Browsing by Author "Ringle, Christian M."
Now showing items 1-4 of 4
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Data generation for composite-based structural equation modeling methods
(Springer, 2020)Examining the efficacy of composite-based structural equation modeling (SEM) features prominently in research. However, studies analyzing the efficacy of corresponding estimators usually rely on factor model data. Thereby, ... -
Partial least squares structural equation modeling-based discrete choice modeling: An illustration in modeling retailer choice
(Springer, 2018)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 ... -
Prediction: coveted, yet forsaken? Introducing a cross-validated predictive ability test in partial least squares path modeling
(Wiley, 2020)Management researchers often develop theories and policies that are forward‐looking. The prospective outlook of predictive modeling, where a model predicts unseen or new data, can complement the retrospective nature of ... -
Structural equation models: From paths to networks (Westland 2019)
(Springer, 2020)Structural equation modeling (SEM) is a statistical analytic framework that allows researchers to specify and test models with observed and latent (or unobservable) variables and their generally linear relationships. In ...
