Measurement of Process-Performances of Turkish Airports Using Network Data Envelopment Analysis

Year 2023, Volume: 7 Issue: 2, 272 - 283, 25.07.2023

Miraç Eren , Murat Ahmet Doğan

https://doi.org/10.30518/jav.1296416

Cited By: 1

Abstract

The air transportation industry is continuously broadening by adding new airports operating with huge investments. Hence, increasing efficiency and productivity in this industry is becoming critical. At the same time, efficiency calculations are also an important tool used to identify weaknesses and contribute to the improvement of the performance of enterprises. Nevertheless, Classic DEA models are commonly insufficient to determine the efficiency of a system due to ignorance of its internal structure and the treatment of the system as a black box. Therefore, if a production system can be divided into subprocesses by using network DEA model, the results will be more plausible and satisfying. Within this scope, this paper aims to evaluate the efficiencies of 39 out of 56 airports in Türkiye from 2015 to 2019. The results indicate that less-populated airports managed by private entrepreneurs are more efficient than others. Moreover, another characteristic of these efficient airports is that they are both domestically and internationally preferred for leisure trips.

Keywords

Türkiye’s Airports, Performance, Network Data Envelopment, DEA, Efficiency, Benchmarking

References

• Abbott, M. (2015). Reform and efficiency of New Zealand's airports. Utilities Policy, 36, 1-9.
• Asker, V. (2016). The measurement of airports operational effectiveness: an application of the major 10 airports in Turkey. Transport & Logistics, 16(41).
• Asker, V., & Battal, Ü. (2017). Operational efficiency measurement at selected airports. Uluslararası Yönetim İktisat ve İşletme Dergisi, 13(13), 351-368.
• Asker, V., & Yaşar, M. (2018). Measurement of efficiency with Data Envelopment Analysis and Malmquist Total Factor Productivity methods: An application on major airports in Turkey. Transport & Logistics: the International Journal, 45, 18.
• ATAG. (2020). A Global Industry‚ Driving Sustainable Development. https://aviationbenefits.org/media/167517/aw-oct-final-atag_abbb-2020-publication-digital.pdf
• Baltazar, M. E., Jardim, J., Alves, P., & Silva, J. (2014). Air transport performance and efficiency: MCDA vs. DEA approaches. Procedia-Social and Behavioral Sciences, 111, 790-799.
• Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science, 30(9), 1078-1092.
• Barros, C. P., & Dieke, P. U. (2007). Performance evaluation of Italian airports: A data envelopment analysis. Journal of Air Transport Management, 13(4), 184-191.
• Barros, C. P. (2008). Airports in Argentina: Technical efficiency in the context of an economic crisis. Journal of Air Transport Management, 14(6), 315-319.
• Bazargan, M., & Vasigh, B. (2003). Size versus efficiency: a case study of US commercial airports. Journal of Air Transport Management, 9(3), 187-193.
• Bloomberg. (2021). Retrieved 27.12.2021 from https://www.bloomberght.com/karaismailoglu-havalimani- sayisi-gelecek-yil-56-dan-61-e-cikacak-2295298
• Chang, Y.-T., Park, H. K., Zou, B., & Kafle, N. (2016). Passenger facility charge vs. airport improvement program
• funds: A dynamic network DEA analysis for US airport financing. Transportation Research Part E: Logistics and Transportation Review, 88, 76-93.
• Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision-making units. European journal of operational research, 2(6), 429-444.
• Chi-Lok, A. Y., & Zhang, A. (2009). Effects of competition and policy changes on Chinese airport productivity: An empirical investigation. Journal of Air Transport Management, 15(4), 166-174.
