References
- Agility. (2025). Emerging Markets Logistics Index. Agility & Transport Intelligence. Retrieved from: https://emli.agility.com/ (01.04.2025)
- Arıkan Kargı, V. S. (2022). Evaluation of Logistics Performance of The OECD Member Countries with Integrated Entropy and Waspas Method. Journal of Management & Economics, 29(4). https://doi.org/10.18657/yonveek.1067480
- Arvis, J. F., Ojala, L., Shepherd, B., Ulybina, D., & Wiederer, C. (2023). Connecting to compete 2023: trade logistics in an uncertain global economy. Logistics Performance Index Indicators. Washington DC: The World Bank. https://doi.org/10.1596/39760
- Barak, S., & Mokfi, T. (2019). Evaluation and selection of clustering methods using a hybrid group MCDM. Expert Systems with Applications, 138, 112817. https://doi.org/10.1016/j.eswa.2019.07.034
- Bennani, M., Jawab, F., Hani, Y., El Mhamedi, A., & Amegouz, D. (2022). Hybrid F-SWARA and F-ENTROPY for the optimization of the weighting of the location criteria of a green logistics platform. IFAC-PapersOnLine, 55(10), 1606-1612. https://doi.org/10.1016/j.ifacol.2022.09.620
- Beysenbaev, R., & Dus, Y. (2020). Proposals for improving the logistics performance index. The Asian Journal of Shipping and Logistics, 36(1), 34-42. https://doi.org/10.1016/j.ajsl.2019.10.001
- Block, S., Emerson, J. W., Esty, D. C., de Sherbinin, A., Wendling, Z. A., Kurczynski, K., ... & Lin, F. (2024). Environmental Performance Index. New Haven, CT: Yale Center for Environmental Law & Policy.
- Bošković, S., Švadlenka, L., Dobrodolac, M., Jovčić, S., & Zanne, M. (2023). An extended AROMAN method for cargo bike delivery concept selection. Decision Making Advances, 1(1), 1-9. https://doi.org/10.31181/v120231
- Bulut, E., & Abacıoğlu, S. (2025). How Criteria Weights Influence Performance in Evaluating Logistic Productivity: An Application in the Emerging Markets Logistics Index. Journal of Productivity, (Productivity for Logistics), 1-28. https://doi.org/10.51551/verimlilik.1518693
- Çakır, S. (2017). Measuring logistics performance of OECD countries via fuzzy linear regression. Journal of Multi-Criteria Decision Analysis, 24(3-4), 177-186. https://doi.org/10.1002/mcda.1601
- Çıray, D., Özdemir, Ü., & Mete, S. (2024). An Evaluation of the logistics Performance Index Using the ENTROPY-based ORESTE Method. Journal of Transportation and Logistics, 9(1), 68-82. https://doi.org/10.26650/JTL.2024.1437070
- Demir, G., Chatterjee, P., & Pamucar, D. (2024). Sensitivity analysis in multi-criteria decision making: A state-of-the-art research perspective using bibliometric analysis. Expert Systems with Applications, 237, 121660. https://doi.org/10.1016/j.eswa.2023.121660
- Dezert, J., Tchamova, A., Han, D., & Tacnet, J. M. (2020, July). The SPOTIS rank reversal free method for multi-criteria decision-making support. In 2020 IEEE 23rd International Conference on Information Fusion (FUSION) (pp. 1-8). IEEE. https://doi.org/10.23919/FUSION45008.2020.9190347
- Durdu, D. (2025). Evaluating Financial Performance with SPC-LOPCOW-AROMAN Hybrid Methodology: A Case Study for Firms Listed in BIST Sustainability Index. Knowledge and Decision Systems with Applications, 1, 92-111. https://doi.org/10.59543/kadsa.v1i.13879
- Ecer, F. (2021). A consolidated MCDM framework for performance assessment of battery electric vehicles based on ranking strategies. Renewable and Sustainable Energy Reviews, 143, 110916. https://doi.org/10.1016/j.rser.2021.110916
- Ekici, Ş. Ö., Kabak, Ö., & Ülengin, F. (2019). Improving logistics performance by reforming the pillars of Global Competitiveness Index. Transport Policy, 81, 197-207. https://doi.org/10.1016/j.tranpol.2019.06.014
- Emerson, P. (2013). The original Borda count and partial voting. Social Choice and Welfare, 40(2), 353-358. https://doi.org/10.1007/s00355-011-0603-9
- Gardas, B. B., Raut, R. D., & Narkhede, B. (2019). Determinants of sustainable supply chain management: A case study from the oil and gas supply chain. Sustainable Production and Consumption, 17, 241-253. https://doi.org/10.1016/j.spc.2018.11.005
