Journals / Uluslararası İşletme ve Ekonomi Çalışmaları Dergisi / 2026 / Cilt: 8 Sayı: 1

Global logistics priorities before and after COVID-19: A multi-criteria assessment of logistics performance index dimensions across income groups

Tuğrul Bayat(AFYON KOCATEPE UNIVERSITY, FACULTY OF ECONOMICS AND ADMINISTRATIVE SCIENCES)0000-0003-1491-6178
Muhammed Karataş(AFYON KOCATEPE UNIVERSITY)0000-0002-3738-7486
Pages
—
Publication Date
2026-10-29
🏛️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 findings reveal different weighting patterns across income groups and periods.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This study examines changes in global logistics priorities by comparing the relative importance of the six Logistics Performance Index (LPI) dimensions across income groups in the 2018 and 2023 evaluation periods.
📊
Key Findings & Data:A fixed common panel of 134 countries is classified into high-, upper-middle-, lower-middle-, and low-income groups to ensure comparability between the two periods.
💡
Conclusion & Impact:The findings reveal different weighting patterns across income groups and periods.
🔬Design:Araştırma Makalesi
📍Scope:2018 Verileri
⚙️Variables:Logistics performance index · COVID-19 · Supply chain resilience

Abstract

This study examines changes in global logistics priorities by comparing the relative importance of the six Logistics Performance Index (LPI) dimensions across income groups in the 2018 and 2023 evaluation periods. A fixed common panel of 134 countries is classified into high-, upper-middle-, lower-middle-, and low-income groups to ensure comparability between the two periods. Criterion weights are determined using three objective weighting methods, LODECI, LOPCOW, and SITDE, and integrated through arithmetic averaging. Countries are then ranked using the ALPAS method. The findings reveal different weighting patterns across income groups and periods. International shipments remain the highest-weighted dimension in the high-income group, while customs retain the highest weight in the lower-middle-income group. In the upper-middle-income group, tracking and tracing remains the highest-weighted dimension in both 2018 and 2023, although its weight decreases from 0.201 to 0.184. The most notable change is observed in the low-income group, where the weight of timeliness decreases from 0.160 to 0.112, while international shipments gain relative importance and tracking and tracing retains the highest weight despite declining from 0.213 to 0.191. Country rankings also show both stability and substantial movements within each income group. Comparisons with AROMAN, CRADIS, MABAC, and MAIRCA, together with scenario-based sensitivity analysis, indicate that the main ranking structure is highly stable. The integrated-ALPAS rankings also show strong agreement with the official World Bank LPI rankings. Overall, the findings indicate that the relative importance of LPI dimensions varies across income groups and evaluation periods, supporting a more context-sensitive interpretation of logistics performance and providing a basis for income-group-specific policy and managerial priorities.

Özet

This study examines changes in global logistics priorities by comparing the relative importance of the six Logistics Performance Index (LPI) dimensions across income groups in the 2018 and 2023 evaluation periods. A fixed common panel of 134 countries is classified into high-, upper-middle-, lower-middle-, and low-income groups to ensure comparability between the two periods. Criterion weights are determined using three objective weighting methods, LODECI, LOPCOW, and SITDE, and integrated through arithmetic averaging. Countries are then ranked using the ALPAS method. The findings reveal different weighting patterns across income groups and periods. International shipments remain the highest-weighted dimension in the high-income group, while customs retain the highest weight in the lower-middle-income group. In the upper-middle-income group, tracking and tracing remains the highest-weighted dimension in both 2018 and 2023, although its weight decreases from 0.201 to 0.184. The most notable change is observed in the low-income group, where the weight of timeliness decreases from 0.160 to 0.112, while international shipments gain relative importance and tracking and tracing retains the highest weight despite declining from 0.213 to 0.191. Country rankings also show both stability and substantial movements within each income group. Comparisons with AROMAN, CRADIS, MABAC, and MAIRCA, together with scenario-based sensitivity analysis, indicate that the main ranking structure is highly stable. The integrated-ALPAS rankings also show strong agreement with the official World Bank LPI rankings. Overall, the findings indicate that the relative importance of LPI dimensions varies across income groups and evaluation periods, supporting a more context-sensitive interpretation of logistics performance and providing a basis for income-group-specific policy and managerial priorities.

