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2019 | 10 | 4 | 743-772

Article title

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

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Abstracts

EN
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.

Year

Volume

10

Issue

4

Pages

743-772

Physical description

Dates

published
2019

Contributors

  • University of Zilina
  • University of Zilina
  • University of Zilina
author
  • University of Zilina
  • University of Economics in Bratislava

References

  • Ahmad, I., Olah, J., Popp, J., & Mate, D. (2018). Does business group affiliation matter for superior performance? Evidence from Pakistan. Sustainability, 10(9). doi: 10.3390/su10093060.
  • Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance, 23(4). doi: 10.2307/2978933.
  • Altman, E. I., Sabato, G., & Wilson, N. (2010). The value of non-financial information in small and medium-sized enterprise risk management. Journal of Credit Risk, 6(2). doi: 10.21314/JCR.2010.110.
  • Arbolino, R., Calucci, F., Cira, A., Ioppolo, G., & Yigitcanlar, T. (2017). Efficiency of the EU regulation on greenhouse gas emissions in Italy: The hierarchical cluster analysis approach. Ecological Indicators, 81. doi: 10.1016/j.ecolind. 2017.05.053.
  • Balcerzak, A.P., Kliestik, T., Streimikiene, D., & Smrcka, L. (2018). Non-parametric approach to measuring the efficiency of banking sectors in European Union countries. Acta Polytechnica Hungarica, 14(7). doi: 10.12700/APH. 14.7.2017.7.4.
  • Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some models for estimating technical and scale inefficiencies in Data Envelopment Analysis. Management Science, 30(9). doi: 10.1287/mnsc.30.9.1078.
  • Banyiova, T., Bielikova, T., & Piterkova, A. (2014). Prediction of agricultural enterprises distress using data envelopment analysis. In 11th international scientific conference European financial systems, Lednice. Czech Republic.
  • Bauer, P., & Edresz, M. (2016). Modelling bankruptcy using Hungarian firm-level data MNB. Budapest, Hungary: Magyar Nemzeti Bank.
  • Beaver, W. (1966). Financial ratios as predictors of failure. Journal of Accounting Research, 4(3). doi: 10.2307/2490171.
  • Bellovary, J., Giacomino, D. E., & Akers, M. D. (2007). A review of bancruptcy prediction studies: 1930 to present. Journal of Financial Education, 33.
  • Binkert, C. H. (1999). Fruherkennung von Unternehmenskrisen mit Hilfe geeigneter Methoden im deutschen und slowakischen Wirtschaftsraum, Ph.D. Thesis. Bratislava, Slovakia: University of Economics in Bratislava.
  • Blanton, T. (2018). Convolutional neural networks, analytical algorithms, and personalized health care: embracing the massive data analysis capabilities of deep learning artificial intelligence systems to complement and improve medical services. American Journal of Medical Research, 5(2). doi: 10.22381/ AJMR5220187.
  • Boda, M. (2009). Predicting bankruptcy of Slovak enterprises by an artificial neural network. Forum Statisticum Slovacum, 9.
  • Bozsik, J. (2010). Artificial neural networks in default forecast. In 8th international conference on applied informatics. Eger, Hungary.
  • Brozyna, J., Mentel, G., & Pisula, T. (2016). Statistical methods of the bankruptcy prediction in the logistics sector in Poland and Slovakia. Transformations in Business & Economics, 15(1).
  • Calabrese, R., & Osmetti, S. A. (2013). Modelling small and medium enterprise loan defaults as rare events: the generalized extreme value regression model. Journal of Applied Statistics, 40(6). doi: 10.1080/02664763.2013.784894.
  • Charnes, A., Cooper, W.W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2. doi: 10.1016/0377-2217(78)90138-8.
