İki Aşamalı Ağ Veri Zarflama Analizinde Çapraz Etkinlik Ölçümü: Türkiye Havalimanları Uygulaması

Abstract

Bu çalışma, bir sistemin genel etkinliğinin sisteminin alt süreçlere ayrıştırılarak ölçülmesini ele almaktadır. Uygulama alanı olarak Türkiye için önemli bir hizmet sektörü olan havayolu sektörü seçilmiştir. Yazın incelendiğinde, Türk havayolu sektöründe yapılan etkinlik çalışmalarında üretim sürecinin tek aşamalı olarak ele alınarak geleneksel Veri Zarflama Analizi (VZA) ile gerçekleştirildiği görülmektedir. Bu çalışmada ise havalimanlarının genel etkinliğinin, operasyonel ve gelir etkinliği olmak üzere iki alt etkinlik bazında, daha ayrıntılı bir şekilde Ağ Veri Zarflama Analizi ile ölçülmesi amaçlanmıştır. Etkinlik analizinde Kao ve Hwang’ın (2008) İlişkisel Ağ Veri Zarflama Analizi modeli kullanılmıştır. Etkinlik değerlerinin sıralanmasında ise Kao ve Liu’nun (2019) Çapraz Etkinlik Ölçümü kullanılarak akran değerlendirmeden yararlanılmıştır. Çalışmada, Devlet Hava Meydanları İşletmesi tarafından işletilen ve 2019 yılı itibarı ile yıllık yolcu trafiği 400.000’in üzerinde olan 27 havalimanı incelenmiştir. Yapılan analizlerde, göreli tam etkinliğe sahip bir havalimanı tespit edilememişken, bazı havalimanlarının alt süreçler bakımından kısmen etkin olduğu bulgusuna ulaşılmıştır.

Keywords

• Ağ Veri Zarflama Analizi
• Havalimanı Etkinliği
• Çapraz Etkinlik Ölçümü

Cross Efficiency Measurement in Two-Stage Network Data Envelopment Analysis: An Application of the Airports in Türkiye

Abstract

This study deals with measuring the overall efficiency of a system by breaking it down into its constituent sub-processes. The airline sector, which is an important service sector for Türkiye, has been chosen as the application area. When the relevant literature is examined, it is seen that the production process in efficiency studies in the Turkish airline sector is carried out with traditional Data Envelopment Analysis (DEA) by considering the efficiency process as a single stage. This study aimed to measure the overall efficiency of airports in more detail on the basis of two sub-activities, operational and revenue efficiency, using Network Data Envelopment Analysis (NDEA). Kao and Hwang’s (2008) Relational Network Data Envelopment Analysis model was used in the efficiency analysis. In ranking efficiency values, peer evaluation was used based on Kao and Liu’s (2019) Cross-Efficiency Measurement approach. In the study, 27 airports operated by the State Airports Authority (DHMİ) with an annual passenger traffic of over 400.000 as of 2019 were examined. In the analysis, it was found that while an airport with relative overall efficiency could not be identified, some airports were partially effective in terms of sub-processes.

Keywords

• Network Data Envelopment Analysis
• Airport Efficiency
• Cross-Efficiency Measurement

References

1. Adler, N., Liebert, V., & Yazhemsky, E. (2013). Benchmarking airports from a managerial perspective. Omega, 41(2), 442–458. https://doi.org/10.1016/j.omega.2012.02.004
2. Altın, F. G., Karaatlı, M., & Budak, İ. (2017). Avrupa’nın en büyük 20 havalimanının çok kriterli karar verme yöntemleri ve veri zarflama analizi ile değerlendirilmesi. Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 22(4), 1049–1064. https://dergipark.org.tr/tr/pub/sduiibfd/issue/52995/704359
3. Ar, İ. M. (2012). Türkiye’deki havalimanlarının etkinliklerindeki değişimin incelenmesi: 2007-2011 dönemi için Malmquist-TFV Endeksi uygulaması. Atatürk Üniversitesi İktisadi ve İdari Bilimler Dergisi, 26(3–4), 143–160. https://dergipark.org.tr/tr/pub/atauniiibd/issue/2707/35812
4. Asker, V. (2016). The measurement of airports operational effectiveness: An application of the major 10 airports in Turkey. Transport & Logistics: the International Journal, 16(41), 1–9.
5. Asker, V., & Battal, Ü. (2017). Operational efficiency measurement at selected airports. International Journal of Management Economics and Business, ICMEB17 Special Issue, 351–368. https://dergipark.org.tr/en/download/article-file/1124049
6. Avcı, T., & Aktaş, M. (2016). Türkiye’de faaliyet gösteren havalimanlarının performanslarının değerlendirilmesi. Uluslararası Alanya İşletme Fakültesi Dergisi, 7(3), 67–77. https://dergipark.org.tr/en/download/article-file/201849
7. Battal, Ü. (2020). Avrupadaki havaalanı grup şirketlerinin finansal performanslarının ölçülmesi: Veri Zarflama Analizi Yöntemi. Ömer Halisdemir Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 13(1), 171–184. https://doi.org/10.25287/ohuiibf.622991
8. Bolat, B., Temur, G. T., & Gürler, H. (2016). Türkiye deki havalimanlarının etkinlik tahmini: Veri zarflama analizi ve yapay sinir ağlarının birlikte kullanımı. Ege Academic Review, 16(OZEL), 1–10. https://doi.org/10.21121/eab.2016OZEL24424

