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Approximating Innovation Potential With Neurofuzzy Robust Model / Aproximación Al Potencial Innovador Con Un Modelo Robusto De Neuro-Fuzzy

Author

Listed:
  • Kasa, Richard

    (Budapest Business School (Hungary))

Abstract

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. / En un plazo increíblemente corto, la globalización económica ha cambiado el orden de la economía, creando nuevos retos y oportunidades a las pequeñas y medianas empresas. Por ello se esta dando la necesidad de crear maneras de medir capacidad de innovación que resulta fundamental para quien debe tomar decisiones politico-economicas. Las compañías no pueden competir en este nuevo entorno a no ser que sean mas innovadoras y respondan de manera más eficiente a las necesidades y preferencias del consumidor-como de hecho se ha mencionado en la Estrategia de Innovación de la UE. Las decisiones no pueden ser tomadas de manera eficiente y adecuada sin el conocimiento de la capacidad de innovación de compañías de un determinada región y/o sector. Esta necesidad está forzando a los economistas a desarrollar un método completo integrado y unificador de medir, aproximar e incluso predecir el rendimiento innovativo tanto a micro como a macro niveles. En este reciente articulo se ha hecho un análisis critico de la literatura que trata sobre aproximaciones y/o predicciones del potencial innovador, mostrando sus defectos y posibles alternativas que eliminarían las limitaciones y desventajas de las mediciones clásicas y métodos predictivos.

Suggested Citation

  • Kasa, Richard, 2015. "Approximating Innovation Potential With Neurofuzzy Robust Model / Aproximación Al Potencial Innovador Con Un Modelo Robusto De Neuro-Fuzzy," Investigaciones Europeas de Dirección y Economía de la Empresa (IEDEE), Academia Europea de Dirección y Economía de la Empresa (AEDEM), vol. 21(1), pages 35-46.
  • Handle: RePEc:idi:jiedee:v:21:y:2015:i:1:p:35-46
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    File URL: http://www.elsevier.es/es-revista-investigaciones-europeas-direccion-economia-empresa-345-resumen-aproximacion-al-potencial-innovador-con-90372439
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    More about this item

    Keywords

    Innovation potential; Approximation; Neural networks; Fuzzy logic; Potencial de innovación; Aproximaciones; Redes neuronales; Fuzzy logic;
    All these keywords.

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C65 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Miscellaneous Mathematical Tools
    • O32 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Management of Technological Innovation and R&D

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