Research of the dynamics of the economic performance of the Serbian economy based on LMAW-DNMA methods
Keywords:
economy, performance, Serbia, LMAW-DNMA method
Abstract
The issue of analyzing the factors of the dynamics of the economic performance of every economy, which means Serbia as well, is continuously very current, challenging, significant, and complex. Adequate control of key factors can significantly influence the achievement of the target economic performance of the economy. The application
of multi-criteria decision-making methods enables adequate control of the key factors of economic performance of the economy. Bearing that in mind, this paper analyzes the dynamics of the economic performance of the Serbian economy in the period 2013 - 2022 based on the LMAW-DNMA method. The top five years according to the economic performance of the Serbian economy according to the LMAW-DNMA method are in order: 2021, 2019, 2018, 2013, and 2022. The worst economic performance of the Serbian economy was achieved in 2014. Recently, the economic performance of the Serbian economy has improved significantly. Adequate management of the analyzed statistical variables (gross domestic product, inflation, agriculture, industry, export, import, capital, income, and taxes) influenced this. Likewise, the geopolitical and economic climate, foreign direct investments, the COVID-19 pandemic, the energy crisis, the digitalization of the entire company's operations, and other factors. Their adequate control can greatly influence the achievement of the target performance of the Serbian economy.
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References
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3 Amin, G. R., & Hajjami, M. (2021). Improving DEA cross-efficiency optimization in portfolio selection. Expert Systems with Applications, 168, 114280.
4 Amirteimoori, A., Mehdizadeh, S., & Kordrostami, S., (2022). Stochastic performance measurement in two-stage network processes: A data envelopment analysis approach. Kybernetika, 58(2), 200-217. DOI: 10.14736/kyb-2022-2-0200
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6 Ayçin, E., &Arsu, T. (2021). Sosyal Gelişme Endeksine Göre Ülkelerin Değerlendirilmesi: MEREC ve MARCOS Yöntemleri ile Bir Uygulama. İzmir Yönetim Dergisi, 2(2), 75-88.
7 Banker, R.D., A. Charnes, A., & Cooper, WW (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science, 30(9), 1078–1092.
8 Chang, X., & Wang, X. (2020). Research Performance Evaluation of University Based on Super DEA Model. 2020 IEEE 9th Joint International Information Technology and Artificial Intelligence Conference (ITAIC), 1252-1255. doi: 10.1109/ITAIC49862.2020.9339131
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10 Chen, W., Li, S. S., Zhang, J., & Mehlawat, M. K. (2020). A comprehensive model for fuzzy multi- objective portfolio selection based on the DEA cross-efficiency model. Soft computing, 24(4), 2515-2526.
11 Chen, W., Li, S. S., Mehlawat, M. K., Jia, L., & Kumar, A. (2021). Portfolio Selection Using Data Envelopment Analysis Cross-Efficiency Evaluation with Undesirable Fuzzy Inputs and Outputs. International Journal of Fuzzy Systems, 23(5), 1478-1509.
12 Chen, Chunhua, Liu, Haohua, Tang, Lijun & Ren, Jianwei. (2021). A Range Adjusted Measure of Super-Efficiency in Integer-Valued Data Envelopment Analysis with Undesirable Outputs. Journal of Systems Science and Information, 9(4), 378-398. https://doi.org/10.21078/JSSI-2021-37821
13 Cooper, W. W., Park, K. S., & Pastor, J. T. (1999). RAM: a range-adjusted measure of inefficiency for use with additive models, and relations to other models and measures in DEA. Journal of Productivity Analysis, 11(1), 5-42. DOI: https://doi.org/10.1023/A:1007701304281
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15 Ecer, F. (2020). Multi-criteria Decision-making comprehensive approach from past to present. Seçkin Publications.
16 Ecer, F., & Aycin, E. (2022). Novel Comprehensive MEREC Weighting-Based Score Aggregation Model for Measuring Innovation Performance: The Case of G7 Countries. Informatica, 131, DOI 10.15388/22-INFOR494
17 Đurić, Z., Jakšić,M. & Krstić, A. (2020). DEA window analysis of insurance sector efficiency in the Republic of Serbia. Economic Themes, 58(3),291-310. doi: 10.2478/ethemes-2020-0017 market.
18 Fenyves, V., & Tarnóczi, T. (2020). Data envelopment analysis for measuring performance in a competitive
Problems and Perspectives in Management, 18(1), 315-325. doi:10.21511/ppm.18(1).2020.27
19 Fotova Čiković, K., & Lozić, J. (2022). Application of Data Envelopment Analysis (DEA) in Information
and Communication Technologies. Tehnički glasnik, 16 (1), 129-134. https://doi.org/10.31803/tg-20210906103816
20 Guo, D., & Cai, Z.Q. (2020). Super-Efficiency Infeasibility in the Presence of Nonradial
Measurement. Mathematical Problems in Engineering, 2020 Article ID 6264852, 7 pages.
https://doi.org/10.1155/2020/6264852
21 Liao, H., & Wu, X. (2020). DNMA: A double normalization-based multiple aggregation for multi-expert multi-criteria decision making. Omega, 94, 102058. methods https://doi.org/10.1016/j.omega.2019.04.001
22 Lee, H.S., Chu, C.W., & J. Zhu, J. (2011). Super-efficiency DEA in the presence of infeasibility. European Journal of Operational Research, 212(1), 141–147.
23 Lin, R. (2020). Cross-efficiency evaluation capable of dealing with negative data: A directional distance function based approach. Journal of the Operational Research Society, 71(3), 505-516.
