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Research background: Big data-driven artificial Internet of Things (IoT) fintech algorithms can provide real-time personalized financial service access, strengthen risk management, and manage, monitor, and mitigate transaction operational risks by operational credit risk management, suspicious financial transaction abnormal pattern detection, and synthetic financial data-based fraud simulation. Blockchain technologies, automated financial planning and investment advice services, and risk scoring and fraud detection tools can be leveraged in financial trading forecasting and planning, cryptocurrency transactions, and financial workflow automation and fraud detection. Algorithmic trading and fraud detection tools, distributed ledger and cryptocurrency technologies, and ensemble learning and support vector machine algorithms are pivotal in predictive analytics-based risk mitigation, customer behavior and preference-based financial product and service personalization, and financial transaction and fraud detection automation. Credit scoring and risk management tools can offer financial personalized recommendations based on customer data, behavior, and preferences, in addition to transaction history, by generative adversarial and deep learning recurrent neural networks. Purpose of the article: We show that blockchain and edge computing technologies, generative artificial IoT-based fintech algorithms, and transaction monitoring and credit scoring tools can be harnessed in financial decision-making processes and loan default rate mitigation for transaction, payment, and credit process efficiency. Generative and predictive artificial intelligence (AI) algorithmic trading systems can drive coherent customer service operations, provide tailored financial and investment advice, and influence financial decision processing, while performing real-time risk assessment and financial and trading risk scenario simulation across fluctuating market conditions. Fraud and money laundering prevention tools, blockchain and financial transaction technologies, and federated and decentralized machine learning algorithms can articulate algorithmic profiling-based transaction data patterns and structures, credit assessment, loan repaying likelihood prediction, and interest rate and credit lending risk management by real-time financial pattern and economic forecast-based credit analysis across investment payment and transaction record infrastructures. Methods: Research published between 2023 and 2024 was identified and analyzed across ProQuest, Scopus, and the Web of Science databases by use of screening and quality assessment software systems such as Abstrackr, AMSTAR, AXIS, CADIMA, CASP, Catchii, DistillerSR, Eppi-Reviewer, MMAT, Nested Knowledge, PICO Portal, Rayyan, ROBIS, and SRDR+. Findings & value added: The main value added derived from the systematic literature review is that generative AI-based operational risk management, fraud detection, and transaction monitoring tools can provide personalized financial support and services and clarify financial and credit decisions and operations by financial decision-making process automation in dynamic business environments based on fraud detection capabilities and transaction data analysis and assessment. The benefits for theory and current state of the art are that credit risk and financial forecasting tools, artificial IoT-based fintech and generative AI algorithms, and algorithmic trading and distributed ledger technologies can be deployed in financial decisionmaking and customer behavior pattern optimization, credit score assessment, and money laundering and fraudulent payment detection. Policy implications reveal that investment management and algorithmic credit scoring tools can streamline financial activity operational efficiency, design financial planning analysis and forecasting, and carry out financial service and transaction data analysis for informed transaction decision-making and fraudulent behav ior pattern and incident detection, taking into account credit history and risk evaluation and improving personalized experiences. [ABSTRACT FROM AUTHOR]

Profitability reveals the key financial ratios that ensure the long-term sustainability of an enterprise's ability to invest in further growth. Without knowledge of the course of development, particularly changes in profitability, enterprises face financial difficulties that threaten their existence. Although the homogeneity tests serve as a reliable tool for identifying changes, their application in financial management often receives insufficient attention. The aim of this research is to evaluate the detection power of homogeneity tests and identify the one with the highest ability based on testing changes in the development of profitability ratios across sectors in the Visegrad Four. Buishand's test, Pettitt's test, the SNHT, and the von Neumann test were run for 8671 enterprises during 2016–2021 and gained from Moody's Orbis. Comparison tables for ROA, ROC, ROE, ROS, ROR, and ROW using Monte Carlo simulation with a million replications identified the number of Slovak, Czech, Polish, and Hungarian enterprises in which heterogeneity was divided according to the Nomenclature of Economic Activities, NACE. The SNHT disclosed the greatest number of changes in the development of all profitability ratios. The results validate the use of selected tests for ratio assessment. Furthermore, business agencies may replicate this approach to determine the economic situation and sector performance. [ABSTRACT FROM AUTHOR]

