ANALYSIS OF THE FINANCIAL PERFORMANCE OF AIRLINE COMPANIES IN STAR ALLIANCE IN THE PERIOD 2018-2022 USING LOPCOW-TOPSIS METHODS

Abstract

This study aims to comparatively evaluate the financial performance of the airlines included in Star Alliance for the period 2018-2022 for pre-COVID-19, COVID-19 and post-COVID-19 periods. For the performance evaluation, 5 criteria and a total of 9 financial performance ratios were used. LOPCOW method was used to determine the criteria (importance) weights of the calculated financial performance ratios and TOPSIS method was used to determine the performance rankings. According to the results of LOPCOW analysis, the most important criterion was determined as net profit/total assets (PR3) for 2018, net profit/total equity (PR2) for 2019 and 2021, short-term debt/total assets (FSR3) for 2020 and net profit/net sales (PR1) for 2022. The criterion with the lowest importance weight is net balance sheet position/equity (CA) for 2018, 2019 and 2021, net sales/ current assets (AT) for 2020, and short-term debt/total assets (FSR3) for 2022. According to TOPSIS performance evaluation results, the best performing airline was Shenzhen Airlines in 2018, 2019 and 2020, Thai Airways International in 2021 and Aegean in 2022. The airline that ranked last in the performance ranking was Croatia Airlines in 2018, Asian Airlines in 2019, Thai Airways International in 2020, Air Canada in 2021 and Air China in 2022.

Keywords

• Star Alliance Airlines
• Financial Performance
• Multi-Criteria Decision Making Techniques
• LOPCOW
• TOPSIS

References

1. Avcı, T., & Çınaroğlu, E. (2018). AHP Temelli TOPSIS Yaklaşımı İle Havayolu İşletmelerinin Finansal Performans Değerlemesi. Cumhuriyet Üniversitesi İktisadi ve İdari Bilimler Dergisi, 19(1), 316-335.
2. Bae, K., Gupta, A., & Mau, R. (2021). Comparative Analysis of Airline Financial and Operational Performances: A fuzzy AHP and TOPSIS Integrated Approach. Decision Science Letters, 10(3), 361-374. http://dx.doi.org/10.5267/j.dsl.2021.2.002.
3. Bakır, M., Akan, Ş., Kiracı, K., Karabasevic, D., Stanujkic, D., & Popovic, G. (2020). Multiple-Criteria Approach of the Operational Performance Evaluation in the Airline Industry: Evidence from the Emerging Markets. Romanian Journal of Economic Forecasting, 23(2), 149-172.
4. 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.
5. Bektaş, S. (2023). Özel Sermayeli Bir Mevduat Bankasının Sürdürülebilirlik Performansının Hibrit ÇKKV Modeliyle Değerlendirilmesi: 2009-2021 Dönemi Akbank Örneği. İzmir İktisat Dergisi, 38(4), 884-907, https://dx.doi.org/10.24988/ije.1216968.
6. 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://dx.doi.org/10.46520/bddkdergisi.1178359.
7. Belton, V., & Stewart, T. (2002). Multiple Criteria Decision Analysis: An Integrated Approach. Springer Science & Business Media.
8. Chang, Y. H., & Yeh, C. H. (2001). Evaluating Airline Competitiveness Using Multi Attribute Decision Making. Omega, 29(5), 405-415.