• DHMI. (2015). DHMI Annual Report 2015. https://www.dhmi.gov.tr/Lists/AnnualReports/Attachments/9/Annual_Report_2015_site.pdf
• DHMI. (2016). DHMI Annual Report 2016. https://www.dhmi.gov.tr/Lists/AnnualReports/Attachments/10/Annual_Report_2016.pdf
• DHMI. (2017). DHMI Annaul Report 2017. https://www.dhmi.gov.tr/Lists/AnnualReports/Attachments/11/Annual_Report_2017.pdf
• DHMI. (2018). DHMI Annual Report 2018. https://www.dhmi.gov.tr/Lists/AnnualReports/Attachments/12/DHMI_2018_Annual_Report.pdf
• DHMI. (2019). DHMI Annual Report 2019. https://www.dhmi.gov.tr/Lists/AnnualReports/Attachments/13/2019.pdf
• DHMI. (2020). https://www.dhmi.gov.tr/Sayfalar/EN/ Statistics.aspx
• Färe, R., & Grosskopf, S. (1996). Productivity and intermediate products: A frontier approach. Economics Letters, 50(1), 65-70.
• Fragoudaki, A., Giokas, D., & Glyptou, K. (2016). Efficiency and productivity changes in Greek airports during the crisis years 2010–2014. Journal of Air Transport Management, 57, 306-315.
• Fung, M. K. Y., Wan, K. K. H., Van Hui, Y., & Law, J. S. (2008). Productivity changes in Chinese airports 1995–2004. Transportation Research Part E: Logistics and Transportation Review, 44(3), 521-542.
• Gillen, D., & Lall, A. (1997). Developing airport productivity and performance measures: an application of data envelopment analysis. Transportation Research Part E: Logistics and Transportation Review, 33(4), 261-273.
• Graham, A. (2018). Airport economics and finance. In The Routledge Companion to Air Transport Management (pp. 189-205). Routledge.
• Gutiérrez, E., & Lozano, S. (2016). Efficiency assessment and output maximization possibilities of European small and medium-sized airports. Research in Transportation Economics, 56, 3-14.
• Güner, S., Cebeci, H. İ., Antunes, J. J. M., & Wanke, P. F. (2021). Sustainable efficiency drivers in Eurasian airports: Fuzzy NDEA approach based on Shannon's entropy. Journal of Air Transport Management, 92, 102039.
• Ha, H.-K., Wan, Y., Yoshida, Y., & Zhang, A. (2013). Airline market structure and airport efficiency: Evidence from major Northeast Asian airports. Journal of Air Transport Management, 33, 32-42.
• Hong, S.-J., & Jeon, M. (2019). The technical efficiency of French regional airports and low-cost carrier terminals. Sustainability, 11(18), 5107.
• Hooper, P. G., & Hensher, D. A. (1997). Measuring total factor productivity of airports—an index number approach. Transportation Research Part E: Logistics and Transportation Review, 33(4), 249-259.
• Kamp, V., Niemeier, H.-M., & Müller, J. (2007). What can be learned from benchmarking studies? Examining the apparent poor performance of German airports. Journal of Airport Management, 1(3), 294-308.
• Kao, C. (2014). Network data envelopment analysis: A review. European journal of operational research, 239(1), 1-16.
• Kao, C., & Hwang, S.-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.
• Kao, C., & Hwang, S.-N. (2010). Efficiency measurement for network systems: IT impact on firm performance. Decision Support Systems, 48(3), 437-446.
• Keskin, B., & Köksal, C. D. (2019). A hybrid AHP/DEA-AR model for measuring and comparing the efficiency of airports. International Journal of Productivity and Performance Management.
• Koçak, H. (2011). Efficiency examination of Turkish airports with DEA approach. International Business Research, 4(2), 204.
• 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.
• Lin, L., & Hong, C. (2006). Operational performance evaluation of major international airports: An application of data envelopment analysis. Journal of Air Transport Management, 12(6), 342-351.
• Lo Storto, C. (2018). The analysis of the cost-revenue production cycle efficiency of the Italian airports: A NSBM DEA approach. Journal of Air Transport Management, 72, 77-85.
