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Brand management plays a crucial role in increasing the loyalty of current customers as well as in gaining new customers. The product brand significantly influences shopping behaviour of customers. Many customers prefer branded products to non-branded ones. Some of them prefer branded products in general; some of them prefer branded products only for selected products. This paper is focused on the analysis of the perception of ten favourite car brands by Slovak consumers with an emphasis on the quality, prestige, image and expected benefits and features. The aims of the paper are divided into three main parts: i) find out the selected car brand rank based on the influence on the purchasing behaviour of Slovak consumers; ii) assess the impact of selected socio-demographic characteristics on the expected benefits as well as on the features that the respondent´s chosen car brand should have; iii) identify the main reason why respondents focus on preferred car brands. The correlation of these factors was tested on a sample of 2002 respondents from the Slovak republic. The analysed data represent a partial result of an extensive survey. The chosen mathematical-statistical methods such as correlation analysis and testing of statistical hypotheses were applied to fulfil the purpose of the research. Based on a survey, the most influential car brands include Skoda and Volkswagen. Especially the Skoda brand has a significant position in Slovakia, given by tradition, history and place of production of these cars.

The Slovak law on expertness defines an expert opinion, determining who can venture it and how to bring it before the court. Yet, do the courts consider expert opinions as their own? The presented study explores the incentives to commissioning expert opinions and the legal environment of this institute. The community of experts has found flaws in the legislation governing expert opinions, arguing that experts tend to bias their opinion in favour of the client and break the oath they took on behalf of the law. Although the Slovak legislation sets parameters to avoid abuse or defamation of the expert opinion, the judicial practice will show if the degree of acceptance of this opinion is higher than in countries with non-enacted legislation on these opinions despite their wide use in legal proceedings. [ABSTRACT FROM AUTHOR]

The aim of this paper was to evaluate the demand for mobile phones in the world at the time of the coronavirus pandemic and to predict future development up until the end of 2022. During the covid-19 pandemic, markets in all sectors were subdued due to limited production and sales. As the production of chips for electronics was one of the deeply affected areas in terms of production slowdown, along with the rapid purchase of electronics at the beginning of the pandemic, there was also a slowdown in sales. The method of describing time series and calculation using linear regression is used. In this work, it is found that the decline in demand for mobile phones was caused by a global pandemic and consumers' uncertainty in maintaining their jobs. Overall, during the covid-19 pandemic, we saw a drop in the number of mobile phone units sold by more than 40,000. Furthermore, it was found that the entire mobile phone market is clearly recovering after the coronavirus crisis.

This study is concerned with forecasts of GDP growth for the Visegrad countries (V4-hereafter) and oil prices simultaneously. The series for GDP and Oil prices are quarterly, covering the period from December 1, 2000, to October 1, 2023. Neural network techniques were performed to generate individual forecasts. The forecasts for oil prices maintain higher accuracy than the GDP ones due to autoregressive lags. In other words, current oil prices absorb significant influence from past prices. The GDP forecasts for the V4 group indicate tremendous shock and moving growth to a lower steady state. The lower growth observed by the end of the period under review can be attributed to two subsequent non-economic shocks: the Russia-Ukraine war and the COVID-19 pandemic. The V4 countries faced difficulties after the outbreak of COVID-19, mainly due to the stringency measures. In addition to accelerating inflation, the war also disrupted energy commodities within supply chains. These and other facts justify the low growth that awaits these countries until 2028. Ultimately, the oil price until 2028 is estimated to oscillate between $60 and $90 per barrel. [ABSTRACT FROM AUTHOR]

The large number of joint stock companies causes that investors strive for better awareness and information in the stock area. The objective of the contribution is to apply the method of discounted FCFE in the valuation of a specific company and to determine the parameters for the model application in practice. The data for the analysis are obtained from the database of Kofola ČeskoSlovensko a.s. The data are taken from the published annual reports for the years 2018 and 2019. First, a financial plan for a period of stability was created. Subsequently, the discount value of the company was calculated using the FCFE model. Finally, the calculation of the company value in the continuation phase was calculated. To achieve the research goal, the individual FCFE need to be discounted by the value of equity using the CAMP model. In conclusion, it can be assumed that the discount rate will remain stable and the prediction of the growth rate will not change. If there are no fundamental changes in the company e.g. in terms of dividend payment, investment activities, or changes in the working capital, it can be assumed that the value of the company should be stabilized until 2021.

Research background: In the Czech Republic, the CZK/EUR currency pair is the most observed one not only by Czech consumers but also by Czech and foreign companies. Not only the political situation in the world and the foreign investors but also other variables have a significant influence on the CZK. Purpose of the article: The aim of this paper is to analyse the development of the CZK/EUR exchange rate, to perform a correlation analysis of the CZK/EUR currency pair and to forecast its future development in today’s unstable global world affected by the Coronavirus pandemics. Methods: In order to fulfil the aim of the paper, the data of the CZK/EUR currency pair from the beginning of the year 1999 to mid-June 2020 was used. The correlation analysis and the forecast of the future development of the exchange rate are performed by means of the, in today’s globalized world very promising, technology of artificial neural networks. Findings & Value added: The analysis and the forecast of the exchange rate development is based on the time series model taking the previous value of the exchange rate and its previous volatility into consideration. The strongest propulsion power for the development of the CZK/EUR exchange rate will be the market atmosphere on the world’s markets and the associated capital transfer between risk assets and safe havens. Provided, that the current situation settled down definitively and the economy returned back to its normal state, it is probable that the Czech koruna would gain some part of its significant loss back within a medium-term period.

