Tackling cyclicity in causal models with cross-sectional data using a partial least square approach. Implication for the sequential model on internet appropriation (2210.12520v1)

Abstract: Working with SEM and crosssectional 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, treated as antecedents and consequences. Our twogiven that variables are step approach allows researchers to identify and measure cyclic effects when working with cross algorithm. Using the resources and appropriation tsectional data and a PLS modelling heory and the sequential model of internet appropriation, w e 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 (2001) social stratification hypothesis regarding future evolution of the digital divide.