  • Chava, S., & Jarrow, R. A. (2004). Bankruptcy prediction with industry effects. Review of Finance, 8(4). doi: 10.1093/rof/8.4.537.
  • Chrastinova, Z. (1998). Metódy hodnotenia ekonomickej bonity a predikcie finančnej situácie poľnohospodárskych podnikov. Bratislava: VÚEPP.
  • Constand, L. R., & Yazdipour, R. (2011). Firm failure prediction models: a critique and a review of recent developments. Advances in entrepreneurial finance: with applications from behavioral finance and economics, 18.
  • Cygler, J., & Wyka, S. (2019). Internal barriers to international R&D cooperation: the case of Polish high tech firms. Forum Scientiae Oeconomia, 7(1). doi: 10.23762/FSO_VOL7_NO1_2.
  • Delina, R., & Packova, M. (2013). Prediction bankruptcy models validation in Slovak business environment. E & M Ekonomie a management, 16(3).
  • Dimitras, A. I., Zanakis, S. H., & Zopoundis, C. (1996). A survey of business failure with an emphasis on prediction method and industrial applications. European Journal of Operational Research, 90. doi: 10.1016/0377-2217(95)00070-4.
  • Divsalar, M., Roodsaz, H., Vahdatinia, F., Norouzzadeh, G., & Behrooz, A. H. (2012). A robust data-mining approach to bankruptcy prediction. Journal of Forecasting, 31(6). doi: 10.1002/for.1232.
  • Dixon, Ch. (2016). Why the global financial crisis had so little impact on the banking systems of emergent East Asia. Journal of Self-Governance and Management Economics, 4(2). doi: 10.22381/JSME4220162.
  • Dvoracek, J., & Sousedikova R. (2006). Forecasting companies’ future economic development. Acta Montanistica Slovaca, 11.
  • Ekes, K. S., & Koloszar, L. (2014). The efficiency of bankruptcy forecast models in the Hungarian SME sector. Journal of Competitiveness, 6(2). doi: 10.7441 /joc.2014.02.05.
  • Fejer-Kiraly, G. (2015). Bankruptcy prediction: a survey on evolution, critiques, and solutions. Acta University Sapientiae, Economics and Business, 3. doi: 10.1515/auseb-2015-0006.
  • Fitzpatrick, P. (1932). A comparison of ratios of successful industrial enterprises with those of failed firms. Certified Public Accountant, 2.
  • Fogarassy, C., Neubauer, E., Mansur, H., Tangl, A., Olah, J., & Popp, J. (2018). The main transition management issues and the effects of environmental accounting on financial performance – with focus on cement industry. Administratie si Management Public, 31. doi: 10.24818/amp/2018.31-04.
  • Freed, N., & Glover, F. (1981). Simple but powerful goal programming approach to the discriminant problem. European Journal of Operational Research, 7. doi: 10.1016/0377-2217(81)90048-5.
  • Frydman, H., Altman, E. I., & Kao D. L. (1985). Introducing recursive partitioning for financial classification: the case of financial distress. Journal of Finance, 40(1). doi: 10.2307/2328060.
  • Gajdka, J., & Stos, D. (1996). The use of discriminant analysis in assessing the financial condition of enterprises. Restructuring in the Process of Transformation and Development of Enterprises. Krakow, Poland: Wydawnictwo Akademii Ekonomicznej w Krakowie.
  • Gandolfi, G., Regalli, M., Soana, M. G., & Arcuri, M. C. (2018). Underpricing and Long-Term performance of IPOs: evidence from European intermediary-oriented markets. Economics, Management, and Financial Markets, 13(3). doi: 10.22381/EMFM13320181.
  • Gavurova, B., Janke, F., Packova, M., & Pridavok, M. (2017). Analysis of impact of using trend variables on bankruptcy prediction models performance. Ekonomicky Casopis, 65(4).
  • Gordini, N. (2014). A genetic algorithm approach for SMEs bankruptcy prediction: empirical evidence from Italy. Expert Systems with Applications, 41(14). doi: 10.1016/j.eswa.2014.04.026.