9. Dağlı, D. (2021). Havayolu İşletmelerinin Covid-19 Öncesi ve Covid-19 Sürecindeki Finansal Performanslarının TOPSIS Yöntemi ile Değerlendirilmesi. İşletme Araştırmaları Dergisi, 13 (3), 2242-2255, https://doi.org/10.20491/isarder.2021.1259.
10. Ecer, F. ve Pamucar, D. (2022). A Novel LOPCOW-DOBI Multi Criteria Sustainability Performance Assessment Methodology: An Application in Developing Country Banking Sector. Omega, 112,112690, 1-17, 10.1016/j.omega.2022.102690.
11. Ellibes, E., & Candan, G. (2021). Financial Performance Evaluation of Airline Companies with Fuzzy AHP and Grey Relational Analysis Methods. EKOIST Journal of Econometrics and Statistics, 34, 37-56, https://dx.doi.org/10.26650/ekoist.2021.34.917326.
12. Feng, C. M., & Wang, R. T. (2000). Performance Evaluation for Airlines Including the Consideration of Financial Ratios. Journal of Air Transport Management, 6(3), 133-142, https://econpapers.repec.org/scripts/redir.pf?u=https%3A%2F%2Fdoi.org%2F10.1016%252FS0969-6997%252800%252900003-X;h=repec:eee:jaitra:v:6:y:2000:i:3:p:133-142.
13. Gülcemal, T., & İzci, A. Ç. (2024). Türk Katılım Bankacılığı Sektörünün Performansının LOPCOW-MOOSRA Modeliyle Analizi. Doğuş Üniversitesi Dergisi, 25(1), 115-134.
14. Hwang, C. L. & Yoon, K. (1981). Multiple Attributes Decision Making Methods and Applications, Berlin: Springer.
15. Işık, Ö. (2019). Entropi ve TOPSIS Yöntemleriyle Finansal Performans İle Pay Senedi Getirileri Arasındaki İlişkinin İncelenmesi. Kent Akademisi, 12(1), 200–213.
16. Köse, Y. (2021). Havacılık Sektöründe Spesifik Finansal Oranlar: Türkiye’deki Havayolu Şirketleri Üzerine Analiz ve Değerlendirme. Finansal Araştırmalar ve Çalışmalar Dergisi, 13(25), 623-636, https://doi.org/10.14784/marufacd.976607.
17. Kurt, G., & Kablan, A. (2022). Covid-19’un, BİST Ulaştırma Endeksinde Faaliyet Gösteren Havayolu İşletmelerinin Finansal Performansı Üzerindeki Etkilerinin, Çok Kriterli Karar Verme Yöntemleri İle Analizi. İşletme Akademisi Dergisi, 3(1), 16-33, https://doi.org/10.26677/TR1010.2022.961.
18. Ömürbek, Y., & Kınay, Ö. (2013). Havayolu Taşımacılığı Sektöründe TOPSIS Yöntemiyle Finansal Performans Değerlendirmesi. Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 18(3), 343-363.
19. Perçin, S., & Aldalou, E. (2018). Financial Performance Evaluation of Turkish Airlines Companies Using Integrated Fuzzy AHP Fuzzy TOPSIS Model. Uluslararası İktisadi ve İdari İncelemeler Dergisi, 18. EYİ Özel Sayısı, 583-598, https://doi.org/10.18092/ulikidince.347925.
20. Pestana Barros, C., & Wanke, P. (2015). An Analysis of African Airlines Efficiency with Two-Stage TOPSIS and Neural Networks. Journal of Air Transport Management, 44-45, 90-102, https://doi.org/10.1016/j.jairtraman.2015.03.002.
21. Sivil Havacılık Genel Müdürlüğü. (2014). Faaliyet Raporu. Erişim Adresi: https://web.shgm.gov.tr/documents/sivilhavacilik/files/kurumsal/faaliyet/2014.pdf. Erişim Tarihi: 13.02.2024.
22. Sivil Havacılık Genel Müdürlüğü. (2022). Faaliyet Raporu. Erişim Adresi: https://web.shgm.gov.tr/documents/sivilhavacilik/files/kurumsal/faaliyet/2022-v2.pdf. Erişim: 13.02.2024.
23. Sümerli Sarıgül, S., Ünlü, M., & Yaşar, E. (2023). Financial Performance Analysis of Airlines Operating in Europe: CRITIC Based MAUT and MARCOS Methods. International Journal of Business and Economic Studies, 5(2), 76-97, https://doi.org/10.54821/uiecd.1257488.
24. Star Alliance. https://www.staralliance.com/en/
25. Teker, D., Teker, S., Polat, E., (2022). Performance of airlines: Topsis analysis. PressAcademia Procedia (PAP), 15, 149-150, https://doi.org/10.17261/Pressacademia.2022.1602.
26. Tzeng, G. H. & Huang, J. J. (2011). Multiple Attribute Desicion Making Methods and Applications. New York: Chapman and Hall/CRC, https://doi.org/10.1201/b11032.
27. Torlak, G., Sevkli, M., Sanal, M., & Zaim, S. (2011). Analyzing Business Competition by Using Fuzzy TOPSIS Method: An Example of Turkish Domestic Airline İndustry. Expert Systems with Applications, 38(4), 3396-3406 https://doi.org/10.1016/j.eswa.2010.08.125.
28. Wang, Y. M., & Elhag, T. M. (2006). Fuzzy TOPSIS Method Based on Alpha Level Sets With An Application to Bridge Risk Assessment. Expert Systems with Applications, 31(2), 309-319, http://dx.doi.org/10.1016/j.eswa.2005.09.040.
29. Wang, Y. J. (2008). Applying FMCDM to Evaluate Financial Performance of Domestic Airlines in Taiwan. Expert Systems with Applications, 34(3), 1837-1845, http://dx.doi.org/10.1016/j.eswa.2007.02.029.
30. Wanke, P., Pestana Barros, C., & Chen, Z. (2015). An Analysis of Asian Airlines Efficiency with Two-Stage TOPSIS and MCMC Generalized Linear Mixed Models. International Journal of Production Economics, 169, 110–126. DOI: 10.1016/j.ijpe.2015.07.028.
31. Zhu, X., Wang, F., Liang, C., Li, J., & Sun, X. (2012). Quality Credit Evaluation Based on TOPSIS: Evidence From Air-Conditioning Market In China. Procedia Computer Science, 9, 1256-1262, https://doi.org/10.1016/j.procs.2012.04.137.