Journals / Anadolu Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi / 2026 / Volume: 27 Issue: 3

COMPARATIVE FINANCIAL PERFORMANCE ANALYSIS OF EV MANUFACTURERS USING CRITIC-BASED TOPSIS, OCRA, VIKOR AND EATWIOS

Şifa Kırhasanoğlu(GIRESUN UNIVERSITY, GÖRELE ACADEMY OF APPLIED SCIENCES, DEPARTMENT OF BANKING AND FINANCE)0000-0002-0214-3166
Murat Özdemir(GIRESUN UNIVERSITY, FACULTY OF ECONOMICS AND ADMINISTRATIVE SCIENCES)0000-0001-6608-6511
Pages
—
Publication Date
2026-09-22
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Overall, although rankings differ across methods, general performance patterns remain similar.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Electric vehicles (EVs), seen as a solution to reduce carbon emissions and reduce energy dependence, continue to gain importance.
📊
Key Findings & Data:Along with this importance, EV manufacturers, who have gained a significant place in the automotive sector, continue to grow.
💡
Conclusion & Impact:Overall, although rankings differ across methods, general performance patterns remain similar.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Electric Vehicle Manufacturers · Financial Performance · Multi-criteria Decision Making (MCDM)

Abstract

Electric vehicles (EVs), seen as a solution to reduce carbon emissions and reduce energy dependence, continue to gain importance. Along with this importance, EV manufacturers, who have gained a significant place in the automotive sector, continue to grow. These companies are recognized as having different characteristics from traditional vehicle manufacturers. Therefore, a different financial evaluation is required compared to automotive companies. There are relatively studies in the literature that specifically examine EV companies. In this context, this study aims to evaluate the financial performance of EV manufacturing companies using their specific financial criteria. The TOPSIS, VIKOR, and OCRA methods were used for financial performance analysis. Furthermore, the companies' efficiency analysis was carried out using the EATWIOS method. The criteria were weighted using the CRITIC method. The findings show that the financial performance rankings vary depending on the method used. NIKOLA consistently ranks first according to the OCRA, TOPSIS, and EATWIOS methods, demonstrating consistent performance. In contrast, VIKOR results differ depending on the value of the v parameter, with XPENG and TESLA ranking first under different scenarios. Overall, although rankings differ across methods, general performance patterns remain similar.

🔖

Cite This Article

Kırhasanoğlu, Ş., Özdemir, M. (2026). COMPARATIVE FINANCIAL PERFORMANCE ANALYSIS OF EV MANUFACTURERS USING CRITIC-BASED TOPSIS, OCRA, VIKOR AND EATWIOS. Anadolu Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, Volume: 27 Issue: 3. https://doi.org/10.53443/anadoluibfd.1882296

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (66)

