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.
Some connections of the theory of hidden markov chains with the graph theory and linear algebra in this paper
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.
This document presents results of non-quantitative observations application and their processing methods, which significantly widen the analytical capabilities of the statistical measurement of the Russian IT market. The need to expand statistical tools that allow to reflect current and future trends in the sectoral development of IT sphere in a fast and visible manner, due to the rapid character of penetration of these services into the Russian market, is argued in the paper.
With the help of business climate indicators and construction of different homogeneous behavior models, analysis of business trends in the financial and economic activities of IT organizations is presented, highlighting the specifics of them functioning within the various cyclic episodes of 2010-2017.
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.
Purpose: Today many programs supporting clusters are introduced in Russia and other countries. The purpose of the research is to provide a relevant quantitative study assessing the effectiveness of cluster policy. Design/methodology/approach: In this paper, the effectiveness of Russia's cluster policy is analyzed using regression analysis. The survey covers data on 516 Russian enterprises divided into two groups: companies from supported clusters and firms that are members of similar but not supported clusters. To the classical variables of Cobb-Douglas production function (companies’ revenue, number of workers, capital of the company) we added cluster program dummy variable. The main question of the research is whether companies in supported clusters operate more effectively than other companies. Findings: The analysis provided quite interesting results. It was found that governmental support which was received by 27 innovative clusters didn’t have any effect on the revenue of the companies. This means that Russian innovation clusters work equally efficiently, regardless of whether they have government support. Research/practical implications: We have not found short-term effects on the enterprises associated with the supported clusters. The obtained results indicate that cluster policy conducted from 2012 to the present time requires adjustment. In this regard, the authors propose recommendations on further implementation of cluster policy. Originality/value: We have described the production function of Russian companies which work in the clusters. We have found that there is no significant effect on companies' output from government supporting of the clusters in Russia. Effectiveness of cluster policy has never been evaluated empirically before this research. Keywords: Cluster, Cluster Policy, Cluster Policy Impact Assessment, Innovative Territorial Clusters
A model for organizing cargo transportation between two node stations connected by a railway line which contains a certain number of intermediate stations is considered. The movement of cargo is in one direction. Such a situation may occur, for example, if one of the node stations is located in a region which produce raw material for manufacturing industry located in another region, and there is another node station. The organization of freight traﬃc is performed by means of a number of technologies. These technologies determine the rules for taking on cargo at the initial node station, the rules of interaction between neighboring stations, as well as the rule of distribution of cargo to the ﬁnal node stations. The process of cargo transportation is followed by the set rule of control. For such a model, one must determine possible modes of cargo transportation and describe their properties. This model is described by a ﬁnite-dimensional system of diﬀerential equations with nonlocal linear restrictions. The class of the solution satisfying nonlocal linear restrictions is extremely narrow. It results in the need for the “correct” extension of solutions of a system of diﬀerential equations to a class of quasi-solutions having the distinctive feature of gaps in a countable number of points. It was possible numerically using the Runge–Kutta method of the fourth order to build these quasi-solutions and determine their rate of growth. Let us note that in the technical plan the main complexity consisted in obtaining quasi-solutions satisfying the nonlocal linear restrictions. Furthermore, we investigated the dependence of quasi-solutions and, in particular, sizes of gaps (jumps) of solutions on a number of parameters of the model characterizing a rule of control, technologies for transportation of cargo and intensity of giving of cargo on a node station.
Event logs collected by modern information and technical systems usually contain enough data for automated process models discovery. A variety of algorithms was developed for process models discovery, conformance checking, log to model alignment, comparison of process models, etc., nevertheless a quick analysis of ad-hoc selected parts of a journal still have not get a full-fledged implementation. This paper describes an ROLAP-based method of multidimensional event logs storage for process mining. The result of the analysis of the journal is visualized as directed graph representing the union of all possible event sequences, ranked by their occurrence probability. Our implementation allows the analyst to discover process models for sublogs defined by ad-hoc selection of criteria and value of occurrence probability
Existing approaches suggest that IT strategy should be a reflection of business strategy. However, actually organisations do not often follow business strategy even if it is formally declared. In these conditions, IT strategy can be viewed not as a plan, but as an organisational shared view on the role of information systems. This approach generally reflects only a top-down perspective of IT strategy. So, it can be supplemented by a strategic behaviour pattern (i.e., more or less standard response to a changes that is formed as result of previous experience) to implement bottom-up approach. Two components that can help to establish effective reaction regarding new initiatives in IT are proposed here: model of IT-related decision making, and efficiency measurement metric to estimate maturity of business processes and appropriate IT. Usage of proposed tools is demonstrated in practical cases.
The manual is intended for students of Department of computer engineering MIEM HSE. In the textbook based on the courses "Economics of firm" and "the development strategy of the organization." Discusses the key conceptual and methodological issues of the theory and practice of Economics and development planning of the organization. The use of textbooks will enable students: to analyze key performance indicators, and use the tools of strategic analysis with reference to concrete situations in contemporary Russian and international business. Special attention is paid to the methods and systems of information support of the life support functions of business organizations and management methodology of innovation and investment. An Appendix contains source data for analysis of competition in a particular industry.
The paper provides a number of proposed draft operational guidelines for technology measurement and includes a number of tentative technology definitions to be used for statistical purposes, principles for identification and classification of potentially growing technology areas, suggestions on the survey strategies and indicators. These are the key components of an internationally harmonized framework for collecting and interpreting technology data that would need to be further developed through a broader consultation process. A summary of definitions of technology already available in OECD manuals and the stocktaking results are provided in the Annex section.
Over the last two decades national policy makers drew special attention to the implementation of policy tools which foster international cooperation in the fields of science, technology, and innovation. In this paper, we look at cases of Russian-German collaboration to examine the initiatives of the Russian government aimed at stimulating the innovation activity of domestic corporations and small and medium enterprises. The data derived from the interviews with companies’ leaders show positive effects of bilateral innovative projects on the overall business performance alongside with major barriers hindering international cooperation. To overcome these barriers we provide specific suggestions relevant to the recently developed Russian Innovation Strategy 2020.