Управленец

2218-5003

2686-7923

10.29141/2218-5003-2025-16-5-7

XIEVBC

https://upravlenets.usue.ru/ru/-2025/1762

Влияние COVID-19 на финансовое состояние отрасли пассажирских авиаперевозок

The COVID-19 pandemic’s impact on the financial performance of the aviation industry

Сейхан

Исмаил Фатих

Сейхан

Исмаил Фатих

Ceyhan

İsmail Fatih

ifceyhan@gmail.comKarapolat

Özden

Karapolat

Özden

Караполат

Озден

karapolatozden@gmail.com

Bartın University (Bartın, Türkiye)Университет Бартына (г. Бартын, Турция)

11112025

165981130507202418122024

CC BY-NC 4.0

Чрезвычайные ситуации, такие как кризис COVID-19, оказывают критическое влияние на финансовые показатели компаний, в частности рентабельность, задолженность и ликвидность. Статья посвящена исследованию воздействия пандемийного кризиса на финансовое состояние индустрии пассажирских авиаперевозок. Методологическая основа представлена теорией перспектив Д. Канемана и А. Тверски. Информационную базу составили данные 20 крупнейших авиакомпаний мира. Период исследования включал два подпериода: допандемийный (2015–2019 гг.) и пандемийный (2020–2021 гг.). Для анализа показателей деятельности авиакомпаний рассчитаны 15 финансовых коэффициентов, распределенных на четыре группы: ликвидность, рентабельность, финансовая структура и оборачиваемость. Данные взвешены с помощью метода энтропии и проанализированы методами многокритериального анализа решений TOPSIS и PROMETHEE. Тест ранговой корреляции Спирмена позволил установить согласованность итогов ранжирования финансовых показателей компаний в допандемийный и пандемийный периоды. Результаты исследования подтвердили, что кризис COVID-19 негативно сказался на финансовых показателях большинства авиакомпаний, но наиболее сильное влияние испытали Air China, British Airways, LATAM Airlines Group и Southwest Airlines (согласно TOPSIS), а также «Аэрофлот», Air China, Air France и Alaska Airlines (согласно PROMETHEE). В то же время оба метода выявили улучшение средних показателей в пандемию у компаний Delta Airlines и Emirates, чей рост может объясняться оптимизацией финансовой стратегии и деятельности компаний, оперативной реакцией на возникающие возможности. Продемонстрировано, что компаниям – лидерам индустрии в допандемийный период не всегда удавалось удержать свои позиции в разгар кризиса.

Extraordinary situations such as the COVID-19 crisis affect various financial indicators of companies, including profitability, indebtedness, and liquidity. This study aims to examine the impact of the pandemic on the financial performance of the global aviation industry. The methodological foundation is based on the prospect theory of Kahneman and Tversky. The sample consists of 20 internationally operating airline companies. The analysis covers two periods: the five years preceding the pandemic (2015–2019) and the two-year pandemic period (2020–2021). Fifteen financial ratios, grouped under liquidity, profitability, financial structure, and activity, were calculated and used as data. The data were first weighted using the entropy method, followed by analysis through the TOPSIS and PROMETHEE which are multi-criteria decision-making methods. Additionally, performance rankings for the pre-pandemic and pandemic periods were compared using Spearman rank correlation, revealing consistency between TOPSIS and PROMETHEE results. Both methods indicate that the pandemic generally had a negative effect on financial performance. According to TOPSIS, the most adversely affected companies were Air China, British Airways, LATAM Airlines Group, and Southwest Airlines. According to PROMETHEE, these were Aeroflot Russian Airlines, Air China, Air France, and Alaska Airlines. However, both methods also identified Emirates and Delta Airlines as companies whose financial performance improved during the pandemic. These improvements may be attributed to timely adjustments in financial strategies, enhanced operational efficiency, and agile responses to emerging opportunities. Furthermore, the findings suggest that companies with strong prepandemic performance did not necessarily retain their leading positions during the pandemic, highlighting the disruptive and unpredictable nature of such extraordinary events.

