The issue of analyzing factors of the dynamics of the economic performance of every economy, which means Bosnia and Herzegovina 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 of Bosnia and Herzegovina. The application of multi-criteria decisionmaking methods enables adequate control of the key factors of the economic performance of the economy of Bosnia and Herzegovina. Bearing that in mind, this paper analyzes the dynamics of the economic performance of the economy of Bosnia and Herzegovina in the period 2013 - 2022 based on the LMAW-DNMA method. The top five years according to the economic performance of the economy of Bosnia and Herzegovina according to the LMAW-DNMA method are in order: 2018, 2019, 2017, 2016, and 2015. The worst economic performance of the economy of Bosnia and Herzegovina was achieved in 2020. Lately, in general, it has significantly improved the economic performance of the economy of Bosnia and Herzegovina. This was influenced by adequate management of the analyzed statistical variables (gross domestic product, inflation, agriculture, industry, export, import, capital, income, taxes, time required to start business - days, and domestic loans provided by the financial sector). Likewise, the geopolitical situation, the economic climate, foreign direct investments, the COVID-19 pandemic, the energy crisis, the digitalization of the company's entire operations, and other factors. In any case, their adequate control can greatly influence the achievement of the target economic performance of the economy of Bosnia and Herzegovina.
Lukić R. Analysis of the efficiency of companies in Serbia based on the DEA Super-Radial approach. Journal of Engineering Management and Competitiveness. 2023;13(1):21–9.
2.
Park W, Kim SG. Integrating quantitative and qualitative methodologies to build a national R&D plan using data envelopment analysis based on R&D stakeholders’ perspectives. PLOS ONE. 17(3):e0265058.
3.
Moghaddas Z, Oukil A, Vaez-Ghasemi M. Global multi-period performance evaluation – New model and productivity index. RAIRO - Operations Research. 2022;56(3):1503–21.
4.
Nguyen HQ, Nguyen VT, Phan DP, Tran QH, Vu NP. Multi-Criteria Decision Making in the PMEDM Process by Using MARCOS, TOPSIS, and MAIRCA Methods. Applied Sciences. 12(8):3720.
5.
Mishra AR, Saha A, Rani P, Hezam IM. An Integrated Decision Support Framework Using Single-Valued-MEREC-MULTIMOORA for Low Carbon Tourism Strategy Assessment. *IEEE Access*. 2022;10:24411–32.
6.
Martić M, Savić G. An application of DEA for comparative analysis and ranking of regions in Serbia with regards to social-economic development. European Journal of Operational Research. 2001;132(2):343–56.
7.
Mandić K, Delibašić B, Knežević S, Benković S. Analysis of the efficiency of insurance companies in Serbia using the fuzzy AHP and TOPSIS methods. *Economic Research*. 2017;30(1):550–65.
8.
Lukić R. Measurement and analysis of profitability dynamics of the banking sector in Serbia based on the FLMAW-MARCOS method. Bankarstvo. 2023;52(1):8–47.
9.
Radojko L. 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):201–19.
10.
Pamučar D, Žižović M, Biswas S, Božanić D. A NEW LOGARITHM METHODOLOGY OF ADDITIVE WEIGHTS (LMAW) FOR MULTI-CRITERIA DECISION-MAKING: APPLICATION IN LOGISTICS. Facta Universitatis, Series: Mechanical Engineering. 19(3):361.
11.
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. 2023;(Vol. 24 No. 2 / 2023).
12.
Lukić R. PERFORMANCE ANALYSIS OF TRADING COMPANIES IN SERBIA BASED ON DIBR - WASPAS METHODS. Proceedings of the 28th International Scientific Conference Strategic Management and Decision Support Systems in Strategic Management. 2023.
13.
Lukić R. Application of PROMETHEE Method in Evaluation of Insurance Efficiency in Serbia. *Revija za ekonomske i poslovne vede. Journal of Economic and Business Sciences*. 2023;10(1):3–19.
14.
Lukic R. Comparative Analysis of Transport and Storage Information Systems of the European Union and Serbia Using Fuzzy LMAW and MARCOS Methods. Economy, Business and Development: An International Journal. 2023;4(1):1–17.
15.
Lukić R. 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. 2023;5(5):69–80.
16.
Lukić R. Influence of Net Working Capital on Trade Profitability in Serbia. *European Journal of Interdisciplinary Studies*. 2023;15(1):48–67.
17.
Lukić R. Analysis of the performance of the Serbian economy based on the MEREC-WASPAS method. *MARSONIA: Časopis za društvena i humanistička istraživanja*. God. 2023;2, br. 1:39–53.
18.
LUKIC R. Measurement and Analysis of The Information Performance of Companies in The European Union and Serbia Based on The Fuzzy LMAW and MARCOS Methods. Informatica Economica. 27(1/2023):17–31.
