The article describes the construction of a predictive model of corporate sustainability, the DACSI Index, for measuring sustainability. The aim of the paper is to propose a predictive model DACSI Index based on economic IEcoi and non-financial indicators IESGi and appropriately selected predictive models DAEco and DAESG for manufacturing companies according to CZ-NACE classification. Predictive models were developed with the use of Multiple Discriminant Analysis (MDA). MDA results showed that the inclusion of non-financial indicators did not result in any significant changes in the classification of companies into individual groups compared to classification on the basis of economic indicators only. From MDA results it also follows that the statistical significance of non-financial indicators is low, but they signal a causal relationship between individual economic and non-financial indicators of sustainability. The results also showed that the predictive model DACSI Index, composed of economic indicators, environmental indicators, social indicators and corporate governance indicators has a much higher accuracy than the predictive model composed of economic indicators only. The essential conclusion of our research into corporate sustainability measurement is that the traditional performance assessment using economic indicators no longer suffices and does not reflect current performance of the company from the long-term perspective, and it is therefore necessary to include both economic and non-financial indicators into the predictive model DACSI Index. And the predictive model DACSI Index is just the type of model that will provide relevant information about the company’s sustainability status to both the owners and investors.
|Journal||Engineering Economics = Inžinerinė ekonomika: mokslo darbai|
|Publication status||Published - 2015|
- Sustanability measurement
- Predictive model
Kocmanova, A., Simanavičienė, Ž., & Docekalova, M. P. (2015). Predictive model for measuring sustainability of manufacturing companies. Engineering Economics = Inžinerinė ekonomika: mokslo darbai, 26(4), 442-451. https://doi.org/10.5755/j01.ee.26.4.11480