- Gelmez, E., Güleş, H. K., & Zerenler, M. (2024). Evaluation of Logistics Performances of G20 Countries Using SD-Based COPRAS and SAW Methods. Journal of Turkish Operations Management, 8(2), 339-353. https://doi.org/10.56554/jtom.1471209
- Gligorić, Z., Gligorić, M., Miljanović, I., Lutovac, S., & Milutinović, A. (2023). Assessing Criteria Weights by the Symmetry Point of Criterion (Novel SPC Method) --Application in the Efficiency Evaluation of the Mineral Deposit Multi-Criteria Partitioning Algorithm. CMES-Computer Modeling in Engineering & Sciences, 136(1). https://doi.org/10.32604/cmes.2023.025021
- Hadzikadunic, A., Stevic, Z., Badi, I., & Roso, V. (2023). Evaluating the logistics performance index of European Union Countries: An integrated multi-criteria decision-making approach utilizing the Bonferroni Operator. Int. J. Knowl. Innov. Stud, 1(1), 44-59. https://doi.org/10.56578/ijkis010104
- Isik, O., Aydin, Y., & Kosaroglu, S. M. (2020). The assessment of the logistics performance index of CEE countries with the new combination of SV and MABAC methods. LogForum, 16(4), 549-559. https://doi.org/10.17270/J.LOG.2020.504
- Ju, M., Mirović, I., Petrović, V., Erceg, Ž., & Stević, Ž. (2024). A Novel Approach for the Assessment of Logistics Performance Index of EU Countries. Economics, 18(1), 20220074. https://doi.org/10.1515/econ-2022-0074
- Kara, K., Bentyn, Z., & Yalçın, G. C. (2022). Determining the logistics market performance of developing countries by entropy and MABAC methods. LogForum, 18(4), 421-434. https://doi.org/10.17270/J.LOG.2022.752
- Khan, M. S., Aziz, G., Bakoben, H. B. M., & Saeed, A. (2025). Implications of Sustainable Logistics on Economic, Environment, and Social Dimensions: Pre-and Post-Implementation of Saudi Vision 2030. Journal of the Knowledge Economy, 1-30. https://doi.org/10.1007/s13132-025-02616-w
- Larson, P. D. (2021). Relationships between logistics performance and aspects of sustainability: A cross-country analysis. Sustainability, 13(2), 623. https://doi.org/10.3390/su13020623
- Linh, N. T. D., Son, N. H., & Thao, D. X. (2025). Evaluating the Impact of Weighting Methods on the Stability of Scores for Alternatives in Multi-Criteria Decision-Making Problems. Engineering, Technology & Applied Science Research, 15(1), 19998-20004. https://doi.org/10.48084/etasr.9518
- Liu, J., Yuan, C., Hafeez, M., & Yuan, Q. (2018). The relationship between environment and logistics performance: Evidence from Asian countries. Journal of cleaner production, 204, 282-291. https://doi.org/10.1016/j.jclepro.2018.08.310
- Lu, M., Xie, R., Chen, P., Zou, Y., & Tang, J. (2019). Green transportation and logistics performance: An improved composite index. Sustainability, 11(10), 2976. https://doi.org/10.3390/su11102976
- Martí, L., Puertas, R., & García, L. (2014). The importance of the Logistics Performance Index in international trade. Applied economics, 46(24), 2982-2992. https://doi.org/10.1080/00036846.2014.916394
- Mešić, A., Miškić, S., Stević, Ž., & Mastilo, Z. (2022). Hybrid MCDM solutions for evaluation of the logistics performance index of the Western Balkan countries. ECONOMICS-Innovative and Economics Research Journal, 10(1). https://doi.org/10.2478/eoik-2022-0004
- Miškić, S., Stević, Ž., Tadić, S., Alkhayyat, A., & Krstić, M. (2023). Assessment of the LPI of the EU countries using MCDM model with an emphasis on the importance of criteria. World Review of Intermodal Transportation Research, 11(3), 258-279. https://doi.org/10.1504/WRITR.2023.132501
- Moldabekova, A., Philipp, R., Reimers, H. E., & Alikozhayev, B. (2021). Digital technologies for improving logistics performance of countries. Transport and Telecommunication, 22(2), 207-216. https://doi.org/10.2478/ttj-2021-0016
- Nayak, N., Pant, P., Sarmah, S. P., & Tulshan, R. (2024). Development of in-country logistics performance index for emerging economies: a case of Indian states. International Journal of Productivity and Performance Management, 73(9), 2926-2950. https://doi.org/10.1108/IJPPM-03-2023-0122
- Ojala, L., & Celebi, D. (2015). The World Bank’s Logistics Performance Index (LPI) and drivers of logistics performance. Proceeding of MAC-EMM, OECD, 3-30.