🔖

Cite This Article

Bayat, T., Karataş, M., Çetiner, B. (2026). Global logistics priorities before and after COVID-19: A multi-criteria assessment of logistics performance index dimensions across income groups. Uluslararası İşletme ve Ekonomi Çalışmaları Dergisi, Cilt: 8 Sayı: 1. https://doi.org/10.54821/bes.1915558

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (50)

  1. Ali, I., Sadiddin, A., & Cattaneo, A. (2023). Risk and resilience in agri-food supply chain SMEs in the pandemic era: A cross-country study. International Journal of Logistics Research and Applications, 26(11), 1602-1620. https://doi.org/10.1080/13675567.2022.2102159
  2. Arvis, J.-F., Ojala, L., Shepherd, B., Saslavsky, D., Busch, C., Raj, A., & Naula, T. (2018). Connecting to compete 2018: Trade logistics in the global economy-The logistics performance index and its indicators. World Bank. https://doi.org/10.1596/29971
  3. Arvis, J.-F., Ojala, L., Shepherd, B., Ulybina, D., & Wiederer, C. (2023). Connecting to compete 2023: Trade logistics in an uncertain global economy. World Bank. https://doi.org/10.1596/39760
  4. Bayraktar, E., Zaim, S., Gudelek, M., & Golgeci, I. (2025). Global logistics efficiency after COVID-19: A cross-country analysis using the logistics performance index. International Journal of Logistics Research and Applications, 29(10), 1275-1306. https://doi.org/10.1080/13675567.2025.2540863
  5. Birkel, H., & Müller, J. M. (2025). Resilient by nature or technology? How Industry 4.0 enhances Supply Chain Resilience until 2035. Supply Chain Management: An International Journal, 30(3), 304-322. https://doi.org/10.1108/scm-07-2023-0340
  6. Božanić, D., Milić, A., Tešić, D., Salabun, W., & Pamučar, D. (2021). D numbers–FUCOM–fuzzy RAFSI model for selecting the group of construction machines for enabling mobility. Facta Universitatis, Series: Mechanical Engineering, 19(3), 447-471. https://doi.org/10.22190/fume210318047b
  7. Bugarčić, F. Ž., Mićić, V., & Stanišić, N. (2023). The role of logistics in economic growth and global competitiveness. Zbornik radova Ekonomskog fakulteta u Rijeci / Proceedings of Rijeka Faculty of Economics, 41(2), 499-520. https://doi.org/10.18045/zbefri.2023.2.499
  8. Christopher, M. (2016). Logistics & supply chain management: Logistics & supply chain management. (5th ed.). UK: Pearson.
  9. Çalık, A., Erdebilli, B., & Özdemir, Y. S. (2023). Novel integrated hybrid multi-criteria decision-making approach for logistics performance index. Transportation Research Record, 2677(2), 1392-1400. https://doi.org/10.1177/03611981221113314
  10. Çelebi, D. (2019). The role of logistics performance in promoting trade. Maritime Economics & Logistics, 21(3), 307-323. https://doi.org/10.1057/s41278-017-0094-4
  11. Dovbischuk, I. (2022). Innovation-oriented dynamic capabilities of logistics service providers and firm performance during the COVID-19 pandemic. The International Journal of Logistics Management, 33(2), 499-519. https://doi.org/10.1108/IJLM-01-2021-0059
  12. Ecer, F., & Pamucar, D. (2022). A novel LOPCOW-DOBI multi-criteria sustainability performance assessment methodology: An application in developing country banking sector. Omega, 112, 102690. https://doi.org/10.1016/j.omega.2022.102690
  13. Gani, A. (2017). The logistics performance effect in international trade. The Asian Journal of Shipping and Logistics, 33(4), 279-288. https://doi.org/10.1016/j.ajsl.2017.12.012
  14. Gao, Y., Zheng, S., Cao, Y., & Cao, Z. (2026). Urban modern agriculture development in major Chinese cities: A multidimensional evaluation and contextual typology. Land, 15(6), 1000. https://doi.org/10.3390/land15061000
  15. Ghobakhloo, M., Iranmanesh, M., Foroughi, B., Tseng, M. L., Nikbin, D., & Khanfar, A. A. (2025). Industry 4.0 digital transformation and opportunities for supply chain resilience: A comprehensive review and a strategic roadmap. Production Planning & Control, 36(1), 61-91. https://doi.org/10.1080/09537287.2023.2252376
