Working capital management is a crucial pillar in corporate finance. The performance of transport companies can be improved by efficient working capital management through cash management, inventory management, and receivables management. This approach aims at sustainable growth of transport companies in international competition. The main aim of the article is to identify statistically significant variables from working capital management describing liquidity and activity, with a focus on corporate performance in the Visegrad Group countries. We designed models for small and medium-sized enterprises for each member state of the Visegrad Group and a universal model for the entire region. We applied a comprehensive model design process using multi-criteria linear regression, mainly on indicators from the Amadea financial statements in IBM SPSS 25. We described the overall sample using descriptive statistics, identify outliers, identify multicollinearity, and design models, and compared with other models describing return on assets. The added value is the explanation of the impact of working capital management on the performance of small and medium-sized transport companies in the Visegrad Group, which make up most companies in this sector. These findings help identify key aspects of working capital management that contribute to business performance. The paper presents a detailed output for future research into the role of working capital in corporate management.

The issue of prediction of financial state, or especially the threat of the financial distress of companies, is very topical not only for the management of the companies to take the appropriate actions but also for all the stakeholders to know the financial health of the company and its possible future development. Therefore, the main aim of the paper is ensemble model creation for financial distress prediction. This model is created using the real data on more than 550,000 companies from Central Europe, which were collected from the Amadeus database. The model was trained and validated using 27 selected financial variables from 2016 to predict the financial distress statement in 2017. Five variables were selected as significant predictors in the model: current ratio, return on equity, return on assets, debt ratio, and net working capital. Then, the proposed model performance was evaluated using the values of the variables and the state of the companies in 2017 to predict financial status in 2018. The results demonstrate that the proposed hybrid model created by combining methods, namely RobustBoost, CART, and k-NN with optimised structure, achieves better prediction results than using one of the methods alone. Moreover, the ensemble model is a new technique in the Visegrad Group (V4) compared with other prediction models. The proposed model serves as a one-year-ahead prediction model and can be directly used in the practice of the companies as the universal tool for estimation of the threat of financial distress not only in Central Europe but also in other countries. The value-added of the prediction model is its interpretability and high-performance accuracy.

In this paper, we analyse the specific behaviour of passengers in personal transport commuting to work or school during the COVID-19 pandemic, based on a sample of respondents from two countries. We classified the commuters based on a two-step cluster analysis into groups showing the same characteristics. Data were obtained from an online survey, and the total sample size consists of 2000 respondents. We used five input variables, dividing the total sample into five clusters using a two-step cluster analysis. We observed significant differences between gender, status, and car ownership when using public transport, cars, and other alternative means of transportation for commuting to work and school. We also examined differences between individual groups with the same socioeconomic and socio-demographic factors. In total, the respondents were classified into five clusters, and the results indicate that there are differences between gender and status. We found that ownership of a prepaid card for public transport and social status are the most important factors, as they reach a significance level of 100%, unlike compared to other factors with importance ranging from 60 to 80%. Moreover, the results demonstrate that prepaid cards are preferred mainly by female students. Understanding these factors can help in planning transport policy by knowing the habits of users.

The situation of the COVID-19 pandemic has had enormous social and economic impacts and has significantly affected the modal split. Many cities worldwide have adopted various blocking policies that affect how people travel. Micromobility systems, such as scooters and bicycle sharing, were among the transport systems affected by COVID-19. Electric scooters and shared bicycles provide comfortable and fast first-/last-mile connections for short-distance rides. The shared nature of these modes, together with the spread COVID-19, has contributed to the declining use of these services. The quantification of the impact of COVID-19 on shared services was demonstrated by this research through various mathematical methods. Satisfaction with the use of alternative modes of transport during the pandemic was determined based on the evaluation of a questionnaire survey. Independence tests of qualitative features and statistically significant associations that were demonstrated with a correspondence analysis were used for comparison. The main conclusion of the research was to point out the reasons for the preference for alternative modes of transport and to highlight the impacts on health and fears of contracting COVID-19 when using micromobility services.

The COVID-19 global pandemic has affected normal human behaviour in day-to-day activities. As a result of various restrictions, people have significantly changed their shopping and mobility to limit the spread of the pandemic. This article aims to determine the association between consumers’ shopping preferences and the frequency of selected daily activities during and before the COVID-19 pandemic using correspondence analysis. The total sample consists of 407 respondents from Slovakia. The data are obtained from an online questionnaire divided into several sections such as socio-demographic factors, shopping preferences, and frequency of selected activities per week. The results show that there is an association between consumers’ preference for shopping in supermarkets and the frequency of family visits per week during the pandemic, among other factors. These findings follow up on previous studies on the consequences of changing mobility as a result of the global crisis.