A Long-Term Performance Analysis of SMEs in Türkiye Using RATMI, MACAD, and L-MABAC Methods

Abstract

In this study, the macroeconomic performance of small and medium-sized enterprises (SMEs) in Türkiye between 2013 and 2022 was examined, with particular attention paid to structural vulnerabilities to external shocks. Prior to evaluating performance, the dimensions of financial efficiency, production efficiency, R&D and innovation efficiency, foreign trade efficiency, and employee efficiency were weighed using objective and subjective multi-criteria methods. Annual performance rankings were determined using the RATMI, MACAD, and Logarithmic-MABAC methods, whilst the model's robustness was tested through scenario-based sensitivity analyses. The research findings indicate that SME performance is highly sensitive to macroeconomic shocks, rather than following a linear, steady growth trajectory. When examining the downturns caused by the financial turmoil of 2018 and the 2020 pandemic, the decline in R&D investment prior to the crisis was consistent with the investment hysteresis and uncertainty theories found in the literature. Furthermore, foreign trade ventures lacking sound technological and financial foundations are highly sensitive and rapidly deteriorate under external pressure. From a methodological perspective, the MACAD and RATMI techniques offer a resilient, highly consistent analytical foundation that mutually corroborates results even under extreme sensitivity scenarios.

Keywords

• Multi-Criteria Decision -Making (MCDM)
• Small and Medium-sized Enterprises (SME) Performance
• Longitudinal Analysis and Simulation

Türkiye’deki KOBİ’lerin RATMI, MACAD ve L-MABAC Yöntemleri Kullanılarak Yapılan Uzun Vadeli Performans Analizi

Abstract

Bu çalışmada, 2013-2022 yılları arasında Türkiye’deki küçük ve orta ölçekli işletmelerin (KOBİ’ler) makroekonomik performansı, özellikle dış şoklara karşı yapısal kırılganlıklar dikkate alınarak incelenmiştir. Performansın değerlendirilmesinden önce finansal etkinlik, üretim etkinliği, ar-ge ve inovasyon etkinliği, dış ticaret etkinliği ve çalışan etkinliği boyutları nesnel ve öznel çok kriterli yöntemler kullanılarak ağırlıklandırılmıştır. Yıllık performans sıralamaları RATMI, MACAD ve Logaritmik-MABAC yöntemleriyle belirlenmiş, modelin sağlamlığı ise senaryolara dayalı duyarlılık analizleriyle test edilmiştir. Araştırma bulguları, KOBİ’lerin performansının doğrusal ve istikrarlı bir büyüme eğrisi izlemek yerine makroekonomik şoklara karşı son derece duyarlı olduğunu göstermiştir. 2018’deki finansal çalkantı ve 2020’deki pandeminin yol açtığı daralmalar incelendiğinde, kriz öncesinde Ar-Ge yatırımlarındaki düşüş, literatürde yer alan yatırım ataleti ve belirsizlik kuramlarıyla tutarlılık sergilemiştir. Ayrıca, sağlam teknolojik ve finansal temellere sahip olmayan dış ticaret girişimlerinin, dış baskı altında hızla kötüleşen seyirde daha yüksek bir hassasiyet sergilediği görülmüştür. Metodolojik açıdan bakıldığında ise, MACAD ve RATMI teknikleri, aşırı duyarlılık senaryolarında bile sonuçları karşılıklı olarak doğrulayan, dayanıklı ve son derece tutarlı bir analitik temel sunmuştur.

Keywords

• Çok Kriterli Karar Verme (ÇKKV)
• Küçük ve Orta Ölçekli İşletmelerin (KOBİ) Performansı
• Boylamsal Analiz ve Simülasyon

References

1. Abdulaal, R. M., & Bafail, O. A. (2022). Two new approaches (RAMS‐RATMI) in multi‐criteria decision‐making tactics. Journal of Mathematics, 2022(1), 6725318. https://doi.org/10.1155/2022/6725318
2. Abdulaal, R. M., Makki, A. A., & Al-Filali, I. Y. (2023). A novel hybrid approach for prioritizing investment initiatives to achieve financial sustainability in higher education institutions using MEREC-G and RATMI. Sustainability, 15(16), 12635. https://doi.org/10.3390/su151612635
3. Akarsu, O., & Çıraklı, ME (2026). Exchange rate dynamics and heterogeneity in firm export behavior: A top-to-bottom analysis. Central Bank Review, 26 (2), 100256. https://doi.org/10.1016/j.cbrev.2026.100256
4. Akgül, Y. (2025). A novel MCDM framework for evaluating corporate financial performance: Evidence from the Turkish insurance sector, Journal of Operational Strategic Analytic, 3 (2), 65–79, https://doi.org/10.56578/josa0
5. Alıcı, A., Ertek, A., & Aktemur, Ş. (2026). Havayolu Sektöründe Çalışan Odaklı Sosyal ve Finansal Performans Analizi: Bütünleşik LOPCOW-COCOSO Yaklaşımı. Black Sea Journal of Engineering and Science, (Advanced Online Publication), 334-350. https://doi.org/10.34248/bsengineering.1831229
6. Alkahtani, B. S. T., Simic, V., Anjum, M., Pamucar, D., Algahtani, O., & Alzaid, S. S. (2025). Decision support system for financial and accounting performance assessment in manufacturing industries. International Journal of Computational Intelligence Systems, 18(1), 314. https://doi.org/10.1007/s44196-025-01066-1
7. Altıntaş, F. F. (2025). A new approach for determining weight coefficients of criteria within the scope of multi-criteria decision making: Logarithmic effect-based measurement (LEBM). Journal of Turkish Operations Management, 9(1), 215-232. https://doi.org/10.56554/jtom.1327595
8. Amira, K. I. (2024). Analysis of capital budgeting decision-making using the ELECTRE decision support system method (case study on export-oriented MSMEs: PT. Mekar Saluyu Group). Jurnal Multidisiplin Indonesia, 3(7), 4198–4212. https://doi.org/10.58344/jmi.v3i7.1678

9. Amoah, J., Belas, J., Dziwornu, R., & Khan, K. A. (2022). Enhancing SME contribution to economic development: A perspective from an emerging economy. Journal of International Studies, 15 (2), 63-76.
