This function allows creating clusters from the characteristics of the articles analyzed and highlights the justifications for the groupings created. By using the FactoMineR 1.34 extension, the hierarchical clustering on principal components (HCPC) function was used ( Husson et al., 2007 Lê et al., 2008). The characteristics considered for carrying out MCA were the “main term”, “research approach”, “type of research”, “constructs” and “research strategies”. In the present research, multiple CA (MCA) was applied – indicated when the elements are described as categorical variables ( Lê et al., 2008). This extension is dedicated to the multivariate analysis of data and allows the manipulation of different types of variables (quantitative or categorical). CA is a multivariate exploratory technique that converts a data matrix into a graphical representation, so that rows and columns are represented by points in a graph ( Greenacre and Hastie, 1987). Correspondence analysis (CA) was also performed. This extension is able to manipulate networks with millions of vertices and edges and provides a series of functions to analyze the properties of social networks, such as subnetworks, intermediation, centrality, among other characteristics ( Csárdi and Nepusz, 2006). The IGRAPH 0.5.5-2 extension (package) was used to analyze graphs and co-authorship networks ( Csárdi and Nepusz, 2006). The R 3.3.2 and R Studio 1.0.136 software were used. It has been identified that 77 articles were published in eight journals. The procedures suggested by Crossan and Apaydin (2010) for conducting bibliometric studies were adopted. However, it was decided to analyze ten of them. In total, 12 “A” concept journals were identified. This paper uses national journals with Concept “A” of the Qualis classification (2016) for journal selection.
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