Journals / Dokuz Eylül Üniversitesi İşletme Fakültesi Dergisi / 2026 / Cilt: 27 Sayı: 1

ASSESSING TRANSPORTATION AND LOGISTICS EFFICIENCY THROUGH MCDM METHODS-EVIDENCE FROM OECD COUNTRIES

ULAŞTIRMA VE LOJİSTİK VERİMLİLİĞİN ÇKKV YÖNTEMLERİ İLE DEĞERLENDİRİLMESİ - OECD ÜLKELERİNDEN KANITLAR

Tuğrul Bayat(AFYON KOCATEPE UNIVERSITY, BOLVADİN ACADEMY OF APPLIED SCIENCES)0000-0003-1491-6178
Pages
170–199
Publication Date
2026-06-29
🏛️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)

The empirical results reveal that digitalization-related criteria, particularly the use of digital platforms in transportation services and broadband internet penetration, exert the strongest influence on overall performance.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Transportation and logistics systems constitute a critical foundation for economic growth, environmental sustainability, and social welfare in modern economies.
📊
Key Findings & Data:However, evaluating transportation and logistics performance requires a multidimensional analytical framework that simultaneously incorporates infrastructure quality, digitalization, environmental impact, financial development, and social dynamics.
💡
Conclusion & Impact:The empirical results reveal that digitalization-related criteria, particularly the use of digital platforms in transportation services and broadband internet penetration, exert the strongest influence on overall performance.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Ulaştırma · Lojistik · Dijitalleşme

Abstract

Transportation and logistics systems constitute a critical foundation for economic growth, environmental sustainability, and social welfare in modern economies. In OECD countries, transportation infrastructure and logistics efficiency play a decisive role in supporting international trade, supply chain integration, and competitiveness. However, evaluating transportation and logistics performance requires a multidimensional analytical framework that simultaneously incorporates infrastructure quality, digitalization, environmental impact, financial development, and social dynamics. This study evaluates transportation and logistics efficiency across OECD countries by employing a Multi-Criteria Decision-Making (MCDM) framework. Criterion weights are objectively determined using the CRITIC method, while country performance rankings are obtained through the MABAC method. To assess the robustness and reliability of the proposed model, sensitivity analysis is conducted using alternative weighting approaches—Entropy, Standard Deviation, and Equal Weight—and alternative ranking techniques, namely ARAS, COPRAS, and VIKOR. The empirical results reveal that digitalization-related criteria, particularly the use of digital platforms in transportation services and broadband internet penetration, exert the strongest influence on overall performance. The United States, South Korea, the Netherlands, France and Japan emerge as the highest-performing countries, whereas Slovakia, Costa Rica, and Colombia display the lowest efficiency levels. Notably, Türkiye ranks seventieth, outperforming several advanced OECD economies. Overall, the findings highlight the strategic importance of digital infrastructure in enhancing transportation and logistics efficiency. The proposed MCDM framework provides policymakers with a data-driven decision-support tool for prioritizing transportation investments and designing integrated logistics, digitalization, and sustainability-oriented policies.

Özet

Ulaştırma ve lojistik sistemleri, ekonomik büyüme, çevresel sürdürülebilirlik ve toplumsal refah açısından modern ekonomilerin temel bileşenleri arasında yer almaktadır. OECD ülkelerinde ulaştırma altyapısı ve lojistik verimlilik, uluslararası ticaretin desteklenmesi, tedarik zincirlerinin entegrasyonu ve rekabet gücünün artırılması açısından belirleyici bir role sahiptir. Bununla birlikte, ulaştırma ve lojistik performansının sağlıklı biçimde değerlendirilebilmesi; altyapı kalitesi, dijitalleşme, çevresel etkiler, finansal gelişmişlik ve sosyal dinamikleri eş zamanlı olarak ele alan çok boyutlu bir analitik çerçeve gerektirmektedir. Bu çalışma, OECD ülkelerinde ulaştırma ve lojistik verimliliğini Çok Kriterli Karar Verme (ÇKKV) yöntemleri kullanarak değerlendirmektedir. Kriter ağırlıkları nesnel olarak CRITIC yöntemi ile belirlenirken, ülkelerin performans sıralamaları MABAC yöntemi aracılığıyla elde edilmektedir. Önerilen modelin sağlamlığını ve güvenilirliğini test etmek amacıyla; Entropi, Standart Sapma ve Eşit Ağırlık yöntemleri ile alternatif ağırlıklandırmalar yapılmış, ARAS, COPRAS ve VIKOR yöntemleri kullanılarak duyarlılık analizi gerçekleştirilmiştir. Elde edilen bulgular, ulaştırma ve lojistik performansı üzerinde dijitalleşme ile ilişkili kriterlerin belirleyici olduğunu göstermektedir. Özellikle ulaştırma hizmetlerinde dijital platformların kullanımı ve geniş bant internet yaygınlığı, genel performans üzerinde en yüksek etkiye sahiptir. Performans sıralamasında Amerika Birleşik Devletleri, Güney Kore, Hollanda, Fransa ve Japonya üst sıralarda yer alırken; Slovakya, Kosta Rika ve Kolombiya en düşük performans gösteren ülkeler olmuştur. Türkiye ise onyedinci sırada yer alarak birçok gelişmiş OECD ülkesini geride bırakmıştır. Sonuç olarak, çalışma ulaştırma ve lojistik verimliliğinin artırılmasında dijital altyapının stratejik önemini ortaya koymaktadır. Önerilen ÇKKV çerçevesi, politika yapıcılara ulaştırma yatırımlarının önceliklendirilmesi ve lojistik, dijitalleşme ve sürdürülebilirlik odaklı politikaların geliştirilmesi için veri temelli bir karar destek aracı sunmaktadır.