10. Anđić, D. (2025). Assessing the applicability of the MCRAT, RAPS, RAMS and RATMI methods in the optimization of a dam geodetic control network. International Journal of Engineering and Geosciences, 10(3), 364-379. https://doi.org/10.26833/ijeg.1630726
11. Angilella, S., Doumpos, M., & Pappalardo, M. R. (2026). An enhanced simulation-based approach for multicriteria evaluation of SMEs’ performance. Annals of Operations Research, 360, 767–813. https://doi.org/10.1007/s10479-026-07036-w
12. Ayçin, E., & Çakin, E. (2019). KOBİ’lerin Finansal Performansının MACBETH-COPRAS Bütünleşik Yaklaşımıyla Değerlendirilmesi. Journal of Yaşar University. https://doi.org/10.19168/jyasar.483594
13. Ayçin, E., & Güçlü, P. (2020). BIST ticaret endeksinde yer alan işletmelerin finansal performanslarının Entropi ve MAIRCA yöntemleri ile değerlendirilmesi. Muhasebe ve Finansman Dergisi, (85), 287-312. ttps://doi.org/10.25095/mufad.673739
14. Aydın, Y. (2025). LOPCOW-RANCOM-RAWEC modeli yardımıyla sigortacılık sektöründe kurumsal finansal performans analizi: Sompo Sigorta örneği. İşletme Araştırmaları Dergisi, 17(2), 1273-1287. https://doi.org/10.20491/isarder.2025.2031
15. Baydaş, M. (2022). The effect of pandemic conditions on financial success rankings of BIST SME industrial companies: a different evaluation with the help of comparison of special capabilities of MOORA, MABAC and FUCA methods. Business & Management Studies: An International Journal, 10(1), 245-260. https://doi.org/10.15295/bmij.v10i1.1997
16. Bektaş, S. (2022). Türk Sigorta Sektörünün 2002-2021 Dönemi için MEREC, LOPCOW, COCOSO, EDAS ÇKKV Yöntemleri ile Performansının Değerlendirilmesi. BDDK Bankacılık ve Finansal Piyasalar Dergisi, 16(2), 247-283. https://doi.org/10.46520/bddkdergisi.1178359
17. Bernanke, B. S., Gertler, M., & Gilchrist, S. (1999). The financial accelerator in a quantitative business cycle framework. Handbook of macroeconomics, 1, 1341-1393.
18. Bernanke, B. S., & Gertler, M. (1995). Inside the black box: the credit channel of monetary policy transmission. Journal of Economic perspectives, 9(4), 27-48.
