Using HJ-Biplot and External Logistic Biplot as Machine Learning Methods for Corporate Social Responsibility Practices for Sustainable Development
| Autor: | Purificación Vicente-Galindo, José Luis Vicente-Villardón, Joel A. Martínez-Regalado, Mónica Jiménez-Hernández, Cinthia Leonora Murillo-Avalos |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
corporate social responsibility (CSR)
Biplot General Mathematics Sample (statistics) Machine learning computer.software_genre Field (computer science) HJ-Biplot Computer Science (miscellaneous) QA1-939 Product (category theory) Engineering (miscellaneous) Sustainable development external logistic biplot sustainable development business.industry machine learning multivariate analysis Work (electrical) Corporate social responsibility Artificial intelligence business computer Social responsibility Mathematics |
| Zdroj: | Mathematics, Vol 9, Iss 2572, p 2572 (2021) Mathematics; Volume 9; Issue 20; Pages: 2572 |
| ISSN: | 2227-7390 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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