отрасль пассажирских авиаперевозок;авиакомпании;финансовые показатели;COVID-19;энтропия.

airline industry;airline companies;financial performance;COVID-19;entropy.

Albers S., Rundshagen V. (2020). European airlines’ strategic responses to the COVID-19 pandemic (January–May, 2020). Journal of Air Transport Management, vol. 87, 101863. https://doi.org/10.1016/j.jairtraman.2020.101863

Bae K.-H.G., Gupta A., Mau R. (2021). Comparative analysis of airline financial and operational performances: A fuzzy AHP and TOPSIS integrated approach. Decision Science Letters, vol. 10, no. 3, pp. 361–374. https://doi.org/10.5267/j.dsl.2021.2.002

Barros C.P., Wanke P. (2015). An analysis of African airlines efficiency with two-stage TOPSIS and neural networks. Journal of Air Transport Management, vol. 44–45, pp. 90–102. https://doi.org/10.1016/j.jairtraman.2015.03.002 Barros C.P., Peypoch N. (2009). An evaluation of European airlines’ operational performance. International Journal of Production Economics, vol. 122, no. 2, pp. 525–533. https://doi.org/10.1016/j.ijpe.2009.04.016

Bauranov A., Parks S., Jiang X., Rakas J., González M.C. (2021). Quantifying the resilience of the U.S. domestic aviation network during the COVID-19 pandemic. Frontiers in Built Environment, vol. 7, 642295. https://doi.org/10.3389/fbuil.2021.642295

Behzadian M., Otaghsara S.K., Yazdani M., Ignatius J. (2012). A state-of the-art survey of TOPSIS applications. Expert Systems with Applications, vol. 39, no. 17, pp. 13051–13069. https://doi.org/10.1016/j.eswa.2012.05.056

Brans J.P., Vincke P. (1985). A preference ranking organisation method (The PROMETHEE method for multiple criteria decisionmaking). Management Science, vol. 31, pp. 647–656. http://dx.doi.org/10.1287/mnsc.31.6.647

Cetin A., Altan S. (2019). Performance evaluation of airline companies with fuzzy TOPSIS methods: An application at Esenboğa airport. Giresun Üniversitesi İktisadi Ve İdari Bilimler Dergisi / Journal of Economics and Administrative Sciences, vol. 5, no. 1, pp. 40–61. (in Turkish)

Doganis R. (2019). Flying off course: Airline economics and marketing. 5th ed. Routledge.

Dube K., Nhamo G. (2020). Vulnerability of nature-based tourism to climate variability and change: Case of Kariba resort town, Zimbabwe. Journal of Outdoor Recreation and Tourism, vol. 29, 100281. https://doi.org/10.1016/j.jort.2020.100281

Ekin E., Okutkan C. (2021). An application related to the problem of facility location selection using the PROMETHEE method. Gaziantep Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi / Journal of Gaziantep University Faculty of Economics and Administrative Sciences, vol. 3, no. 1, pp. 46–58. (in Turkish)

Ersoy N. (2016). Evaluation of corporate sustainability performance using multi criteria decision making methods: The case of Arçelik. Master’s thesis, Akdeniz University. Ulusal Tez Merkezi. (in Turkish)

Ertikin K. (2019). Comparison of financial performance via TOPSIS and PROMETHEE multi-criteria decision making methods: An application on construction sector in Istanbul stock exchange. Master’s thesis, Bursa Uludağ University. Ulusal Tez Merkezi. (in Turkish)

Hwang C.L., Yoon K. (1981). Multiple attribute decision making: Methods and applications. Springer-Verlag, New York. http:// dx.doi.org/10.1007/978-3-642-48318-9

Jaap B., Saxon S., Lind N. (2022). Understanding the pandemic’s impact on the aviation value chain. McKinsey report. https://www. mckinsey.com/industries/travel/our-insights/understanding-the-pandemics-impact-on-the-aviation-value-chain.

