Segmentation in PLS path modeling framework results is a critical issue in social sciences. The assumption that data is collected from a single homogeneous population is often unrealistic. Sequential clustering techniques on the manifest variables level are ineffective to account for heterogeneity in path model estimates. Three PLS path model related statistical approaches have been developed as solutions for this problem. The purpose of this paper is to present a study on sets of simulated data with different characteristics that allows a primary assessment of these methodologies.Partial Least Squares; Path Modeling; Unobserved Heterogeneity
Structural Equation Models assume homogeneity across the entire sample. In other words, all the unit...
When applying structural equation modeling methods, such as partial least squares (PLS) path modelin...
In classical model fitting techinques, such as traditional Multiple Linear Regression models (MLR) ...
When applying multivariate analysis techniques in information systems and social science disciplines...
Modeling has often failed to meet expectations, mostly because of the difficulty of comprehending re...
Partial least squares-based path modeling with latent variables is a methodology that allows to esti...
When collecting survey data for a specific study it is usual to have some background information, in...
This is the peer reviewed version of the following article: Giuseppe Lamberti, Aluja, T., G. S. The ...
A large proportion of information systems research is concerned with developing and testing models p...
The problem of heterogeneity represents a very important issue in the decision-making process. Furth...
Uncovering unobserved heterogeneity is a requirement to obtain valid results when using structural e...
A large proportion of information systems research is concerned with developing and testing models p...
When applying the partial least squares structural equation modeling (PLSSEM) method, the assumption...
A large proportion of information systems research is concerned with developing and testing models p...
Abstract When applying structural equation modeling methods, such as partial least squares (PLS) pat...
Structural Equation Models assume homogeneity across the entire sample. In other words, all the unit...
When applying structural equation modeling methods, such as partial least squares (PLS) path modelin...
In classical model fitting techinques, such as traditional Multiple Linear Regression models (MLR) ...
When applying multivariate analysis techniques in information systems and social science disciplines...
Modeling has often failed to meet expectations, mostly because of the difficulty of comprehending re...
Partial least squares-based path modeling with latent variables is a methodology that allows to esti...
When collecting survey data for a specific study it is usual to have some background information, in...
This is the peer reviewed version of the following article: Giuseppe Lamberti, Aluja, T., G. S. The ...
A large proportion of information systems research is concerned with developing and testing models p...
The problem of heterogeneity represents a very important issue in the decision-making process. Furth...
Uncovering unobserved heterogeneity is a requirement to obtain valid results when using structural e...
A large proportion of information systems research is concerned with developing and testing models p...
When applying the partial least squares structural equation modeling (PLSSEM) method, the assumption...
A large proportion of information systems research is concerned with developing and testing models p...
Abstract When applying structural equation modeling methods, such as partial least squares (PLS) pat...
Structural Equation Models assume homogeneity across the entire sample. In other words, all the unit...
When applying structural equation modeling methods, such as partial least squares (PLS) path modelin...
In classical model fitting techinques, such as traditional Multiple Linear Regression models (MLR) ...