Artikel
Approximating innovation potential with neurofuzzy robust model
In a remarkably short time, economic globalisation has changed the world's economic order, bringing new challenges and opportunities to SMEs. These processes pushed the need to measure innovation capability, which has become a crucial issue for today's economic and political decision makers. Companies cannot compete in this new environment unless they become more innovative and respond more effectively to consumers' needs and preferences - as mentioned in the EU's innovation strategy. Decision makers cannot make accurate and efficient decisions without knowing the capability for innovation of companies in a sector or a region. This need is forcing economists to develop an integrated, unified and complete method of measuring, approximating and even forecasting the innovation performance not only on a macro but also a micro level. In this recent article a critical analysis of the literature on innovation potential approximation and prediction is given, showing their weaknesses and a possible alternative that eliminates the limitations and disadvantages of classical measuring and predictive methods.
- Language
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Englisch
- Bibliographic citation
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Journal: Investigaciones Europeas de Dirección y Economía de la Empresa (IEDEE) ; ISSN: 1135-2523 ; Volume: 21 ; Year: 2015 ; Issue: 1 ; Pages: 35-46 ; Amsterdam: Elsevier
- Classification
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Wirtschaft
Neural Networks and Related Topics
Model Construction and Estimation
Miscellaneous Mathematical Tools
Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
- Subject
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Innovation potential
Approximation
Neural networks
Fuzzy logic
- Event
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Geistige Schöpfung
- (who)
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Kasa, Richard
- Event
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Veröffentlichung
- (who)
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Elsevier
- (where)
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Amsterdam
- (when)
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2015
- DOI
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doi:10.1016/j.iedee.2014.02.001
- Handle
- Last update
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10.03.2025, 11:42 AM CET
Data provider
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Artikel
Associated
- Kasa, Richard
- Elsevier
Time of origin
- 2015
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