Goodness of fit

Pure mathematics

Causal model

Data mining

Mathematics

Computer science

Path analysis (statistics)

Model selection

Machine learning

Philosophy

Predictive modelling

Field (mathematics)

Partial least squares regression

Latent variable

Artificial intelligence

Predictive power

Structural equation modeling

Statistics

Epistemology

Econometrics

Industrial Management & Data Systems

Purpose Partial least squares structural equation modeling (PLS-SEM) has become popular in the information systems (IS) field for modeling structural relationships between latent variables as measured by manifest variables. However, while researchers using PLS-SEM routinely stress the causal-predictive nature of their analyses, the model evaluation assessment relies exclusively on criteria designed to assess the path model's explanatory power. To take full advantage of the purpose of causal prediction in PLS-SEM, it is imperative for researchers to comprehend the efficacy of various quality criteria, such as traditional PLS-SEM criteria, model fit, PLSpredict, cross-validated predictive ability test (CVPAT) and model selection criteria. Design/methodology/approach A systematic review was conducted to understand empirical studies employing the use of the causal prediction criteria available for PLS-SEM in the database of Industrial Management and Data Systems (IMDS) and Management Information Systems Quarterly...

Demystifying the role of causal-predictive modeling using partial least squares structural equation modeling in information systems research