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.
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.
Subject and purpose of work: The subject of analysis and evaluation are foreign direct investments (FDI) in Poland with particular emphasis on the Lublin Voivodship as a peripheral region. The aim of the paper is to present the investment attractiveness of the voivodship, the state of investment and ways to enhance the investment attractiveness of the region.
Materials and methods: This paper is based on statistical data from the Central Statistical Office (GUS), the National Bank of Poland (NBP) and other institutions, as well as published literature of this topic.
Results: This paper presents theoretical foundations of foreign investments, characteristics of the inflow of foreign direct investments to Poland in the years 2000-2017, their origin and directions of their use and distribution in the country. FDI is presented in detail in Lublin Voivodship, which is considered to be a peripheral region of Poland and the European Union.
Conclusions: FDI inflow to Poland was uneven in time, and investments were concentrated in the Masovian Voivodship and a few other voivodships of Western and Central Poland. Lublin Voivodship, despite activities increasing its investment attractiveness, still has unused opportunities for application of foreign investments.
In videogames industry, time series analysis can be very useful in determining the general evolution and behaviour of the market dynamics. These methods are applicable to any time series forecasting problem, regardless of the application sector. This article discusses time series approaches to forecast the sales of console games for the Italian market. In particular two univariate techniques were evaluated, exponential smoothing and the SARIMA technique. The aim is to exploit the capabilities of these statistical methods in order to have a comparison of the results and to choose the most accurate model through an ex-post evaluation. Using monthly time-series data from November 2005 to September 2017, the selection of the most suitable model was indicated by the smallest value of the measures of accuracy (MAPE, sMAPE, RMSE) for the out-of-sample observations regarding the period October 2017-September 2018. The implementation of the models was done using Forecast PRO and Gretl. The time series involved is related to the sales regarding the first party manufacturers of consoles and handhelds (Microsoft, Sony and Nintendo).
Good graphical presentation of data is useful during the whole analysis process from the first glimpse into the data to the model fitting and presentation of results. The most popular way of longitudinal data presentation are separate (for each wave, in cross-sectional dimension) comparisons of figures. However, plotting the data over time is useful in suggesting appropriate modeling techniques to deal with the heterogeneity observed in the trajectories. The main aim of this paper is to present the changing perceptions of the financial situation in Poland using different graphical tools for the heterogonous discrete longitudinal data sets and present demographics features for those changes. We will focus on the most important features of the categorical longitudinal data – category sequences and their graphical presentation. We aim to characterize the analyzed sequences on the basis of unidimensional indicators and composite complexity measures, as well as using mainly TraMineR [Gabadinho et al. 2017] package of R.
In a duration analysis of enterprises, as a rule there are determined four basic functions related to the time of their duration, i.e.: the density function; the distribution function; the survival function, and the hazard function. It turns out that the hazard function and its cumulative version are the key to understanding modern survival analysis. The aim of the paper is to indicate the best method of the estimation of the values of individual functions in survival analysis based on other functions. The paper provides compiled and classified information on particular functions used in the non-parametric duration analysis of enterprises. It examines some theoretical and practical problems related to the determination of, among others, the hazard function and the cumulative hazard function on the basis of data in cohort tables and the results of the estimation of the survival function with the use of the Kaplan-Meier method. The considerations included in the paper are illustrated with the results of analyses for enterprises established in the Łódzkie Voivodeship in 2001-2015 (including those which went into liquidation).
Statistical data on foreign trade are collected in all EU member states separately and then passed on to Eurostat where the data are aggregated. Continuous actions are to ensure that all datasets collected at national level are fully comparable. The aim of the paper is to provide a classification as well as an ordering of CN chapters (2-digit codes) according to the quality of data on intra-Community trade of goods. Data were taken from Eurostat’s COMEXT database. In ordering the chapters, we utilized the distance from the ideal solution with GDM as the distance measure. The study reveals a structure of goods subject to intra-Community trade that is supplementary to the official nomenclature. In addition, we provided CN chapters ordering according to the overall level of irregularities in reported mirror values of ICS and ICA. The results we obtained are of practical value for both researchers and authorities interested in foreign trade.