19. Biswas, S., & Pamučar, D. S. (2021). Combinative distance based assessment (CODAS) framework using logarithmic normalization for multi-criteria decision making. Serbian Journal of Management, 16(2). https://doi.org/10.5937/sjm16-27758
20. Bitarafan, M., Hosseini, K. A., Hashemkhani Zolfani, S., & Ziari, K. (2025). Evaluating earthquake resilience in urban areas: A novel fuzzy RANCOM approach: M. Bitarafan et al. Environment, Development and Sustainability, 1-38. https://doi.org/10.1007/s10668-025-06393-z
21. Bitarafan, M., Hosseini, K. A., Hashemkhani Zolfani, S., & Ziari, K. (2026). Evaluating earthquake resilience in urban areas: A novel fuzzy RANCOM approach. Environment, Development and Sustainability, 28, 2483–2520. https://doi.org/10.1007/s10668-025-06393-z
22. Brzozowski, M., & Tchorek, G. (2017). Exchange rate risk as an obstacle to export activity. The Polish Journal of Economics, 115-141. https://doi.org/10.33119/gn/100752
23. Ceballos, B., Lamata, M. T., & Pelta, D. A. (2016). A comparative analysis of multi-criteria decision-making methods. Progress in Artificial Intelligence, 5(4), 315-322. https://doi.org/10.1007/s13748-016-0093-1
24. Chang, S.-C., Chang, H.-H., & Lu, M.-T. (2021). Evaluating Industry 4.0 technology application in SMEs: Using a hybrid MCDM approach. Mathematics, 9(4), 414. https://doi.org/10.3390/math9040414
25. Chen, X., & Shen, A. (2026). Smart decision framework for financial planning and investment optimization. Scientific Reports, 16, 13089. https://doi.org/10.1038/s41598-026-43270-9
26. Costa, M., Delbono, F., & Linguiti, F. (2026). Employment resilience and business organization: the case of Italian micro firms. Applied Economics. https://doi.org/10.1080/00036846.2026.2613165
27. Dixit, A. K., & Pindyck, R. S. (1994). Investment under uncertainty. Princeton university press.
28. Durdu, D. (2025). Evaluating financial performance with SPC-LOPCOW-MARCOS hybrid methodology: a case study for firms listed in BIST sustainability index. Knowledge and Decision Systems with Applications, 1, 92-111. https://doi.org/10.59543/kadsa.v1i.13879
29. Ecer, F., & Pamucar, D. (2022). A novel LOPCOW‐DOBI multi‐criteria sustainability performance assessment methodology: An application in developing country banking sector. Omega, 112, 102690. https://doi.org/10.1016/j.omega.2022.102690
30. Ecer, F., Küçükönder, H., Kaya, S. K., & Görçün, Ö. F. (2023). Sustainability performance analysis of micro-mobility solutions in urban transportation with a novel IVFNN-Delphi-LOPCOW-CoCoSo framework. Transportation research part a: policy and practice, 172, 103667. https://doi.org/10.1016/j.tra.2023.103667
31. Ecer, F., Tanrıverdi, G., Yaşar, M., & Görçün, Ö. F. (2025). Sustainable aviation fuel supplier evaluation for airlines through LOPCOW and MARCOS approaches with interval-valued fuzzy neutrosophic information. Journal of Air Transport Management, 123, 102705. https://doi.org/10.1016/j.jairtraman.2024.102705
32. Eggers, F. (2020). Masters of disasters? Challenges and opportunities for SMEs in times of crisis. Journal of Business Research, 116, 199 - 208. https://doi.org/10.1016/j.jbusres.2020.05.025
33. Enaifoghe, A., & Vezi-Magigaba, M. F. (2023). Enhancing the role and contributions of small and medium enterprises (SMEs) in a globalized and innovative economy: Challenges and opportunities [Preprint]. Preprints.org. https://doi.org/10.20944/preprints202305.1748.v1,
34. Enjolras, M., Camargo, M., & Schmitt, C. (2020). Evaluating innovation and export capabilities of SMEs: Toward a multi-criteria decision-making methodology. Journal of technology management & innovation, 15(3), 17-32. http://dx.doi.org/10.4067/S0718-27242020000300017
35. Essel, R. (2025). Business Financial Strategies and SMEs Performance: Evidence from China’s Shenzhen Stock Exchange. SEDME (Small Enterprises Development, Management & Extension Journal), 52, 180 - 210. https://doi.org/10.1177/09708464251326392
36. Farjam, F., Shojaei, P., & Askarifar, K. (2023). A conceptual model for open innovation risk management based on the capabilities of SMEs: A multi-level fuzzy MADM approach. Technovation, 127, 102844. https://doi.org/10.1016/j.technovation.2023.102844
37. Gonçalves, J. M., Ferreira, F. A., Ferreira, J. J., & Farinha, L. M. (2019). A multiple criteria group decision-making approach for the assessment of small and medium-sized enterprise competitiveness. Management Decision, 57(2), 480-500. https://doi.org/10.1108/MD-02-2018-0203
38. Gönüllü, O., Çakır, N., & Küçükkocaoğlu, G. (2026). Analysing the Financial Performance of Borsa Istanbul Energy Companies: Integrated LOPCOW-MAIRCA MCDM Approach. Sosyoekonomi, 34(68), 33-58. https://doi.org/10.17233/sosyoekonomi.2026.02.02
39. Gupta, H., & Barua, M. K. (2018). A novel hybrid multi-criteria method for supplier selection among SMEs on the basis of innovation ability. International Journal of Logistics Research and Applications, 21(3), 201-223. https://doi.org/10.1080/13675567.2017.1382457
40. Gülcemal, T., & İzci, A. Ç. (2023). Türk katılım bankacılığı sektörünün performansının LOPCOW-MOOSRA modeliyle analizi. Doğuş Üniversitesi Dergisi, 25(1), 115-134. https://doi.org/10.31671/doujournal.1287120
41. Hamza, T., Barka, Z., Verdie, J. F., & Al Sayah, M. (2024). Economic policy uncertainty and SMEs’ investment efficiency in France: does competitive pressure matter?. European Business Review, 36(4), 449-467. https://doi.org/10.1108/EBR-03-2023-0078
42. Han, J., Pei, J., and Tong, H. (2023). Data Mining Concepts and Techniques (4th Eddition). Morgan Kaufmann Publications, Cambridge US.
