Will PLS have to become factor-based to survive and thrive?

Structural equation modelling (SEM) is a general method that aims at estimating models with latent variables (LVs), where the LVs are measured indirectly and with some imprecision via questionnaires. This is done usually employing question-statements answered on Likert-type scales. In this paper we discuss various forms of SEM, and demonstrate that composite-based models, common in classic partial least squares (PLS) implementations, are poorly aligned with the very idea of SEM. We argue that minimisation of type I and II errors, or false positives and negatives respectively in hypothesis testing, can only happen if LVs are implemented as factors (and not as composites). This requires the use of modern, factor-based PLS methods, which have some advantages not only over classic PLS implementations, but also over covariance-based SEM approaches. Our main goal with this paper is to stimulate debate, whether pro or against our views. If we are generally correct in our thinking, the impact on how quantitative research is conducted in the field of information systems, as well as many other fields, could be quite dramatic. The reason for this is the widespread use of SEM in information systems, business, and the behavioural sciences.