Research Note—Partial Least Squares and Models with Formatively Specified Endogenous Constructs: A Cautionary Note

Information systems researchers have recently begun to propose models that include formatively specified constructs, and largely rely on partial least squares (PLS) to estimate the parameters of interest in those models. In this research, we focus on those cases where the formatively specified constructs are endogenous to other constructs in the research model in addition to their own manifest indicators, which are quite common in published research in the discipline, and analyze whether PLS is a valid statistical technique for estimating those models. Although there is evidence that covariance-based approaches can accurately estimate them, this is the first research that examines whether PLS can indeed do so. Through a theoretical analysis based on the inner workings of the PLS algorithm, which is later validated and extended through a series of Monte Carlo simulations, we conclude that is not the case. Specifically, estimates obtained from PLS are capturing something other than the relationship of interest when the formatively specified constructs are endogenous to others in the model. We show how our results apply more generally to a class of models, and discuss implications for future research practice.

• 저널Information Systems Research · 25(4) · 761–778
• 토픽Big Data and Business Intelligence · Management Information Systems
• DOI10.1287/isre.2013.0493
• 저자Miguel I. Aguirre‐Urreta, George M. Marakas