  16. Gopisetty, Y. B., & Sama, H. R. (2025). Skewness impact through distributional evaluation (SITDE) method. Journal of the Operational Research Society, 76(6), 1204-1224. https://doi.org/10.1080/01605682.2024.2416910
  17. Gürler, H. E., Özçalıcı, M., & Pamucar, D. (2024). Determining criteria weights with genetic algorithms for multi-criteria decision-making methods: The case of logistics performance index rankings of European Union countries. Socio-Economic Planning Sciences, 91, 101758. https://doi.org/10.1016/j.seps.2023.101758
  18. Hausman, W. H., Lee, H. L., & Subramanian, U. (2013). The impact of logistics performance on trade. Production and Operations Management, 22(2), 236-252. https://doi.org/10.1111/j.1937-5956.2011.01312.x
  19. Hobbs, J. E. (2020). Food supply chains during the COVID-19 pandemic. Canadian Journal of Agricultural Economics, 68(2), 171-176. https://doi.org/10.1111/cjag.12237
  20. Hosseini Shekarabi, S. A., Kiani Mavi, R., & Romero Macau, F. (2025). Supply chain resilience: A critical review of risk mitigation, robust optimisation, and technological solutions and future research directions. Global Journal of Flexible Systems Management, 26(3), 681-735. https://doi.org/10.1007/s40171-025-00458-8
  21. Ivanov, D. (2020). Predicting the impacts of epidemic outbreaks on global supply chains: A simulation-based analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case. Transportation Research Part E: Logistics and Transportation Review, 136, 101922. https://doi.org/10.1016/j.tre.2020.101922
  22. Ivanov, D. (2024). Transformation of supply chain resilience research through the COVID-19 pandemic. International Journal of Production Research, 62(23), 8217-8238. https://doi.org/10.1080/00207543.2024.2334420
  23. Ivanov, D., & Dolgui, A. (2020). Viability of intertwined supply networks: Extending the supply chain resilience angles towards survivability. A position paper motivated by COVID-19 outbreak. International Journal of Production Research, 58(10), 2904-2915. https://doi.org/10.1080/00207543.2020.1750727
  24. 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
  25. Jüttner, U. (2005). Supply chain risk management: Understanding the business requirements from a practitioner perspective. The International Journal of Logistics Management, 16(1), 120-141. https://doi.org/10.1108/09574090510617385
  26. Koyuncu, T., Doğaner, B., & Alola, A. A. (2023). The criticality of transport and export activities in the economic prosperity of high-middle income countries: The role of logistics performance. Urban, Planning and Transport Research, 11(1), 2182353. https://doi.org/10.1080/21650020.2023.2182353
  27. Martí, L., Martín, J. C., & Puertas, R. (2017). A DEA-logistics performance index. Journal of Applied Economics, 20(1), 169-192. https://doi.org/10.1016/S1514-0326(17)30008-9
  28. 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
  29. 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, 10(1), 13-34. https://doi.org/10.2478/eoik-2022-0004
  30. 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
  31. Notteboom, T., Pallis, T., & Rodrigue, J.-P. (2021). Disruptions and resilience in global container shipping and ports: The COVID-19 pandemic versus the 2008-2009 financial crisis. Maritime Economics & Logistics, 23(2), 179-210. https://doi.org/10.1057/s41278-020-00180-5
  32. Özekenci, E. K. (2026). Comparative analysis of the logistics performance index of Central and Eastern European countries: A hybrid LOPCOW-RAWEC model. Central European Business Review, 15(1), 89-109. https://doi.org/10.18267/j.cebr.408
  33. Pala, O. (2024). Assessment of the social progress on European Union by logarithmic decomposition of criteria importance. Expert Systems with Applications, 238, 121846. https://doi.org/10.1016/j.eswa.2023.121846