  1. Alshahapy, H., Bozeman III, J. F., Carley, S., Nock, D., & Matisoff, D. (2025). Modern challenges facing electric vehicle adoption: A review of barriers to adoption, supply chain challenges, and equity. Environmental Research Letters, 20(2), 023002. https://doi.org/10.1088/1748-9326/ada6de
  2. Altın, H. (2023). Borsa İstanbul’da ilk halka arzların CRITIC tabanlı OCRA ve COPRAS yöntemleri ile analizi. KOCATEPEİİBFD, 25(2), 226–245. https://doi.org/10.33707/akuiibfd.1291707
  3. Ayaydın, H., Shırzadı, S. A., Balıkçı, H. C., Sharabati, A.-A. A., & Barut, A. (2026). Entropy-based VIKOR and TOPSIS approaches for financial performance ranking: Evidence from Turkish cement companies listed on Borsa Istanbul. Quality & Quantity: International Journal of Methodology, 60(2), 5583–5605. https://doi.org/10.1007/s11135-025-02494-z
  4. Aytekin, A. (2020). Türkiye’de önde gelen şirketlerin etkinlik, farklılık ve performans ölçümü. İşletme Araştırmaları Dergisi, 12(3), 2555–2573.
  5. Baležentis, A., Baležentis, T., & Misiunas, A. (2012). An integrated assessment of Lithuanian economic sectors based on financial ratios and fuzzy MCDM methods. Technological and Economic Development of Economy, 18(1), 34–53. https://doi.org/10.3846/20294913.2012.656151
  6. Behzadian, M., Otaghsara, S. K., Yazdani, M., & Ignatius, J. (2012). A state-of the-art survey of TOPSIS applications. Expert Systems with Applications, 39(17), 13051–13069. https://doi.org/10.1016/j.eswa.2012.05.056
  7. Bozdoğan, T., Odabas, A., & Çetin, Ö. O. (2023). Financial performance of BIST-Cement companies analysis by TOPSIS and ELECTRE methods. Dumlupınar Üniversitesi Sosyal Bilimler Dergisi, (78), 491–510. https://doi.org/10.51290/dpusbe.1368085
  8. Braun, M., & Larrain, B. (2005). Finance and the business cycle: International, inter-industry evidence. The Journal of Finance, 60(3), 1097–1128. https://doi.org/10.1111/j.1540-6261.2005.00757.x
  9. Bülbül, S. E., & Köse, A. (2016). Türk sigorta sektörünün PROMETHEE yöntemi ile finansal performans analizi. Marmara Üniversitesi İktisadi ve İdari Bilimler Dergisi, 38(1), 187–210. https://doi.org/10.14780/iibd.29194
  10. Bulut, E., & Şimşek, A. İ. (2024). Evaluation of financial performance of BIST participation banks: CAMELS and multi-criteria decision making (MCDM) approach. Alanya Akademik Bakış, 8(3), 923–940. https://doi.org/10.29023/alanyaakademik.1511040
  11. Büyükselçuk, E. Ç., & Tozan, H. (2022). Integrated Entropy-EDAS Methods for the Electrified Car Selection Problem. Kocaeli Journal of Science and Engineering, 5(ICOLES2021 Special Issue), 38-47. https://doi.org/10.34088/kojose.1015129
  12. Çalış, N., & Sakarya, Ş. (2022). Covid-19 döneminde ve öncesinde firmaların finansal performanslarının CRITIC temelli CoCoSo yöntemi ile analizi; BİST otomotiv sektörü üzerine bir uygulama. Bingöl Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 6(2), 287–322. https://doi.org/10.33399/biibfad.1142744
  13. Candan, G. (2020). Efficiency and performance analysis of economics research using hesitant fuzzy AHP and OCRA methods. Scientometrics, 124(3), 2527–2549. https://doi.org/10.1007/s11192-020-03558-5
  14. Chatterjee, P., & Chakraborty, S. (2012). Material selection using preferential ranking methods. Materials & Design, 35, 384–393. https://doi.org/10.1016/j.matdes.2011.09.027
  15. Çilek, A., & Karavardar, A. (2023). Hibrit Entropi ve EATWIOS teknikleri ile Türk kamu bankalarının verimlilik analizi. Business & Management Studies: An International Journal (BMIJ), 11(1), 136–151. https://doi.org/10.15295/bmij.v11i1.2185
  16. Çilek, A., & Şeyranlıoğlu, O. (2025). Measuring the financial performance of reinsurance companies in Türkiye with LODECI, CRADIS and AROMAN MCDM methods. International Journal of Business and Economic Studies, 7(1), 1–18. https://doi.org/10.54821/uiecd.1587675
