This study examines Industry 4.0-based technologies, focusing on the barriers to their implementation in European small- and medium-sized enterprises (SMEs). The purpose of this research was to determine the most significant obstacles that prevent SMEs from implementing smart manufacturing, as well as to identify the most important components of such an operationalization and to evaluate whether only large businesses have access to technological opportunities given the financial complexities of such an adoption. The study is premised on the notion that, in the setting of cyber-physical production systems, the gap between massive corporations and SMEs may result in significant disadvantages for the latter, leading to their market exclusion by the former. The research aim was achieved by secondary data analysis, where previously gathered data were assessed and analyzed. The need to investigate this topic originates from the fact that SMEs require more research than large corporations, which are typically the focus of mainstream debates. The findings validated Industry 4.0′s critical role in smart process planning provided by deep learning and virtual simulation algorithms, especially for industrial production. The research also discussed the connection options for SMEs as a means of enhancing business efficiency through machine intelligence and autonomous robotic technologies. The interaction between Industry 4.0 and the economic management of organizations is viewed in this study as a possible source of significant added value.

The scientific periodical journal (indexed on Web of Science) published by the Tomas Bata University in Zlin offers results of basic and applied economic research in the English language.

The current COVID-19 pandemic has affected every aspect of consumer behavior—their expenses, investments, and financial reserves, as well as their financial and social wellbeing. As a consequence of different restrictions, consumers and their shopping patterns have changed significantly; thus, the factors that influence new purchase patterns need to be identified to help traders, retailers, and marketers develop appropriate strategies to respond to crucial consumer changes in the market. A categorical analysis (Pearson’s chi-square test) and correspondence analysis (simple and multivariate) were applied to a sample of 425 Slovak respondents to reveal the most important factors impacting consumers’ financial situations, as well as the effects on the maintenance of new shopping habits established during the pandemic period. The results revealed that consumers’ income, age, and sector of occupation play important roles in the context of new shopping patterns. These findings are in agreement with other global studies, confirming both the worldwide impact of the pandemic on consumer behavior and the importance of national studies on consumer shopping behavior in order for state authorities, traders, marketers, and entrepreneurs to be able to take necessary measures.

Research background: Since the first bankruptcy prediction models were developed in the 60?s of the 20th century, numerous different models have been constructed all over the world. These individual models of bankruptcy prediction have been developed in different time and space using different methods and variables. Therefore, there is a need to analyse them in the context of various countries, while the question about their suitability arises. Purpose of the article: The analysis of more than 100 bankruptcy prediction models developed in V4 countries confirms that enterprises in each country prefer different explanatory variables. Thus, we aim to review systematically the bankruptcy prediction models developed in the countries of Visegrad four and analyse them, with the emphasis on explanatory variables used in these models, and evaluate them using appropriate statistical methods. Methods: Cluster analysis and correspondence analysis were used to explore the mutual relationships among the selected categories, e.g. clusters of explanatory variables and countries of the Visegrad group. The use of the cluster analysis focuses on the identification of homogenous subgroups of the explanatory variables to sort the variables into clusters, so that the variables within a common cluster are as much similar as possible. The correspondence analysis is used to examine if there is any statistically significant dependence between the monitored factors ? bankruptcy prediction models of Visegrad countries and explanatory variables. Findings & Value added: Based on the statistical analysis applied, we confirmed that each country prefers different explanatory variables for developing the bankruptcy prediction model. The choice of an appropriate and specific variable in a specific country may be very helpful for enterprises, researchers and investors in the process of construction and development of bankruptcy prediction models in conditions of an individual country.

Research background: Financial risk management is the task of monitoring financial risks and managing their impact. Financial risk is often perceived as the risk that a company may default on its debt payments. The issue of the debt, default or prosperity of the company are presented in the article as one of the ways of the risk management. A prediction of corporate default is an inseparable element of the risk management. Mainly the consequences of risk are the engine of research and development of methods and models, which enable to predict economic and financial situation in specific conditions of global economies. Purpose of the article: The main aim of the presented article is to assess financial risks of Slovak entities, realized by the identification of significant factors and determinants affecting the prosperity of Slovak companies. Methods: To conduct the research we have used the data of Slovak enterprises, obtained from annual financial reports covering the year 2015 and the calculated financial ratios of profitability, activity, liquidity and indebtedness that may affect the financial health of the company were applied in the regression analysis. Realizing the multiple regression analysis, the statistically significant determinants that affect the future financial development of the company are identified, as well as the regression model of the bankruptcy prediction. Findings & Value added: In the research aimed at the management of financial risks in Slovak enterprises, we focused on the revelation of significant economic risk factors using multiple regression. The results suggest that the most significant predictors are net return on capital, cash ratio, quick ratio, current ratio, net working capital, RE/TA ratio, current debt ratio, financial debt ratio and current assets turnover based on which the decision about the future company default can be made. These factors are significant enough to manage financial risks and to affect the profitability and prosperity of the company.