Research background: Generative artificial intelligence (AI) and machine learning algorithms support industrial Internet of Things (IoT)-based big data and enterprise asset management in multiphysics simulation environments by industrial big data processing, modeling, and monitoring, enabling business organizational and managerial practices. Machine learning-based decision support and edge generative AI sensing systems can reduce persistent labor shortages and job vacancies and power productivity growth and labor market dynamics, shaping career pathways and facilitating occupational transitions by skill gap identification and labor-intensive manufacturing job automation by path planning and spatial cognition algorithms, furthering theoretical implications for management sciences. Generative AI fintech, machine learning algorithms, and behavioral analytics can assist multi-layered payment and transaction processing screening with regard to authorized push payment, account takeover, and synthetic identity frauds, flagging suspicious activities and combating economic crimes by rigorous verification processes. Purpose of the article: We show that edge device management functionalities of cloud industrial IoT and virtual robotic simulation technologies configure plant production and route planning processes across cyber-physical production and industrial automation systems in multi-cloud immersive 3D environments, leading to tangible business outcomes by reinforcement learning and convolutional neural networks. Labor-augmenting automation and generative AI technologies can impact employment participation, increase wage and wealth inequality, and lead to potential job displacement and massive labor market disruptions. The deep learning capabilities of generative AI fintech in terms of adaptive behavioral analytics and credit scoring mechanisms can enhance financial transaction behaviors and algorithmic trading returns, identify fraudulent payment transactions swiftly, and improve financial forecasts, leading to customized investment recommendations and well-informed financial decisions. Methods: Machine learning-based study selection process and text mining systematic review management software and tools leveraged include Abstrackr, CADIMA, Colandr, DistillerSR, EPPI-Reviewer, JBI SUMARI, METAGEAR package for R, SluRp, and SWIFT-Active Screener. Such reference management systems are harnessed for methodologically rigorous evidence synthesis, study selection and characteristic extraction, predictive document classification, machine learning-based citation and record screening, bias assessment, article retrieval automation, and document classification and prioritization. Findings & value added: Industrial IoT and 3D augmented reality technologies can create business value by streamlining virtual product and remote asset management across extended reality-based navigation and robotic autonomous systems in smart factory environments by generative AI and machine learning algorithms, articulating business organizational level and theory of management implications. 3D simulation and operational modeling tools can execute and complete complex cognitive task-oriented and knowledge economy jobs, producing first-rate quality outputs swiftly while leading to unemployment spells, labor market disruptions, job displacement losses, and reduced earnings by machine learning clustering and spatial cognition algorithms. Generative AI decentralized finance, interoperable blockchain networks, cash flow management tools, and asset tokenization can mitigate fraud risks, enable digital fund and crypto investing servicing, and automate treasury operations by integrating real-time payment capabilities, routing and configurable workflows, and lending and payment technologies.

The financial health of enterprises and their continued profitability and competitiveness in the market are influenced considerably by the level of earnings achieved. Enterprises are forced to report the best possible results to demonstrate financial strength and competitiveness and to provide a good accounting for investors and creditors. Thus, the main objective of the study is to investigate whether there is any mutual dependence between corporate financial stability and earnings management. To measure these categories, Altman’s Z score was used to determine the financial health of enterprises, and the Beneish M-score and modified Jones model were applied to detect earnings manipulation. Using the chi-square test, the results revealed a statistically significant dependence between financial distress and earnings manipulation. Then, a multivariate statistical technique of correspondence analysis was applied to the categorical data to find categories of factors that are mutually correspondent. Based on a dataset of 11,105 enterprises operating in the Visegrad countries, the results found that enterprises that are threatened by bankruptcy or located in the gray zone tend to manipulate their earnings to maintain credibility, creditworthiness, and competitiveness. Because the financial health of an enterprise provides a potential incentive for earnings manipulation, state authorities, regulators, and policy-makers may benefit from the findings of the study.

Research background: Corporate social responsibility (CSR) demonstrates that a business should be interested in broader social issues rather than on merely those impacting profit margins. Thus, enterprises across all sectors need to focus on the pillars of CSR, which can ultimately give them a competitive advantage. Previous research papers have focused mainly on the level of CSR in particular industries or how CSR activities are communicated in these industries. Purpose of the article: The paper focuses on demonstrating the level of CSR performance in the chosen central European countries in the context of corporate earnings and specifying the impact of the economic sectors on the level of CSR performance, which is mapped by the environmental, social, and governance (ESG) score. Methods: This study used the ESG score, an indicator of the level of CSR performance, and financial and accounting data of 490 publicly traded enterprises from Central Europe. It also applied correlation analysis, the Kruskal–Wallis test and cluster agglomerative hierarchical clustering. Findings & value added: The results have proved that the CSR performance of central European enterprises is positively associated with the level of corporate earnings in all NACE sectors. This knowledge broadens the existing literature on this topic. The study also revealed statistically significant differences in the development of the CSR concept across the sectors. Then, cluster agglomerative hierarchical clustering identified the groups of sectors with homogenous approaches to CSR. This provides information on the homogeneity or heterogeneity of CSR performance across different industries, which is useful information not only for investors and other stakeholders, but also for researchers.

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 learning-assisted 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, multi-sensory 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.

The research’s objective was to develop a thorough model of the viability of SMEs in the V4 countries based on predetermined variables. The following factors were defined: human resources management, business ethics, corporate social responsibility, company digitalisation, environmental considerations, financial management, and sustainability of SMEs. The model was developed on the solid empirical research carried out in the Visegrad Four countries in June 2022 by the renowned external agency MNFORCE using a sample of 1,398 respondents and the “Computer Assisted Web Interviewing” technique. According to the research, the developed complex model of SMEs’ sustainability depending on the listed criteria is statistically significant. Each investigated factor has a favourable impact on SMEs’ sustainability. The study’s findings supported the notion that the environmental aspects of the business have the strongest positive impact on the long-term sustainability of SMEs. The study’s findings indicate that the management of human resources, finances, and the degree of digitalisation of SMEs significantly influence the viability of businesses. The findings provide an important platform for managers responsible for the sustainability of the SME segment at the worldwide level (International council for small business), national level (particularly for V4 nations), as well as institutions supporting SMEs and SME owners.

Research background: Although experts emphasise the indisputable importance and influence of small and medium-sized enterprises (SMEs) in the economy, many authors highlight the need for further development of the corporate social responsibility (CSR) concept within the context of SMEs. Most empirical studies focus on the perception of CSR from the perspective of customers, employees, or other stakeholders, while the perspective of entrepreneurs remains largely unexplored. Purpose of the article: This study aims to identify and quantify the impact of selected factors, such as business ethics (BE), human resource management (HRM), financial management (FM), and environmental aspects (EA), on the willingness of SMEs to implement CSR in the V4 region. Methods: The attitudes of SME owners and top managers of SMEs were examined using computer-assisted web interviews. Data were collected in June 2022 from a sample of 1,396 SMEs within the V4 region. Quantitative methods, including factor analysis and structural equation modelling, were used to evaluate the hypotheses. Findings & value added: All examined factors, including BE, FM, HRM, and EA, are significant determinants that positively influence the level of CSR implementation in the SME segment. Among these, the perceptions of owners and top managers regarding EAs exert the most substantial positive impact on CSR implementation, followed by HRM and FM, each with a moderately positive effect. BE has the least positive impact on CSR. These findings contribute valuable insights that can inform the effective implementation of CSR and offer practical implications for academics, institutions supporting SMEs, and policymakers.