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Identification of Regional Innovation Clusters: Empirical Evidence in Russia

Identification of Regional Innovation Clusters: Empirical Evidence in Russia. Anna Bykova National Research University “Higher School of Economics” 5 th April, 2012 Moscow. The brief story about the research…. Outline of the Presentation.

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Identification of Regional Innovation Clusters: Empirical Evidence in Russia

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  1. Identification of Regional Innovation Clusters: Empirical Evidence in Russia Anna Bykova National Research University “Higher School of Economics” 5th April, 2012 Moscow

  2. The brief story about the research…

  3. Outline of the Presentation

  4. Motivation for the Research: Theoretical Problems and Practical Implications • Glokalization: General consensus that the most competitive companies generate innovation in cooperation within localized regions (Porter, 2002, 2011; Elllison et al., 2010) • Many gaps in theory and empirical results of innovation cluster’s concept (Chesbrough, 2006; Immarino, McCann, 2006; Audretsch, Feldman, 1996; Maggioni et al., 2006 and others) • Regional Innovation Scoreboard 2006: strong positive and significance relation between success clusters’ presence and regional innovation development • The particular significance of research in this area in Russia stems from the fact: • the high degree of uneven socio-economic development of regions • a life in “isolation” from each other do not allow to raise their competitiveness • These perspectives become even worse after the crisis • Companies’ location should be considered not only as a factor of their effectiveness, but also as factor of their survival

  5. Research Questions The Research Problem: Analysis of external factors promoting the concentration and relationships between companies The study of cluster appearing and developing terms

  6. Research Design

  7. Research Design: The scorecard

  8. The data

  9. The sample description

  10. Methodology of Cluster Identification

  11. Udmurtia republic Orenburg obl. Mari El rep. Penza obl. Chuvashiaya rep. Kirov obl. Ulyanovsk obl. Mordovia rep. Saratov obl. Kurgan obl. Bashkortostan rep. Chelyabinsk obl. Nigh. Novgorod obl. Permskiy krai Tatarstan rep. Samara obl. Sverdlovsk obl. Tyumen obl. Main results: Cluster mapping(Regions with “Stars” clusters)

  12. Main results: Cluster mapping(Regions with “Stars” clusters) The differences between groups are statically significant

  13. Conclusion & Discussion • The 3rd stars-group of regions has a greater chance on cluster success • In the 3rd stars-group we can identify the innovation proto-clusters in different industries • As an additional argument (first step in analysis of clusters) for further investigation of clusters on lower level of aggregation • An argument for interaction and innovation infrastructure development at working out industrial and innovation region policies

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