  • Granato, D., Santos, J. S., Escher, G. B., Ferreira, B. L., & Maggio, R. M. (2018). Use of principal component analysis (PCA) and hierarchical cluster analysis (HCA) for multivariate association between bioactive compounds and functional properties in foods: a critical perspective. Trends in Food Science & Technology, 72. doi: 10.1016/j.tifs.2017.12.006.
  • Grice, J. S., & Dugan, M. T. (2001). The limitations of bankruptcy prediction models: some cautions for the researcher. Review of Quantitative Finance and Accounting, 17. doi: 10.1023/A:1017973604789.
  • Gruszczynski, M., Ciesielski, P., & Domeracki, M. (2005). New bankruptcy prediction models for Polish companies. Working paper, Department of Applied Econometrics, Warsaw School of Economics.
  • Guha, S., Rastogi, R., & Shim, K. (2000). ROCK: a robust clustering algorithm for categorical attributes. Information Systems, 25(5). doi: 10.1109/ICDE.1999 .754967.
  • Gulka, M. (2016). The prediction model of financial distress of enterprises operating in conditions of SR. Biatec, 24(6).
  • Gupta, Y., Rao, R. P., & Bagchi, P.K. (1990). Linear goal programming as an alternative to multivariate discriminant analysis: a note. Journal of Business Finance and Accounting, 17(4). doi: 10.1111/j.1468-5957.1990.tb01146.x.
  • Gupta, J., Gregoriou, A., & Healy, J. (2014). Forecasting bankruptcy for SMEs using hazard function: to what extent does size matter? Review of Quantitative Finance and Accountings, 45(4). doi: 10.1007/s11156-014-0458-0.
  • Gurcik, L. (2002). G-index – the financial situation prognosis method of agricultural enterprises. Agricultural Economics, 48(8).
  • Hadasik, D. (1998). The bankruptcy of enterprises in Poland and methods of its forecasting. Poznan, Poland: Wydawnictwo Akademii Ekonomicznej w Poznaniu.
  • Hajdu, O., & Virag, M. (2001). Hungarian model for predicting financial bankruptcy. Society and Economy in Central and Eastern Europe, 23(12).
  • Harumova, A., & Janisova, M. (2014). Rating Slovak enterprises by scoring functions. Ekonomicky casopis, 62(5).
  • Higgs, N. T. (1991). Practical and innovative uses of correspondence analysis. Statistician, 40(2). doi: 10.2307/2348490.
  • Hill, M. O. (1974). Correspondence analysis: a neglected multivariate method. Applied Statistics, 23(3). doi: 10.2307/2347127.
  • Hurtosova, J. (2009). Development of rating model as a toll to assess the enterprise credibility. Ph.D. Thesis. Bratislava, Slovakia: University of Economics in Bratislava.
  • Jagiełło, R. (2013). Discriminant and Logistic Analysis in the Process of Assessing the Creditworthiness of Enterprises. Materiały i Studia, Zeszyt 286. Warszawa: NBP.
  • Jakubik, P., & Teply, P. (2011). The JT index as an indicator of financial stability of corporate sector. Prague Economic Papers, 20(2).
  • Javadi, S., Hashemy, S.M., Mohammadi, K., Howard, K.W.F., & Neshat, A. (2017). Classification of aquifer vulnerability using K-means cluster analysis. Journal of Hydrology, 549. doi: 10.1016/j.jhydrol.2017.03.060.
  • Kalouda, F., & Vanicek, R. (2013). Alternative bankruptcy models – first results. In 10th international scientific conference European financial systems, Telc, Czech Republic.
  • Karas, M., & Reznakova, M. (2013). Bankruptcy prediction model of industrial enterprises in the Czech Republic. International Journal of Mathematical Models and Methods in Applied Sciences, 7(5).
  • Karas, M., & Reznakova, M. (2015). A parametric or nonparametric approach for creating a new bankruptcy prediction model: the evidence from the Czech Republic. International Journal of Mathematical Models and Methods in Applied Sciences, 8.