Research background: Connected Internet of Robotic Things (IoRT) and cyber-physical process monitoring systems, industrial big data and real-time event analytics, and machine and deep learning algorithms articulate digital twin smart factories in relation to deep learningassisted smart process planning, Internet of Things (IoT)-based real-time production logistics, and enterprise resource coordination. Robotic cooperative behaviors and 3D assembly operations in collaborative industrial environments require ambient environment monitoring and geospatial simulation tools, computer vision and spatial mapping algorithms, and generative artificial intelligence (AI) planning software. Flexible industrial and cloud computing environments necessitate sensing and actuation capabilities, cognitive data visualization and sensor fusion tools, and image recognition and computer vision technologies so as to lead to tangible business outcomes. Purpose of the article: We show that generative AI and cyber--physical manufacturing systems, fog and edge computing tools, and task scheduling and computer vision algorithms are instrumental in the interactive economics of industrial metaverse. Generative AI-based digital twin industrial metaverse develops on IoRT and production management systems, multisensory extended reality and simulation modeling technologies, and machine and deep learning algorithms for big data-driven decision-making and image recognition processes. Virtual simulation modeling and deep reinforcement learning tools, autonomous manufacturing and virtual equipment systems, and deep learning-based object detection and spatial computing technologies can be leveraged in networked immersive environments for industrial big data processing. Methods: Evidence appraisal checklists and citation management software deployed for justifying inclusion or exclusion reasons and data collection and analysis comprise: Abstrackr, Colandr, Covidence, EPPI Reviewer, JBI-SUMARI, Rayyan, RobotReviewer, SR Accelerator, and Systematic Review Toolbox. Findings & value added: Modal actuators and sensors, robot trajectory planning and computational intelligence tools, and generative AI and cyber--physical manufacturing systems enable scalable data computation processes in smart virtual environments. Ambient intelligence and remote big data management tools, cloud-based robotic cooperation and industrial cyber-physical systems, and environment mapping and spatial computing algorithms improve IoT-based real-time production logistics and cooperative multi-agent controls in smart networked factories. Context recognition and data acquisition tools, generative AI and cyber--physical manufacturing systems, and deep and machine learning algorithms shape smart factories in relation to virtual path lines, collision-free motion planning, and coordinated and unpredictable smart manufacturing and robotic perception tasks, increasing economic performance. This collective writing cumulates and debates upon the most recent and relevant literature on cognitive digital twin-based Internet of Robotic Things, multi-sensory extended reality and simulation modeling technologies, and generative AI and cyber--physical manufacturing systems in the immersive industrial metaverse by use of evidence appraisal checklists and citation management software. [ABSTRACT FROM AUTHOR]

Studies on creative accounting date back to the latter part of the 20th century. Creative accounting is still a big challenge in financial accounting. The problem of financial statement manipulation might be investigated, for instance, from an accounting, legal, ethical, or psychological perspective. This research aims to identify the main motives for the use of creative accounting and to find out whether corporate culture has an impact on the motives leading to the use of creative accounting. Data collection took place from 18 November 2023 to 18 December 2022. In the research, we used Pearson's χ2 test to determine the dependence of the studied variables in contingency tables. Subsequently, correspondence analysis was used. The type of corporate culture does not have an impact on the motives that lead to creative accounting. It was proven that the type of corporate culture has an impact on the performance of creative accounting actions based on the request of a senior employee. The uniqueness of the research lies in the investigation of creative accounting from a psychological and managerial point of view in the territory of the Slovak Republic. [ABSTRACT FROM AUTHOR]

