What is he best known for?
The fields of study he is best known for:
- Marketing
- Statistics
- Management
His primary areas of investigation include Partial least squares regression, Partial least squares path modeling, Structural equation modeling, Covariance and Econometrics.
His Partial least squares regression research is multidisciplinary, incorporating perspectives in Omnibus test, Empirical research, Information retrieval and Rendering.
His studies deal with areas such as LISREL, Path, Latent variable and Operations research as well as Partial least squares path modeling.
Jörg Henseler has included themes like Variance, Categorical variable and Artificial intelligence in his Structural equation modeling study.
The Covariance study combines topics in areas such as Monte Carlo method and Consistency.
As a member of one scientific family, Jörg Henseler mostly works in the field of Econometrics, focusing on Statistical power and, on occasion, Sample size determination.
His most cited work include:
- A new criterion for assessing discriminant validity in variance-based structural equation modeling (4208 citations)
- The use of partial least squares path modeling in international marketing (3589 citations)
- An empirical comparison of the efficacy of covariance-based and variance-based SEM (1360 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Marketing, Structural equation modeling, Partial least squares regression, Partial least squares path modeling and Path.
He works mostly in the field of Marketing, limiting it down to concerns involving Advertising and, occasionally, Co-creation.
His Structural equation modeling research focuses on Covariance and how it connects with Consistency.
His Partial least squares regression research includes themes of Empirical research, Econometrics, Factor analysis and Statistical power.
The Partial least squares path modeling study which covers Strengths and weaknesses that intersects with Point.
His research in Path intersects with topics in Algorithm, Data science and Artificial intelligence.
He most often published in these fields:
- Marketing (28.97%)
- Structural equation modeling (37.38%)
- Partial least squares regression (35.51%)
What were the highlights of his more recent work (between 2019-2021)?
- Marketing (28.97%)
- Structural equation modeling (37.38%)
- Partial least squares path modeling (29.91%)
In recent papers he was focusing on the following fields of study:
Jörg Henseler mainly investigates Marketing, Structural equation modeling, Partial least squares path modeling, Partial least squares regression and Field.
His Marketing study combines topics from a wide range of disciplines, such as Creativity and Pragmatism.
Jörg Henseler combines subjects such as Composite number, Monte Carlo method, Applied mathematics and Empirical research with his study of Structural equation modeling.
Jörg Henseler conducts interdisciplinary study in the fields of Partial least squares path modeling and Context through his works.
His Partial least squares regression study integrates concerns from other disciplines, such as Specification and Business value.
His study in Business value is interdisciplinary in nature, drawing from both Test, Latent variable, Causal information, Econometrics and Estimator.
Between 2019 and 2021, his most popular works were:
- How to perform and report an impactful analysis using partial least squares: Guidelines for confirmatory and explanatory IS research (90 citations)
- How to perform and report an impactful analysis using partial least squares: Guidelines for confirmatory and explanatory IS research (90 citations)
- Using confirmatory composite analysis to assess emergent variables in business research (8 citations)
In his most recent research, the most cited papers focused on:
- Marketing
- Statistics
- Management
Partial least squares path modeling, Partial least squares regression, Structural equation modeling, Monte Carlo method and Business value are his primary areas of study.
The concepts of his Structural equation modeling study are interwoven with issues in Specification, Empirical research and Management information systems.
His work carried out in the field of Empirical research brings together such families of science as Covariance, Machine learning, Artificial intelligence and Identification.
The Monte Carlo method study combines topics in areas such as Algorithm, Sample size determination, Outlier and Distortion.
His Business value research is multidisciplinary, relying on both Test, Latent variable, Causal information, Econometrics and Estimator.
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