43. Heidary Dahooie, J., Estiri, M., Zavadskas, E. K., & Xu, Z. (2022). A novel hybrid fuzzy DEA-fuzzy ARAS method for prioritizing high-performance innovation-oriented human resource practices in high tech SME’s. International Journal of Fuzzy Systems, 24(2), 883-908. https://doi.org/10.1007/s40815-021-01162-2
44. Huang, W., & Ichikohji, T. (2024). How dynamic capabilities enable Chinese SMEs to survive and thrive during COVID-19: Exploring the mediating role of business model innovation. PLOS ONE, 19. https://doi.org/10.1371/journal.pone.0304471
45. Inegbedion, H. E., Thikan, P. R., David, J. O., Ajani, J. O., & Peter, F. O. (2024). Small and medium enterprise (SME) competitiveness and employment creation: the mediating role of SME growth. Humanities and Social Sciences Communications, 11(1), 1-10. https://doi.org/10.1057/s41599-023-02434-y
46. International Finance Corporation. 2024. IFC and Small and Medium Enterprises. © World Bank. http://hdl.handle.net/10986/42083 License: CC BY-NC-ND 3.0 IGO.” https://doi.org/10.1596/42083
47. International Trade Centre. (2023). SME competitiveness outlook 2023: Small businesses in fragility: From survival to growth. Geneva: International Trade Centre. https://www.intracen.org/resources/publications/sme-competitiveness-outlook-2023
48. Jameel, T., Yasin, Y., & Riaz, M. (2026). An integrated hybrid MCDM framework for renewable energy prioritization in sustainable development. Spectrum of Decision Making and Applications, 3(1), 124-150. https://doi.org/10.31181/sdmap31202640
49. Jing, S., Hou, K., & Zhanwen, N. (2024). Advancing an evaluation model: How do family SMEs select innovation scheme in lean management? Soft Computing, 28, 4125–4149. https://doi.org/10.1007/s00500-023-09389-x
50. Johnson, R. C. (2018). Measuring Global Value Chains. Annual Review of Economics. https://doi.org/10.3386/w24027
51. Južnik Rotar, L., Kontošić Pamić, R., & Bojnec, Š. (2019). Contributions of small and medium enterprises to employment in the European Union countries. Economic research-Ekonomska istraživanja, 32(1), 3296-3308. https://doi.org/10.1080/1331677X.2019.1658532
52. Kahveci, E., Avunduk, Z. B., Daim, T., & Zaim, S. (2025). The role of flexibility, digitalization, and crisis response strategy for SMEs: Case of COVID-19. Journal of Small Business Management, 63(3), 1198-1235. https://doi.org/10.1080/00472778.2024.2377671
53. Karthee, K., Sankar, S. V., & Raj, S. Y. (2018). Improvement of overall performance of micro/small scale industries (MSSI) using multi-criteria decision making (MCDM). IOP Conference Series: Materials Science and Engineering, 402(1), 012119. https://doi.org/10.1088/1757-899X/402/1/012119
54. Kaya, F. (2025). Küçük ve orta büyüklükteki işletmelerin ekonomik kalkınmadaki rolü (Dünya ülkelerinin deneyimleri). Uluslararası Türk Dünyası Bilimsel Araştırmalar Dergisi, 6(2), 208-218. https://doi.org/10.54970/turkuaz.1797446,
55. Keshtpour, A., & Chakrabortty, R. K. (2025). The selection of saltwater desalination technology using new measurement alternatives by a combination of angle and distance (MACAD) method: a case study. Environment Systems and Decisions, 45(3), 41. https://doi.org/10.1007/s10669-025-10034-1
56. Kiessling, T., Dabić, M., Yadav, S., Huck, N., & Maley, J. F. (2024). Supply chain disruptions and need for resilience: SMEs direct/indirect exporting and rapid internationalization. IEEE transactions on engineering management, 72, 115-133. 10.1109/TEM.2024.3514701
57. Kizielewicz, B., Tomczyk, T., Gandor, M., & Sałabun, W. (2024). Subjective weight determination methods in multi-criteria decision-making: A systematic review. Procedia Computer Science, 246, 5396-5407. https://doi.org/10.1016/j.procs.2024.09.673
58. KOSGEB (2025). Türkiye’deki Kobilere İlişkin Bazı İstatistiki Göstergeler. https://www.kosgeb.gov.tr/site/tr/genel/detay/349/rapor-ve-istatistikler
