Researchers and practitioners appreciate the various advantageous features that PLS-SEM, as a component-based approach to SEM, offers in practical applications. Although strategic management research relatively early on recognized PLS-SEM's flexibility regarding handling various modeling...

Discussions concerning different structural equation modeling methods draw on an increasing array of concepts and related terminology. As a consequence,misconceptions about the meaning of terms such as reflective measurement and common factor models as well as formative measurement and composite...

Partial least squares structural equation modeling (PLS-SEM) has become a popular method for estimating path models with latent variables and their relationships. A common goal of PLS-SEM analyses is to identify key success factors and sources of competitive advantage for important target...

Commonly used discrete choice model analyses (e.g., probit, logit and multinomial logit models) draw on the estimation of importance weights that apply to different attribute levels. But directly estimating the importance weights of the attribute as a whole, rather than of distinct attribute...