Kaygisiz D. (2019). Stock selection by PROMETHEE ranking method: An application on BIST-30 index. Master’s thesis, Dokuz Eylul University. Ulusal Tez Merkezi. (in Turkish)

Keles M.K. (2023). Measuring the efficiency of traditional airline companies with data envelopment analysis before and after the pandemic. Mehmet Akif Ersoy Üniversitesi Iktisadi Ve Idari Bilimler Fakültesi Dergisi / Mehmet Akif Ersoy University Journal of Economics and Administrative Sciences Faculty, vol. 10, issue 3, pp. 1976–1987. https://doi.org/10.30798/makuiibf.1220155. (in Turkish)

Kiraci K., Tanriverdi G., Akan E. (2023). Analysis of factors affecting the sustainable success of airlines during the COVID-19 pandemic. Transportation Research Record, vol. 2677, issue 4, pp. 350–379. https://doi.org/10.1177/03611981221104462

Kottas A., Madas M. (2018). Efficiency evaluation of major European airlines through DEA. Journal of Air Transport Management, vol. 68, pp. 48–64.

Kursuncu O., Secilmis N. (2023). The effect of Covid-19 on the financial performance of ground handling: The example of Çelebi aviation holding. Journal of Aviation, vol. 7, no. 1, pp. 83–92. https://doi.org/10.30518/jav.1205072

Mintah E.O., Gulko N. (2023). The impact of the COVID-19 pandemic on social and environmental reporting and financial performance of airlines operating in the UK. Journal of Financial Reporting and Accounting, preprint. https://doi.org/10.1108/ JFRA-01-2023-0032

Nguyen H.P., Nguyen T.T.M., Bui V.D. (2022). Understanding the impacts of the COVID-19 pandemic on the aviation and tourism industry. International Journal of Knowledge-Based Development (IJKBD), vol. 12, no. 3/4. https://doi.org/10.1504/IJKBD.2022.128903

Oral C., Kipkip E. (2019). TOPSIS and PROMETHEE methods for performance measurement of the transportation sector: An application on BIST. Mehmet Akif Ersoy Üniversitesi Sosyal Bilimler Enstitüsü Dergisi / Mehmet Akif Ersoy University Journal of Social Sciences Institute, vol. 11, issue 30, pp. 1005–1015. https://doi.org/10.20875/makusobed.594265. (in Turkish)

Oxley D., Jain C. (2015). Global air passenger market: Riding out periods of turbulence, travel and tourism competitiveness report. International Air Transport Association. World Economic Forum.

Oztel A. (2016). A new approach in the selection of a multi-criteria decision-making method. Doctoral thesis, Gazi University. (in Turkish)

Sahin T. (2014). ERP system selection based on AHP and PROMETHEE method: An application in NGOs. Master’s thesis, Süleyman Demirel University. (in Turkish)

Subasi H. (2011). Comparison of TOPSIS and AHP methods used in multi-criteria decision making and an application. Master’s thesis, Marmara University. (in Turkish)

Teker S., Teker D., Guner A. (2016). Financial performance of top 20 airlines. Procedia – Social and Behavioral Sciences, vol. 235, pp. 603–610. https://doi.org/10.1016/j.sbspro.2016.11.035

Uzun S. (2015). Main engine and generator selection in shipbuilding process: AHP, TOPSIS and PROMETHEE application. Master’s thesis, Gebze Technical University. Ulusal Tez Merkezi. (in Turkish)

Wang Y.-J. (2008). Applying FMCDM to evaluate financial performance of domestic airlines in Taiwan. Expert Systems with Applications, vol. 34, issue 3, pp. 1837–1845. https://doi.org/10.1016/j.eswa.2007.02.029

Wanke P., Barros C.P., Chen Z. (2015). An analysis of Asian airlines efficiency with two-stage TOPSIS and MCMC generalized linear mixed models. International Journal of Production Economics, vol. 169, pp. 110–126. https://doi.org/10.1016/j. ijpe.2015.07.028

Zavadskas E.K., Turskis Z., Kildienė S. (2014). State of art surveys of MCDM methods. Technological and Economic Development of Economy, vol. 22, no.1, pp. 165–179.

Zelvi A. (2019). Financial performance measurement of private banks in turkey: Using TOPSIS and PROMETHEE methods. Master’s thesis, Dokuz Eylül University. Ulusal Tez Merkezi. (in Turkish)