🔖

Cite This Article

Bayat, T. (2026). ASSESSING TRANSPORTATION AND LOGISTICS EFFICIENCY THROUGH MCDM METHODS-EVIDENCE FROM OECD COUNTRIES. Dokuz Eylül Üniversitesi İşletme Fakültesi Dergisi, Cilt: 27 Sayı: 1, 170–199. https://doi.org/10.24889/ifede.1710853

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (77)

  1. Akçakanat, Ö., Aksoy, E. ve Teker, T. (2018). CRITIC ve MDL temelli EDAS yöntemi ile TR-61 bölgesi bankalarının performans değerlendirmesi. Süleyman Demirel Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 1(32), 1-24. E
  2. Ahmed, Z., Ahmad, M., Rjoub, H., Kalugina, O. A., & Hussain, N. (2021). Economic growth, renewable energy consumption, and ecological footprint: Exploring the role of environmental regulations and democracy in sustainable development. Sustainable Development. 30, 595–605. https://doi.org/10.1002/SD.2251
  3. Altay Topçu, B. (2021). Dijital Ekonomi ve Göstergelerinin Sürdürülebilir Kalkınma Üzerindeki Etkisi: AB Ülkeleri Örneği. Uluslararası Yönetim Akademisi Dergisi, 4(2), 455-465.
  4. Akyol, H., Akar, T. ve Akar, G. (2023). Dijitalleşme ve ekonomik büyüme: AB ülkelerinden yeni kanıtlar. Balıkesir Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 26(49), 99-114.
  5. Arslan, T., & Khisty, C. J. (2020). Transportation challenges in emerging economies: The case of Turkey. Transport Policy, 85, 1-12. https://doi.org/10.1016/j.tranpol.2019.10.003
  6. Arvis, J. F., Raballand, G., & Marteau, J. F. (2007). The cost of being landlocked: logistics costs and supply chain reliability (Vol. 4258). World Bank Publications.
  7. Arvis, J. F., Saslavsky, D., Ojala, L., Shepherd, B., Busch, C., & Raj, A. (2018). Connecting to Compete 2018: Trade Logistics in the Global Economy – The Logistics Performance Index and Its Indicators. World Bank.
  8. Ayçin, E. (2020). Personel seçim sürecinde CRITIC ve MAIRCA yöntemlerinin kullanılması. İşletme, 1(1), 1-12.
  9. Bardakci, M., Erdoğdu, A., ve Barut, A. (2020). G-7 ve E-7 Ülkelerinde Lojistik Performans, Ekonomik Büyüme ve Finansal Gelişme İlişkisi. İnsan ve Toplum Bilimleri Araştırmaları Dergisi. 4(4), 1011–1030. https://doi.org/10.47428/itobiad.799017
  10. Barro, R. (1990). Government Spending in a Simple Model of Endogenous Growth. Journal of Political Economy, 98(5), 102–125.
  11. 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.
  12. Çetin, Ö. O., ve Karataş, M. (2024). BİST’te işlem gören otomotiv şirketlerinin kârlılık performansının LOPCOW ve MABAC yöntemleriyle analizi. Nevşehir Hacı Bektaş Veli Üniversitesi SBE Dergisi, 14(3), 1470-1496.
  13. Demirbaş, S., Üre, N., Demir, M., & Karaköy, C. (2021). The effect of financial development on logistics performance: Evidence from emerging markets. Sustainable Economics and Management Studies, 1(1), 1–13. https://scioasis.mycpanel.rs/sems/index.php/sems/article/view/24
  14. 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
  15. Din, M. U., Jiang, C., Bhatti, M. I., & Shahid, M. (2022). Population density: An underlying mechanism between road transportation and environmental quality. Frontiers in Environmental Science, 10. https://doi.org/10.3389/fenvs.2022.940911
  16. Downs, A. (2004). Still Stuck in Traffic: Coping with Peak-Hour Traffic Congestion. Brookings Institution Press. European Bank for Reconstruction and Development (EBRD). (2022). Transition report 2022: Policies for a green economy. https://www.ebrd.com
  17. European Commission. (2023). Transport in the European Union: Current trends and challenges. Publications Office of the European Union. https://transport.ec.europa.eu