  • Kasgari, A. A., Divsalar, M., Javid, M. R., & Ebrahimian,S. J. (2013). Prediction of bankruptcy Iranian corporations through artificial neural network and Pro- bit-based analyses. Neural Computing and Applications, 23(3,4). doi: 10.1007/s00521-012-1017-z.
  • Kaufman, L., & Rousseeuw, P. (2005). Finding groups in data: an introduction to custer analysis. Hoboken: Wiley.
  • Kiviluoto, K. (1998). Predicting bankruptcies with self organizing map. Neurocomputing, 21. doi: 10.1016/S0925-2312(98)00038-1.
  • Kiestik, T., Kliestikova, J., Kovacova, M., Svabova, L., Valaskova, K., Vochozka, M., & Olah, J. (2018). Prediction of financial health of business entities in transition economies. New York, New York: Addleton Academic Publishers.
  • Kliestik, T., Valaskova, K., Kliestikova, J., Kovacova, M., & Svabova, L. (2019). Bankruptcy prediction in transition economies. Zilina, Slovakia: EDIS.
  • Korab, V. (2001). One approach to small business bankruptcy prediction: the case of the Czech Republic. In VII SIGEFF congress new logistics for the new economy. Naples: SIGEFF International Association for FUZZY SET, University Degli Studi Di Napoli, Federico II.
  • Korol, T. (2004). Assessment of the accuracy of the application of discriminatory methods and artificial neural networks for the identification of enterprises threatened with bankruptcy. Gdansk: Doctoral dissertation.
  • Korol, T. (2010). Forecasting bankruptcies of companies using soft computing techniques. Finansowy Kwartalnik Internetowy “e-Finanse”, 6.
  • Kovacova, M., & Kliestik, T. (2017). Logit and Probit application for the prediction of bankruptcy in Slovak companies. Equilibrium. Quarterly Journal of Economics and Economic Policy, 12(4). doi: 10.24136/eq.v12i4.40.
  • Kristof, S., & Koloszar, L. (2014). The Efficiency of Bankruptcy Forecast Models in the Hungarian SME Sector. Journal of Competitiveness, 6. doi: 10.7441/joc.2014.02.05.
  • Lane, W. R., Looney, S. W., Wansley, J. W. (1986). An application of the Cox proportional hazards model to bank failure. Journal of Banking and Finance, 10(4). doi: 10.1016/S0378-4266(86)80003-6.
  • Luoma, M., & Laitinen, E. K. (1991). Survival analysis as a tool for company failure prediction. Omega International Journal of Management Science, 19(6). doi: 10.1016/0305-0483(91)90015-L.
  • Machek, O., Smrcka, L., & Strouhal, J. (2015). How to predict potential default of cultural organizations. In 7th international scientific conference finance and performance of firms in science, education and practice, 7.
  • Maczynska, E. (1994). Assessment of the condition of the enterprise. Simplified methods. Zycie Gospodarcze, 38.
  • Mangasarian, O. L. (1965). Linear and nonlinear separation of patterns by linear programming. Operation research, 13. doi: 10.1287/opre.13.3.444.
  • McKee, T. E. (2003). Rough sets bankruptcy prediction models versus auditor signaling rates. Journal of Forecasting, 22. doi: 10.1002/for.875.
  • McKee, T. E. (2000). Developing a bankruptcy prediction model via rough sets theory. International Journal of Intelligent Systems in Accounting Finance and Management, 9(3). doi: 10.1002/1099-1174(200009)9:33.0.CO;2-C.
  • Messier, W. F., & Hansen, J. V. (1988). Including rules for expert system development: An example using default and bankruptcy data. Management Science, 34(2).
  • Michaluk, K. (2003). Effectiveness of corporate bankruptcy models in Polish economic conditions. Corporate Finance in the Face of Globalization Processes. Warszawa, Poland: Wydawnictwo Gda´ nskiej Akademii Bankowej.
