In the era of industrial digitalization, companies are increasingly investing in tools and solutions that allow their processes, machines, employees, and even the products themselves, to be integrated into a single integrated network for data collection, data analysis, the evaluation of company development, and performance improvement. To study the impact of Industry 4.0 on the company we used Porter’s (1985) value chain model, which is particularly useful when paying particular attention to corporate areas which have a primary role in customer value creation. Since the primary impact of Industry 4.0 is perceived in value-creating processes, and has so far had the greatest transformative effect in this area, the model can be considered to be appropriate. The objective of our research is to discover how companies operating in Hungary interpret the phenomenon of Industry 4.0, what Internet of Things (IoT) tools they use to support their processes, and what critical issues they face during adaptation. We applied a dual methodology in our investigation: We sent an online questionnaire to manufacturing and logistical service companies to investigate the IoT tools they use, and the problems they face, and received 43 answers we could evaluate. We also conducted four expert interviews with manufacturing firms to get deeper insights into the application, critical issues and development phases of IoT tools. During our research, we found that the spread of real-time data across companies—given the availability of appropriate analytical tools and methods—can have a significant impact on the entire company. In the case of CPS (Cyber Physical System), CPPS and Big Data Technologies, companies using them have been evaluated as having a higher level of logistic service, more efficient processes with their partners, improved cooperation between certain logistic functions, and higher market and financial performance and competitiveness. Applying more efficient production processes, and achieving better productivity and economies of scale, might also result in increased economic sustainability. Furthermore, we have found that companies have started on the path to digital evolution, and investments of this type have already begun.

The Role and Impact of Industry 4.0 and the Internet of Things on the Business Strategy of the Value Chain—The Case of Hungary

Research background: Since the first bankruptcy prediction models were developed in the 60?s of the 20th century, numerous different models have been constructed all over the world. These individual models of bankruptcy prediction have been developed in different time and space using different methods and variables. Therefore, there is a need to analyse them in the context of various countries, while the question about their suitability arises.
Purpose of the article: The analysis of more than 100 bankruptcy prediction models developed in V4 countries confirms that enterprises in each country prefer different explanatory variables. Thus, we aim to review systematically the bankruptcy prediction models developed in the countries of Visegrad four and analyse them, with the emphasis on explanatory variables used in these models, and evaluate them using appropriate statistical methods.
Methods: Cluster analysis and correspondence analysis were used to explore the mutual relationships among the selected categories, e.g. clusters of explanatory variables and countries of the Visegrad group. The use of the cluster analysis focuses on the identification of homogenous subgroups of the explanatory variables to sort the variables into clusters, so that the variables within a common cluster are as much similar as possible. The correspondence analysis is used to examine if there is any statistically significant dependence between the monitored factors ? bankruptcy prediction models of Visegrad countries and explanatory variables.
Findings & Value added: Based on the statistical analysis applied, we confirmed that each country prefers different explanatory variables for developing the bankruptcy prediction model. The choice of an appropriate and specific variable in a specific country may be very helpful for enterprises, researchers and investors in the process of construction and development of bankruptcy prediction models in conditions of an individual country.

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Systematic review of variables applied in bankruptcy prediction models of Visegrad group countries

The issue of the debt, bankruptcy or non-bankruptcy of a company is presented in this article as one of the ways of conceiving risk management. We use the Amadeus database to obtain the financial and accounting data of Slovak enterprises from 2015 and 2016 to calculate the most important financial ratios that may affect the financial health of the company. The main aim of the article is to reveal financial risks of Slovak entities and to form a prediction model, which is done by the identification of significant predictors having an impact on the health of Slovak companies and their future prosperity. Realizing the multiple regression analysis, we identified the significant predictors in conditions of the specific economic environment to estimate the corporate prosperity and profitability. The results gained in the research are extra important for companies themselves, but also for their business partners, suppliers and creditors to eliminate financial and other corporate risks related to the unhealthy or unfavorable financial situation of the company.