  18. Febriyanto, A., Sasana, H., Prakoso, J., & Satriagasa, M. (2024). The impact of land, sea, and air transportation infrastructure on economic growth in G7 countries. Journal of Applied Economics in Developing Countries, 9(2), 50-59. http://dx.doi.org/10.20961/jaedc.v9i2.90655
  19. Gigović, L., Pamučar, D., Božanić, D. & Ljubojević, S. (2017). Application of the GIS-DANP-MABAC Multi-Criteria Model for Selecting the Location of Wind Farms: A Case Study of Vojvodina, Serbia, Renewable Energy, 103, 501-521.
  20. Gomes, S., Lopes, J. M., ve Ferreira, L. (2022). The Impact of the Digital Economy on Economic Growth: The case of OECD countries. Revista de Administração Mackenzie, 23(6), 1–31.
  21. Gökgöz, F., & Yalçın, E. (2023). Evaluating the transport sustainability of OECD countries with MCDM approach. In Proceedings of the Global Conference on Transport and Traffic Engineering (GCTTE) (pp. 73-81).
  22. Guild, R. L. (2000). Infrastructure Investment and Interregional Development: Theory, Evidence, and Implications for Planning. Journal of Planning Literature.
  23. Gürler, H. E. (2024). Ulaşım altyapı performansının çok kriterli karar verme yöntemleriyle değerlendirilmesi: Seçilmiş ülkeler üzerine bir analiz. İktisadi İdari ve Siyasal Araştırmalar Dergisi, 9(24), 238-256. https://doi.org/10.25204/iktisad.1454785
  24. Hassan, I., Alhamrouni, I., & Azhan, N. H. (2023). A CRITIC–TOPSIS Multi-Criteria Decision-Making Approach for Optimum Site Selection for Solar PV Farm. Energies, 16(10), 4245. https://doi.org/10.3390/en16104245
  25. IATA. (2020). Economic Performance of the Airline Industry. https://www.iata.org
  26. International Energy Agency (IEA). (2022). Transport and carbon emissions. https://www.iea.org
  27. International Energy Agency (IEA). (2023a). CO₂ Emissions in 2022. https://www.iea.org/reports/co2-emissions-in-2022
  28. International Energy Agency (IEA). (2023b). Global EV Outlook 2023. https://www.iea.org
  29. International Monetary Fund (IMF). (2023). World Economic Outlook: Navigating Global Divergences. https://www.imf.org/en/Publications/WEO
  30. International Telecommunication Union (ITU). (2023a). ICT Development Index. https://www.itu.int
  31. International Telecommunication Union (ITU). (2023b). Measuring Digital Development: Facts and figures 2023. https://www.itu.int/en/ITU-D/Statistics
  32. International Transport Forum (ITF). (2019). ITF Transport Outlook 2019. OECD Publishing, Paris, https://www.oecd.org/publications/itf-transport-outlook-2019_transp_outlook-en-2019-en.htm
  33. International Transport Forum (ITF). (2022). Transport Outlook 2022. https://www.itf-oecd.org
  34. Kabaklarlı, E. & Atasoy, B. S. (2019). Broadband Infrastructure and Economic Growth: A Panel Data Approach for Selected Countries. DIEM, 4(1), 105-114.
  35. Khan, S. A. R., & Qianli, D. (2017). Does national scale economic and environmental indicators spur logistics performance? Evidence from UK. Environmental Science and Pollution Research, 24, 26692–26705. doi:10.1007/s11356-017-0222-9
  36. Konečný, V., Kolarovszki, P., & Machová, V. (2019). The influence of demographic development and population structure on the demand for public passenger transport. LOGI – Scientific Journal on Transport and Logistics, 10(1), 21–30. https://doi.org/10.2478/logi-2019-0017
  37. Konu, A. (2020). Sürdürülebilir Kalkınma ve Dijital Ekonomi İlişkisi: AB Ülkeleri İçin Bir Araştırma. Akademik İncelemeler Dergisi, 15(2), 655-678. https://doi.org/10.17550/akademikincelemeler.738292
  38. Konya, S. (2024). OECD Ülkelerinde Dijital Ekonomi-Ekonomik Büyüme İlişkisinin İncelenmesi: Bir Panel Veri Analizi. Sakarya Üniversitesi İktisat Dergisi, 13(3), 163-182.
