The main aim of this study is to find a method to measure the intellectual capital (IC) of an organization which is able to combine management and measurement views, to reflect the newest concepts regarding IC, and to take into consideration the “vague” interactions between IC categories. We posit the idea that a Fuzzy Expert System (FES) model can address these issues, since it takes account of the qualitative nature of most IC indicators and the different IC subcategories. The main advantage of an IC score developed through a FES model is to provide a reliable IC index. The model presented in this article applied to data derived from the Austrian universities’ IC reports is a pilot model, sufficiently flexible for individual adaptations and adjustments. The main limitation of the study is that further tests can be carried out only in the presence of available and comparable IC data which are currently not available.

Measuring intellectual capital in the university sector using a fuzzy logic expert system

VELTRI, Stefania
;
2014-01-01

Abstract

The main aim of this study is to find a method to measure the intellectual capital (IC) of an organization which is able to combine management and measurement views, to reflect the newest concepts regarding IC, and to take into consideration the “vague” interactions between IC categories. We posit the idea that a Fuzzy Expert System (FES) model can address these issues, since it takes account of the qualitative nature of most IC indicators and the different IC subcategories. The main advantage of an IC score developed through a FES model is to provide a reliable IC index. The model presented in this article applied to data derived from the Austrian universities’ IC reports is a pilot model, sufficiently flexible for individual adaptations and adjustments. The main limitation of the study is that further tests can be carried out only in the presence of available and comparable IC data which are currently not available.
2014
Intellectual capital index; Austrian universities; fuzzy logic expert system
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11770/155582
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