19.
Pendharkar PC. Hybrid radial basis function DEA and its applications to regression, segmentation and cluster analysis problems. Machine Learning with Applications. 2021;6:100092.
20.
Podinovski VV, Bouzdine-Chameeva T. Optimal solutions of multiplier DEA models. Journal of Productivity Analysis. 2021;56(1):45–68.
21.
Popović G, Pucar Đ, Smarandache F. MEREC-Cobra Approach in E-Commerce Development Strategy Selection. *Journal of Process Management and New Technologies*. 2022;10(3–4):66–74.
22.
Radonjić L. Comparative analysis of the regional efficiency in Serbia: DEA approach. Industrija. 2020;48(2):7–20.
23.
Rani P, Mishra AR, Saha A, Hezam IM, Pamucar D. Fermatean fuzzy Heronian mean operators and MEREC‐based additive ratio assessment method: An application to food waste treatment technology selection. International Journal of Intelligent Systems. 2022;37(3):2612–47.
24.
Rasoulzadeh M, Edalatpanah SA, Fallah M, Najafi SE. A multi-objective approach based on Markowitz and DEA cross-efficiency models for the intuitionistic fuzzy portfolio selection problem. *Decision Making: Applications in Management and Engineering*. 2022;5(2):241–59.
25.
Rostamzadeh R, Akbarian O, Banaitis A, Soltani Z. APPLICATION OF DEA IN BENCHMARKING: A SYSTEMATIC LITERATURE REVIEW FROM 2003–2020. Technological and Economic Development of Economy. 2021;27(1):175–222.
26.
Sala-Garrido R, Mocholí-Arce M, Maziotis A, Molinos-Senante M. Benchmarking the performance of water companies for regulatory purposes to improve its sustainability. npj Clean Water. 6(1).
27.
Stević Ž, Miškić S, Vojinović D, Huskanović E, Stanković M, Pamučar D. Development of a Model for Evaluating the Efficiency of Transport Companies: PCA–DEA–MCDM Model. Axioms. 11(3):140.
28.
Stojanović I, Puška A, Selaković M. A multi-criteria approach to the comparative analysis of the global innovation index on the example of the Western Balkan countries. ECONOMICS. 2022;10(2):9–26.
29.
Toslak M, Aktürk B, Ulutaş A. MEREC ve WEDBA Yöntemleri ile Bir Lojistik Firmasının Yıllara Göre Performansının Değerlendirilmesi. *Avrupa Bilim ve Teknoloji Dergisi*. 2022;33:363–72.
30.
Tone K. A slacks-based measure of super-efficiency in data envelopment analysis. *European Journal of Operational Research*. 2002;143:32–41.
31.
Tsai CM, Lee HS, Gan GY. A New Fuzzy DEA Model for Solving the MCDM Problems in Supplier Selection. Journal of Marine Science and Technology. 29(1).
32.
Vojteški KD, Lukić R. Evaluation of the efficiency of providers of financial leasing in Serbia. *Glasnik društvenih nauka - Journal of Social Sciences*. 2022;14(XIV):113–44.
33.
Zhu N, He K. The efficiency of major industrial enterprises in Sichuan province of China: A super slacks-based measure analysis. Journal of Industrial and Management Optimization. 2023;19(2):1328.
34.
Đurić Z, Jakšić M, Krstić A. DEA Window Analysis of Insurance Sector Efficiency in the Republic of Serbia. Economic Themes. 2020;58(3):291–310.
35.
Amini A, Alinezhad A, Yazdipoor F. A TOPSIS, VIKOR and DEA integrated evaluation method with belief structure under uncertainty to rank alternatives. *International Journal of Advanced Operations Management*. 2019;11(3):171–88.
36.
Amin GR, Hajjami M. Improving DEA cross-efficiency optimization in portfolio selection. *Expert Systems with Applications*. 2021;168:114280.
37.
Amirteimoori A, Mehdizadeh S, Kordrostami S. Stochastic performance measurement in two-stage network processes: A data envelopment analysis approach. Kybernetika. :200–17.
38.
Andersen P, Petersen NC. A procedure for ranking efficient units in data envelopment analysis. *Management Science*. 1993;39:1261–4.
39.
Ayçin E, Arsu T. Sosyal Gelişme Endeksine Göre Ülkelerin Değerlendirilmesi: MEREC ve MARCOS Yöntemleri ile Bir Uygulama. *İzmir Yönetim Dergisi*. 2021;2(2):75–88.
40.
Banker RD, Charnes A, A., Cooper WW. Some models for estimating technical and scale inefficiencies in data envelopment analysis. *Management Science*. 1984;30(9):1078–92.
41.
