With the ever-increasing acceptance of the need to empirically validate theories in the social science disciplines (e.g., Sheth, 1971), data and multivariate analysis techniques (e.g., Hair et al., 2010; Hair et al., 2011b; Mooi and Sarstedt, 2011) play a central role in today’s research. The evolution of structural equation modeling (SEM) methods is perhaps the most important and influential s...

Software Engineering (SE) researchers are increasingly paying attention to organizational and human factors. Rather than focusing only on variables that can be directly measured, such as lines of code, SE research studies now also consider unobservable variables, culture trust. To measure latent scholars have adopted Partial Least Squares Structural Equation Modeling (PLS-SEM), which is one mem...

Partial least squares structural equation modeling (PLS-SEM) is one of the most widely used methods multivariate data analysis. Although previous research has discussed different aspects PLS-SEM, little been done to explain attributes various PLS-SEM statistical applications. The objective this editorial discuss multiple applications, including SmartPLS, WarpPLS, and ADANCO. It written based on...

An important source of bias in structural equation modeling (SEM) employing the partial least squares method (PLS) is missing data. Deletion methods, such as listwise and pairwise deletion, have traditionally been used to deal with missing data. These methods are perceived as leading to selective loss of data and significant related biases. Missing data imputation methods, on the other hand, do...

In many domains (marketing, social sciences, etc.) the variables of interest are often not directly observable. Complex causal relationships between such latent variables may be modeled by means of Structural Equation Models (SEM). Two different methodologies exist for the estimation of such models: SEM-ML (Maximum Likelihood Approach to Structural Equation Models), also known as LISREL (Linear...

Structural equation modeling using partial least squares (PLS-SEM) has become a main-stream modeling approach in various disciplines. Nevertheless, prior literature still lacks a practical guidance on how to properly test for differences between parameter estimates. Whereas existing techniques such as parametric and non-parametric approaches in PLS multi-group analysis solely allow to assess di...

Two complementary schools have come to the fore in the field of Structural Equation Modelling (SEM): covariance-based SEM and component-based SEM. The first approach has been developed around Karl Jöreskog and the second one around Herman Wold under the name "PLS" (Partial Least Squares). Hwang and Takane have proposed a new component-based SEM method named Generalized Structured Component Anal...

This ‘hands-on’ workshop will demonstrate how to obtain and use sophisticated, but (currently) free, visual software for assessing the measurement and structural model characteristics of path-based SEM quantitative models using a partial least squares (PLS) approach. The workshop will be presented as a teaching aid for faculty, doctoral students, and for other researchers who are engaged in SEM...