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.

Research background: Covid-19 pandemic had a strong impact on the economy and capital market. In times of crisis, it is important for investors to be able to diversify their investment portfolio in order to mitigate risk. However, the growing trend towards capital market integration may make it ineffective. Research on financial integration, during the Covid-19 period, has started to develop, mainly in major global capital markets. It is, therefore, important to extend this research to other capital markets. The purpose of the article: This contribution aims to analyze financial integration in the stock indexes of the capital markets of Austria (ATX), Slovenia (SBITOP), Hungary (BUDAPEST SE), Lithuania (OMX VILNIUS), Poland (WIG), the Czech Republic (PX PRAGUE), Russia (MOEX) and Serbia (BELEX 15), in the context of the global pandemic (COVID-19). Methods: To measure the unit roots in the time series, we used ADF, PP, and KPSS tests, and Clemente et al. (1998) test to detect structural breaks. To ana-lyse financial integration, we applied the Gregory and Hansen integration test, and to validate the robustness of results, we use the impulse-response function (IRF) methodology, with Monte Carlo simulations, as they provide a dynamic analysis generated from the VAR model estimates. Findings & Value added: The results suggest very significant levels of integration, which decreases the chances of portfolio diversification in the long-term. Evidence shows 47 pairs of integrated stock market indexes (out of 56 possible). The stock indexes ATX, BUDAPESTE SE, BELEX 15 show financial integration with all other indexes. On the contrary, the index of OMX VILNIUS shows only 3 integrations. Results also show that most of the significant structural breaks occurred in March 2020. The analysis of the relationship between markets, in the short term, shows positive/negative co-movements, with statis-tical significance and with a persistence longer than one week.

Research background: Internationalisation of entrepreneurial activities provides an ample opportunity for the growth and sustainability of small and medium-sized enterprises (SMEs). When making their business activities international, SMEs are facing various obstacles. Investigating the key factors of internationalisation and their impact on SME decision making constitutes a key factor of the research work. Purpose of the article: The main purpose of the paper is to examine the impact of selected factors of SME internationalisation on the positive perception of the market risk. Higher export costs, differences in legal environment, taxes, and linguistic and cultural differences were set to be the significant factors influencing the positive perception of the market risk. Bearing the main purpose in mind, the Visegrad Four (V4 ? Czech Republic, Slovakia, Poland, Hungary) were examined. Methods: The questionnaire in mother tongue of each country in online and paper form was used. The questionnaire consists of 77 questions divided into several sections. Likert five-point scale was used to allow the respondents to express their opinion. In total, 1585 responses were completed. A regression analysis was used to verify the statistical significance of the independent variables and to quantify the causal relationships (determining the direction and strength of the effect) on the dependent variable. As the variables are of one metric, a linear regression model (LRM) was selected to validate the hypothesis. Findings & value added: The internationalisation of SME activities brings along many risks and barriers that SMEs need to overcome if they want to enter international markets. Many SMEs consider the barriers and restrictions to enter foreign markets to be severe. Therefore, they decide to do their business domestically. It was established in the research that only 30.2% of the respondents expanded their business activities internationally. The biggest impact on the positive assessment of the market risk was found to have linguistic and cultural differences. They do not have a negative effect on the positive perception of the market risk, so they do not hinder SMEs in their international expansion. The second most important was the factor of the export costs. The factor of possible increased costs is quite important for SMEs in making their decisions whether to expand internationally or not. This factor also shapes a positive approach to assessing the market risk by SMEs. In the research, SMEs confirmed that potential higher costs do not represent a major barrier for them in shaping their attitudes towards the market risk. The remaining factors being analysed, namely differences in legal environment and taxes, did not have a significant impact on the positive assessment of the market risk internationally. The practical implications can be found in new information about market risk in process of internationalisation provided from four selected countries.

Accurate prediction of stock market values is a challenging task for over decades. Prediction of stock prices is associated with numerous benefits including but not limited to helping investors make wise decisions to accumulate profits. The development of the share price is a dynamic and nonlinear process affected by several factors. What is interesting is the unpredictability of share prices due to the global financial crisis. However, classical methods are no longer sufficient for the application of share price development prediction.However, over-relying on prediction data can lead to losses in the case of software malfunction. This paper aims to innovate the prediction management when predicting the share price development over time by the use of neural networks. For the contribution, the data on the prices of CEZ, a.s. shares obtained from the Prague Stock Exchange database. The stock price data are available for the period 2012-2017. In the case of Statistica software, the multilayer perceptron networks (MLP) and the radial basis function networks (RBF) are generated. In the case of Matlab software, the Support Vector Regression (SVR) and the Back-Propagation Neural Network (BPNN) are generated. The networks with the best characteristics are retained and based on the statistical interpretation of the results, and all are applicable in practice. In all data sets, MLP networks show stable performance better than in the case of SVR and BPNN networks. As for the final assessment, the deviation of 2.26% occurs in the most significant differential of the maximal and the minimal prediction. It is not necessarily significant regarding the price of one stock. However, in the case of purchasing or selling a large number of stocks, the difference may seem significant. Therefore, in practice, the application of two networks is recommended: MLP 1-2-1 and MLP 1-5-1. The first network always represents a pessimistic, minimal prediction. The second one of the recommended networks is an optimistic, maximal prediction. The actual situation should correspond to the interval of the difference between the optimistic and pessimistic prediction. Keywords: Statistica software, Matlab software, stock price development, neural networks, prediction.

