What is he best known for?
The fields of study he is best known for:
- Statistics
- Marketing
- Management
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.
His most cited work include:
- PLS-SEM: Indeed a Silver Bullet (6310 citations)
- A primer on partial least squares structural equation modeling (PLS-SEM) (5601 citations)
- A new criterion for assessing discriminant validity in variance-based structural equation modeling (4208 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Structural equation modeling (68.84%)
- Partial least squares regression (63.04%)
- Econometrics (31.88%)
What were the highlights of his more recent work (between 2017-2021)?
- Structural equation modeling (68.84%)
- Partial least squares regression (63.04%)
- Marketing (21.38%)
In recent papers he was focusing on the following fields of study:
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.
Between 2017 and 2021, his most popular works were:
- When to use and how to report the results of PLS-SEM (1001 citations)
- Predictive model assessment in PLS-SEM: guidelines for using PLSpredict (176 citations)
- Partial least squares structural equation modeling in HRM research (164 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Marketing
- Management
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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