Research background: Bitcoin is defined as digital money in a peer-to-peer decentralized payment network, an amalgam hybrid between fiat and commodity currency without a real value. This digital currency is also independent of any government or currency administration. Purpose of the article: This article explores whether bitcoin works as a medium of exchange or relates to assets, focusing on its current use and future utility regarding its characteristics. Methods: Analysing bitcoin statistical features, we found no connection with traditional asset categories such as stock, bonds and commodities either in intermediate time, or periods of financial crises. Findings & Value added: The study suggests that investors’ abiding interest in bitcoins can have a positive impact on their liquidity in the real time.

The USA decided to regulate the trade more by imposing tariffs on specific types of traded goods. It is therefore more interesting to find out whether the current technologies based on artificial intelligence with time series influenced by extraordinary factors such as the trade war between two powers are able to work. The objective of the contribution is to examine and subsequently equalize two time series – the USA import from the PRC and the USA export to the PRC. The dataset shows the course of the time series at monthly intervals between January 2000 and July 2019. 10,000 multilayer perceptron networks (MLP) are generated, out of which 5 with the best characteristics are retained. It has been proved that multilayer perceptron networks are a suitable tool for forecasting the development of the time series if there are no sudden fluctuations. Mutual sanctions of both states did not affect the result of machine learning forecasting.

The objective of the contribution is to introduce a methodology for considering seasonal fluctuations in equalizing time series using artificial neural networks on the example of the Czech Republic and the People´s Republic of China trade balance. The data available is the data on monthly balance for the period between January 2000 and July 2018, that is, 223 input data. The unit is Euro. The data for the analysis are available on the World Bank web pages etc. Regression analysis is carried out using artificial neural networks. There are two types on neural networks generated, multilayer perceptron networks (MLP) and radial basis function networks (RBF). In order to achieve the optimal result, two sets of neural structures are generated. There are generated a total of 10,000 neural structures, out of which only 5 with the best characteristics are retained. Finally, the results of both groups of retained neural networks are compared. The contribution this paper brings is the involvement of variables that are able to forecast a possible seasonal fluctuation in the time series development when using artificial neural networks. Moreover, neural networks have been identified that achieve slightly better results than other networks, specifically these are the neural networks 1. MLP 13-6-1 and 3. MLP 13-8-1.

The goal of the paper was to assess the safety of riding shared e-scooters and determine the causes of accidents involving them. Primary data for the research were obtained through a combination of quantitative and qualitative content analysis based on deep learning by analysing data from the WoS database entered into the VOSviewer system. A quantitative observation method was used to determine the number of shared e-scooter users who do not use safety equipment such as helmets. The data obtained were processed using the Wilcoxon test designed to test the homogeneity of two random samples in the univariate case. The observation of shared e-scooter users and social networks of e-scooter service providers showed that safety equipment is not adequately used; in addition, the users often do not know the traffic rules and often violate them. Of the 256 monitored cases of using shared e-scooters, none of the users wore a safety helmet. It can thus be concluded that it is necessary to promote the rules of the safe use of shared e-scooters. Further research could focus on proposing a methodology for assessing traffic accidents involving e-scooters and a general overview of recommendations for e-scooter safe use. [ABSTRACT FROM AUTHOR]

Foreign trade has been and is considered to be very important. Trade balance measurement provides one of the best analyzes of a country's external economic relations. It serves as a monetary expression of economic transactions between a certain country and its foreign partners over a certain period. The aim of this paper is to compare the accuracy of time series alignment by means of regression analysis and neural networks on the example of the trade balance of the Czech Republic and the People's Republic of China. Trade balance data between the Czech Republic and the People's Republic of China is used. This is a monthly balance starting in 2000 and ending in July 2018. First, a linear regression is made followed by regression using artificial neural networks. A comparison of both methods at expert level and experience of the evaluator, the economist, is performed. Optically, the LOWESS curve appears to be best out of the linear regression and the fifth preserved RBF 1-24-1 network seems the mot appropriate out of neural networks.