• Lozano, S., Gutiérrez, E., & Moreno, P. (2013). Network DEA approach to airports performance assessment considering undesirable outputs. Applied Mathematical Modelling, 37(4), 1665-1676.
• Lozano, S., Gutiérrez, E., & Salmerón, J. L. (2009). Network DEA models in transportation. Application to airports. German Aviation Research Society Seminar on Airport Benchmarking,
• Lu, W., Park, S. H., Huang, T., & Yeo, G. T. (2019). An analysis for Chinese airport efficiency using weighted variables and adopting CFPR. The Asian Journal of Shipping and Logistics, 35(4), 230-242.
• Merkert, R., & Mangia, L. (2014). The efficiency of Italian and Norwegian airports: A matter of management or the level of competition in remote regions? Transportation Research Part A: Policy and Practice, 62, 30-38.
• Ngo, T., & Tsui, K. W. H. (2020). A data-driven approach for estimating airport efficiency under endogeneity: An application to New Zealand airports. Research in Transportation Business & Management, 34, 100412.
• Olfat, L., Amiri, M., Soufi, J. B., & Pishdar, M. (2016). A dynamic network efficiency measurement of airports performance considering sustainable development concept: A fuzzy dynamic network-DEA approach. Journal of Air Transport Management, 57, 272-290.
• Örkcü, H. H., Balıkçı, C., Dogan, M. I., & Genç, A. (2016). An evaluation of the operational efficiency of Turkish airports using data envelopment analysis and the Malmquist productivity index: 2009–2014 case. Transport Policy, 48, 92-104.
• 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.
• Pacagnella Junior, A. C., Hollaender, P. S., Mazzanati, G. V., & Bortoletto, W. W. (2020). Infrastructure and Flight Consolidation Efficiency of Public and Private Brazilian International Airports: A Two-Stage DEA and Malmquist Index Approach. Journal of Advanced Transportation, 2020.
• Pathomsiri, S., Haghani, A., Dresner, M., & Windle, R. J. (2008). Impact of undesirable outputs on the productivity of US airports. Transportation Research Part E: Logistics and Transportation Review, 44(2), 235-259.
• Pels, E., Nijkamp, P., & Rietveld, P. (2001). Relative efficiency of European airports. Transport Policy, 8(3), 183-192. Ripoll-Zarraga, A. E., & Mar-Molinero, C. (2020). Exploring the reasons for efficiency in Spanish airports. Transport Policy, 99, 186-202.
• Sarkis, J. (2000). An analysis of the operational efficiency of major airports in the United States. Journal of Operations Management, 18(3), 335-351.
• Seiford, L. M., & Zhu, J. (1999). Profitability and marketability of the top 55 US commercial banks. Management Science, 45(9), 1270-1288.
• Song, K. H., Choi, S., & Han, I. H. (2020). Competitiveness Evaluation Methodology for Aviation Industry Sustainability Using Network DEA. Sustainability, 12(24), 10323.
• Stichhauerova, E., & Pelloneova, N. (2019). An efficiency assessment of selected German airports using the DEA model. Journal of Competitiveness, 11(1), 135-151.
• Tsui, W. H. K., Balli, H. O., Gilbey, A., & Gow, H. (2014). Operational efficiency of Asia–Pacific airports. Journal of Air Transport Management, 40, 16-24.
• TUIK.(2020). https://data.tuik.gov.tr/Kategori/ GetKategori? p=dis-ticaret-104&dil=1
• Ülkü, T. (2015). A comparative efficiency analysis of Spanish and Turkish airports. Journal of Air Transport Management, 46, 56-68.
• Wanke, P., & Barros, C. (2017). Efficiency thresholds and cost structure in Senegal airports. Journal of Air Transport Management, 58, 100-112.
• Yoshida, Y. (2004). Endogenous-weight TFP measurement: methodology and its application to Japanese- airport benchmarking. Transportation Research Part E: Logistics and Transportation Review, 40(2), 151-182.
• Yu, M.-M. (2010). Assessment of airport performance using the SBM-NDEA model. Omega, 38(6), 440-452.