The COVID-19 pandemic has become a key event that has changed our lives. Businesses had to change their daily operations, and working from home has become the norm. Almost everyone who is working from home must use some type of digital technology. It is possible to say that business activities and business models have undergone some form of digital transformation due to the COVID-19 pandemic. Therefore, we chose a company that was affected by these changes, and the main goal of our research was the identification of how the digital transformation that was caused by the pandemic changed the business models of the selected company. Because the analyzed company is operating in the financial sector, this article is a case study. The basic activities and elements of the business models of the selected company, before and during the pandemic, were identified using the Business Model Canvas. We propose a set of key performance indicators (KPIs), based on the Balanced Scorecard, for measuring the newly identified activities and elements of the business models that arose in the company as a result of the COVID-19 pandemic. The knowledge gained in this article shows how the digital transformation changed the functioning of the selected company. The conclusions of this article are mainly for the companies that are considering their own digitalization, which contributes to the long-term sustainability of banking.

Historically, new technologies have always increased the efficiency of production processes. Production process efficiency increases productivity. With the growth of productivity, there is usually an increase in sales and profit. Today’s world is mainly influenced by digital technologies. The digitization of production processes leads to the digital transformation of the business and natural economy sectors. If, like other technologies in the past, the effects of digital technologies are associated with a growth in efficiency, productivity and thus also revenues and profits, the aim of this article is to propose a model to assess the potential of a digital transformation from a macroeconomic perspective. The proposed model, which was based on a composite indicator expressing the potential for digital transformation, was quantified for a certain period and for a selected sample of countries. The potential for digital transformation can occur in any country, regardless of the purchasing power of its population and GDP per capita. We can assume that the economic benefits of digital technologies are obvious. Businesses operating in any country innovate and are driven by digital technologies. This is also reflected in employment. New workers who know how to work with digital technologies are needed. Therefore, the results of the assessments and the proposal itself serve as a basis for national policy makers to create national strategies for digital transformation.

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Electromobility is one form of transport that is suitable for achieving the goals of carbon neutrality. Therefore, if stakeholders want to be informed, the aim of the article is to examine trends in the field of electromobility. Trends are explored using a systematic literature review. The selected areas are specific factors affecting electromobility, which are market characteristics and value-added services. A specific area of inquiry is important for the reason that if businesses providing or dealing with electromobility are successful in the market, it is necessary to analyse their market position or characteristics of the market in which they operate. If it is clear which laws apply in the market, the company can adapt its offer to the trends that affect the given business segment. The same applies to policy makers, new knowledge can be applied in policy making. Data, about the market and services that are offered on the market, provide knowledge to stakeholders that focus their activities on electromobility. Furthermore, this article provides not only information about the current state of affairs, but also further directions research should take to map electromobility from point of view of market characteristics and value-added services.

This study examines the ethical problems of using artificial intelligence (AI) applications in higher education, focusing on activities performed by university educators. Drawing on Slovak legislation that defines educators’ responsibilities, the study classifies their activities into three categories: teaching, scientific research, and other (academic management and self-directed professional development). From standpoint of methodology, a thematic review of 42 open-access, peer-reviewed articles published between 2022 and 2025 was conducted across the Web of Science and Scopus databases. Relevant AI applications and their associated ethical issues were identified and thematically categorized. Results of this study show that AI applications are extensively used across all analysed areas of university educators’ activities. Most notably used are applications that are generative language models, editing and paraphrasing tools, learning and assessment software, management and search tools, visualizing and design tools, and analysis and management systems. Their adoption raises ethical concerns which can be thematically grouped into six categories: privacy and data protection, bias and fairness, transparency and accountability, autonomy and oversight, governance gaps, and integrity and plagiarism. The results provide universities with a structured analytical framework to assess and address ethical risks related to AI use in specific academic activities. Although the study is limited to open-access literature, it offers a conceptual foundation for future empirical research and the development of ethical, institutionally grounded AI policies in higher education.

This article investigates the utilization of eye-tracking methodology to monitor the driver’s activities and attention during the arrival and departure procedures of train operations on Slovak Railway (ŽSR) line no. 120, Bratislava–Žilina. Previous studies conducted in 2020 formed the basis of the current research, which focused on two train stations and two railway stops located on the Žilina–Púchov track section. The results of the experiment allowed for a greater understanding of the driver’s cognitive processes, thereby leading to increased safety and sustainability in the railway transport system. It is noteworthy that the employed measurement methodology and technology had no detrimental effect on train operation, or operational and thus passenger safety. Thus, the results of this experiment provide a sound foundation for further exploration into human–machine (driver–train) interaction in actual traffic conditions.