A path analysis on the strategic determinants of the average revenue per user in the Saudi telecom sector

Abstract

This study compared a restructured hierarchical regression using structural equation modeling (SEM) with a path analysis SEM Regression using The Rules of Casual Order.  The dataset originated from Jones & Alshammari (2017) which studied the Value-Added Intellectual Coefficient (VAIC) determinants and capital expenditures (CAPEX) effects on the average revenue per user (ARPU). The comparisons showed CLE and CEEcap explained 61% of ARPU.   For every 1 unit of change in CLE and CEEcap combined, produces 2 units of change in ARPU.  The results on HCE_cap and SCE_cap were inconsistent, regression weights were insignificant at the p ≤ .001 level, and both determinants did not correlate with Revenue.  This study showed that causation can be established prior to any multivariate or SEM statistical procedures. The rules of casual order are an effective way of designing a model based on reality and show the true effects among observed variables.