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.
- Sprache
-
Englisch
- Erschienen in
-
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
- Klassifikation
-
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
- Thema
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Innovation potential
Approximation
Neural networks
Fuzzy logic
- Ereignis
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Geistige Schöpfung
- (wer)
-
Kasa, Richard
- Ereignis
-
Veröffentlichung
- (wer)
-
Elsevier
- (wo)
-
Amsterdam
- (wann)
-
2015
- DOI
-
doi:10.1016/j.iedee.2014.02.001
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:42 MEZ
Datenpartner
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Objekttyp
- Artikel
Beteiligte
- Kasa, Richard
- Elsevier
Entstanden
- 2015
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