59. Kulatilaka, N., & Perotti, E. C. (1992). Strategic investment timing under uncertainty. London School of Economics.
60. Lee, R. (2021). The effect of supply chain management strategy on operational and financial performance. Sustainability, 13(9), 5138. https://doi.org/10.3390/su13095138
61. Lia, D. A. Z., & Natswa, S. L. (2021). Assessment of Financial Ratio Analysis for Evaluation of Small Medium Enterprises (SME) Performance (Study on IRT Ramayana Agro Mandiri Batu City 2016-2019),“. International Journal of Multicultural and Multireligious Understanding, 8(8). https://doi.org/10.18415/ijmmu.v8i8.2889
62. Ma, J., Fan, Z. P., & Huang, L. H. (1999). A subjective and objective integrated approach to determine attribute weights. European Journal of Operational Research, 112(2), 397-404. https://doi.org/10.1016/S0377-2217(98)00141-6
63. Malik, F. S., & Terzidis, O. (2026). The metamorphosis of organizational resilience: a strategic roadmap for SMEs in the digital age. Review of Managerial Science, 1-35. https://doi.org/10.1007/s11846-026-01002-3
64. Maruf, M., & Özdemir, K. (2021). Türkiye’deki ticari bankalara ait web sitelerin performanslarının SWARA ve ARAS yöntemi ile sıralanması. OPUS International Journal of Society Researches, 18(Yönetim ve Organizasyon Özel Sayısı), 1514-1537. https://doi.org/10.26466/opus.888184
65. Matroushi, H. A., Jabeen, F., & Ali, S. A. (2018). Prioritising the factors promoting innovation in emirati female-owned SMEs: AHP approach. International Journal of Entrepreneurship and Innovation Management, 22(2/3), 220–250. https://doi.org/10.1504/IJEIM.2018.091768
66. Mattam, M. R., & Padal, K. B. (2026). Evaluation of drone propeller materials through geometrics components using multi criteria decision making methods. In EPJ Web of Conferences (Vol. 345, p. 01052). EDP Sciences.
67. Melnik-Leroy, G. A., & Dzemyda, G. (2021). How to influence the results of MCDM?—Evidence of the impact of cognitive biases. Mathematics, 9(2), 121. https://doi.org/10.3390/math9020121
68. Mishra, A. R., Pamucar, D., Rani, P., & Hezam, I. M. (2025). Single-valued neutrosophic distance measure-based MEREC-RANCOM-WISP for solving sustainable energy storage technology problem. Cognitive Computation, 17(2), 87. https://doi.org/10.1007/s12559-025-10437-x
69. Muneeb, F. M., Karbassi Yazdi, A., Hanne, T., & Mironko, A. (2025). Small and medium-sized enterprises in emerging markets and foreign direct investment: An integrated multi-criteria decision-making approach. Applied Economics, 57(25), 3327–3344. https://doi.org/10.1080/00036846.2024.2337777
70. Nanda, S., Rani, R., Karnik, N., Khanna, L., & Agarwal, N. (2024). Surveying the crucial role of financial management in small and medium enterprises. Multidisciplinary Reviews. https://doi.org/10.31893/multirev.2023ss097.
71. Ndiaye, N., Razak, L. A., Nagayev, R., & Ng, A. (2018). Demystifying small and medium enterprises' (SMEs) performance in emerging and developing economies. Borsa Istanbul Review, 18(4), 269–281. https://doi.org/10.1016/j.bir.2018.04.003
72. Ng, H. S., & Kee, D. M. H. (2012). The issues and development of critical success factors for the SME success in a developing country. International Business Management, 6(6), 680-691.
73. Nkwinika, E., & Akinola, S. (2023). The importance of financial management in small and medium-sized enterprises (SMEs): an analysis of challenges and best practices. Technology Audit and Production Reserves, 5(4(73), 12–20. https://doi.org/10.15587/2706-5448.2023.285749.
74. Odu, G. O. (2019). Weighting methods for multi-criteria decision making technique. Journal of Applied Sciences and Environmental Management, 23(8), 1449-1457. DOI: https://dx.doi.org/10.4314/jasem.v23i8.7
75. OECD (2023), OECD SME and Entrepreneurship Outlook 2023, OECD Publishing, Paris, https://doi.org/10.1787/342b8564-en.
