Should PLS become factor-based or should CB-SEM become composite-based? Both!
本文批判性讨论了一篇主张PLS应转向因子模型的EJIS论文，澄清CB-SEM也能处理复合模型，并通过情景分析证明PLSF-SEM方法在几乎所有方面都劣于CB-SEM和一致PLS，因此暂不推荐使用。
This paper critically discusses a recent EJIS paper entitled "Will PLS have to become factorbased to survive and thrive?", which laments that covariance-based structural equation modelling cannot handle composite models, postulates that PLS should become factor-based, and advocates the use of the PLSF-SEM method.We clarify that covariance-based structural equation modelling can handle composite models, e.g. using the so-called Henseler -Ogasawara specification, agree that PLS in its original form cannot consistently estimate the parameters of reflective measurement models, and reiterate that consistent PLS is a viable means to overcome this limitation.Finally, we study the performance of PLSF-SEM through scenario analyses.Based on our results, we conclude that PLSF-SEM is inferior to covariancebased structural equation modelling and consistent PLS in almost all respects.Therefore, we cannot currently recommend the use of PLSF-SEM, and it is up to future research to demonstrate potential advantages over existing structural equation modelling techniques.