  • Mihalovic, M. (2016). Performance comparison of multiple discriminant analysis and Logit models in bankruptcy prediction. Economics and Sociology, 9(4). doi: 10.14254/2071-789X.2016/9-4/6.
  • Nath R., Jackson, W. M., & Jones, T. W. (1992). A comparison of the classical and the linear programming approaches to the classification problem in discriminant analysis. Journal of Statistical Computation and Simulation, 41. doi: 10.1080/00949659208811392.
  • Nemec, D., & Pavlik, M. (2016). Predicting insolvency risk of the Czech companies. In International scientific conference quantitative methods in economics (Multiple criteria decision making XVIII). Bratislava, Slovakia.
  • Neumaierova, I., & Neumaier, I. (2002). Vykonnost a trzni hodnota firmy. Prague, Czech Republic: Grada Publishing.
  • Odom, M., & Sharda, R. (1990). A neural network model for bankruptcy prediction. Proceedings of the Second IEEE International Joint Conference on Neural Networks. San Diego, USA, 63-68.
  • Ouenniche, J., & Tone, K. (2017). An out-of-sample evaluation framework for DEA with application in bankruptcy prediction. Annals of Operations Research, 254(1-2). doi: 10.1007/s10479-017-2431-5.
  • Pisula, T., Mentel, G., & Brozyna, J. (2013). Predicting bankruptcy of companies from the logistics sector operating in the Podkarpacie region. Modern Management Review, 18. doi: 10.7862/rz.2013.mmr.33.
  • Pisula, T., Mentel, G., & Brozyna, J. (2015). Non-statistical methods of analyzing of bankruptcy risk. Folia Oeconomica Stetinensia, 15. doi: 10.1515/foli-2015-0029.
  • Platt, H. D., & Platt, M. B. (1990). Development of a class of stable predictive variables: the case of bankruptcy prediction. Journal of Business Finance & Accounting, 17(1). doi: 10.1111/j.1468-5957.1990.tb00548.x.
  • Pociecha, J., Pawelek, B., Baryla, M., & Augustyn, S. (2014). Statistical methods of forecasting bankruptcy in the changing economic situation. Krakow, Poland: Fundacja Uniwersytetu Ekonomicznego w Krakowie.
  • Pogodzinska, M., & Sojak, S. (1995). The use of discriminant analysis in predicting bankruptcy of enterprises. AUNC Ekonomia, 25(299).
  • Popp, J., Olah, J., Machova, V., & Jachowicz, A. (2018). Private equity market of the Visegrad group. Ekonomicko-manazerske spektrum, 12(1). doi: 10.26552/ems.2018.1.
  • Ptak-Chmielewska, A. (2016). Statistical models for corporate credit risk assessment - rating models. Acta Universitatis Lodziensis Folia Oeconomica 3. doi: 10.18778/0208-6018.322.09.
  • Ravi Kumar, P., & Ravi, V. (2007). Bankruptcy prediction in banks and firms via statistical and intelligent techniques-A review. European Journal of Operational Research, 180(1). doi: 10.1016/j.ejor.2006.08.043.
  • Rohacova, V., & Kral P. (2015). Corporate failure prediction using DEA: an application to companies in the Slovak Republic. In 18th applications of mathematics and statistics in economics, International Scientific Conference. Jindrichuv Hradec, Czech Republic.
  • Salaga, J., Bartosova, V., & Kicova, E. (2015). Economic value added as a measurement tool of financial performance. Procedia Economics and Finance, 26. doi: 10.1016/S2212-5671(15)00877-1.
  • Schonfelder, B. (2003). Debt collection and bankruptcies in Slovakia: a study of institutional development. Post-Communist Economies, 15(2). doi: 10.1080/ 14631370308097.
  • Slowinski, R., & Zopounidis, C. (1995). Application of the rough set approach to evaluation of bankruptcy risk. Intelligent Systems in Accounting, Finance and Management, 4. doi: 10.1002/j.1099-1174.1995.tb00078.x.
  • Sion, G. (2018). How artificial intelligence is transforming the economy. Will cognitively enhanced machines decrease and eliminate tasks from human workers through automation? Journal of Self-Governance and Management Economics, 6(4). doi: 10.22381/JSME6420185.
