This study aims to analyse whether the characteristics of a company’s corporate social responsibility (CSR) orientation could improve the accuracy rates of small enterprise (SE) bankruptcy prediction models. A sample made up of 382 Italian SEs is analysed and, by applying logistic regression, a SE default prediction model is designed using both CSR orientation characteristics and financial ratios as default predictors. The accuracy of this model is then compared to that of a second model based only on financial ratios as predictive variables. The main results are: i) using CSR orientation characteristics significantly improves the effectiveness of SE default prediction modelling; ii) the smaller a firm the higher the increase in the prediction accuracy that can be obtained by using CSR characteristics as default predictors; iii) SE default prediction modelling should be separately implemented for different size groups of firms.

Using corporate social responsibility orientation characteristics for small enterprise default prediction (Online Published: December 27, 2017) / Ciampi Francesco. - In: WSEAS TRANSACTIONS ON BUSINESS AND ECONOMICS. - ISSN 2224-2899. - ELETTRONICO. - 15:(2018), pp. 113-127.

Using corporate social responsibility orientation characteristics for small enterprise default prediction (Online Published: December 27, 2017)

Ciampi Francesco
2018

Abstract

This study aims to analyse whether the characteristics of a company’s corporate social responsibility (CSR) orientation could improve the accuracy rates of small enterprise (SE) bankruptcy prediction models. A sample made up of 382 Italian SEs is analysed and, by applying logistic regression, a SE default prediction model is designed using both CSR orientation characteristics and financial ratios as default predictors. The accuracy of this model is then compared to that of a second model based only on financial ratios as predictive variables. The main results are: i) using CSR orientation characteristics significantly improves the effectiveness of SE default prediction modelling; ii) the smaller a firm the higher the increase in the prediction accuracy that can be obtained by using CSR characteristics as default predictors; iii) SE default prediction modelling should be separately implemented for different size groups of firms.
2018
15
113
127
Goal 9: Industry, Innovation, and Infrastructure
Ciampi Francesco
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1106089
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