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Research background: Currently the topic of a company’s intellectual capital is being widely investigated by various researchers. Nevertheless, only a small number of studies on the compa-ny’s intellectual capital impact on its market value were conducted. What is more, the concept of a company’s intellectual capital itself is not unified. There are some discrepancies in defining a company’s intellectual capital, unifying structural model of the company’s intellectual capital, and harmonizing the research methods and models of how to evaluate a company’s intellectual capital. Purpose of the article: The aim of the article is to examine various scientific approaches of the company’s intellectual capital and its impact on the market value of a respective company; to prepare a model of company’s intellectual capital and its impact on the market value. What is more, the aim of this article is to check and test the model effectiveness using an example of the Baltic States listed companies. Methods: Data on 58 Baltic States’ companies that are listed in Nasdaq Baltic stock exchange were taken as the basis of the research. Based on four component model (human capital, structural capital, juridical capital, relational capital) a set of indicators for assessing company’s intellectual capital was formed. Expert evaluation was used in order to assign weights for different structural parts of intellectual capital. An exploratory factor analysis was conducted in order to find out what factors are the most significant for a company’s intellectual capital. In order to find out how specific elements affect company’s intellectual capital, a pair-wise multiple correlation and re-gression analysis were used. An average comparison method was used to reveal differences between companies of different countries. Findings & Value added: The study contributes to the Baltic States’ knowledge on intellectual capital. It was detected that it is appropriate to use human capital, structural capital, juridical capital, and relational capital structural parts as components of company’s intellectual capital while investigating its impact on market value of a respective company. According to the ob-tained results, the model of a company’s intellectual capital and its impact on the market value was created, optimized, and its validity checked using exploratory factor analysis. The model was used to test the Baltic States listed companies and how their intellectual capital affects the market value. It was identified that intellectual capital in the listed companies of the Baltic States has a positive impact on their market value. Nevertheless, the study revealed that intellectual capital structural parts do not equally affect the market value of listed companies. The findings support the conclusion that human capital and relational capital have the greatest influence on the market value of listed companies. Companies where structural capital comprises the largest proportion of intellectual capital had lower levels of intellectual capital aggregated index, which could be exam-ined in future studies. Keywords: intellectual capital; human capital; structural capital; juridical capital; relational capital; market value.

Research background: Importance of intangible resources for country’s economic growth is widely recognized. However, empirical evidence of this influence is hard to show due to measurement limitations of intangible resources. Majority of empirical studies concentrates on the analysis of a specific type of intangible resource’s influence on economic growth. National intellectual capital concept provides background for an integrated assessment of the country's intangible resources. This new approach enables the estimation of intangible resources’ influence to economic growth in a more complex way. Purpose of the article: a) To examine various scientific approaches of the national intellec-tual capital and its impact on the economic growth; b) to offer a measurement model of the national intellectual capital influence on economic growth; c) to evaluate the specific Euro-pean Union countries’ intellectual capital’s effect on their economic growth. Methods: Econometric analysis; refined factor value computation method using the stand-ardized regression coefficients; the SAW method; expert evaluation, cluster analysis; corre-lation and regression analyses. Findings & Value added: A review of the economic growth theories showed that structural components of intellectual capital (human capital, structural capital, social capital, relational capital) in economic growth theories are analyzed as key determinants of economic growth. Our proposed research methodology consists time lag between variables and this let us evaluate casual relation. Empirical analysis of 25 European Union countries’ intellectual capital’s effect on their economic growth rate revealed that national intellectual capital and the countries’ level of economic development have statistically significant impact on eco-nomic growth rate. The analysis of intellectual capital components’ influence on economic growth rate of 25 European Union countries showed that only human capital and the level of economic development have statistically significant influence. A more comprehensive hu-man capital’s influence on economic growth analysis revealed that 63.1 percent of the long-term economic growth rate in 25 European Union countries can be explained by differences in their economic development level and differences in educational achievement factor values. Moreover, analysis of national intellectual capital effect on economic growth in separate clusters allowed to identify influence differences in each group of countries. Keywords: national intellectual capital; human capital; structural capital; social capital; relational capital; economic growth.