  17. Demirgüneş, K. (2016). The effect of liquidity on financial performance: Evidence from Turkish retail industry. International Journal of Economics and Finance, 8(4), 63–79. https://doi.org/10.5539/ijef.v8n4p63
  18. Diakoulaki, D., Mavrotas, G., & Papayannakis, L. (1995). Determining objective weights in multiple criteria problems: The CRITIC method. Computers & Operations Research, 22(7), 763–770. https://doi.org/10.1016/0305-0548(94)00059-H
  19. Dirie, M. A. (2017). Finansal performansın TOPSİS çok kriterli karar verme yöntemi ile belirlenmesi: Ana metal sanayi işletmeleri üzerine bir uygulama [Master’s thesis, Dokuz Eylül University]. Ulusal Tez Merkezi
  20. Ecer, F. (2020). Çok kriterli karar verme: Geçmişten günümüze kapsamlı bir yaklaşım. Seçkin Yayıncılık.
  21. Ferraro, A., & Garofalo, A. (2025). Navigating the transition to green mobility: Assessing the ımpact of tax incentives on vehicle efficiency and market dynamics in Italy. Corporate Social Responsibility and Environmental Management, 32(3), 3167–3180. https://doi.org/10.1002/csr.3121
  22. Filonchyk, M., Peterson, M. P., Zhang, L., Hurynovich, V., & He, Y. (2024). Greenhouse gases emissions and global climate change: Examining the influence of CO2, CH4, and N2O. Science of the Total Environment, 935, 173359. https://doi.org/10.1016/j.scitotenv.2024.173359
  23. Gao, Z., Xie, H., Yang, X., Zhang, L., Yu, H., Wang, W., … Chen, S. (2023). Electric vehicle lifecycle carbon emission reduction: A review. Carbon Neutralization, 2(5), 528–550. https://doi.org/10.1002/cnl2.81
  24. Gökdemir, T., & Emel, G. G. (2023). BIST BANKA’da işlem gören bankaların COVID-19 pandemi dönemindeki finansal performanslarının farklı kriter ağırlıklandırma yöntemleri ile analizi. Business & Management Studies: An International Journal (BMIJ), 11(3), 1163–1190. https://doi.org/10.15295/bmij.v11i3.2249
  25. Görçün, Ö. F., Zolfani, S. H., & Çanakçıoğlu, M. (2022). Analysis of efficiency and performance of global retail supply chains using integrated fuzzy SWARA and fuzzy EATWOS methods. Oper Manag Res, 15, 1445–1469. https://doi.org/10.1007/s12063-022-00261-z
  26. Hwang, C.-L., & Yoon, K. (1981). Multiple attribute decision making: Methods and applications. Springer-Verlag. https://doi.org/10.1007/978-3-642-48318-9
  27. İç, Y. T., Çelik, B., Kavak, S., & Baki, B. (2022). An integrated AHP-modified VIKOR model for financial performance modeling in retail and wholesale trade companies. Decision Analytics Journal, 3, 100077. https://doi.org/10.1016/j.dajour.2022.100077
  28. IEA. (2025). Global EV outlook 2025. https://www.iea.org/reports/global-ev-outlook-2025
  29. Jariah, A., & Lukiana, N. (2023). Management of financial performance based on capital structure, liquidity ratio, coverage and activity. Assets: Jurnal Ilmiah Ilmu Akuntansi, Keuangan Dan Pajak, 7(2), 1–7. https://doi.org/10.30741/assets.v7i2.1030
  30. Kahraman, İ. K., & Nazlıoğlu, E. H. (2025). Çok kriterli karar verme yöntemleri ile aracı kurumların finansal performans analizi: Entropi-TOPSIS yöntemi. Erciyes Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, (71), 131–137. https://doi.org/10.18070/erciyesiibd.1683551
  31. Karaoğlan, S., & Şahin, S. (2018). BİST XKMYA işletmelerinin finansal performanslarının çok kriterli karar verme yöntemleri ile ölçümü ve yöntemlerin karşılaştırılması. Ege Akademik Bakış, 18(1), 63–80.
  32. Kaya, A., Pamucar, D., Gürler, H. E., & Ozcalici, M. (2024). Determining the financial performance of the firms in the Borsa Istanbul sustainability index: Integrating multi criteria decision making methods with simulation. Financial Innovation, 10(1), 21. https://doi.org/10.1186/s40854-023-00512-3
  33. Kendirli, S., & Yıldırım, M. V. (2022). BIST’te kayıtlı otomotiv imalat sektörü şirketlerinin finansal performanslarının topsis yöntemi ile incelenmesi. Global Journal of Economics and Business Studies, 11(21), 87–97.