76. Orden-Cruz, C., Paule-Vianez, J., Sánchez-Escobedo, M. C., & Moutinho, V. (2024). EPU and SMEs' financial performance: Industry vs. service sector. Journal of Open Innovation: Technology, Market, and Complexity, 10(3), 100320. https://doi.org/10.1016/j.joitmc.2024.100320
77. Ozanne, L. K., Chowdhury, M., Prayag, G., & Mollenkopf, D. A. (2022). SMEs navigating COVID-19: The influence of social capital and dynamic capabilities on organizational resilience. Industrial Marketing Management, 104, 116-135. https://doi.org/10.1016/j.indmarman.2022.04.009
78. Özden, Ü. H., Başar, Ö. D., & Kalkan, S. B. (2024). ÇKKV yöntemlerinden RAMS ve RATMI ile AB ülkelerinin sosyoekonomik sürdürülebilirliklerine göre sıralanması. Balkan & Near Eastern Journal of Social Sciences (BNEJSS), 10.
79. Özdıl, Z., Kartal, C., & Karaboğa, M. A. (2025). Analysis of logistics performance of EU 27 countries with LOPCOW and EDAS methods. Third Sector Social Economic Review, 60(1), 681-693. https://doi.org/10.15659/3.sektor-sosyal-ekonomi.25.02.2540
80. Özyürek, H., Yalçın, G. C., Kara, K., Polat, M., & Şahin, G. (2025). Performance evaluation of healthcare companies with hybrid multi-criteria decision-making (MCDM) methods during the covid-19 pandemic. J. Oper. Strat. Anal, 3, 134-161. https://doi.org/10.56578/josa030205.
81. Pagaddut, J. (2021). The financial factors affecting the financial performance of Philippine MSMEs. Universal Journal of Accounting and Finance, 9(6): 1524-1532. https://doi.org/10.13189/ujaf.2021.090629
82. Pamučar, D., & Ćirović, G. (2015). The selection of transport and handling resources in logistics centers using Multi-Attributive Border Approximation area Comparison (MABAC). Expert systems with applications, 42(6), 3016-3028. https://doi.org/10.1016/j.eswa.2014.11.057
83. Pamučar, D., Žižović, M., Biswas, S., & Božanić, D. (2021). A new logarithm methodology of additive weights (LMAW) for multi-criteria decision-making: Application in logistics. Facta universitatis, series: mechanical engineering, 19(3), 361-380. Doi: 10.22190/FUME210214031P
84. Ponhan, K., & Sureeyatanapas, P. (2022). A comparison between subjective and objective weighting approaches for multi-criteria decision making: A case of industrial location selection. Engineering and applied science research, 49(6), 763-771. https://ph01.tci-thaijo.org/index.php/easr/article/view/250477
85. Pu, G., Qamruzzaman, M., Mehta, A., Naqvi, F., & Karim, S. (2021). Innovative finance, technological adaptation and SMEs sustainability: The mediating role of government support during COVID-19 pandemic. Sustainability, 13(16), 9218. https://doi.org/10.3390/su13169218
86. Raby, S., & Chowdhury, R. H. (2025). Examining the impact of adaptive financial strategies on SME performance: insights from the COVID-19 pandemic. Small Business Economics, 65, 895 - 913. https://doi.org/10.1007/s11187-025-01011-8
87. Ram, C., & Montibeller, G. (2013). Exploring the impact of evaluating strategic options in a scenario-based multi-criteria framework. Technological Forecasting and Social Change, 80, 657-672. https://doi.org/10.1016/j.techfore.2012.09.019.
88. Rashid, A., Waqar, S.M. Exchange rate fluctuations, firm size, and export behavior: an empirical investigation. Small Bus Econ 49, 609–625 (2017). https://doi.org/10.1007/s11187-017-9849-7
89. Restrepo-Morales, J. A., Giraldo-Betancur, E. A., López-Cadavid, D. A., Grados-Vásquez, M. M., & Olórtiga-Cóndor, L. W. (2023). Enhanced organizational performance: Integrating dimensions for sustainable growth. Sustainability, 15(21), 15186. https://doi.org/10.3390/su152115186
90. Roffia, P., Simón-Moya, V., & Sendra García, J. (2021). Board of director attributes: effects on financial performance in SMEs. International Entrepreneurship and Management Journal, 18, 1141 - 1172. https://doi.org/10.1007/s11365-020-00715-5
91. Rong, Y., Yu, L., Liu, Y., Simic, V., & Garg, H. (2024). The FMEA model based on LOPCOW-ARAS methods with interval-valued Fermatean fuzzy information for risk assessment of R&D projects in industrial robot offline programming systems. Computational and Applied Mathematics, 43(1), 25. https://doi.org/10.1007/s40314-023-02532-2
92. Safaei Ghadikolaei, A., Khalili Esbouei, S., & Antucheviciene, J. (2014). Applying fuzzy MCDM for financial performance evaluation of Iranian companies. Technological and Economic Development of Economy, 20(2), 274-291. https://doi.org/10.3846/20294913.2014.913274
93. Sahabuddin, M., & Khan, I. (2021). Multi-criteria decision analysis methods for energy sector's sustainability assessment: Robustness analysis through criteria weight change. Sustainable Energy Technologies and Assessments, 47, 101380. https://doi.org/10.1016/j.seta.2021.101380
94. Sałabun, W., Urbaniak, K. (2020). A New Coefficient of Rankings Similarity in Decision-Making Problems. In: Krzhizhanovskaya, V., et al. Computational Science – ICCS 2020. ICCS 2020. Lecture Notes in Computer Science, vol 12138. Springer, Cham. https://doi.org/10.1007/978-3-030-50417-5_47
95. Sarkar, A., Goswami, S. S., & Gupta, K. K. (2027). Sensitivity analysis and validation in MCDM methods: A comprehensive review with advancements, applications, and future directions. Spectrum of Decision Making and Applications, 1–14. https://doi.org/10.31181/sdmap41202769
96. Schulze Brock, P., Katsinis, A., Lagüera González, J., Di Bella, L., Odenthal, L., Hell, M., Lozar, B. And Secades Casino, B., Annual report on European SMEs 2024/2025, SME Performance Review, Publications Office of the European Union, Luxembourg, 2025, https://data.europa.eu/doi/10.2760/7714438, JRC142263.
