An analysis of the investments intervention effect from operational programmes in the programming period 2007–2013 upon the R&D infrastructure of the Czech public universities is presented. The analysis was based upon publicly available data, universities´ annual economic reports, and evaluations and analyses. A few indicators have been selected to quantify the effect of significant extension and upgrade of the universities´ R&D infrastructure where investments from structural funds amounted to 36 % of the universities´ total R&D expenditure. The effect of the financial intervention upon the performance in basic research was evaluated firstly by making use of the increase of publications number in impacted journals in the time windows 2009–2011 and 2015–2017, i.e. before the effective launch of the interventions, and after their termination. The share of foreign public funds (structural funds excluded) in the total R&D expenditure was the second indicator used. The effect upon the applied research performance was evaluated by comparing the difference of the number of patents and by the change in the share of private sources in the R&D expenditure. The analyses show an increase of the number of publications whereas the change in the share of the foreign public funds in the total R&D expenditure did not induce any positive trend. In parallel with the number of publications, the number of patents increased, too. The change in the share of the private sources in the R&D expenditure was unequivocally associated with a positive trend, especially in the out-of-Prague technical universities. For a more robust evaluation of the effect of the interventions financed by the structural funds an analogous analysis should be carried out after a longer time than the mere three years after the termination of the interventions.
This paper aims to find out how the research consortia supported in the Competence Centres programme were created, what motives and factors decide on the involvement of various actors in these consortia. It is based on a combination of a questionnaire survey, structured interviews and analysis of quantitative data from the Research, Development and Innovation Information System. The most frequent motives of consortium members for involvement in competence centres were the development of their own research activities and access to knowledge / facilities shared by partners. The main beneficiary, or a narrow group of beneficiaries forming the core of the consortium, played a decisive role in shaping the consortium. The main factor for the involvement of individual members was the combination of previous experiences with partners and their competences. Furthermore, participating enterprises have developed and extensive research and development activities, do not compete, and their activities are complementary or linked. The differences in motives and factors for each type of partner can indicate the hierarchical arrangement of consortium members.
The aim of this contribution is to evaluate the cooperation of the European countries in projects related to the AI in the 7th Framework Programme (FP7) and in the current Horizon 2020 Programme. The analysis is based on the information obtained from European Commission database eCORDA (External COmmon Research DAtawarehouse). Dynamic scientometric maps were constructed to describe in more detail the collaboration of European subjects in the EC funded AI research. Within the FP7, 1223 projects in the AI field received roughly 2,8 billion €. In the current H2020 programme the EC has already provided 2,1 billion € for 1081 projects in AI. In the FP7, higher education institutions dominated in both the number of awarded project and the received financial support. In the H2020 programme, a profound shift favouring business enterprise sector occurred. Approximately one third of AI projects in the FP7 was in the thematic area Information and Communication Technologies. In the H2020 programme the EC extended the support further to other thematic areas including global societal challenges especially in the field of transport and security. The extent of the involvement of the member countries varies extensively. The countries above average participating in the AI projects are Germany, Italy, Austria, Spain, Portugal, and Slovenia. The Czech Republic falls in the group of less participating countries. The Czech Republic also exhibits a smaller increase of the participation between RP7 and H2020. Universities involved in approximately two thirds of AI project have dominated in the Czech participation in the FP7. In the H2020 program their share in the AI project decreased by approximately 10 percent points. On the other side, their share of financial support was conserved. The most participating universities were the Czech Technical University Prague (24 projects, and 30% share of the EC contribution for the Czech AI projects) and the Brno University of Technology (14 projects, and 12,4% financial share). In the business enterprise sector Honeywell International s. r. o. attained the highest participation. The Czech subjects collaborate most frequently with German, British, Italian and French research teams.
The results of happiness analysis are presented in the form of a World Happiness Report that covers 156 countries and 17 different indicators. In the article model-based clustering ensemble is built to determine what selected European countries have similar patterns of happiness. The results are analyzed using multidimensional scaling and a decision tree to find out what factors determine cluster memberships. In the empirical part, three clusters were detected The first contains countries: Austria, Denmark, Finland, Germany, Ireland, Luxembourg, the Netherlands, Norway, Sweden, Switzerland and the United Kingdom. They have the highest values for all the variables, except the negative affect. The second cluster contains seven countries: Bulgaria, Estonia, Hungary, Lithuania, Poland, Romania and Slovakia. This cluster is also the most homogeneous one. The third cluster contains eight countries: Cyprus, the Czech Republic, France, Greece, Italy, Portugal, Slovenia and Spain.
