The scientific periodical journal (indexed on Web of Science) published by the Tomas Bata University in Zlin offers results of basic and applied economic research in the English language.

Research background: Internet of Things devices and sensors, artificial intelligence-based digital asset trading and digital twin-based extended reality technologies, and autonomous robotic and enterprise resource planning systems can be leveraged in 3D semantic scene completion and metaverse-based commercial transactions across Internet of Things-based business environments. Distributed ledger and enterprise business technologies, shop-floor digital twin synthetic data, and 3D simulation and visualization systems configure integrated multi-physics workflows in hyper-realistic immersive industrial environments for artificial intelligence-based business value. Digital twin-based Internet of Robotic Things, robotic swarm and multi-modal machine learning algorithms (with regard to enterprise total factor productivity), and virtual and augmented reality simulation technologies are pivotal in spatial planning processes. Industrial product data and manufacturing value chain management support digital twin-based virtual factory modeling in collaborative immersive 3D visualization environments. Purpose of the article: We show that interconnected business process management and metaverse economic organizational structures, immersive economic and entrepreneurial knowledge image-based modeling (for big data-driven product development processes), and remote autonomous equipment control and monitoring integrate digital twin-enabled 6G Tactile Industrial Internet of Things, deep reinforcement learning and image processing algorithms, and event-driven signal processing for collaborative economic value co-creation. Deep learning-based visual recognition and industrial extended reality technologies, 3D production management modeling, and Internet of Things industrial and mobile sensing networks are pivotal in production operation management, as deep learning-based multi-source data fusion assists autonomous industrial manufacturing processes across interactive 3D immersive business and synthetic manufacturing environments. Collaborative robotic cyber-physical production and generative Artificial Intelligence of Things-based systems (in terms of managerial business value), artificial intelligence-based perceptual and cognitive technologies, and spatial mapping and machine intelligence algorithms enhance manufacturing process visualization, as industrial big data sharing and interoperability are functional in 3D semantic scene completion for sustainable business and economic growth across big data-driven immersive virtual industrial manufacturing environments. Methods: We inspected Tracxn (the Industrial Metaverse section) for the first 100 companies in terms of Tracxn score for X-corn status (i.e., Minicorn, Soonicorn, or none), total equity funding (USD), and company stage (i.e., Seed, Funding Raised, Unfunded, Public, Acquired, Acqui-Hired, and Series A, B, C, D), and identified three main topics for analysis that would lead to tangible business outcomes. We examined the performance management of shop floor virtualization: connected digital twins increase production and logistics process optimization in production environments across the industrial metaverse, facilitating photorealistic production system 3D modelling and simulation. We appraised integrated diagnostic functionalities of real-time simulation implementation for error elimination and machine parameter adjustment in immersive planned production lines by synthetic image data sets and collaborative workflows. We determined digital twin-based data synthesis operational procedures and interconnected use cases across industrial scalable infrastructures for value chain efficiency. Findings & value added: We identified the specific integrated operational simulation functions and production tasks, key performance indicators of shop floor autonomous and value creation systems, and industrial process parameters for predictive quality and fault detection, resulting in production loss reduction by use of industrial metaverse technologies. By use of operational data with regard to the technological management of the selected companies, quantitative analysis determines how immersive collaborative business process and extended reality-driven industrial metaverse technologies lead to economic value co-creation across 3D digital twin factories and cyber-physical manufacturing enterprises. The main value added derived from our research is that cloud-based collaborative 3D visualization and neuromorphic computing systems, 6G sensing and holographic simulation technologies, and machine intelligence and environment awareness algorithms (for business performance and productivity) can be leveraged in machine vision-based defect prediction, detection, diagnosis, and management. Virtual reality space convergence and object connection operate in Internet of Things-based sensing device performance monitoring across Internet of Things-based business environments. Virtual assembly lines and manufacturing enterprises necessitate machine learning-based production forecasting techniques, 3D object detection and tracking, and industrial autonomous and cyber-physical production systems, supporting spatial computing and predictive maintenance algorithms in collaborative immersive virtual environments.

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.

This contribution considers the theoretical, methodological, and analytical aspects of implementing a strategic approach to the management of corporate environmental responsibility in practice. The economic and normative approach to understanding the essence of corporate environmental responsibility is revealed; the key factors are systematized. Based on the generalization of theoretical and methodological provisions for corporate environmental responsibility, the authors formulated a concept for the strategic management thereof. An approach to understanding the content and forms of environmental responsibility at different stages of a company’s lifecycle is formulated. The main indicators that enable the analysis of corporate environmental responsibility from the point of view of the chain “inputs-processes-outputs” are systematized. Analytical studies of corporate environmental responsibility are conducted on the basis of information concerning automotive companies, in particular in terms of the following main areas: the study of global trends, monitoring of environmental goals and their reflection in development strategy, comprehensive analysis of environmental responsibility, the study of the balance of environmental and economic indicators. To achieve the aforementioned, the following were used: quantitative and qualitative methods, analytical and comparative methods of processing, analysis and synthesis of statistical information, economic and mathematical modelling, etc. The mechanism of transforming global environmental challenges into environmental responsibility management in practice is substantiated. An organizational mechanism is put forward for developing an environmental responsibility management system based on a strategic approach focused on implementation in practice. The key tools for implementing theoretical and methodological provisions in management practice are also identified.

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.

The objective of the contribution is to propose a new methodology for determining the optimal credit absorption capacity of an enterprise while maintaining the positive function of financial leverage, i.e., the maximum possible loan that would continuously bring benefit to the enterprise. The proposed methodology determines the credit absorption capacity of an enterprise according to EVA Equity and EVA Entity. Based on a theoretical analysis of both indicators, the possibility of applying the proposed methodology for this purpose was proved. To verify the theoretical assumptions, the optimal credit absorption capacity of enterprises operating in the agricultural sector of the CR was determined. The data used for the purposes of the contribution were obtained from the Albertina database for the years 2012–2018. The credit absorption capacity of the monitored enterprises ranged from CZK 6.88 million to CZK 9.6 million. The article also determines the optimal ratio of equity to debt capital.