Research background: It is essential to examine the correlation between liquidity and enterprise activity in the Visegrad Group, which comprises Central European countries, given the current economic conditions. These variables are the basis for economic synergy and operate as crucial factors in determining the financial sustainability and competitiveness of firms in the region. Purpose of the article: This essay aims to offer a comprehensive comprehension of the correlation between liquidity and activity in V4 firms. We examine the reciprocal impact and collective contribution of these two factors on regional economic dynamics. We aim to analyse the fundamental elements that influence this connection and provide precise suggestions for the commercial setting. Methods: We utilised an amalgamation of quantitative and statistical methodologies in order to accomplish the predetermined goals. Median values for specific liquidity and activity indicators among V4 enterprises were computed using Microsoft Excel for the quantitative analysis. Furthermore, the significance of variations between years was evaluated by employing the Friedman test. Findings & Value added: Our research demonstrates that activity and liquidity have a substantial effect on the V4 economy. We have identified and illustrated the variation of important factors that influence this relationship across industries. This article enhances comprehension of economic synergy in the region and provides business leaders, economists, and the general public with an interest in Central European business matters with insightful information. [ABSTRACT FROM AUTHOR]

Research background: Cryptocurrency is a digital currency that is intended for online trading. It uses and implements the principles of cryptography to create a distributed, decentralized and secure digital currency. Virtual money is a new and promising branch of the virtual economy that brings many advantages and disadvantages in a global sense. Many people have become involved in cryptocurrency hype because high investments in this digital money have been seized during the pandemic. The rise in revenues from this digital money has gripped the world globally. Purpose of the article: The basic purpose and chosen goal is to analyze the use of cryptomen trading during the global Covid-19 pandemic, as well as investing in these alternative sources of investment, which are gaining more attention every day precisely because of their freedom and detachment. Methods: The article will analyze data that will be compared based on the years before the Covid-19 pandemic and during the Covid-19 pandemic. Based on these data, the investment activity of people, companies, corporations is compared. Findings & Value added: Based on the results in the article, it was found that during the Covid-19 pandemic, the interest in investing in cryptocurrencies increased compared to the interest in investing in cryptocurrencies before the pandemic. The overall result is that people are moving to a new way of holding money, as cryptocurrencies are a new way to the future, as banks are unable to provide such returns from client deposits as cryptocurrencies, but they are associated with much greater risk.

Research background: Creative accounting is a set of skills that allow people to produce an artistic, scientific or other creative activity that creates new, unusual and acceptable ideas, which are transformed into financial statements, which then form a positive view of the company’s economic activities. Purpose of the article: Creative accounting is a problem all over the world, as it adversely affects the financial statements in each country. The article describes models that help reveal the creativity of accountants and financiers. Procedures for identifying possible accounting delays are based on analytical models. In particular, it is complex statistical methods or data collection techniques that identify hidden, unusual patterns that indicate fraud. These analytical models are therefore used to detect various errors and fraud in accounting. Methods: Analytical models will be available to identify different methods of accounting abuse. Interest is placed on the analytical models of Beneish model and the Modified Jones model. The main objective of using these models is to reveal creative accounting in sector A - Agriculture, forestry and fishing. Fraud is investigated using sophisticated analytical methods that reveal improved data. Findings & Value added: Creative accounting in the conditions of the Slovak Republic but also globally is still not possible completely to reveal. The value added of this article can be mentioned as the benefit for potential business partners as well as investors interested in the analysed sector and the other hand for the other for the state administration.

