Research background: The paper investigates the earnings management phenomenon in the context of Central European countries, attempting to identify the factors and incentives that can influence earnings management behavior on a sample of 8,156 enterprises from Slovakia, the Czech Republic, Hungary, and Poland. Purpose of the article: The main purpose of the manuscript is to prove that there are significant differences in earnings management practices (measured by discretionary accruals) across the countries and to find the firm-specific features that influence the way enterprises manage their earnings. Methods: The modified Jones model was used to calculate the discretionary accruals, which are further analyzed across the countries. The statistically significant differences were confirmed across the countries. Thus, the impact of the economic sector, firm size, firm age, legal form, and ownership structure on earnings management behavior is studied by the Kruskal-Wallis test. The Dunn-Bonferroni post hoc tests then revealed the significant differences across the categories of the investigated earnings management determinants. To find the association between the particular earnings management practice (income-increasing or income-decreasing manipulation), correspondence analysis was used to visualize the mutual relations. Findings & value added: The results of the realized investigation revealed that the economic sector is one of the most important earnings management determinants, as its statistical significance was confirmed in each analyzed country. The correspondence analysis determined specific sectors, where income-increasing manipulation with earnings is practiced (NACE codes F, J, K, M, N), and vice versa, income-decreasing earnings management is characteristic for enterprises in sectors A, C, D, G or L. In specific economic conditions, firm size is also a relevant indicator (Hungary), or firm age and legal form and ownership structure (Poland). The recognition of crucial earnings management incentives may be helpful for authorities, policymakers, analysts and auditors when identifying various techniques and practices of earnings manipulation which could vary across the sectors and taking necessary measures to mitigate potential financial risks.

Research background: Cognitive computing and robotic technologies, enterprise digital twin system modeling, and sensory perception algorithms optimize industrial big data exchange and production collaboration, production floor management, and smart device 3D simulation and visualization in the Industry 5.0 metaverse and virtual shop floor environments. Enterprise metaverse business operations, multi-granularity cognitive computing, and industrial big data fusion simulation integrate virtual and augmented reality technologies, collaborative robotic and industrial cyber-physical production systems, and artificial intelligence-enabled edge computing and Internet of Everything devices in mobile edge computing environments. Cloud-based production and digital twin Internet of Things networks, 3D immersive virtual reality and realistic 3D scene construction technologies, and cyber-physical production and business process management systems articulate smart production engineering and management, artificial intelligence-driven physics simulation, and Internet of Things-based robotic manufacturing in highly realistic industrial product representations and 3D virtual spaces with regard to big data-driven business decisions. Purpose of the article: We show that 3D immersive virtual reality and digital twin metaverse technologies, spatial scanning modeling, and autonomous robotic and virtual factory simulation systems are pivotal in immersive 3D process management, industrial manufacturing production value, and knowledge accumulation in synthetic simulated environments. 3D simulation-based industrial processes and immersive experiences can be attained through cognitive computing and robotic technologies, multi-modal information fusion, autonomous intelligence generation, and multiple production process management in immersive 3D metaverse environments. Immersive, multisensory, and augmented digital experiences can be attained through 3D factory simulation and immersive extended reality technologies, cognitive robotic process automation, autonomous robotic and industrial machine learning systems, and task allocation optimization in computer-generated 3D virtual environments. Methods: We analyzed and synthesized common operations for the first 60 companies in industrial metaverse on ensun (AI-based supplier sourcing tool’s) website in terms of key takeaway, working industry, type of company, and specialized areas, and identified three main topics. Findings & value added: The main value added derived from our research is that industrial metaverse 3D simulation and modeling, digital twin and remote fault diagnosis technologies, multiphysics simulation and predictive maintenance tools assist industrial big data monitoring and management, Internet of Things-based robotic manufacturing, and multiple processing tasks in 3D digital twin factories. Collaborative autonomous manufacturing operations, artificial intelligence-driven physics simulation, and smart industrial devices and processes necessitate industrial metaverse decentralized federated learning, cognitive computing and robotic technologies, and cognitive digital twins in virtual shop floor environments, generating economic value. 3D simulation and visualization technologies, business intelligence and digital twin-based cyber-physical production systems, and big data-driven forecasting and real-time collision detection algorithms can be harnessed in robotic automation processes, intelligent manufacturing upgrading, and sustainable industrial value creation across 3D digital twin factories and distributed computing environments.

