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

Joel A. Martínez-Regalado, Cinthia Leonora Murillo-Avalos, Purificación Vicente-Galindo, Mónica Jiménez-Hernández and José Luis Vicente-Villardón
Additional contact information
Joel A. Martínez-Regalado: Departamento de Estadística, Campus Miguel de Unamuno, Universidad de Salamanca, 37008 Salamanca, Spain
Cinthia Leonora Murillo-Avalos: Departamento de Estadística, Campus Miguel de Unamuno, Universidad de Salamanca, 37008 Salamanca, Spain
Purificación Vicente-Galindo: Departamento de Estadística, Campus Miguel de Unamuno, Universidad de Salamanca, 37008 Salamanca, Spain
Mónica Jiménez-Hernández: Centro de Investigación de Estadística Multivariante Aplicada (CIEMA), Universidad de Colima, Colima 28040, Mexico
José Luis Vicente-Villardón: Departamento de Estadística, Campus Miguel de Unamuno, Universidad de Salamanca, 37008 Salamanca, Spain

Mathematics, 2021, vol. 9, issue 20, 1-16

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.

Keywords: machine learning; multivariate analysis; HJ-Biplot; external logistic biplot; corporate social responsibility (CSR); sustainable development (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/2227-7390/9/20/2572/pdf (application/pdf)
https://www.mdpi.com/2227-7390/9/20/2572/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:9:y:2021:i:20:p:2572-:d:655692

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().