Financial Risk Measurement and Prediction Modelling for Sustainable Development of Business Entities Using Regression Analysis

The literature on energy-growth nexus has consistently stated that economic development, which leads to environmental degradation in its early stages, is the only way to ensure environmental sustainability. The paper examines the interrelationships between the performance on sustainability indicators and economic growth in European Union. The data for sustainability indicators such as human development index, financial development index, urban population, renewable energy consumption, non-renewable energy consumption, ecological footprints, carbon emissions, and economic growth indicator that is gross domestic product are analyzed for 27 EU countries. The study employs Arellano-Bond dynamic panel data estimation, system dynamic panel data estimation, and Augmented Mean Group model. The results show a two-way positive relationship between economic growth and non-renewable energy and a two-way negative relationship between economic growth and renewable energy. Besides, ecological footprint, non-renewable energy consumption, and carbon emissions are shown to positively impact economic growth. In contrast, renewable energy consumption is shown to negatively impact economic growth. The results reveal explain the reasoning for unimpressive performance of Europe on the front of climate action, and average performance on clean energy, notwithstanding reasonable economic growth. The novelty of the study lies in finding out that the shift towards renewable energy may not seem economically viable in the short-run, but after a point of time, renewable energy consumption is bound to positively impact the economic growth in line with the Environmental Kuznets Curve.

Exploring the nexus between non-renewable and renewable energy consumptions and economic development: Evidence from panel estimations

Financial ratios play an important role in revealing corporate financial soundness, a role which helps to maintain the competitive position of an enterprise, with the achievement of stable development contributing to the elimination of potential financial risks. This paper aims to analyse and compare financial ratios used in the models of transition countries. The analysis focuses on the prediction of the future financial development of a particular enterprise as well as the determination of potential dependencies among the nation in consideration of financial ratios and country of origin. More than 400 prediction models of the Slovak Republic, the Czech Republic, Poland, Hungary, Romania, Lithuania, Latvia, Estonia, Croatia, Russia, Ukraine and Belarus were analysed. The crucial significance of financial ratios in divergent conditions is revealed using a cluster analysis, categorical data and a correspondence analysis. The cluster analysis identified similarities among three groups of countries: i) Belarus, Estonia, Croatia and Latvia; ii) Lithuania, Russia, Hungary and Ukraine, and iii) Czech Republic, Slovakia, Romania and Poland. The results of the correspondence analysis indicate that the individual groups of countries prefer different financial ratios in developing models of prediction of financial distress, differences which arose as a consequence of common changing political, market and economic conditions within each group of nations. In contrary to results suggested by our findings, the most frequently used financial ratios in the prediction models throughout the countries remain current ratio, total-liabilities-to-total-assets ratio, and total-sales-to-total-assets ratio.

Remaining Financially Healthy and Competitive: The Role of Financial Predictors

Every corporation has an economic and moral responsibility to its stockholders to perform well financially. However, the number of bankruptcies in Slovakia has been growing for several years without an apparent macroeconomic cause. To prevent a rapid denigration and to prevent the outflow of foreign capital, various efforts are being zealously implemented. Robust analysis using conventional bankruptcy prediction tools revealed that the existing models are adaptable to local conditions, particularly local legislation. Furthermore, it was confirmed that most of these outdated tools have sufficient capability to warn of impending financial problems several years in advance. A novel bankruptcy prediction tool that outperforms the conventional models was developed. However, it is increasingly challenging to predict bankruptcy risk as corporations have become more global and more complex and as they have developed sophisticated schemes to hide their actual situations under the guise of "optimization" for tax authorities. Nevertheless, scepticism remains because economic engineers have established bankruptcy as a strategy to limit the liability resulting from court-imposed penalties.