  34. Kırhasanoğlu, Ş. (2025). Elektrikli araç üretim şirketlerinde muhasebe temelli finansal göstergelerin kârlılığa etkisi: Panel veri yaklaşımıyla ampirik bir analiz (Publication No. 971801) [Doctoral dissertation, Giresun Üniversitesi]. Ulusal Tez Merkezi.
  35. Krabec, T., & Čižinská, R. (2021). Financial performance of the automotive industry in selected EU countries. International Advances in Economic Research, 27(4), 325–327. https://doi.org/10.1007/s11294-022-09837-8
  36. Magazzino, C., Costantiello, A., Laureti, L., Leogrande, A., & Gattone, T. (2025). Greenhouse gas emissions and road infrastructure in Europe: A machine learning analysis. Transportation Research Part D: Transport and Environment, 139, 104602. https://doi.org/10.1016/j.trd.2025.104602
  37. Mshelia, R., Jibatswen, T., Babarinde, F., & Yusuf, R. (2021). Assessment of vehicular greenhouse gas emissions and potentials for reduction in a university campus: Case study of Bayero university Kano. International Journal of Environment and Geoinformatics, 8(3), 301–306. https://doi.org/10.30897/ijegeo.837262
  38. Opricovic, S., & Tzeng, G. H. (2004). Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS. European Journal of Operational Research, 156(2), 445–455. https://doi.org/10.1016/S0377-2217(03)00020-1
  39. Organisation for Economic Co-operation and Development. (2025). Environmental outlook on the triple planetary crisis: Stakes, evolution and policy linkages. OECD Environmental Outlook. OECD Publishing. https://doi.org/10.1787/257ffbb6-en
  40. Orhan, M., Altın, H., & Aytekin, M. (2020). Çok kriterli karar verme yöntemleriyle finansal performans değerlendirme: Ulaştırma alanında bir uygulama. Turkish Studies-Economics, Finance, Politics, 15(1), 395. https://doi.org/10.29228/TurkishStudies.40149
  41. Özarı, Ç., & Çanakçıoğlu, M. (2025). Borsa İstanbul’daki araç üretici firmaların finansal performanslarının analizi. Mali Çözüm, 35(187), 109–135.
  42. Özbek, A. (2021). Çok kriterli karar verme yöntemleri ve Excel ile problem çözümü. Seçkin Yayıncılık.
  43. Paksoy, Ö., & Duran, Z. (2023). Otomotiv sektörünün Covid-19 sürecindeki finansal performansının CRITIC ve MOOSRA yöntemleri ile değerlendirilmesi. Muhasebe ve Denetime Bakış Dergisi, (68), 227–248. https://doi.org/10.55322/mdbakis.1131780
  44. Pala, O. (2021). BIST inşaat endeksinde bütünleşik CCSD-COCOSO tabanlı finansal performans analizi. Afyon Kocatepe Üniversitesi Sosyal Bilimler Dergisi, 23(4), 1500–1513. https://doi.org/10.32709/akusosbil.912504
  45. Parkan, C. (1994). Operational competitiveness ratings of production units. Managerial and Decision Economics, 15(3), 201–221. https://doi.org/10.1002/mde.4090150303
  46. Parkan, C., & Wu, M. L. (1999). Decision making and performance measurement models with application to robot selection. Computers & Industrial Engineering, 36(3), 503–523. https://doi.org/10.1016/S0360-8352(99)00114-8
  47. Peters, M. L., & Zelewski, S. (2010). Performance Measurement mithilfe des Operational Competitiveness Ratings (OCRA). Wirtschaftswissenschaftliches Studium, 39(5), 224–229. https://doi.org/10.15358/0340-1650-2010-5-224
  48. Peters, M. L., Zelewski, S., & Bruns, A. S. (2012). Extended version of EATWOS concerning satisficing levels for input quantities. In T. Blecker, W. Kersten, & C. M. Ringle (Eds.), Pioneering supply chain design: A comprehensive insight into emerging trends, technologies and applications (pp. 303–318). Josef Eul Verlag.
  49. Shao, W., Yang, K., & Bai, X. (2021). Impact of financial subsidies on the R&D intensity of new energy vehicles: A case study of 88 listed enterprises in China. Energy Strategy Reviews, 33, 100580. https://doi.org/10.1016/j.esr.2020.100580
  50. Shaw, V. K., Biswas, S., & Guha, B. (2026). A modified EATWIOS framework for efficiency assessment: A case study on the banking sector. Management Science Advances, 3(1), 187–226. https://doi.org/10.31181/msa31202644
