Tackling Cyclicity in Causal Models with Cross-Sectional Data Using a Partial Least Squares Approach: Implications for the Sequential Model of Internet Appropriation

Abstract

Working with SEM and cross-sectional data, and depending on the studied phenomenon, assuming an acyclic model may mean that we obtain only a partial view of the mechanisms that explain causal relationships between a set of theoretical constructs, given that variables are treated as antecedents and consequences. Our two-step approach allows researchers to identify and measure cyclic effects when working with cross-sectional data and a PLS modelling algorithm. Using the resources and appropriation theory and the sequential model of internet appropriation, we demonstrate the importance of considering cyclic effects. Our results show that opportunities for physical access followed by digital skills acquisition enhance internet usage (acyclic effects), but also that internet usage intensity, in reverse, reinforces both digital skills and physical access (cyclic effects), supporting Norris (Digital divide: civic engagement, information poverty, and the internet worldwide. Cambridge University Press, Cambridge, 2001) social stratification hypothesis regarding future evolution of the digital divide.