IDEAS home Printed from https://ideas.repec.org/a/bbn/journl/2012_2_3_kasa.html
   My bibliography  Save this article

Measuring Innovation Potential at SME Level with a Neurofuzzy Hybrid Model

Author

Listed:
  • RICHARD KASA

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

Abstract

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
    as

    Download full text from publisher

    File URL: http://tbs.ubbcluj.ro/RePEc/bbn/journl/Negotia_2_2012.pdf
    File Function: Revised version, 2012
    Download Restriction: no

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bbn:journl:2012_2_3_kasa. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Cornelia Pop). General contact details of provider: http://edirc.repec.org/data/fbubbro.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.