Bankruptcy Prevention: New Effort to Reflect on Legal and Social Changes

Research background: Misleading financial reporting has a negative impact on all stakeholders since financial records are the primary source of information on financial stability, economic activity, and financial health of any company. The handling of them is primarily the responsibility of managers or owners and reasons for doing so may differ. Their common denominator is the artificial creation of information asymmetry to get different types of benefits. It is, therefore, logical that the issue of detecting opportunistic earnings management comes to the fore.
Purpose of the article: The purpose of the study is to create a discriminant model of the detection of earnings manipulators in the conditions of the Slovak economy. 
Methods: We used the discriminant analysis to create a model to identify fraudulent companies, based on the real data on companies that were convicted from misleading financial reporting in connection with tax fraud in the years 2009?2018. The model is inspired by the Beneish model, which is one of the most applied fraud detection methods at all.
Findings & Value added: In order to achieve more accurate detection results, we extended the original model by taking into account the values of indicators from three consecutive years, i.e. by taking into account the development of the potential tendency of companies to be involved in opportunistic earnings management. Our model correctly identified 86.4% of fraudulent companies and overall reaches 84.1% classification ability. Both models were applied on empirical data on 1,900 Slovak companies from the years 2016?2018, while their overlap was 32.7% for fraudulent companies and 38.4% for non-fraud companies. This is a very useful result, as the application of both models rein-forces the results obtained and the identical classification of the company into fraudulent indicates that the manipulation of earnings occurs with a high probability.

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Detecting earnings manipulation and fraudulent financial reporting in Slovakia

Prediction of the financial difficulties of companies has been dealt with over the last years by scientists and economists worldwide. Several prediction models mostly focused on a particular sector of the national economy, have been created also in Slovakia. The main purpose of this paper is to create new prediction models for small and medium-sized companies in Slovakia, based on real data from the Amadeus database from the years 2016–2018. We created prediction models of financial difficulties of companies for 1 year in advance and also a model for 2 years prediction. These models are based on the combination of two methods, discriminant analysis and logistic regression that belong, among others, to the group of the most commonly used methods to derive prediction models of financial difficulties of the companies. The overall prediction powers of the combined model are 90.6%, 93.8% and 90.4%. The results of this analysis can be used for early prediction of the financial difficulties of the company, that could be very useful for all the stakeholders.

Business Failure Prediction for Slovak Small and Medium-Sized Companies

Background and Purpose: Models of identifying and predicting earnings management in companies by using ac­cruals are in general based on the dependence between total assets of companies and various profit measures. In this paper, we focused on an initial dependency analysis between these business indicators in the Visegrad group’s business entities. We explore the mentioned relationships, verify, and quantify the strength of the dependencies between earnings levels of companies (in terms of economic evaluation of the return on business capital in absolute terms) and the value of their total assets (i.e. business capital tied in the assets without its further classification and analysis).
Methodology: We use descriptive statistics as well as a correlation analysis based on the real business data on almost 300 thousand companies in the V4 countries from the Amadeus database, covering the period from 2013 to 2017. Finally, we use a comparative analysis to identify disproportion among the results that were found out for each of the analysed countries.
Results: The analysis showed that Slovak companies have the average values of profit measures and total assets comparable to Hungarian companies. Czech and Polish companies have several times higher average values of profit measures and also of total assets than Slovak and Hungarian companies. The analysis of the development of the profit measures and the total assets of the companies over the years showed significant differences across the four countries during the period covered by this study.
Conclusion: The analysis of relationships between total assets of the companies and their profit measures showed that the strength of these dependencies among countries is very similar, and over the years, these results did not change. The results of this study can be further used in the creation of the earnings management model in enterpris­es, both in Slovakia and in other V4 countries.
Keywords: profit measures; total assets; earnings management; correlation

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Lucia Svabova

Papers

Bankruptcy Prevention: New Effort to Reflect on Legal and Social Changes

Financial Risk Measurement and Prediction Modelling for Sustainable Development of Business Entities Using Regression Analysis

Detecting earnings manipulation and fraudulent financial reporting in Slovakia

Business Failure Prediction for Slovak Small and Medium-Sized Companies

Dependency Analysis Between Various Profit Measures and Corporate Total Assets for Visegrad Group’s Business Entities