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EN
Business suiweys can be applied in a wide rangę of fields as well as interpreted in a variety of ways, from many different angles. Among others, it is possible to analyze relationships between economic situation, in the light of qualitative and quantitative indicators, and the competitiveness of enterprises. Business survey indicators facilitate carrying out such an analysis due to the fact tliat they absorb both external, macroeconomic changes and those taking place at the level of given enterprises. The possibilities of application and interpretation of qualitative indicators of business surveys change in time. The significance of this method varies with the influence of the external, macroeconomic eiwironment on the situation of the enterprises sector. Such very dynamie changes of external factors have been taking place for the last ten years. They concern both the world economy and countries in transition. This, in tum, is a result of the revolution in computing, widespread privatization processes in the world, as well as a rapidly growing number of substitutes. Tliese generał tendencies are accompanied by economic policies in individual countries, for example in Poland. Due to the above mentioned macroeconomic factors, the extent to which companies can manipulate prices in order to gain profit is diminishing and, at the same time, it tends to be replaced by cost reduction. As a result, a widespread decrease of inflation can be observed and the demand - supply - price relations are not as distinct as in the early phases of market economies. Thus, a new paradigm of enterprises’ conipetitiveness emerges. It consists in the ability of a company to react to changes. Therefore, an economic situation of an enterprise nowadays reflects its adaptation to changes, in addition to a classic cycle of market conditions known from earlier periods. In the above context, the aim of the paper is the assessment of the changing competitive situation of enterprises in the Iight of business surveys. The analysis will be carried out with the use of qualitative indicators, as well as selected quantitative macroeconomic ones.
EN
One of the most important economic indicators developed on the basis of agents’ opinions is consumer confidence index. Such a situation stems from the fact that consumption is usually the most important element of total demand. In well developed economies in which consumer confidence indexes have been used for many years a lot of attention is paid to analysis of their behavior. They are the element of composite leading indicators developed among others by the European Commission and the US Trade Department. In the emerging economies, in which analyses of consumer behavior were introduced relatively recently, qualitative data on consumer confidence is treated with less attention. This suggests conducting research in such a field for new EU member states. Countries which joined EU in 2004 r. are: the Czech Republic, Estonia, Cyprus, Latvia, Lithuania, Hungary, Malta, Poland, Slovenia, Slovakia. This group of countries was subject to analysis presented in the paper. The main aim of the research is to find and analyze relationships between consumer confidence indexes and chosen macroeconomic variables. Research questions refer to common direction in such an analysis conducted worldwide: to identify factors influencing consumers’ opinion and check whether changes of consumer confidence lead to changes of chosen economic variables. Consumption in the state of equilibrium depends on propensity to consumption (measured by confidence index) and on possibilities which are represented by disposable income. Quantitative monthly data used in the research refers to purchasing power of households and consumption expenditure. On the other hand the scope of the research was aimed at main economic time series which can influence agents expectations. Composite consumer confidence index cannot be commonly applied to describe volatility of various types of consumption, so in the research simple (component) indicators were used as well. The analysis of linear relationships is based on the cross correlations. In order to find lags and leads there were estimated Pearson correlation coefficients for shifts ±12 months. The analysis of linear relationships was extended by Granger’s causality test in order to verify whether quantitative variables influence consumer’s answers. Reverse relationships were also verified. In order to track nonlinear relationships neural networks module of Statistica was used. In the case algorithm of optimal data set for model time series was applied. Achieved results allow to identify relationships between analyzed economic time series, but also can be treated as first step for introducing consumer confidence indicators to economic forecasting in chosen developing EU economies..
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