  34. Pamučar, D., Macura, D., Tavana, M., Božanić, D., & Knežević, N. (2022). An integrated rough group multicriteria decision-making model for the ex-ante prioritization of infrastructure projects: The Serbian railways case. Socio-Economic Planning Sciences, 79, 101098. https://doi.org/10.1016/j.seps.2021.101098
  35. Ponomarov, S. Y., & Holcomb, M. C. (2009). Understanding the concept of supply chain resilience. The International Journal of Logistics Management, 20(1), 124-143. https://doi.org/10.1108/09574090910954873
  36. 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
  37. Saslavsky, D., & Rastogi, C. (2020). Facilitating air freight: Policies and actions. World Bank. https://doi.org/10.1596/33717
  38. See, K. F., Guo, Y., & Yu, M. M. (2024). Enhancing logistics performance measurement: An effectiveness-based hierarchical data envelopment analysis approach. INFOR: Information Systems and Operational Research, 62(3), 449-479. https://doi.org/10.1080/03155986.2024.2309420
  39. Slack, N., Brandon-Jones, A., & Johnston, R. (2019). Operations management. (9th ed.). London, UK: Prentice Hall/Financial Times. Available at: https://za.pearson.com/content/dam/region-growth/south-africa/pearson-south-africa/TVET/localTitles/documents/9781775789345_Operations_Management_Global_and_Southern_African_Perspectives_3rd_Edition_sample_chapter.pdf
  40. Stević, Ž., Ersoy, N., Başar, E. E., & Baydaş, M. (2024). Addressing the global logistics performance index rankings with methodological insights and an innovative decision support framework. Applied Sciences, 14(22), 10334. https://doi.org/10.3390/app142210334
  41. Tekgün, B., Koşaroğlu, Ş. M., Zolfani, S. H., Akbulut, O. Y., & Işık, Ö. (2026). Comparative assessment of financial market development in African economies: An integrated MCDM framework based on CRISUS-LODECI-WENSLO weighting and ARTASI ranking. Mathematics, 14(10), 1677. https://doi.org/10.3390/math14101677
  42. Ulutaş, A., Topal, A., & Ecer, F. (2025). The Alternative Prioritization and Assessment System (ALPAS) method for environmental performance evaluation. Mathematics, 13(20), 3333. https://doi.org/10.3390/math13203333
  43. UNCTAD. (2021). Review of maritime transport 2021. United Nations UNCTAD/RMT/2021, Sales No. E.21.II.D.21. Available at: https://digitallibrary.un.org/record/4042157?v=pdf
  44. World Bank. (2020). The impact of COVID-19 on logistics. World Bank. Available at: http://documents.worldbank.org/curated/en/944061596087531129
  45. World Bank. (2020a). COVID-19 trade watch. World Bank. Available at: http://documents.worldbank.org/curated/en/819571634057406094
  46. World Bank. (2020b). Global economic prospects. World Bank. https://doi.org/10.1596/978-1-4648-1553-9
  47. Ye, C., Cai, T., Zhang, C., & Cao, W. (2025). Sustainability-based evolution of port hinterlands: Green performance and spatial impacts in the Bohai Rim. Ecological Indicators, 178, 114095. https://doi.org/10.1016/j.ecolind.2025.114095
  48. Yu, M. M., & Rakshit, I. (2025). An alternative assessment approach to global logistics performance evaluation: Common weight H‐DEA approach. International Transactions in Operational Research, 32(2), 839-862. https://doi.org/10.1111/itor.13360
  49. Yürüyen, A. A., & Altay, H. (2025). Evaluating the logistics performance of one Belt One Road project countries with multi criteria decision making methods. Journal of Mehmet Akif Ersoy University Economics and Administrative Sciences Faculty, 12(2), 750-774. https://doi.org/10.30798/makuiibf.1609904
  50. Zhao, G., Vazquez-Noguerol, M., Liu, S., & Prado-Prado, J. C. (2024). Agri-food supply chain resilience strategies for preparing, responding, recovering, and adapting in relation to unexpected crisis: A cross-country comparative analysis from the COVID-19 pandemic. Journal of Business Logistics, 45(1), e12361. https://doi.org/10.1111/jbl.12361