Using HJ-Biplot and External Logistic Biplot as Machine Learning Methods for Corporate Social Responsibility Practices for Sustainable Development

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.

Corporate Social Responsibility and Sustainability: From a Corporate Governance Perspective

“…In Model (8), the moderating variable DA is the quality of accounting information. In this study, the modified Jones model was used to calculate the manipulable accrued profit, with larger values indicating poorer accounting information quality and smaller values indicating better quality.…”

“…The interaction term coefficient β 3 between CSR and internal control and management capability is greater than 0 and is significant at the 1% level, indicating that internal control and management capability can have a positive moderating effect on CSR and corporate sustainable development. Column (4) of Table 4 shows the test results of Model (8). The interaction term coefficient β 3 between CSR and accounting information quality is greater than 0 and significant at the 1% level, indicating that the poorer the quality of accounting information, the more significant the contribution of CSR to corporate sustainable development.…”

“…Therefore, CSR is crucial for achieving sustainable development [6,7]. CSR implies the need for companies to protect and enhance the present and future welfare of society and organizations through various business and social actions, and to guarantee just and sustainable benefits to multiple stakeholders [8]. Most of the existing literature studies the sustainable development of enterprises from the perspective of entrepreneurship and macroeconomic factors, but few consider the impact of social responsibility.…”

Association of Dietary Intake of Polyphenols with an Adequate Nutritional Profile in Postpartum Women from Argentina

“…Unlike traditional techniques or other biplot methods (JK-Biplot and GH-Biplot), the HJ-Biplot achieves optimal quality for both rows (individuals) and columns (variables) in the same reference system (Nieto-Librero et al, 2017). Thus, its use has increased over the last 20 years in multiple fields (food industry, health, and social sciences) because HJ-Biplot provides options for evaluating associations between multiple observations and variables, with fewer restrictions than conventional methods, and the advantage of being specifically designed for the inspection of data matrices (Martínez-Regalado et al, 2021). Ruiz et al (2018) highlighted four main advantages: a simultaneous examination of multiple variables in the same graphical representational plane; different comparisons at individual and group levels; the relationship between measures of the same and/or different origins; and no need to perform a posteriori contrast.…”

Development of a Methodology for Forecasting the Sustainable Development of Industry in Russia Based on the Tools of Factor and Discriminant Analysis

“…A qualitative methodology for assessing and predicting processes and phenomena should be based on statistical methods and techniques, in particular, regression, factor, and discriminant analysis. Their illumination is reflected in the works of Oda et al [40], Martínez-Regalado et al [41], Yadav et al [42], Tavassoli and Farzipoor Saen [43], and other works.…”