An artificial intelligence method for digital government assessment

Abstract

Development of accurate measurement systems to assess digital government advance is a critical topic of digital agenda of academia and governments around the world. There are several quantitative approaches such as rankings and indicators that have contributed to measure the progress of digital government initiatives in the public sector, but more sophisticated computational tools are usually unexploited. This article proposes a computational multi-parametric analysis of multiple metrics of digital government advance using a computational technique, the neural networks, for the analysis of the evolution of digital government ranking of Mexican states during the period 2009-2015. Neural networks analysis has been used in different areas such as scientometric performance profiles, and disciplines like physics, chemistry, management, economics and demography. The neural networks analysis helps to identify clusters of characterizations that represents digital government advance patterns of performance. It also locates various profiles of digital government progress with similar patterns of performance and atypical behaviors (outliers) which are difficult to identify with classical tools. The results of this computational technique are robust showing that artificial intelligence tools are useful instruments to evaluate digital government advance overtime.