An evaluation of PLS based complex models: the roles of power analysis, predictive relevance and GOF index

Abstract

Structural equation modeling (SEM) is an important tool to estimate a network of causal relationships linking two or morecomplex concepts. The PLS approach to SEM, also known as component based SEM, is becoming more prominent forestimating large complex models due to its soft modeling assumptions. This ‘soft modeling’ refers to the greater flexibility ofPLS technique in developing and validating the complex models. However, to establish rigor in such complex modeling, thisstudy highlights the critical roles of power analysis, predictive relevance and GoF index. The findings of the study show thatpower analysis is essential to establish conjectures based on IT artifacts, predictive relevance is vital to measure how wellobserved values are reproduced by the model and finally, GoF index is crucial for assessing the global validity of a complexmodel.

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