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Working with SEM and crosssectional data, and depending on the studiedphenomenon, assuming an acyclic model may mean that we obtain only a partialview of the mechanisms that explain causal relationships between a set oftheoretical constructs, treated as antecedents and consequences. Our twogiventhat variables are step approach allows researchers to identify and measurecyclic effects when working with cross algorithm. Using the resources andappropriation tsectional data and a PLS modelling heory and the sequentialmodel of internet appropriation, w e demonstrate the importance of consideringcyclic effects. Our results show that opportunities for physical accessfollowed by digital skills acquisition enhance internet usage (acycliceffects), but also that internet usage intensity, in reverse, reinforces bothdigital skills and physical access (cyclic effects), supporting Norris (2001)social stratification hypothesis regarding future evolution of the digitaldivide.

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