The article focuses on highlighting the role of life cycle costing (LCC) in the preparatory and implementation phase of residential projects. It involves the evaluation of several investment scenarios in the pre-investment phase, the choice between variants of the design of the entire building or its parts, and the choice of variants of structures and equipment with acceptable parameters. An innovative method of evaluating the life cycle of buildings is described in the article. This method was tested in selected residential projects realized by Skanska in the Czech Republic. Experience from construction practice shows that the choice of variants, constructions, or equipment of buildings only on the basis of the lowest acquisition costs (lowest bid prices) is wrong. The LCC calculation tool has been designed to model life cycle costs of individual variants of construction designs with different input parameters. It is possible to analyze the components or equipment that have the greatest impact on total life cycle costs. The article presents a tool that evaluates the long-term economic efficiency of the proposed residential buildings in terms of analysis of life cycle costs. The article will also expand the knowledge of the professional and general public about the importance of examining investment and operating costs already in the phase of construction preparation.

Research background. The need for an in-time, efficient and effective adaptation of small and medium-sized enterprises in the service sector to the technical, economic, social and environmental conditions of Industry 4.0 and Society 4.0 is a current problem caused by the introduction of new information and communication technologies, cyber-physical systems and artificial intelligence into all economic industries and areas of human life. The advent of new technologies is changing the entire value chains of Industry 4.0 - Labor 4.0 - Education 4.0 - Thinking 4.0, creating conditions for new business models but also pressure on flexibility and personalization of services or triggering new requirements for cyber security and the interdisciplinarity of access of small and medium-sized enterprises - providers and users of services. The current global COVID-19 health pandemic (2020-2021), with its economic and social impacts on all economic industries, has accelerated the use of disruptive technologies, principles, procedures, methods and tools of Industry 4.0 and Society 4.0 and the need for the support of small and medium-sized enterprises in the service sector in adapting to changes in the business environment. Purpose of the article. The main objective of the research is to validate, evaluate and discuss the functionality and content of the created methodology of adaptation of small and medium-sized enterprises in the service sector to the implementation of the principles, procedures, methods and tools of Society 4.0 and its applicability in business practice, specifically focusing on small and medium-sized enterprises in the area of knowledge-intensive services. An integral part of the research is to assess selected negative and positive impacts of the COVID-19 pandemic on business processes in the context of issues being tackled. Methodology. The methodological procedure to validate the functionality and content of the created methodology of the adaptation of small and medium-sized enterprises in the service sector to the conditions of Society 4.0 is based on the strategy of mixed research. An explorative sequence design was used as a key design of mixed dynamically oriented research, during which quantitative data collection, analysis and evaluation formed the basis for qualitative analysis and evaluation, which resulted in the interpretation and discussion of the research results and, at the end of the paper, the formulation of the issues that will be the next area of scientific research activity of the author’s team. In the quantitative part of the research, Fisher’s exact test was used for the verification of difference statistics in data obtained from testing a sample of micro, small and medium-sized enterprises and from external project application sponsors providing services to this category of enterprises. In the qualitative part of the research, the Atlas.ti software was used for the evaluation of information obtained with respect to the methodology of adaptation of small and medium-sized enterprises for encoding, processing and interpretation of qualitative data and semantic networks were created for the individual sub-categories of evaluation. Findings & Value Added. The research result proved that the adequacy of the content and functionality of the checked methodology of adaptation of SMEs strongly corresponds to the needs and requirements of micro and small enterprises in particular that conduct business in the area of knowledge-intensive services. Barriers, limits, needs and potential in individual categories of micro, small and medium-sized enterprises were identified which are related to the use of technologies and tools of Society 4.0, to new requirements for knowledge and skills qualifications of labor and to the relation of SMEs to innovations in business processes. The results of validation of the methodology of adaptation of SMEs in the knowledge-intensive service sector confirmed that the created methodology of adaptation of small and medium-sized enterprises is applicable to the implementation of the principles, procedures, methods and tools of Society 4.0. in the real conditions of business practice.