Les clusters en Russie
Ces derniers temps, le nombre et la qualité des clusters ont pris une expansion visible en Russie. Avant l’adoption d’une politique de clusters au niveau fédéral, à la fi n de la décennie 2000, seules quelques régions avaient fait part de leur intention de contribuer au développement de ceux qui existaient sur leur territoire et dont peu marchaient vraiment. La situation, en outre, a radicalement changé au cours des dernières années.
Intellectual capital is very heterogeneous so it’s usual practice to divide it into some groups of more similar and homogeneous intellectual assets. It’s widespread to distinguish human capital (knowledge, skills of employees etc.), structural capital (business‐processes, innovations, corporate culture etc.) and relational capital (brand, reputation, relationships with customers etc.). The literature supports the significance of intellectual capital influence on company’s value creation. Researchers find a strong dependence of corporate performance on intellectual assets in different countries and economy branches. But their findings about a character of intellectual capital transformation in corporate value are ambiguous. Importance of human, structural and relational capital and interrelationships between them vary highly across papers. It may be explained by high firm specificity of corporate value creation. It doesn’t mean impossibility of intercompany research but requires a comparability of analyzed firms. Empirical researches on the theme of intellectual capital are often limited to particular country and industry. This restriction makes investigated companies more comparable. But we suppose there is a lot of other significant aspects of firm specificity that may impact on transformation of intellectual assets into corporate value such as firm size, amount of intangible assets, total firm efficiency etc. These variables are sometimes considered as additional factors of corporate value. But we suppose these criteria may define the model of corporate value creation in principle. This study is targeted to reveal some main types of companies and investigate a specificity of corporate value creation model for each of them. We expect to discover significant differences in models mostly related to importance and significance of particular intellectual assets. This paper is empirical and quantitative. Our sample embraces about 200 large public European industrial companies from 7 countries (Denmark, Germany, Great Britain, Finland, Netherlands, Portugal and Spain) for 2005‐2009 years. The database includes: 1. Information from financial statement. The source is Amadeus database (Bureau Van Dijk). 2. A set of nonfinancial proxy indicators (quantitative and qualitative) displaying a state of human, structural and relational capital. This data has been collected from open Internet sources such as companies’ sites. Methodology of the research combines statistic methods (cluster analysis and factor analysis) and econometrics (regression analysis). Clustering distinguishes some main types of companies. Factor analysis constructs integral indices for human, structural and relational capital on the base of initial proxy set. Regression is an instrument of modeling the corporate value creation. We found significant differences between models of corporate value creation. Human, structural and relational capitals differently transform into firm value in each type of companies. Our findings have some practical implications. For example prioritizing investments in intellectual assets should take into account a firm’s specificity more deeply. This study comprises research findings from the ‘Intellectual Capital Evaluation” Project carried out within The Higher School of Economics’ 2011 Academic Fund Program.
The author researches the issues of usage of economical and statistical indices of functioning of special economical zones. The combination of economical approach with technocratic favors the creation of complex method of assessment of usefulness and efficiency of innovations, their screening, distribution of limited resources and also presupposes formation of wide applied aspect.
In the current climate of sanctions imposed against Russia by several countries in 2014, special attention should be given to high-tech sectors of the economy as a key source of import substitution on the domestic market. One of the important policy measures is to support the development of high-tech, specialized clusters by forming new linkages and strengthening existing ones between small and medium-sized businesses, large enterprises, and research organizations. The starting point for an effective cluster policy is to define areas with high potential for clustering of these industries. The paper presents an original method to identify potential clusters and tests the method on Russian regions. We show that most of the state-supported pilot innovative territorial clusters are being developed in regions and sectors that have a high level of cluster potential. A typology of existing clusters depends on the index of clustering potential. We identified regions that have similar or comparatively favourable conditions for creating clusters in the pilot sectors.