24 Lukic, R., Sokic, M., & Kljenak, D.V. (2017). Efficiency analysis of the banking sector in the Republic of Serbia. Business Excellence and Management, 7, 5–17.
25 Lukic, R. (2018). Analysis of the efficiency of insurance companies. In: Insurance in the post- crisis era, Belgrade: Faculty of Economics, University of Belgrade. ISBN: 978-86-403-1548-7.
26 Lukic, R., & Hadrovic Zekic, B. (2019). Evaluation of efficiency of trade companies in Serbia using the DEA approach. Proceedings of the 19th International Scientific Conference Business Logistics In Modern Management October 10-11, Osijek, Croatia, Josip Juraj Strossmayer University of Osijek, Faculty of Economics in Osijek, 145-165.
27 Lukić, R., & Kozarević, E. (2019). Analysis of selected countries' trade efficiency based on the DEA models. December 2019, Conference: The Sixth Scientific Conference with International Participation "Economy of Integration" ICEI 2019 - (E) Migrations And Competitiveness Of South Eastern European Countries. At: Tuzla, Bosnia and Herzegovina, 61-71.
28 Lukić, R., Hanić, H., & Bugarčić, M. (2020). Analysis of profitability and efficiency of trade-in Serbia. Economic Analusis, 53(2), 39-50.
29 Lukić, R. (2021). Evaluation of the efficiency of public companies in Serbia using the ARAS method. Proceedings of the Conference, 8, 43-53.
30 Lukic, R. (2022a) Analysis of efficiency factors of companies in Serbia based on artificial neural networks. Анали Економског факултетa у Суботици – The Annals of the Facult y of Economics in Subotica, 58(47), 097-115. DOI: 10.5937/AnEkSub2247097L
31 Lukic, R. (2022b). Evaluation of financial performance and efficiency of companies in Serbia. Journal of engineering management and competitiveness (JEMC), 12(2), 132-141. DOI: 10.5937/JEMC2202132L
32 Lukic, R.(2022c). Measurement and Analysis of the Dynamics of Financial Performance and Efficiency of Trade in Serbia Based on the DEA Super-Radial Model. Review of International Comparative Management, 23(5), 630-645. DOI: 10.24818/RMCI.2022.5.630
33 Lukić, R.(2023a). Measurement and Analysis of The Information Performance of Companies in The European Union and Serbia Based on The Fuzzy LMAW and MARCOS Methods. Informatica Economică vol. 27, no. 1, 17 – 31. DOI: 10.24818/issn14531305/27.1.2023.02
34 Lukić, R. (2023b). Analysis of the performance of the Serbian economy based on the MEREC- WASPAS method. MARSONIA: Časopis za društvena i humanistička istraživanja, God. 2, br. 1, 39-53.
35 Lukić, R. (2023c). Influence of Net Working Capital on Trade Profitability in Serbia. European Journal of Interdisciplinary Studies, 15(1), 48-67. DOI: http://doi.org/10.24818/ejis.2023.04
36 Lukić, R.(2023d). Analysis of the performance of companies in Serbia listed on the Belgrade stock exchange. Zbornik radova / Conference Proceedings, Računovodstvo i revizija u teoriji i praksi / Accounting and audit in theory and practice, Banja Luka College / Besjeda Banja Luka, 5(5),69-80. DOI 10.7251/ZRRRTP2301069L
37 Lukić, R. (2023e). Comparative analysis of transport and storage information systems of the European Union and Serbia using fuzzy LMAW and MARCOS methods. Economy, 27 Business & Development, 4(1). 1-17DOI: 0.47063/ebd.00011
38 Lukić, R. (2023f). Application of PROMETHEE Method in Evaluation of Insurance Efficiency in Serbia. Revija za ekonomske in poslovne vede, Journal of Economic and Business Sciences, 10(1), 3-19. DOI: ttps://doi.org/10.55707/eb.v10i1.121
39 Lukić, R.(2023g). Performance analysis of trading companies in Serbia based on DIBR – WASPAS methods. Conference proceedings [Elektronski izvor] / 28th International Scientific Conference Strategic Management and Decision Support Systems in Strategic Management SM 2023, Subotica, 18-19 May, 2023. - Subotica: Faculty of Economics, 2023, 361-372. DOI: 10.46541/978-867233-416-6_47
40 Lukic, R. (2023h). Analysis of the Trade Performance of the European Union and Serbia on the Base
of FF-WASPAS and WASPAS Methods. Review of International Comparative Management, 24(2),
228-250. DOI: 10.24818/RMCI.2023.2.228
41 Lukic, R. (2023i). Analysis of the efficiency of companies in Serbia based on the DEA super-radial
approach. Journal of Engineering Management and Competitiveness (JEMC), 13(1), 21-29. DOI: 0.5937/JEMC2301021L
42 Lukic, R.(2023j). Measurement and Analysis of Dynamics of Financial Performance and
Efficiency of Trade in Serbia Using Iftopsis and Topsis Methods. Management and Economics Review,
8(2), 201-219. DOI: 10.24818/mer/2023.06-06
43 Lukic, R. (2023k). Merenje i analiza dinamike profitabilnosti bankarskog sektora u Srbiji na bazi FLMAW-MARCOS metoda. Measurement and Analysis of Profitability Dynamics of the Banking Sector
in Serbia Based on the FLMAW-MARCOS Method. Banking – Bankarstvo, 8-47. DOI:
10.5937/bankarstvo2301028L
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Published
2024-11-28
Section
Economics (Microeconomics, Macroeconomics, International Economics)