The production growth rates, the generated volumes of waste by classes of hazard, an extent of fuel and energy resources and fresh water used for coal mining, as well as the volumes of pollutant emissions into the atmosphere of Ukrainian mining enterprises have been analyzed. It has been found that achieving the decoupling (reduction of anthropogenic load) is only possible under the condition of introducing the cleaner production technologies, non-waste technologies, innovations, replacement of obsolete capital funds, and the like. Mere changes in points will not significantly affect the situation, thus, companies should develop their own approach to the sustainable development by using the frameworks as templates. The calculations have been made of the decoupling phenomenon based on the volumes of emission, the volumes of generated waste by classes of hazard and used fuel and water resources, which showed an increased anthropogenic environmental impact and economic imbalance of the mining sector. The research has been made into the use of frameworks as an auxiliary tool for the implementation by mining companies of sustainable development principles. The developed DPSIR framework, illustrated with examples from foreign experience, is conceptual in character and makes it possible to understand the cause and effect relationships of the enterprise activities impact, including mining companies, on the environment and the mechanisms of better response to the resulting consequences.

China, by GDP, is the second largest economic power, and hence also a key player in the field of international relations. As far as the EU is concerned, it is China's largest trading partner. From this point of view, it is clear that monitoring export and import development between these partners is essential. This paper therefore aims to compare two useful methods, namely the accuracy of time series alignment through regression analysis and artificial neural networks, to assess the evolution of the EU and the People's Republic of China trade balance. Data on the export and import trends of these two partners since 2000 have been used, and it is clear that the trade balance was completely different that year than it is now. The development over time is interesting. The most appropriate curve is selected from the linear regression, and from the neural networks three useful neural structures are selected. We also look at the prediction of future developments while taking into account seasonal fluctuations.

A remarkable success depends on a variety of factors, one of which is the ability to motivate the employees, increasing motivation of managers, measuring business processes and measuring and observing values of the company. The tangible results are also shown by the indicator of return on equity (ROE). The article aims at reflecting the importance of ROE for the EVA Equity (economic value added for shareholders) calculation of Motor Jikov Strojírenská, a.s. The data come from Albertina database. These are details of financial statements from 2000-2015. As a matter of fact, weighted average costs of capital, alternative cost value of equity capital and EVA Equity are calculated. Software Statistica and its sophisticated tool data mining - automated neural networks was used for finding a correlation between EVA Equity indicator and ROE. In addition, 10,000 neural networks were generated, five of which with the best results have been stored. The results show that EVA Equity is not dependent on the ROE rate.

Stock price forecasting is highly important for the entire market economy as well as the investors themselves. However, stock prices develop in a non-linear way. It is therefore rather complicated to accurately forecast their development. A number of authors are now trying to find a suitable tool for forecasting the stock prices. One of such tools is undoubtedly artificial neural network, which have a potential of accurate forecast based even on non-linear data. The objective of this contribution is to use neural networks for forecasting the development of the ČEZ, a. s. stock prices on the Prague Stock Exchange for the next 62 trading days. The data for the forecast have been obtained from the Prague Stock Exchange database. These are final prices at the end of each trading day when the company shares were traded, starting from the beginning of the year 2012 till September 2017. The data are processed by the Statistica software, generating multiple layer perceptron (MLP) and radial basis function (RBF) networks. In total, there are 10,000 neural network structures, out of which 5 with the best characteristics are retained. Using statistical interpretation of the results obtained, it was found that all retained networks are applicable in practice.

The present study systematically reviews the existing research on algorithmic monitoring systems, remote sensing tools, and metaverse engagement metrics. Our findings clarify that virtual immersive workspaces integrate image recognition tools, employee monitoring software, and spatial data mining algorithms in extended reality environments, and we contribute to the literature by indicating that digital twin technologies, 3D immersive content, and blockchain-enabled decentralized applications shape the virtual reality visualization environment in relation to remote workforce. Throughout May 2022, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including "virtual economy of the metaverse" + "immersive engagement and geospatial mapping technologies," "employee behavioral data," and "workplace tracking systems." As research published in 2022 was inspected, only 163 articles satisfied the eligibility criteria. By taking out controversial or ambiguous findings (insufficient/ irrelevant data), outcomes unsubstantiated by replication, too general material, or studies with nearly identical titles, we selected 31 mainly empirical sources. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AMSTAR, Dedoose, Distiller SR, and SRDR. [ABSTRACT FROM AUTHOR]

The purpose of this study is to examine menstrual cycle tracking apps integrating sexual and reproductive processes and self-care practices. In this article, we cumulate previous research findings indicating that fetal development tracking apps assist expecting parents in health information seeking. We contribute to the literature on fertility trackers and birth control reminders assisting in unintended pregnancy prevention, family planning, and abortion counseling by showing that mobile health apps may preserve users' satisfaction throughout their prenatal care. Throughout January 2022, we performed a quantitative literature review of the Web of Science, Scopus, and ProQuest databases, with search terms including "fertility awareness apps" + "bodily self-tracking devices," "gynecological health monitoring," and "sexual and reproductive processes." As we inspected research published between 2016 and 2022, only 177 articles satisfied the eligibility criteria. By removing controversial findings, outcomes unsubstantiated by replication, too imprecise material, or having similar titles, we decided upon 24, generally empirical, sources. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AXIS, Dedoose, MMAT, and SRDR. [ABSTRACT FROM AUTHOR]