Family businesses began to emerge in Slovakia after the change of social establishment in 1989, and since then they represent a significant group of business entities with a significant contribution to the economy, and have significant growth potential. Innovations have become a driving force for the future opportunities of these companies. Based on empirical research, this paper aims to identify the innovation activities of small and medium-sized family businesses in Slovakia and to determine their impact on the company’s economic results. We can state that out of small and medium-sized family businesses included in the survey, 76.5% have implemented innovations in the last five years. We use statistical tests to verify the research hypotheses. We can state that there is a statistically significant relationship between the size of the company and the number of types of introduced innovations, as well as between the generation running the company and the number of types of introduced innovations. Second-generation family businesses can, therefore, be considered more innovative than first-generation family businesses. We investigate the impact of the COVID-19 coronavirus pandemic on innovation activities in these companies. It is interesting that in 30.6% of family businesses the COVID-19 coronavirus pandemic positively affected their innovation activities.

The correct real estate property price estimation is significant not only in the real estate market but also in the banking sector for collateral loans and the insurance sector for property insurance. The paper focuses on both traditional and advanced methods for real estate property valuation. Attention is paid to the analysis of the accuracy of valuation models. From traditional methods, a regression model is used for residential property price estimation, which represents the hedonic approach. Modern advanced valuation methods are represented by the artificial neural network, which is one of the soft computing techniques. The results of both methods in residential property market price estimation are compared. The analysis is performed using data on residential properties sold on the real estate market in the city of Nitra in the Slovak Republic. To estimate the residential property prices, artificial neural networks trained with the Levenberg-Marquart learning algorithm, the Bayesian Regularization learning algorithm, and the Scaled Conjugate Gradient learning algorithm, and the regression pricing model are used. Among the constructed neural networks, the best results are achieved with networks trained with the Regularization learning algorithm with two hidden layers. Its performance is compared with the performance of the regression pricing model, and it can state that artificial neural networks can considerably improve prediction accuracy in the estimation of residential property market price. Doi: 10.28991/esj-2020-01250 Full Text: PDF

The presented paper deals with the regionalization of the electoral support of the Czech Pirate Party (Pirates) in regional elections using methods and techniques of spatial data analysis. The aim is to answer the question whether the territorial distribution of Pirate electoral support allows this party to participate in governance at the regional level and thus influence the form of regional policy in individual regions. The results of the analysis show that the spatial distribution of Pirates’ electoral support in regional elections differed quite significantly not only from the pattern found in the elections to the Chamber of Deputies of the Czech Parliament and elections to the European Parliament, but also between individual regional elections. This suggests the current lack of anchorage of Pirates’ electoral support in regional politics, but at the same time, it may have its origins in the second-order character of regional elections and the candidacy of many local and regional entities in regional elections. On the other hand, the results of the regional elections in 2020 meant that the Pirates received seats in all regional councils, but especially in nine of the thirteen regions they joined the regional government (similarly to two years earlier when they joined government of capital city of Prague), gaining the opportunity to influence, with regard to its priorities, the form of regional governance in most Czech regions.

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A municipality’s budget is a tool that significantly affects the long-term economic potential of the area. In addition, it is an important tool for the management of the municipality, in relation to the effective provision of public services for inhabitants. To ensure them, it uses the revenues that the local self-government receives from various sources. The aim of the paper is to characterize and to compare the mechanism of creating revenues of the local self-government in the Slovak and Czech Republic and, at the same time, to analyze the relationships between individual groups of local revenues in the time period 2009–2018. We analyzed the basic groups of municipal revenues: total revenues, current revenues, and capital revenues. For the analysis, we used selected mathematical–statistical methods (trend lines, correlation coefficient). Although both countries were part of one country, both have a dual model of public administration and have undergone fiscal decentralization; the structure and sources of local self-government revenues are different. However, a common attribute is the dependence of local self-government on state revenues. Tax revenues are the most important part of current budget revenues. Despite fiscal decentralization, local budget revenues are dependent on the state. In the Slovak Republic, share taxes from the state represent 74% of the total tax revenues of municipalities, and in the Czech Republic, 85% of the total tax revenues of municipalities.