97. Sen, S., Savitskie, K., Mahto, R. V., Kumar, S., & Khanin, D. (2023). Strategic flexibility in small firms. Journal of Strategic Marketing, 31(5), 1053-1070. https://doi.org/10.1080/0965254X.2022.2036223
98. Sheikh, R., & Senfi, S. (2024). A Novel Opportunity Losses-Based Polar Coordinate Distance (OPLO-POCOD) Approach to Multiple Criteria Decision-Making. Journal of Mathematics, 2024, 8845886, https://doi.org/10.1155/2024/8845886.
99. Sitorus, F., & Brito-Parada, P. R. (2022). The selection of renewable energy technologies using a hybrid subjective and objective multiple criteria decision making method. Expert Systems with Applications, 206, 117839. https://doi.org/10.1016/j.eswa.2022.117839
100. Soyak, A. B. (2019). KOBİ'lere yönelik performans değerlendirme aracı geliştirilmesi: KOBİGEN (Yayımlanmamış doktora tezi). İstanbul Üniversitesi Sosyal Bilimler Enstitüsü, İstanbul.
101. Su, D. N., Tra, D. L., Huynh, H. M. T., Nguyen, H. T. H., & O'Mahony, B. (2021). Enhancing resilience in the Covid-19 crisis: lessons from human resource management practices in Vietnam. Current Issues in Tourism, 24, 3189 - 3205. https://doi.org/10.1080/13683500.2020.1863930
102. Sumrit, D., & Keeratibhubordee, J. (2024). Risk assessment framework for reverse logistics in waste plastic recycle industry: A hybrid approach incorporating FMEA decision model with AHP-LOPCOW- ARAS under trapezoidal fuzzy set. Decision Making: Applications in Management and Engineering, 8(1), 42–81. https://doi.org/10.31181/dmame812025984
103. Suyadal, M., Gül, Y., Karamaşa, Ç., & Pala, O. (2025). Performance evaluation of Turkish sustainability funds via fuzzy MCDM approach. International Journal of Contemporary Economics and Administrative Sciences, 15(2), 1121–1159. https://doi.org/10.5281/zenodo.18166858
104. Şan, Y., & Şenyiğit, E. (2025). Correlation based sensitivity analysis and suitability evaluation of multicriteria decision-making methods according to different normalization techniques. Sigma Journal of Engineering and Natural Sciences – Sigma Mühendislik ve Fen Bilimleri Dergisi, 43(4), 1321−1338, https://doi.org/10.14744/sigma.2025.00123.
105. Şimşek, A. B., & Şimşek, A. (2026). Applying RATMI (Ranking by Alternatives Trace to Median Index) for Ranking Countries Based on Sustainability Performance. In Multi-Criteria Decision Making Methods and Sustainable Applications in the Digital Age (pp. 273-300). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3693-8789-4.ch009
106. Tambunan, T. SME development, economic growth, and government intervention in a developing country: The Indonesian story. J Int Entrepr 6, 147–167 (2008). https://doi.org/10.1007/s10843-008-0025-7
107. Toktaş, P. (2025). Assessment of provinces based on business losses following the february 6 earthquakes using the integrated RANCOM and KEMIRA-M methods. Sustainability, 17(21), 9439. https://doi.org/10.3390/su17219439
108. Triantaphyllou, E., & Sánchez, A. (1997). A sensitivity analysis approach for some deterministic multi-criteria decision-making methods*. Decision Sciences, 28, 151-194. https://doi.org/10.1111/j.1540-5915.1997.tb01306.x.
109. Tümenbatur, A., & Aras, Y. Z. (2025). Küçük ve orta ölçekli işletmelerde performans ölçümü: aile çiftçiliği uygulaması. Maltepe Üniversitesi Akademik Bakış Dergisi, 1(1), 42-52.
110. Türegün, N. (2022). Financial performance evaluation by multi-criteria decision-making techniques. Heliyon, 8(5), e09361.