  51. Smith, K. A., Ball, T., Conen, F., Dobbie, K. E., Massheder, J., & Rey, A. (2018). Exchange of greenhouse gases between soil and atmosphere: Interactions of soil physical factors and biological processes. European Journal of Soil Science, 69(1), 10–20. https://doi.org/10.1111/ejss.12539
  52. Solaymani, S., & Botero, J. (2025). Reducing carbon emissions from transport sector: Experience and policy design considerations. Sustainability, 17(9), 3762. https://doi.org/10.3390/su17093762
  53. Süzülmüş, S. (2025). Tekstil sektöründe CRITIC temelli copras ve mabac yöntemleri ile finansal performans değerlendirilmesi. Bingöl Üniversitesi Sosyal Bilimler Enstitüsü Dergisi (BUSBED), 15(29), 214–234. https://doi.org/10.29029/busbed.1581938
  54. Tazegül, A. (2025). Çok kriterli karar verme yöntemi ile finansal performans değerlendirmesi: BİST 50 şirketleri üzerine bir uygulama. Ünye İktisadi ve İdari Bilimler Fakültesi Dergisi, 7(2), 106–127.
  55. Terzioğlu, M. K., Kurt, E. S., Yaşar, A., & Köken, M. (2022). BİST100-enerji sektörü finansal performansı: SWARA-VIKOR ve SWARA-WASPAS. Alanya Akademik Bakış, 6(2), 2439–2455. https://doi.org/10.29023/alanyaakademik.1079820
  56. Topal, A. (2021). Çok kriterli karar verme analizi ile elektrik üretim şirketlerinin finansal performans analizi: Entropi tabanlı Cocoso yöntemi. Business & Management Studies: An International Journal, 9(2), 532–546. https://doi.org/10.15295/bmij.v9i2.1794
  57. Türegün, N. (2022). Financial performance evaluation by multi-criteria decision-making techniques. Heliyon, 8(5), Article e09361. https://doi.org/10.1016/j.heliyon.2022.e09361
  58. Tutar, H., Kavas, Y. B., Medetoğlu, B., & Koltai, J. (2025). Analysis of financial performance of deposit banks in Turkey using multi-criteria decision-making methods. Journal of International Studies, 18(1), 196–215. https://doi.org/10.14254/2071-8330.2025/18-1/12
  59. Velnampy, T., & Niresh, J. A. (2012). The relationship between capital structure and profitability. Global Journal of Management and Business Research, 12(13), 67–74.
  60. Vieira, E. S. (2018). Debt policy and firm performance of family firms: The impact of economic adversity. International Journal of Managerial Finance, 14(2), 186–206. https://doi.org/10.1108/IJMF-03-2016-0062
  61. Wu, J.-Z., & Zhang, Q. (2011). Multicriteria decision making method based on intuitionistic fuzzy weighted entropy. Expert Systems with Applications, 38(1), 916–922. https://doi.org/10.1016/j.eswa.2010.07.073
  62. Yadav, S. K., & Kapoor, R. (2018). Financial performance ranking of automotive companies in India using TOPSIS method. International Journal of Business Excellence, 16(2), 149–161. https://doi.org/10.1504/IJBEX.2018.094702
  63. Yalçın, G. C., Kara, K., & Özyürek, H. (2025). Evaluating financial performance of companies in the Borsa Istanbul Sustainability Index using the CRITIC-MABAC method. Spectrum of Operational Research, 2(1), 364–387. https://doi.org/10.31181/sor21202530
  64. Zavadskas, E. K., & Turskis, Z. (2011). Multiple criteria decision making (MCDM) methods in economics: An overview. Technological and Economic Development of Economy, 17(2), 397–427. https://doi.org/10.3846/20294913.2011.593291
  65. Zhang, H., Irfan, M., Ai, F., Al-Aiban, K. M., & Abbas, S. (2024). Analyzing barriers to the adoption and development of electric vehicles: A roadmap towards sustainable transportation system. Renewable Energy, 233, 121136. https://doi.org/10.1016/j.jclepro.2012.01.011
  66. Zhang, X. F., Zhang, S. Y., Hu, Z. Y., Yu, G., Pei, C. H., & Sa, R. N. (2012). Identification of connection units with high GHG emissions for low-carbon product structure design. Journal of Cleaner Production, 27, 118–125. https://doi.org/10.1016/j.renene.2024.121136