Research background: Manipulation and the use of creative accounting or earnings management have become an increasingly popular topic in the history of researchers. Since 2002, this issue has attracted the attention of scientists and economists around the world. On the Slovak market, more than 30 insurance companies are actually operating, and some analyses have revealed that some of these insurance companies are engaging in activities that do not comply with the law. Purpose of the article: The article aims to use selected models for the detection of fraudulent financial reporting to determine whether there are unfair activities in insurance companies in Slovakia. At the same time, we evaluate the reliability of selected models and recommend the best models for this sector. Methods: Based on the set criteria, the Beneish model with five parameters and the Beneish model with eight parameters are applied to selected 20 companies in the Financial and Insurance activities sector to determine which companies have manipulated the financial statements. The analysis is performed using real data on Slovak companies from the Amadeus database. Findings & Value added: For the Financial and Insurance activities industry, we recommend using the Beneish model with eight parameters. Comparing the two models, we can conclude that this model is more accurate and thorough. The reason is also that this model works with more data from the financial statements.

Despite the COVID-19 pandemic, the current era offers the ultimate possibility for prosperous corporate life, especially in the transport sector. Industry 4.0 covers artificial intelligence, big data, or industrial IoT, and thus spatial cognition algorithms, traffic flow prediction, autonomous vehicles, and smart sustainable mobility are not far away. The mentioned tools have already been implemented by enterprises in emerging countries. This exploration focused on transportation within the V4 region from 2016–2021. This article aims to confirm the positive sequel of applying Industry 4.0 to chosen indicators of profitability. The positive, negative, or no shift in the development of 534 businesses was based on Pettitt's test. The Pearson chi-square test disclosed the significant dependency between Industry 4.0 and shifts in profitability ratios. Then, more than 25% of enterprises involved in Industry 4.0 had positive shifts in ROA, ROC, ROS, and ROR. The research proved not only its balanced effect but also its augmented force through the z-test of proportion. This investigation may provide multiple proofs for connected sectors with transportation to adapt the tools of Industry 4.0 and deliver the call for the governments in the V4 region to make this tool more achievable. [ABSTRACT FROM AUTHOR]

Businesses should come up with a strategy, plans, and goals so that their total assets can make a profit during the transformation process. Utilizing various features of a property can generate this income. This comparison provides evidence of profitability. During the global economic downturn, a number of businesses encountered issues that caused their payment situations and profitability to deteriorate. The goal of this article is to ascertain whether particular profitability indicators also revealed the pandemic-related global crisis, particularly in the Visegrad Group countries. This analysis was conducted based on categories of business size. Specifically, 8671 enterprises were analyzed. The evaluation of indicators revealed whether there was a significant change in a negative direction, a significant change in a positive direction, or no significant change. It was possible to make a clear diagram of the companies that took part in the study and to figure out the median values in order to compare the results of the chosen profitability indicators. Correspondence analysis was conducted so that conclusions could be more accurate. According to the findings of this study, indicators of ROA, ROE, and ROS did not change significantly across enterprise size categories in the years preceding, during, and after the pandemic. Since the government regulations of the V4 countries had a significant impact on these businesses, the change was most obvious in the case of small businesses within the ROS indicator. The added value of the article is derived from its analysis of selected profitability indicators in the largest group of Central European nations and its relevance. [ABSTRACT FROM AUTHOR]

The aim of this systematic review is to synthesize and analyze existing evidence on delivery apps use during the COVID-19 pandemic. With increasing evidence of app-based sales systems, there is an essential demand for comprehending whether perceived control over food delivery apps may strengthen consumer feelings. In this research, prior findings were cumulated indicating that consumer habits in identifying information as regards ready-to-eat food and making decisions accordingly have reconfigured online delivery. We carried out a quantitative literature review of ProQuest, Scopus, and the Web of Science throughout January 2022, with search terms including “delivery app” + “COVID-19 pandemic,” “consumer satisfaction judgment,” “behavioral intention,” and “purchase decision.” As we analyzed research published between 2020 and 2022, only 159 papers met the eligibility criteria. By removing controversial or unclear findings (scanty/unimportant data), results unsupported by replication, undetailed content, or papers having quite similar titles, we decided on 24, chiefly empirical, sources. Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AXIS, Distiller SR, ROBIS, and SRDR. [ABSTRACT FROM AUTHOR]