Chang X, Wang X. Research Performance Evaluation of University Based on Super DEA Model. 2020 IEEE 9th Joint International Information Technology and Artificial Intelligence Conference (ITAIC). 2020. p. 1252–5.
42.
Chen W, Gai Y, Gupta P. Efficiency evaluation of fuzzy portfolio in different risk measures via DEA. Annals of Operations Research. 2018;269(1–2):103–27.
43.
Chen W, Li SS, Zhang J, Mehlawat MK. A comprehensive model for fuzzy multi-objective portfolio selection based on DEA cross-efficiency model. *Soft Computing*. 2020;24(4):2515–26.
44.
Chen W, Li SS, Mehlawat MK, Jia L, Kumar A. Portfolio Selection Using Data Envelopment Analysis Cross-Efficiency Evaluation with Undesirable Fuzzy Inputs and Outputs. *International Journal of Fuzzy Systems*. 2021;23(5):1478–509.
45.
Chen C, Liu H, Tang L, Ren J. A Range Adjusted Measure of Super-Efficiency in Integer-Valued Data Envelopment Analysis with Undesirable Outputs. *Journal of Systems Science and Information*. 2021;9(4):378–98.
46.
Cooper WW, Park KS, Pastor JT. Journal of Productivity Analysis. 1999;11(1):5–42.
47.
DEMİR G. ANALYSIS OF THE FINANCIAL PERFORMANCE OF THE DEPOSIT BANKING SECTOR IN THE COVID-19 PERIOD WITH LMAW-DNMA METHODS. Sivas Soft Bilisim Proje Danismanlik Egitim Sanayi ve Ticaret Limited Sirketi.
48.
Ecer F. *Multi-criteria Decision-making comprehensive approach from past to present*. 2020.
49.
Ecer F, Aycin E. Novel Comprehensive MEREC Weighting-Based Score Aggregation Model for Measuring Innovation Performance: The Case of G7 Countries. Informatica. 2023;53–83.
50.
Alam TE, González AD, Raman S. Benchmarking of academic departments using data envelopment analysis (DEA). Journal of Applied Research in Higher Education. 2023;15(1):268–85.
51.
Fenyves V, Tarnóczi T. Data envelopment analysis for measuring performance in a competitive market. Problems and Perspectives in Management. 2020;18(1):315–25.
52.
Fotova Čiković K, Lozić J. Application of Data Envelopment Analysis (DEA) in Information and Communication Technologies. Tehnički glasnik. 2022;16(1):129–34.
53.
Guo D, Cai ZQ. Super-Efficiency Infeasibility in the Presence of Nonradial Measurement. Mathematical Problems in Engineering. 2020;2020:1–7.
54.
Liao H, Wu X. DNMA: A double normalization-based multiple aggregation method for multi-expert multi-criteria decision making. Omega. 2020;94:102058.
55.
Zhu J. Super-efficiency DEA in the presence of infeasibility. *European Journal of Operational Research*. 2011;212(1):141–7.
56.
Lin R. Cross-efficiency evaluation capable of dealing with negative data: A directional distance function based approach. *Journal of the Operational Research Society*. 2020;71(3):505–16.
57.
Lukic R, Sokic M, Kljenak DV. Efficiency analysis of the banking sector in the Republic of Serbia. *Business Excellence and Management*. 2017;7:5–17.
58.
Lukic R. Analysis of the efficiency of insurance companies. In: *Insurance in the post-crisis era*. 2018.
59.
Lukic R, Hadrovic Zekic B. Evaluation of efficiency of trade companies in Serbia using the DEA approach. In: *Proceedings of the 19th International Scientific Conference Business Logistics In Modern Management*, October 10-11. 2019. p. 145–65.
60.
Lukić R, Kozarević E. Analysis of selected countries trade efficiency based on the DEA models. In: *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. 2019. p. 61–71.
61.
Lukić R, Hanić H, Bugarčić M. Analysis of profitability and efficiency of trade in Serbia. *Economic Analysis*. 2020;53(2):39–50.
62.
Lukić R. Evaluation of the efficiency of public companies in Serbia using the ARAS method. *Proceedings of the Conference*. 2021;8:43–53.
63.
Lukić R. Analysis of efficiency factors of companies in Serbia based on artificial neural networks. Anali Ekonomskog fakulteta u Subotici. 2022;(47):97–115.
64.
Lukić R. Evaluation of financial performance and efficiency of companies in Serbia. Journal of Engineering Management and Competitiveness. 2022;12(2):132–41.
65.
Lukic R. 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*. 2022;23(5):630–45.
Citation
Copyright
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
The statements, opinions and data contained in the journal are solely those of the individual authors and contributors and not of the publisher and the editor(s). We stay neutral with regard to jurisdictional claims in published maps and institutional affiliations.