  39. Keshavarz Ghorabaee, M., Zavadskas, E. K., Olfat, L., & Turskis, Z. (2015). Multi-criteria inventory classification using a new method of evaluation based on distance from average solution (EDAS). Informatica, 26(3), 435-451.
  40. Limao, N., & Venables, A. J. (2001). Infrastructure, geographical disadvantage, transport costs, and trade. The World Bank Economic Review, 15(3), 451–479. https://doi.org/10.1093/wber/15.3.451
  41. Litman, T. (2021). Evaluating Transportation Equity: Guidance for Incorporating Distributional Impacts in Transport Planning. Victoria Transport Policy Institute. Retrieved from https://www.vtpi.org/equity.pdf
  42. Mahpour, A., Farzin, I., Baghestani, A., Ashouri, S., Javadi, Z., & Asgari, L. (2023). Modeling the impact of logistic performance, economic features, and demographic factors of countries on the seaborne trade. The Asian Journal of Shipping and Logistics, 39(2), 60-66.
  43. McKinsey & Company. (2022). The future of mobility: Reinventing transport in a digital era. https://www.mckinsey.com
  44. Mukul, E., Büyüközkan, G., & Güler, M. (2019). Strategic analysis of intelligent transportation systems. Beykoz Akademi Dergisi, 148-158. https://doi.org/10.14514/byk.m.26515393.2019.sp/148-158
  45. Nguyen, H.-Q., Nguyen, V.-T., Phan, D.-P., Tran, Q.-H., & Vu, N.-P. (2022). Multi-Criteria Decision Making in the PMEDM Process by Using MARCOS, TOPSIS, and MAIRCA Methods. Applied Sciences, 12(8), 3720. https://doi.org/10.3390/app12083720
  46. Organisation for Economic Co-operation and Development (OECD) (2017). International freight, in ITF transport outlook 2017 (Paris: OECD Publishing). doi:10.1787/9789282108000-6-en
  47. Organisation for Economic Co-operation and Development (OECD). (2020). Transport Infrastructure Investment and Maintenance Spending. OECD Publishing.
  48. Organisation for Economic Co-operation and Development (OECD). (2021a). Digital transformation in transport. https://www.oecd.org
  49. Organisation for Economic Co-operation and Development (OECD). (2022). OECD Transport Outlook 2022. OECD Publishing. https://doi.org/10.1787/transp_outlook-en-2022
  50. Organisation for Economic Co-operation and Development (OECD). (2023a). Transport Outlook 2023. OECD Publishing.
  51. Organisation for Economic Co-operation and Development (OECD). (2023b). Economic Outlook for OECD countries. https://www.oecd.org
  52. Özbek, S., ve Oğul, B. (2022). Finansal Kuznets Eğrisi Hipotezinin Geçerliliği: Türkiye Ekonomisi Üzerine Kısa ve Uzun Dönemli Zaman Serisi Bulguları. Dokuz Eylül Üniversitesi İşletme Fakültesi Dergisi, 23(1), 81-97. https://doi.org/10.24889/ifede.1052625
  53. Özdemir, L. (2017). Relationship between financial development and logistics performance and their effects on the competitiveness: an empirical cross-country study (Doctoral dissertation, Middle East Technical University (Turkey)).
  54. Özdemir, L., & Küçükkaya, E. (2023). The Strength of the Association Between Financial Development and Logistics Performance. Yönetim ve Ekonomi Dergisi, 30(3), 511-530.
  55. Özgün, F. (2024). Küreselleşmenin Çevreye Etkisi: Türkiye Üzerine Bir Uygulama. Dokuz Eylül Üniversitesi İşletme Fakültesi Dergisi, 25(2), 130-152. https://doi.org/10.24889/ifede.1501231
  56. Pamučar, D. & Ćirović, G. (2015). The Selection of Transport and Handling Resources in Logistics Centers Using Multi-Attributive Border Approximation Area Comparison (MABAC), Expert Systems with Applications, 42(6), 3016-3028. https://doi.org/10.1016/j.eswa.2014.11.057
  57. Pamučar, D., Petrović, I. & Ćirović, G. (2018). Modification of the Best– Worst and MABAC methods: A novel approach based on interval- valued fuzzy-rough numbers, Expert Systems with Applications, 91, 89-106.