- Oufella, S. (2024). Hybrid use of Borda count and PROMETHEE method for maintenance strategy selection problem. Foundations of Computing and Decision Sciences, 49(2), 139-160. https://doi.org/10.2478/fcds-2024-0009
- Özbek, H. E., & Özekenci, E. K. (2023). Investigation of digital logistics market performance in developing countries with hybrid MCDM methods. JOEEP: Journal of Emerging Economies and Policy, 8(2), 559-576.
- Özekenci, E. K. (2023). Assessing the logistics market performance of developing countries by SWARA-CRITIC based CoCoSo methods. LogForum, 19(3), 5. https://doi.org/10.17270/J.LOG.2023.857
- Özekenci, E. K. (2024). Assessment of the Logistics Performance Index of OPEC Countries with ENTROPY, CRITIC and LOPCOW-based EDAS Methods. Journal of Transportation and Logistics, 9(2), 260-279. https://doi.org/10.26650/JTL.2024.1339285
- Özekenci, E. K. (2025). A Hybrid MPSI-Extended AROMAN Decision-making Model for Assessing Green Logistics Performance: The Case of Asia-Pacific Countries. Logforum 21(1), 7. https://doi.org/10.17270/J.LOG.001154
- Polat, M., Kara, K., & Acar, A. Z. (2023). Competitiveness based logistics performance index: An empirical analysis in Organisation for Economic Co-operation and Development countries. Competition and Regulation in Network Industries, 24(2-3), 97-119. https://doi.org/10.1177/17835917231185890
- Puška, A., Štilić, A., Pamučar, D., Božanić, D., & Nedeljković, M. (2024). Introducing a Novel multi-criteria Ranking of Alternatives with Weights of Criterion (RAWEC) model. MethodsX, 102628. https://doi.org/10.1016/j.mex.2024.102628
- Rezaei, J., van Roekel, W. S., & Tavasszy, L. (2018). Measuring the relative importance of the logistics performance index indicators using Best Worst Method. Transport policy, 68, 158-169. https://doi.org/10.1016/j.tranpol.2018.05.007
- Starostka-Patyk, M., Bajdor, P., & Białas, J. (2024). Green logistics performance Index as a benchmarking tool for EU countries environmental sustainability. Ecological Indicators, 158, 111396. https://doi.org/10.1016/j.ecolind.2023.111396
- Topal, A., & Ulutaş, A. (2024). Evaluating the Logistics Performance of G8 Nations Using Multi-Criteria Decision-Making Models. J. Intell. Manag. Decis, 3, 150-158. https://doi.org/10.56578/jimd030302.
- Ulutaş, A., & Karaköy, Ç. (2019). An analysis of the logistics performance index of EU countries with an integrated MCDM model. Economics & Business Review, 5(4). https://doi.org/10.18559/ebr.2019.4.3
- Van Dua, T., & Thinh, H. X. (2023). RSMVC: A new-simple method to select the cutting tool base on multi criteria. Journal of Applied Engineering Science, 21(1), 167-175. https://doi.org/10.5937/jaes039772
- 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
- Wang, H., Pan, C., Wang, Q., & Zhou, P. (2020). Assessing sustainability performance of global supply chains: An input-output modeling approach. European journal of operational research, 285(1), 393-404. https://doi.org/10.1016/j.ejor.2020.01.057
- Więckowski, J., Kizielewicz, B., & Sałabun, W. (2024a). A multi-dimensional sensitivity analysis approach for evaluating the robustness of renewable energy sources in European countries. Journal of Cleaner Production, 469, 143225. https://doi.org/10.1016/j.jclepro.2024.143225
- Więckowski, J., Wątróbski, J., Shkurina, A., & Sałabun, W. (2024b). Adaptive multi-criteria decision making for electric vehicles: A hybrid approach based on RANCOM and ESP-SPOTIS. Artificial Intelligence Review, 57(10), 270. https://doi.org/10.1007/s10462-024-10901-4
- Wollenberg, A., Lazarini, J. G. O. C., Lazarini, J. J. C., Parra, L. F. O., & Kakade, A. S. (2023). Green supply chains: a comparative efficiency analysis in the gulf and beyond. In Social Change in the Gulf Region: Multidisciplinary Perspectives (pp. 475-492). Singapore: Springer Nature Singapore.
- Yildirim, B. F., & Adiguzel Mercangoz, B. (2020). Evaluating the logistics performance of OECD countries by using fuzzy AHP and ARAS-G. Eurasian Economic Review, 10(1), 27-45. https://doi.org/10.1007/s40822-019-00131-3
- Zhang, W., Zhang, M., Zhang, W., Zhou, Q., & Zhang, X. (2020). What influences the effectiveness of green logistics policies? A grounded theory analysis. Science of the Total Environment, 714, 136731. https://doi.org/10.1016/j.scitotenv.2020.136731