2022 - Research.com Social Sciences and Humanities in Germany Leader Award
Christian M. Ringle mostly deals with Partial least squares regression, Structural equation modeling, Partial least squares path modeling, Econometrics and Factor analysis. The Partial least squares regression study combines topics in areas such as Latent variable, Data mining and Applied mathematics. His Structural equation modeling study incorporates themes from Popularity, Covariance and Management science.
His work deals with themes such as Marketing research and Interdependence, which intersect with Popularity. He has researched Partial least squares path modeling in several fields, including LISREL, Least squares and Artificial intelligence. His Econometrics research includes themes of Mediation, Model complexity and Family business.
Christian M. Ringle mainly focuses on Structural equation modeling, Partial least squares regression, Econometrics, Partial least squares path modeling and Marketing. His study in Structural equation modeling is interdisciplinary in nature, drawing from both Management science, Covariance, Variance, Factor analysis and Data science. His studies in Management science integrate themes in fields like Marketing research and Strategic management.
His Partial least squares regression study combines topics in areas such as Segmentation, Latent variable, Data mining and Applied mathematics. Christian M. Ringle has included themes like Regression analysis, Empirical research and Causal model in his Econometrics study. His Partial least squares path modeling study integrates concerns from other disciplines, such as Estimator, Mathematical optimization and Identification.
Christian M. Ringle mainly investigates Structural equation modeling, Partial least squares regression, Marketing, Econometrics and Data science. Structural equation modeling is a subfield of Statistics that Christian M. Ringle explores. To a larger extent, he studies Machine learning with the aim of understanding Partial least squares regression.
His study in the field of Service, Loyalty, Consumer satisfaction and Service quality is also linked to topics like Accommodation. The concepts of his Econometrics study are interwoven with issues in Regression analysis and Banking sector. His research in Data science intersects with topics in Higher education and Relevance.
His scientific interests lie mostly in Structural equation modeling, Partial least squares regression, Marketing, Endogeneity and Econometrics. His work in the fields of Structural equation modeling, such as Partial least squares path modeling, intersects with other areas such as Methodological research. His work carried out in the field of Partial least squares regression brings together such families of science as Latent variable, Data mining, Predictive power, Applied mathematics and Key.
Many of his research projects under Marketing are closely connected to Consumption with Consumption, tying the diverse disciplines of science together. His Endogeneity research incorporates themes from Control variable, Regression analysis, Quality and Statistical power. His work on Omitted-variable bias and Instrumental variable as part of general Econometrics study is frequently linked to Term and Data treatment, therefore connecting diverse disciplines of science.
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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.
The use of partial least squares path modeling in international marketing
Jörg Henseler;Christian M. Ringle;Rudolf R. Sinkovics.
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: 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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