X-STATIS: A Multivariate Approach to Characterize the Evolution of E-Participation, from a Global Perspective

Abstract

This paper aims to categorize countries by their e-participation index, according to political, capacity, and governmental environment factors; examine how they are projected based on these factors; and analyze whether this projection corresponds to the current state of e-participation development. It is the first study to provide an overview of the e-participation level using multivariate analysis techniques for three-way data analysis, specifically, the X-STATIS methodology and cluster analysis. These techniques enable the simultaneous representation of countries, factors, conditions, trajectories, and groupings, taking into account national conditions in the evolution of e-participation from 2008 to 2016. The results show that when the conditions of each country interact with the level of e-participation development, and depending on the economic development, 7% of countries are lagging behind in e-participation evolution, given their institutional and political capacity. This delay is particularly relevant in countries that enjoy a higher level of socioeconomic status. Meanwhile, 38% are above the level they would correspond to.

The Waste Hierarchy at the Business Level: An International Outlook

“…The methods of the STATIS family are applied in cases where the data have a three-way structure, that is, sets of three-entry data matrices made up of the same individuals (in the rows) and the same variables (in the columns) in different occasions or conditions [41,42]. As shown in Figure 1, this is our case, with a first way for countries (or alternatively sectors), a second way for variables representing waste management elements/practices, and a third way representing the study period (2016-2020).…”

“…Thus, from the study of the intrastructure, we can analyze the evolution over time of the individuals of our matrix (in our case, the 28 countries from which the sampled firms originate). Through principal component analysis, the information contained in the "compromise" matrix is represented in a two-dimensional subspace by projecting the rows of each of the k original matrices onto the resulting compromise subspace [42,51]. If we consider V r to be the first r eigenvectors matrix from the compromise analysis, the coordinates of the rows of the matrix X k are the rows of X k D p V r and the columns are the rows of X t k D n U r , where U r are the first r eigenvectors of X c D p X t c D n .…”

“…We first analyze the commonalities between the years from the study of the interstructure by graphing the vectorial correlation coefficient between matrices, i.e., between years, in a factorial plane representing the strength of the relationships between matrices from different years [42,51,52]. It gives a graphical estimate of the coefficient in which we find acute angles between the vectors, indicating strong relationships between the years that occur in a gradual way (Figure 4).…”