  58. Robinson, M. (2010) Transport Infrastructure Fiasco Tarnishes Australia’s Reputation http://www.economy.com/dismal/analysis/121668 Retrieved:10.01.2025.
  59. Rodrigue, J. P. (2020). The Geography of Transport Systems, 5th edition, Routledge, London.
  60. Sasana, H. (2017). Earmarking Tax Optimization to Support the Effective and Sustainable Infrastructure. Jurnal Ekonomi Pembangunan: Kajian Masalah Ekonomi Dan Pembangunan, 18(2), 194-204. https://doi.org/10.23917/jep.v18i2.4168
  61. Simic, V., Gokasar, I., Deveci, M., & Švadlenka, L. (2022). Mitigating climate change effects of urban transportation using a type-2 neutrosophic MEREC-MARCOS model. IEEE Transactions on Engineering Management, 71, 3233-3249.
  62. Szymańska, E. & Zalewski, R. I. (2020). VIKOR-based assessment of EU seaports’ competitiveness. Sustainability, 12(5), 1936.
  63. Tzeng, G. H., & Huang, J. J. (2011). Multiple attribute decision making: methods and applications. CRC press.
  64. United Nations Environment Programme (UNEP). (2022). Global Environment Outlook 2022: Transport and climate change. https://www.unep.org/resources/global-environment-outlook-2022
  65. Uyar, A., Kuzey, C., Koseoglu, M. A., & Karaman, A. S. (2023). Travel and tourism competitiveness index and the tourism sector development. Tourism Economics, 29(4), 1005-1031.
  66. Wan, B., Wan, W., Hanif, N., & Ahmed, Z. (2022) Logistics performance and environmental sustainability: Do green innovation, renewable energy, and economic globalization matter?. Frontiers in Environmental Science, 10, 996341. https://doi.org/10.3389/fenvs.2022.996341
  67. Weng, X., Yuan, C., Li, X., & Li, H. (2024). Research on the Construction of a Risk Assessment Indicator System for Transportation Infrastructure Investment under Public–Private Partnership Model. Buildings, 14(6), 1679. https://doi.org/10.3390/buildings14061679
  68. WEF (World Economic Forum). (2022). Travel & Tourism Development Index 2021: Rebuilding for a Sustainable and Resilient Future. Access on the internet: https://www.weforum.org/publications/travel-and-tourism-development-index-2021/.
  69. WEF (World Economic Forum). (2023). The future of logistics: Digital transformation. https://www.weforum.org/reports/the-future-of-logistics-digital-transformation
  70. Wong, M. C., & Yip, T. L. (2019). Influence of transportation infrastructure on the relationship between institutions and economic performance. Maritime Business Review, 4(4), 395‑412. https://doi.org/10.1108/MABR-09-2019-0038
  71. World Bank. (2020). Connecting to compete 2020: Trade logistics in the global economy – The logistics performance index and its indicators. Washington, DC: World Bank.
  72. World Bank. (2022). Logistics Performance Index (LPI) 2022: Connecting to compete. https://lpi.worldbank.org
  73. World Bank. (2023). Logistics Performance Index (LPI) report. https://lpi.worldbank.org
  74. Yazdani, M., Zarate, P., Kazimieras Zavadskas, E., & Turskis, Z. (2019). A combined compromise solution (CoCoSo) method for multi-criteria decision-making problems. Management Decision, 57(9), 2501-2519. https://doi.org/10.1108/MD-05-2017-0458
  75. 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.
  76. Zhang, L., Li, Z., Kirikkaleli, D., Adebayo, T. S., Adeshola, I., & Akinsola, G. D. (2021). Modeling CO2 emissions in Malaysia: An application of maki cointegration and wavelet coherence tests. Environmental Science and Pollution Research, 28, 26030–26044. doi:10.1007/s11356-021-12430-x
  77. Zhao, P., & Li, S. (2016). Restraining transport inequality in growing cities: Can spatial planning play a role? International Journal of Sustainable Transportation, 10(10), 947–959. https://doi.org/10.1080/15568318.2016.1165483