Abstract
In recent years, social responsibility has been revolutionizing sustainable development. After the development of new mathematical techniques, the improvement of computers’ processing capacity and the greater availability of possible explanatory variables, the analysis of these topics is moving towards the use of different machine learning techniques. However, within the field of machine learning, the use of Biplot techniques is little known for these analyses. For this reason, in this paper we explore the performance of two of the most popular techniques in multivariate statistics: External Logistic Biplot and the HJ-Biplot, to analyse the data structure in social responsibility studies. The results obtained from the sample of companies representing the Fortune Global 500 list indicate that the most frequently reported indicators are related to the social aspects are labour practices and decent work and society. On the contrary, the disclosure of indicators is less frequently related to human rights and product responsibility. Additionally, we have identified the countries and sectors with the highest CSR in social matters. We discovered that both machine learning algorithms are extremely competitive and practical to apply in CSR since they are simple to implement and work well with relatively big datasets.
Highlights
In recent decades, there has been a notable increase in interest worldwide in relation to social impacts, which has caused direct influence on the management of corporate social responsibility (CSR) practices, mainly by large corporations [1,2,3,4]
The first and fourth quadrant show the countries corresponding to the companies that most frequently disclose the aspects of society (SO), human resources (HR) and labour practices and decent work (LA)
This work has a dual objective, explore the performance of two of the most popular techniques in Multivariate statistics: External Logistic Biplot and the HJ-Biplot for the analysis of the structure of the data and evaluate the commitment to CSR that the world’s largest companies have, by inspecting the indicators of the social dimension reported to the Global Reporting Initiative (GRI) with these multivariate Machine Learning techniques
Summary
There has been a notable increase in interest worldwide in relation to social impacts, which has caused direct influence on the management of corporate social responsibility (CSR) practices, mainly by large corporations [1,2,3,4]. The scope of the proposed objectives was achieved with the use of the Biplot [39], a statistical technique whose graphic representation combines individuals (global companies) and variables (GRI social indicators). The second technique applied is the HJ-Biplot [41] to evaluate the similarities between the countries that serve the companies in the study, since, unlike other techniques, it makes it easier to visually detect the behaviour of geographic areas with respect to various dimensions (GRI social indicators) [42], in addition to achieving the highest quality of representation for rows and columns in the same reference system [43]. We evaluate the commitment to CSR that the world’s largest companies have by inspecting the indicators of the social dimension more and less reported to the GRI with these multivariate Machine Learning techniques.
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