9. Charnes, A., Cooper, W. W., Golany, B., Seiford, L., & Stutz, J. (1985). Foundations of data envelopment analysis for Pareto-Koopmans efficient empirical production functions. Journal of Econometrics, 30(1), 91-107. https://doi.org/10.1016/0304-4076(85)90133-2
10. Chiou, Y.-C., & Chen, Y.-H. (2006). Route-based performance evaluation of Taiwanese domestic airlines using data envelopment analysis. Transportation Research Part E: Logistics and Transportation Review, 42(2), 116–127. https://doi.org/10.1016/j.tre.2005.09.005
11. Çınaroğlu, E., & Avcı, T. (2017). Türkiye’de faaliyette bulunan büyük havalimanlarının iç ve dış hat performanslarının karşılaştırılması. Business & Management Studies: An International Journal, 5(4), 55–75. https://doi.org/10.15295/bmij.v5i4.184
12. Cook, W. D., Tone, K., & Zhu, J. (2014). Data envelopment analysis: Prior to choosing a model. Omega, 44, 1–4. https://doi.org/10.1016/j.omega.2013.09.004
13. Devlet Hava Meydanları İşletmesi Genel Müdürlüğü [DHMİ]. (2020a, Mayıs). 2019 Havayolu Sektör Raporu. Strateji Geliştirme Daire Başkanlığı. https://www.dhmi.gov.tr/Lists/HavaYoluSektorRaporlari/Attachments/13/2019 Havayolu Sektör Raporu.pdf
14. Devlet Hava Meydanları İşletmesi Genel Müdürlüğü [DHMİ]. (2020b, Mayıs). 2019 Faaliyet Raporu. https://www.dhmi.gov.tr/Lists/FaaliyetRaporlari/Attachments/20/49453_1_DHMI2019FaaliyetRaporu(pdf)_FaaliyetRaporu_2019webformati.pdf
15. Doyle, J., & Green, R. (1994). Efficiency and cross-efficiency in DEA derivations, meanings and uses. Journal of the Operational Research Society, 45(5), 567–578. https://doi.org/10.1057/jors.1994.84
16. European Comission. (2005). Community guidelines on financing of airports and start-up aid to airlines departing from regional airports (2005/C 312/01). Official Journal of the European Union. https://eur-lex.europa.eu/legal-content/EN/ALL/?uri=CELEX%3A52005XC1209%2803%29
17. Färe, R., & Grosskopf, S. (1996). Productivity and intermediate products: A frontier approach. Economics Letters, 50(1), 65–70. https://doi.org/10.1016/0165-1765(95)00729-6
18. Färe, R., Grosskopf, S., & Whittaker, G. (2007). Network DEA. In J. Zhu & W. D. Cook (Eds.), Modeling data irregularities and structural complexities in data envelopment analysis (pp. 209-240). Springer US. https://doi.org/10.1007/978-0-387-71607-7_12
19. Gramani, M. C. N. (2012). Efficiency decomposition approach: A cross-country airline analysis. Expert Systems with Applications, 39(5), 5815–5819. https://doi.org/10.1016/j.eswa.2011.11.086
20. Güner, S., Ergüzel, O. Ş., & Cebeci, H. İ. (2019). Uluslararası havalimanlarının operasyonel etkinliğinin değerlendirilmesi: Bölgesel bir karşılaştırma. Alphanumeric Journal, 7(3), 37–44. https://doi.org/10.17093/alphanumeric.499533
21. Halkos, G. E., Tzeremes, N. G., & Kourtzidis, S. A. (2014). A unified classification of two-stage DEA models. Surveys in operations research and management science, 19(1), 1-16. https://doi.org/10.1016/j.sorms.2013.10.001
22. Hong, J.-D., & Jeong, K.-Y. (2019). Combining data envelopment analysis and multi-objective model for the efficient facility location–allocation decision. Journal of Industrial Engineering International, 15(2), 315–331. https://doi.org/10.1007/s40092-018-0294-2
23. Kao, C. (2017). Network data envelopment analysis (Vol. 240). Springer International Publishing. https://doi.org/10.1007/978-3-319-31718-2
24. Kao, C., & Hwang, S.-N. N. (2008). Efficiency decomposition in two-stage data envelopment analysis: An application to non-life insurance companies in Taiwan. European Journal of Operational Research, 185(1), 418–429. https://doi.org/10.1016/j.ejor.2006.11.041
25. Kao, C., & Liu, S.-T. (2019). Cross efficiency measurement and decomposition in two basic network systems. Omega, 83, 70–79. https://doi.org/10.1016/j.omega.2018.02.004
