Nowadays there is a large amount of information at our disposal, which is increasing day by day, and right now the question is not whether we have a method to process it, but which method is most effective, faster and best. When processing large databases, with different data, the formation of homogeneous groups is recommended. This paper presents the financial performance of Hungarian and Romanian food retail companies by using two well-known cluster analyzing methods (K-Mean and K-Medoid) based on ROS (Return on Sales), ROA (Return on Assets) and ROE (Return on Equity) financial ratios. The research is based on two complete databases, including the financial statements for five years of all retail food companies from one Hungarian and one Romanian county. The hypothesis of the research is: in the case of large databases with variable quantitative data, cluster analysis is necessary in order to obtain accurate results and the method chosen can bring different results. It is justified to think carefully about choosing a method depending on the available data and the research aim. The aim of this study is to highlight the differences between the results of these two grouping procedures. Using the two methods we reached different results, which means a different evaluation of financial performance. The results demonstrate that the method chosen for grouping may influence the assessment of the financial performance of companies: the K-Mean method produces a greater variety of groups and the range of results obtained after grouping is larger; whereas, the group distribution and the results obtained by the K-Medoid method are more balanced.

Money launderers and corrupt entities refine methods to evade detection, making artificial intelligence (AI) and machine learning (ML) essential for countering these threats. AI automates identity verification using diverse data sources, including government databases and social media, analysing client data more effectively than traditional methods. This study uses bibliometric analysis to examine AI and ML in anti-money laundering and anti-corruption efforts. A sample of 746 documents from 477 sources from Scopus shows a 14.33% annual growth rate and an average document age of 3.51 years, highlighting the field's actuality and rapid development. The research indicates significant international collaboration in documents. The main clusters of keywords relate to the implementation of AI and ML in (1) avoiding fraud and cybersecurity, (2) AML compliance, (3) promotion of transparency in combating corruption, etc. Addressing ethical concerns, privacy, and bias is crucial for the fair and effective use of AI and ML in this area.

In the wave of digitalization, organizations are increasingly focused on whether to prioritize digital culture or organizational strategy for the use of artificial intelligence (AI); there are mixed opinions, particularly when AI-augmented HRM draws attention as a tool for achieving sustainable organizational performance (SOP) in developing countries. This study aims to explore the influence of digital culture and organizational strategy on AI-augmented HRM and SOP, focusing on the mediating role of AI-augmented HRM in these relationships. To investigate the hypothesized relationships, 219 sample data were gathered from employees associated with HRM-oriented activities in Bangladesh, and SPSS 23 and AMOS software were used to test the SEM model. The results proved that digital culture has an insignificant effect and organizational strategy has a significant effect on AI-augmented HRM, and AI-augmented HRM has a substantial effect on SOP and partially mediates the relationship between organizational strategy and SOP. Based on the results, we infer that the successful implementation of AI-augmented HRM can lead to organizational sustainability in developing countries, where organizational strategy plays a pivotal role rather than digital culture. This research incorporates the resource-based view (RBV) and dynamic capabilities theories, which are crucial for the groundbreaking development of the research model. The results suggest that managers and responsible authorities should prioritize organizational strategy over digital culture when implementing AI-augmented HRM systems to ensure sustainability in developing countries. However, in the long run, organizations also need to concentrate on generating digitally favorable environments.

The capital structure of agricultural enterprises in the Visegrad countries | Veronika Fenyves, Károly Pető, János Szenderák, Mónika Harangi-Rákos | Agricultural Journals

In today’s increasingly competitive markets, it is essential to be able to determine the position of a company as opposed to its competitors. Today the traditional financial ratios are most widely used to measure corporate performance, but more and more authors begin to criticize their use. It is difficult to use financial ratios as a complex measurement tool. It is crucial to use an appropriate method or tool to measure corporate performance, which can measure the company’s performance in a complex way represented by one indicator. In this study, the Data Envelopment Analysis (DEA) method is used, which is one of the potential tools available. Several researchers have used the DEA method to measure corporate performance. Many authors consider DEA as a useful tool for measuring corporate performance, while others criticize it. The authors analyze the performance of retail food companies in Hungary’s Northern Great Plain region. The companies analyzed were chosen from the region investigated, and they have “food retail grocery store” as their main activity, and they had six cleared annual reports in the period 2012–2017. There was a total of 887 companies in the region examined, and 563 (63.5%) met the conditions. The analysis was made using the time-series data of companies for 2012–2017 based on their financial reports, and the authors dealt with various possibilities for extending DEA, which can support its more accurate use. Based on evaluating the retail food companies’ performance in the Northern Great Plain region, one can state that the efficiency of companies shows a very mixed picture over the years examined. The study suggests solutions to the indicated problem. The findings indicate that the application of extended DEA methods gives better results; that is, one can get better estimates of the efficiency of companies.