  • Sourial, N., Wolfson Ch., Zhu, B., Gletcher, J., Karunananthan, S., Bandeen-Roche, K., Beland, F., & Bergman, H. (2010). Correspondence analysis is a useful tool to uncover the relationships among categorical variables. Journal of Clinical Epidemiologia, 63(6). doi: 10.1016/j.jclinepi.2009.08.008.
  • Spanos, M., Dounias, M., & Zopounidis, C. (1999). A fuzzy knowledge-based decision aiding method for the assessment of financial risk: the case of corporate bankruptcy prediction.In European symposium on intelligent techniques (ESIT).
  • Stevens, J. P. (2002). Applied multivariate statistics for the social sciences. New Jersey Lawrence Erlbaum.
  • Svabova, L., Kramarova, K., & Durica, M. (2018). Prediction model of firm´s financial distress. Ekonomicko-manazerske spektrum, 12(1). doi: 10.26552/ems.2018.1.16-29.
  • Uradnicek, V. (2016). Variantne Metody Predikcie Financneho Zdravia Podnikov v Podmienkach Dynamickeho Ekonomickeho Prostredia. Banska Bystrica, Slovakia: Belianum.
  • Valaskova, K., Kliestik, T., & Kovacova, M. (2018). Management of financial risks in Slovak enterprises using regression analysis. Oeconomia Copernicana, 9(1). doi: 10.24136/oc.2018.006.
  • Valecky, J., & Slivkova, E. (2012). Microeconomic scoring model of Czech firms’ bankruptcy. Ekonomicka Revue, 15(1). doi: 10.7327/cerei.2012.03.02.
  • Vavrina, J., Hampel, D., Janova, J. (2013). New approach for the financial distress classification in agribusiness. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 16(4). doi: 10.11118/actaun201361041177.
  • Virag, M., & Kristof, T. (2005). Neural neutworks in bankruptcy prediction - a comparative study on the basis of the first Hungarian bankruptcy model. Acta Oeconomica, 55. doi: 10.1556/AOecon.55.2005.4.2.
  • Virag, M., & Kristof, T. (2014). Is there a trade-off between the predictive power and the interpretability of bankruptcy models? The case of the first Hungarian bankruptcy prediction model. Acta Oeconomica, 64(4). doi: 10.1556/AOecon .64.2014.4.2.
  • Vochozka, M., Strakova, J., & Vachal, J. (2015). Model to predict survival of transportation and shipping companies. Nase More, 62. doi: 10.17818/nm/ 2015/si4.
  • Ward, J. H. (1963). Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association, 58.
  • Wedzki, D. (2000). The problem of using the ratio analysis to predict the bankruptcy of Polish enterprises - Case study. Bank i Kredyt, 5.
  • Wrzosek, M., & Ziemba, A. (2009). Construction of a rating based on a bankruptcy prediction model. Edinburgh, UK: Credit Research Center, The University of Edinburgh.
  • Zopoudinis, C. (1987). A multicriteria decision-making methodology for the evaluation of the risk of failure and an application. Foundations of Control Engineering, 12(1).
  • Zopoudinis, C., & Dimitras, A. I. (1998). Multicriteria decision aid methods for the prediction of business failure. Dordrecht: Kluwer Academic Publishers.
  • Zopounidis, C., & Doumpos, M. (1999). A multicriteria aid methodology for sorting decision problems: The case of financial distress. Computational Economics, 14(3). doi: 10.1023/A:1008713823812.
  • Zvarikova, K., Spuchlakova, E., a Sopkova, G. (2017). International comparison of the relevant variables in the chosen bankruptcy models used in the risk management. Oeconomia Copernicana, 8(1). doi: 10.24136/oc.v8i1.10.

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Publication order reference

Identifiers

Biblioteka Nauki
19106225

YADDA identifier

bwmeta1.element.ojs-doi-10_24136_oc_2019_034
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