26. Karaoğlu, M. (2014). Evaluation of Turkish airports efficiencies using data envelopment analysis. In R. Banker, A. Emrouznejad, H. Bal, İ. Alp, & M. A. Cengiz (Eds.), Data Envelopment Analysis and Performance Measurement: Proceedings of the 11th International Conference of DEA (pp. 195–200). https://doi.org/10.13140/RG.2.1.3353.2966
27. Kıyıldı, R. K., & Karaşahin, M. (2006). Türkiye’deki hava alanlarının veri zarflama analizi ile altyapı performansının değerlendirilmesi. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 10(3), 391–397. https://doi.org/10.19113/sdufbed.16973
28. Koçak, H. (2011). Efficiency examination of Turkish airports with DEA approach. International Business Research, 4(2), 204–209. https://doi.org/10.5539/ibr.v4n2p204
29. Köçken, K., Timor, M., & Karakaplan, M. U. (2022). COVID-19 Pandemisi öncesinde ve pandemi döneminde Türkiye’deki havalimanı etkinliklerinin üç aşamalı veri zarflama analizi ile belirlenmesi. Avrupa Bilim ve Teknoloji Dergisi, 35, 643–652. https://doi.org/10.31590/ejosat.1085597
30. Lee, M. J., & Kim, C. (2018). A network DEA aeronautical and non-aeronautical production model: An application to South Korea airports. Journal of Economic Structures, 7(1), 1–12. https://doi.org/10.1186/s40008-018-0130-2
31. Li, Y., Wang, Y., & Cui, Q. (2015). Evaluating airline efficiency: An application of Virtual Frontier Network SBM. Transportation Research Part E: Logistics and Transportation Review, 81, 1–17. https://doi.org/10.1016/j.tre.2015.06.006
32. Liu, D. (2016). Measuring aeronautical service efficiency and commercial service efficiency of East Asia airport companies: An application of Network Data Envelopment Analysis. Journal of Air Transport Management, 52, 11–22. https://doi.org/10.1016/j.jairtraman.2015.12.001
33. lo Storto, C. (2018). Ownership structure and the technical, cost, and revenue efficiency of Italian airports. Utilities Policy, 50, 175–193. https://doi.org/10.1016/j.jup.2018.01.003
34. Lovell, C. A. K. (1993). Production frontiers and productive efficiency. In H. O. Fried, C. A. K. Lovell, & S. S. Schmidt (Eds.), The measurement of productive efficiency: Techniques and applications (pp. 3–67). Oxford University Press.
35. Lozano, S., Gutiérrez, E., & Moreno, P. (2013). Network DEA approach to airports performance assessment considering undesirable outputs. Applied Mathematical Modelling, 37(4), 1665–1676. https://doi.org/10.1016/j.apm.2012.04.041
36. Lu, W.-M., Hung, S.-W., Kweh, Q. L., Wang, W.-K., & Lu, E.-T. (2014). Production and marketing efficiencies of the U.S. airline industry: A two-stage network DEA approach. In W. Cook & J. Zhu (Eds.), Data envelopment analysis. international series in operations research & management science (Vol. 208; pp. 537–568). Springer New York LLC. https://doi.org/10.1007/978-1-4899-8068-7_21
37. Maghbouli, M., Amirteimoori, A., & Kordrostami, S. (2014). Two-stage network structures with undesirable outputs: A DEA based approach. Measurement, 48(1), 109–118. https://doi.org/10.1016/j.measurement.2013.10.032
38. Mallikarjun, S. (2015). Efficiency of US airlines: A strategic operating model. Journal of Air Transport Management, 43, 46–56. https://doi.org/10.1016/j.jairtraman.2014.12.004
39. Orhan, M. (2019). Türkiye’deki havalimanlarının etkinliklerinin veri zarflama analizi ile değerlendirilmesi. Turkish Studies-Economics, Finance, Politics, 14(4), 1455–1472. https://doi.org/10.29228/TurkishStudies.39243
40. Ömürbek, N., Öksüz Demirgubuz, M., & Tunca, M. Z. (2013). Hizmet sektöründe performans ölçümünde veri zarflama analizinin kullanımı: havalimanları üzerine bir uygulama. Süleyman Demirel Üniversitesi Vizyoner Dergisi, 4(9), 21–43. https://dergipark.org.tr/tr/pub/vizyoner/issue/23008/246064