Ruslana Taratula, Oleksandra Kovalyshyn, Zoriana Ryzhok and Svitlana Malakhova
The work has approved the methods of partial economic evaluation of lands by the indicators of yield capacity of agricultural crops, payback of expenditures and differential income for a typical agricultural enterprise, located on the territory of the natural-agricultural province of the Western Forest-steppe. The research supplies proposals concerning improvement of the methods of economic evaluation of lands in Ukraine on the example of the indicators of economic evaluation of the land use at the farming enterprise “GREEN GARDEN”. The indicators are used for the growing of agricultural crops, planning their yield capacity, comparison of the economic fertility of soils and determination of the economic results of growing agricultural crops under the current production conditions in a defined working area.
The research proposes the optimization of land-use management, applying a metrical game on the basis of indicators of the economic evaluation of lands to define the optimal share of agricultural crops in crop rotation. This method can be used to optimize land use in any region. Application of mathematical modeling by indicators of differential income ensures that maximum gross income is obtained under the mixed strategy of the game on better and worse soils in the enterprise.
The concept of competitiveness has been addressed by economic theorists and policy makers for several hundreds of years, with both groups trying to understand the drivers of economic prosperity and social welfare. This contribution does not aim to address all theoretical thoughts that may contribute to understanding the roots of the competitiveness of locations. The goal is to address the major useful theoretical contributions that permit to identify the main drivers of a territory’s competitiveness and therefore to assess the competitiveness of a specific location according to strong criteria. The first section presents the major contributions found in the classical and neo-classical theories. The second section and the third section concentrate on two majors schools providing significant thoughts on the competitiveness of locations: the Economic Geography (EG) School and the International Business (IB) School.
Martin Cupal, Marek Sedlačík and Jaroslav Michálek
When concluding a property insurance agreement, adjustment of the insured amount poses a certain risk. From the policyholder’s point of view, the risk measure translates into the chosen target amount, which should correspond to the insurable value.
The aim of the research is to determine a statistical model for prediction of the insurable value with using current models in the Czech Republic. The model for insurable value prediction proposed in this paper accepts the risk of decision making under uncertainty suitably. The model’s foundation is a synthesis of four core models discussing the addressed issue. The methodology is based on a classification tree created by the CART method, and multivariate linear regression. After the classification tree is created, the input variables which contributed to the classification are used in the regression model.
The database consists of 125 family houses which went through a detailed examination (they were documented, measured, and their technical state and legal status were determined), and described in experts’ reports.
The obtained results showed a high degree of statistical association of selected predictors with the estimated insurable value of property, as well as with the acceptable risk, and subsequently, a relatively low percentage of misclassified objects. The proposed multiple regression model proved to be statistically significant and can be used for objective estimations of insurable values free of insurance companies’ strategy. The designed methodology may be applied in other areas as well, for example, in decision-making processes at the population level in crisis situations.
The aim of the paper is to identify and assess the role of economic sciences in relation to competitiveness and globalisation, two basic concepts of the market economy. This role is to explain and interpret their essence but also to determine their potential practical usefulness, primarily in the context of economic policy development. The considerations mentioned in the article are, as a rule, of a general and universal nature and do not relate especially to any particular countries or groups of countries. The basic method employed in the study is a critical analysis of the subject literature. The paper consists of an introduction, three sections, and conclusions. Section 1 contains a basic discussion of the subject of economic sciences and describes their four features: cognitive productivity, practical usefulness, dismal nature and beauty. Section 2 presents the contribution of economic sciences to understanding and interpreting the phenomenon of competitiveness. Section 3 focuses on defining and elucidating the idea of globalisation and an examination of its most important aspects. The paper ends with eight conclusions formulated on the basis of this discussion.
Subject and purpose of work: The purpose of the study is to determine the variables determining the level of synthetic measure of economic efficiency in listed companies of the industry sector as part of their enterprise life cycle.
Materials and methods: The article uses data from annual unitary financial statements of industrial enterprises according to the classification of the Warsaw Stock Exchange and data describing the macroeconomic situation of the state economy. The research period covered the years 1999-2012. In order to examine which factors determine the level of economic efficiency at each stage of the life cycle of enterprises, estimation of econometric models was carried out.
Results: In the models obtained for companies in the growth and maturity stage, statistically significant determinants were obtained only in the field of internal factors. In the models estimated for companies in the stages of launch, shake-out and decline, statistically significant conditions were identified, both in terms of external factors and in the area of internal factors.
Conclusions: A comprehensive assessment of the conditions for the level of economic efficiency of enterprises should take into account both factors dependent on the enterprise (microeconomic) as well as those determined by the environment (macroeconomic) and beyond its control. It is therefore necessary for managers of enterprises to have extensive and up-to-date knowledge of factors and conditions that are significant in shaping the level of economic efficiency.