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Measuring Innovation Potential at SME Level with a Neurofuzzy Hybrid Model



    () (University of Miskolc, Institute of Management Sciences, Hungary)


Measuring innovation has become a crucial issue of today’s economical and political decision makers. In a remarkably short time, economic globalisation has changed the world's economic order, bringing new challenges and opportunities to SMEs. Companies cannot compete in this new environment unless it becomes more innovative and responds more effectively to consumers' needs and preferences – says the EU’s innovation strategy. Decision makers cannot make right and efficient decisions without knowing the capability for innovation of companies of a sector or a region. This need is forcing economists to develop an integrated, unified and complete method of measuring, approximating and even forecast the innovation performance not only on macro level but also on micro level. In this article I intended to show that the recent methods of measuring innovation potential are obsolete, marginally used and have weak statistical performance and effectiveness. Why? Because the world has changed! There are new requirements for social and economical modelling and building expert systems, we have enormous amount of data in a stochastic reality and even the nature of data has been changed. This is especially true in the field of management. Innovation has a so plastic and ductile concept system that it cannot be measured and described (ad absurdum forecasted) by classical crisp methods. It requires soft and intelligent methods. In the article I will show my alternative for measuring innovation potential with a new method which is accurate, strict and significant at the same time, plastic and stable at the same time and simultaneously can handle linguistic variables and blurred (fuzzy) variables. This model possesses efficient studying, adaptive responding, right decision making, information granulation and lingual communication. Via these issues problem solving, pattern recognition, linguistic procession, system design and effective forecasting and estimating can be reached.

Suggested Citation

  • Richard Kasa, 2012. "Measuring Innovation Potential at SME Level with a Neurofuzzy Hybrid Model," JOURNAL STUDIA UNIVERSITATIS BABES-BOLYAI NEGOTIA, Babes-Bolyai University, Faculty of Business.
  • Handle: RePEc:bbn:journl:2012_2_3_kasa

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    File Function: Revised version, 2012
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    References listed on IDEAS

    1. El Ouardighi, Fouad & Kim, Bowon, 2010. "Supply quality management with wholesale price and revenue-sharing contracts under horizontal competition," European Journal of Operational Research, Elsevier, vol. 206(2), pages 329-340, October.
    2. Clifford Zinnes & Yair Eilat & Jeffrey Sachs, 2001. "Benchmarking competitiveness in transition economies," The Economics of Transition, The European Bank for Reconstruction and Development, vol. 9(2), pages 315-353, July.
    3. Wei-ping Wu, 2008. "Dimensions of Social Capital and Firm Competitiveness Improvement: The Mediating Role of Information Sharing," Journal of Management Studies, Wiley Blackwell, vol. 45(1), pages 122-146, January.
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    More about this item


    innovation potential; neural network; fuzzy logic; measurement;

    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
    • O32 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Management of Technological Innovation and R&D
    • O47 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - Empirical Studies of Economic Growth; Aggregate Productivity; Cross-Country Output Convergence


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