Composite-based methods like partial least squares (PLS) path modeling have an advantage over factor-based methods (like CB-SEM) because they yield determinate predictions, while factor-based methods’ prediction is constrained in this regard by factor indeterminacy. To maximize practical relevance, research findings should extend beyond the study’s own data. We explain how PLS practices, deriving, at least in part, from attempts to mimic factor-based methods, have hamstrung the potential of PLS. In particular, PLS research has focused on parameter recovery and overlooked predictive validity. We demonstrate some implications of considering predictive abilities as a complement to parameter recovery of PLS by reconsidering the institutionalized practice of mapping formative measurement to Mode B estimation of outer relations. Extensive simulations confirm that Mode A estimation performs better when sample size is moderate and indicators are collinear while Mode B estimation performs better when sample size is very large or true predictability (R²) is high.
Becker, Jan-Michael, Arun Rai and Edward E. Rigdon (2013), “Predictive Validity and Formative Measurement in Structural Equation Modeling: Embracing Practical Relevance," Proceedings of the International Conference on Information Systems (ICIS).