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

TL;DR

This paper introduces a partial least squares approach to identify cyclic causal effects in cross-sectional data, revealing complex reciprocal relationships in internet appropriation and digital divide dynamics.

Contribution

It proposes a novel PLS-based method to detect cyclic effects in SEM with cross-sectional data, extending causal analysis beyond acyclic assumptions.

Findings

Physical access and digital skills acquisition mutually reinforce each other.

Internet usage intensity boosts both digital skills and physical access.

Results support the social stratification hypothesis of the digital divide.

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.