111. Ulutaş, A., & Karaköy, Ç. (2019). An analysis of the logistics performance index of EU countries with an integrated MCDM model. Economics & Business Review, 5(4).
112. Ulutaş, A., Topal, A., Görçün, Ö. F., & Ecer, F. (2024). Evaluation of third-party logistics service providers for car manufacturing firms using a novel integrated grey LOPCOW-PSI-MACONT model. Expert Systems with Applications, 241, 122680. https://doi.org/10.1016/j.eswa.2023.122680
113. Varga, J. (2021). Defining the economic role and benefits of micro, small and medium-sized enterprises in the 21st century with a systematic review of the literature. Acta Polytechnica Hungarica, 18(11), 209-228.,
114. Venkatesan, R. (2024). Impact of financial management practices on performance measurement of small and medium enterprises in Tamil Nadu. SEDME (Small Enterprises Development, Management & Extension Journal), 51(2), 176–194. https://doi.org/10.1177/0970846424125416
115. Wang, J., Lau, W. Y., & Baskaran, A. (2025). Innovating financial metrics for SME development: A factor-based analysis of Chinese enterprises. African Journal of Science, Technology, Innovation and Development, 1-14. https://hdl.handle.net/10520/ejc-aa_ajstid_vahead-of-print_nahead-of-print_a1
116. Wiȩckowski, J., & Sałabun, W. (2023). Sensitivity analysis approaches in multi-criteria decision analysis: A systematic review. Appl. Soft Comput., 148, 110915. https://doi.org/10.1016/j.asoc.2023.110915.
117. Więckowski, J., Różewski, P., & Sałabun, W. (2025). Evaluating relationship functions in the RANCOM method and their effects on criteria weighting accuracy. Procedia Computer Science, 270, 5744-5753. https://doi.org/10.1016/j.procs.2025.10.043
118. Więckowski, J., Wątróbski, J., & Sałabun, W. (2024a). Inaccuracies in expert judgment: Comparative analysis of RANCOM and AHP methods in housing location selection problem. IEEE Access. doi: 10.1109/ACCESS.2024.3443451
119. Więckowski, J., Wątróbski, J., Shkurina, A., & Sałabun, W. (2024b). Adaptive multi-criteria decision making for electric vehicles: a hybrid approach based on RANCOM and ESP-SPOTIS. Artificial Intelligence Review, 57(10), 270. https://doi.org/10.1007/s10462-024-10901-4
120. Więckowski, J., Kizielewicz, B., Shekhovtsov, A., and Sałabun, W. (2023). RANCOM: A novel approach to identifying criteria relevance based on inaccuracy expert judgments, Eng. Appl. Artif. Intell., vol. 122, Jun. 2023, Art. no. 106114. https://doi.org/10.1016/j.engappai.2023.106114
121. Woźniak, M., Duda, J., Gąsior, A., & Bernat, T. (2019). Relations of GDP growth and development of SMEs in Poland. Procedia Computer Science, 159, 2470-2480. https://doi.org/10.1016/j.procs.2019.09.422
122. Wu, H. Y., Tzeng, G. H., & Chen, Y. H. (2009). A fuzzy MCDM approach for evaluating banking performance based on Balanced Scorecard. Expert systems with applications, 36(6), 10135-10147. https://doi.org/10.1016/j.eswa.2009.01.005
123. Zahoor, N., & Lew, Y. (2023). Enhancing international marketing capability and export performance of emerging market SMEs in crises: Strategic flexibility and digital technologies. International Marketing Review, 40(5), 1083–1111. https://doi.org/10.1108/IMR-12-2021-0350
124. Zanakis, S. H., Solomon, A., Wishart, N., & Dublish, S. (1998). Multi-attribute decision making: A simulation comparison of select methods. European journal of operational research, 107(3), 507-529. https://doi.org/10.1016/S0377-2217(97)00147-1
125. Zavadskas, E. K., & Turskis, Z. (2008). A new logarithmic normalization method in games theory. Informatica, 19(2), 303-314. https://doi.org/10.3233/INF-2008-19(2)09
126. Zhang, L., Fakieh, B., & Shang, L. I. (2022a). Financial management of asset-liability ratio of small-and medium-sized enterprises in dynamic nonlinear system. Fractals, 30(02), 2240064. https://doi.org/10.1142/S0218348X22400643
127. Zhang, Z., Zhu, H., Zhou, Z.-G., & Zou, K. (2022b). How does innovation matter for sustainable performance? Evidence from small and medium-sized enterprises. Journal of Business Research. https://doi.org/10.1016/j.jbusres.2022.08.034
128. Zhou, Q., Edafioghor, T., Wu, C.-H., & Doherty, B. (2022). Building organisational resilience capability in small and medium‐sized enterprises: The role of high‐performance work systems. Human Resource Management Journal. https://doi.org/10.1111/1748-8583.12479