41. Özsoy, V. S., & Örkcü, H. H. (2021). Structural and operational management of Turkish airports: a bootstrap data envelopment analysis of efficiency. Utilities Policy, 69, 101180. https://doi.org/10.1016/j.jup.2021.101180
42. Öztürk, E., Durak, H. A., & Bal, H. (2014). Evaluation of Turkish airport efficiencies using data envelopment analysis and Moora Method. In M. A. C. Rajiv Banker, Ali Emrouznejad, Hasan Bal, Ihsan Alp (Eds.), Data Envelopment Analysis and Performance Measurement: Proceedings of the 11th International Conference of DEA (pp. 189–194). https://doi.org/10.13140/RG.2.1.3353.2966
43. Peker, İ., & Baki, B. (2009). Veri zarflama analizi ile Türkiye havalimanlarında bir etkinlik ölçümü uygulaması. Çukurova Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 18(2), 72–88. https://dergipark.org.tr/tr/pub/cusosbil/issue/4382/60125
44. Sarkis, J. (2007). Preparing your data for DEA. In Modeling data irregularities and structural complexities in Data Envelopment Analysis (pp. 305–320). Springer US. https://doi.org/10.1007/978-0-387-71607-7_17
45. Seiford, L. M., & Zhu, J. (1999). Profitability and marketability of the top 55 U.S. commercial banks. Management Science, 45(9), 1270–1288. https://doi.org/10.1287/mnsc.45.9.1270
46. Sexton, T. R., Silkman, R. H., & Hogan, A. J. (1986). Data envelopment analysis: Critique and extensions. New Directions for Program Evaluation, 1986(32), 73–105. https://doi.org/10.1002/ev.1441
47. Shao, Y., & Sun, C. (2016). Performance evaluation of China’s air routes based on network data envelopment analysis approach. Journal of Air Transport Management, 55, 67–75. https://doi.org/10.1016/j.jairtraman.2016.01.006
48. Tavassoli, M., Faramarzi, G. R., & Farzipoor Saen, R. (2014). Efficiency and effectiveness in airline performance using a SBM-NDEA model in the presence of shared input. Journal of Air Transport Management, 34, 146–153. https://doi.org/10.1016/j.jairtraman.2013.09.001
49. Ulutaş, B., & Ulutaş, B. (2009, July 29–August 1). An analytic network process combined data envelopment analysis methodology to evaluate the performance of airports in Turkey [Paper presentation]. 10th International Symposium on the Analytic Hierarchy Process. University of Pittsburgh, Pennsylvania, United States. https://www.isahp.org/2009Proceedings/Final_Papers/90_Ulutas_DEA_ANP_TurkishAirport_Performance_REV_FIN.pdf
50. Ülkü, T. (2015). A comparative efficiency analysis of Spanish and Turkish airports. Journal of Air Transport Management, 46, 56–68. https://doi.org/10.1016/j.jairtraman.2015.03.014
51. Wanke, P. F. (2013). Physical infrastructure and flight consolidation efficiency drivers in Brazilian airports: A two-stage network-DEA approach. Journal of Air Transport Management, 31, 1–5. https://doi.org/10.1016/j.jairtraman.2012.09.001
52. Yavuz, İ. (2003). Verimlilik ve etkinlik ölçümüne yeni yaklaşımlar ve illere göre imalat sanayiinde etkinlik karşılaştırmaları (Yayın No. 667). Milli Prodüktivite Merkez Yayınları.
53. Yazgan, A. E., & Karkacier, O. (2015). Veri Zarflama Analizi ile etkinlik ölçümleri ve havalimanı işletmeciliği sektöründe bir uygulama. Uluslararası Alanya İşletme Fakültesi Dergisi, 7(2), 15–28. https://dergipark.org.tr/tr/pub/uaifd/issue/21603/232022
54. Yu, M.-M. (2010). Assessment of airport performance using the SBM-NDEA model. Omega, 38(6), 440–452. https://doi.org/10.1016/j.omega.2009.11.003
55. Zhu, J. (2011). Airlines performance via two-stage network DEA approach. Journal of CENTRUM Cathedra: The Business and Economics Research Journal, 4(2), 260–269. https://papers.ssrn.com/abstract=1931862
56. Zhu, J. (2014). Quantitative models for performance evaluation and benchmarking. Springer International Publishing. https://doi.org/10.1007/978-3-319-06647-9