker G. Kuppelwieser.
Partial Least Squares Structural Equation Modeling: Rigorous Applications, Better Results and Higher Acceptance
Joseph F. Hair;Christian M. Ringle;Marko Sarstedt.
Long Range Planning (2013)
Editor's comments: a critical look at the use of PLS-SEM in MIS quarterly
Christian M. Ringle;Marko Sarstedt;Detmar W. Straub.
Management Information Systems Quarterly (2012)
Common Beliefs and Reality About PLS: Comments on Rönkkö and Evermann (2013)
Joerg Henseler;Joerg Henseler;Theo K. Dijkstra;Marko Sarstedt;Marko Sarstedt;Christian M. Ringle;Christian M. Ringle.
The Use of Partial Least Squares Structural Equation Modeling in Strategic Management Research: A Review of Past Practices and Recommendations for Future Applications
Joseph F. Hair;Marko Sarstedt;Torsten M. Pieper;Christian M. Ringle.
When to use and how to report the results of PLS-SEM
Joseph F. Hair;Jeffrey J. Risher;Marko Sarstedt;Christian M. Ringle.
European Business Review (2019)
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