The financial management of a business is an integral part of strategic management. Small and medium-sized enterprises (SMEs) are vulnerable to the presence of crisis events in the business environment. In this context, senior managers and owners need to react promptly to emerging changes in a given business segment. The aim of the article is to quantify the effect of crisis events in business and firm’s CSR implementation in business on the financial performance in the SMEs. The empirical research incorporated 1,090 SMEs in the business environment of four Central European countries. The research hypotheses were evaluated using structural equation modelling. The empirical findings confirmed the positive impact of CSR implementation in the enterprise on its financial management. The research results did not confirm the negative effects of crisis events in business on the financial management. Crisis phenomena have direct effects on the sustainability of the enterprise; the organizational activities of the enterprise; the functioning of the enterprise in the market and the management of the enterprise. The findings are used as a basis for the preparation of strategic projects of national and international policies helping to cope with crisis phenomena in the business environment of the SME segment.

Research background: Deep and machine learning-based algorithms can assist in COVID-19 image-based medical diagnosis and symptom tracing, optimize intensive care unit admission, and use clinical data to determine patient prioritization and mortality risk, being pivotal in qualitative care provision, reducing medical errors, and increasing patient survival rates, thus diminishing the massive healthcare system burden in relation to severe COVID-19 inpatient stay duration, while increasing operational costs throughout the organizational management of hospitals. Data-driven financial and scenario-based contingency planning, predictive modelling tools, and risk pooling mechanisms should be deployed for additional medical equipment and unforeseen healthcare demand expenses. Purpose of the article: We show that deep and machine learning-based and clinical decision making systems can optimize patient survival likelihood and treatment outcomes with regard to susceptible, infected, and recovered individuals, performing accurate analyses by data modeling based on vital and clinical signs, surveillance data, and infection-related biomarkers, and furthering hospital facility optimization in terms of intensive care unit bed allocation. Methods: The review software systems employed for article screening and quality evaluation were: AMSTAR, AXIS, DistillerSR, Eppi-Reviewer, MMAT, PICO Portal, Rayyan, ROBIS, and SRDR. Findings & value added: Deep and machine learning-based clinical decision support tools can forecast COVID-19 spread, confirmed cases, and infection and mortality rates for data-driven appropriate treatment and resource allocations in effective therapeutic and diagnosis protocol development, by determining suitable measures and regulations and by using symptoms and comorbidities, vital signs, clinical and laboratory data and medical records across intensive care units, impacting the healthcare financing infrastructure. As a result of heightened use of personal protective equipment, hospital pharmacy and medication, outpatient treatment, and medical supplies, revenue loss and financial vulnerability occur, also due to expenses related to hiring additional staff and to critical resource expenditures. Hospital costs for COVID-19 medical care, screening, treatment capacity expansion, and personal protective equipment can lead to further financial losses while affecting COVID-19 frontline hospital workers and patients.

The paper aims to find disparities in the perception of sustainability-related criteria’s impact on the sustainability of the SMEs in the countries of the V4 based on gender. The case study was conducted on 1398 responses from owners or top managers in Visegrad Group countries (V4). The questionnaire contained 52 questions. Data collection was conducted by an external agency using the CAWI methodology. The statistical hypotheses were verified by applying structural equation modelling (SEM method). The empirical findings are important for many authors and researchers. The environmental aspect of entrepreneurship is the most important factor, having a strong positive impact on the sustainability of SMEs. This includes active involvement in environmental protection, environmental responsibility as a part of SME governance, and the perception of owners regarding the environment in firm management. Gender disparities are important in evaluating financial management, the level of digitalisation in a firm, human resource management, and business ethics, and their effect on the sustainability of SMEs.

Each phase of an enterprise's life cycle brings different impacts on corporate financial health. Financial distress may be associated with the emergence of a crisis, failure and / or bankruptcy. To identify these negative corporate phenomena timely, you need to know the appropriate symptoms. These symptoms occur in every business, but not every business...