The Assess Model of Intellectual Capital and a Company's Value Added CohesionNowadays intangible assets are especially important in every company and can help to increase a company's value added. The importance is so huge that many companies invest more money in intellectual capital than in material assets. Why has this happened? Scientists answer this question very quickly and easily - many companies have already been disappointed and damaged by their materials, goods, equipment, buildings, cars, machinery that cost a lot of money but do not give effective productivity. On the contrary, intellectual capital that usually costs only the salary of an employee brings significant benefits.The research purposeis to evaluate the cohesion between intellectual capital and a company's value added and to provide the model of this cohesion.The methods usedare analysis of scientific literature, GBN matrix method, expert evaluation, average comparison method, and Kendall's coefficient of concordance.Scientific aims:to reveal the cohesion between intellectual capital and a company's value added; to introduce a model of a company's value added and its intellectual capital; to demonstrate the results of expert evaluation on the model of intellectual capital and a company's value.The findingsare as follows: intellectual capital is considered as a unit of social capital, communicational capital, and psychological capital; intellectual capital has a huge influence for the growth of a company's value added; employee motivation is the most important factor either for the growth of intellectual capital or a company's value added.Conclusions:expert evaluation was performed in order to investigate the importance of intellectual capital factors for the growth of intellectual capital itself and a company's value added. Experts were taken from two areas: business environment and academic environment. It is possible that experts from other environments could answer the questions in a completely different way, and this model could be improved even more.

The article analyses creativity and innovativeness in organisations of service business in Lithuania. Relation between creativity and innovativeness is given, and concepts of creativity and innovativeness, as well as the main research models, are presented. Statistical data and questionnaire survey data analyses are used for the research of the factors of creativity and innovativeness in Lithuania. Research results have shown that more than half of the organisations of the service sector in Lithuania are assessed as not creative; also factors that stimulate creativity are not sufficient for the development of creativity. It is necessary to create a work environment favourable for innovations that would help reveal and develop the creativity of human capital.

This paper is devoted to a review of the Multicriteria Framework of financial decisions of bank loan portfolio management as a financial model supporting financial decision-making, which builds pointed models taking into account the peculiarities of a problem. The goal here is to bridge the gap between decision-making disciplines of financial economics and the need for adequate decision support. Finance has traditionally recognized the two-objective situation of risk versus return, with the fundamental goal of wealth maximization. However, not well known are the new ways of approaching financial problems when three or more criteria exist. The modern portfolio management approach involves complex goals where they interact with one another; each has its own possible interpretation of wealth maximization, subjects to concern about risk, safety, liquidity, profitability, social responsibility, environmental protection, employee welfare, and so on. Consequently, it may well be appropriate to pursue a multiple objective approach to many financial decision-making problems. This article is concentrated upon building up a model with multicriteria analysis that provides financial decision makers and analysts with a wide-range methodology well suited to the complexity of modern bank loan portfolio management decision-making.

This research shows how U.S. and German government bond yields can determine the EUR/USD exchange rate in the short run. After presenting the discussion on fundamental, technical and microstructure approach exchange rate determination models, a conclusion is made that out of the components of fundamental models the interest rates could be the best determinants for explaining exchange rate fluctuations in the short term. For the research the mostly traded currency pair, the EUR/USD, was chosen and 2-year, 10-year U.S. and German government bond yields were selected as determinants of the exchange rate. After performing the linear regression procedure it has shown that the model can determine 5 per cent of the daily EUR/USD fluctuations with a change in 2-year U.S. government debt yield being the greatest determinant in the model. It affects the exchange rate as it is stated in the uncovered interest rate parity model – when the yield increases, the USD declines against the euro and vice versa. Another finding is that an increase in the German 10-year government bond yield increases the price of the euro and the increase in the U.S. 10-year debt yield leads to an appreciation of the USD.