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Assessing the logistics market performance of developing countries by SWARA-CRITIC based CoCoSo methods

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EN
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Background: The logistics market performance of developing countries has been measured by the Agility Emerging Markets Logistics Index [AEMLI] report since 2014. The main objective of this study is to propose a new model to assess the logistics market performance of developing countries and rank them based on this performance. Correspondingly, the AEMLI indicators were selected as the main criteria for assessing the logistics market performance of developing countries in this study. Methods: In the current study, the AEMLI indicators, which are domestic logistics opportunities [DLO], international logistics opportunities [ILO], business fundamentals [BF] and digital readiness [DR], were used as criteria to assess the logistics market performances of developing countries. First, the weights of the criteria were computed by a combination of subjective [SWARA] and objective [CRITIC] methods. Then, the CoCoSo method was used to rank developing countries according to their logistics market performance. Results: The findings indicate that BF is the most significant criterion, followed by ILO, DR and DLO. Based on the results of the proposed model, China, India, the United Arab Emirates [UAE], Malaysia, and Saudi Arabia had the best logistics market performance in 2022, while Angola, Myanmar, Mozambique, Venezuela, and Libya had the worst logistics market performance in 2022. Additionally, some differences in the ranking of developing countries according to logistics market performance can be observed in the proposed model compared to the AEMLI 2023 report. Conclusion: To the best of the author’s knowledge, this is the first study to examine logistics market performance through the combination of two weighting methods (both subjective and objective). The current study also contributes to the existing literature by providing insight into logistics market performance for carriers, shippers, distributors, policy makers, and others who focus on the world’s emerging markets.
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375--394
Opis fizyczny
Bibliogr. 28 poz., tab., wykr.
Twórcy
  • Faculty of Economics and Administrative Sciences, Department of International Business Management, Çağ University, Mersin, Turkey
Bibliografia
  • 1. Agility. (2023). Agility Emerging Markets Logistics Index 2023 Report. Available from: https://www.agility.com/en/emerging-markets-logistics-index/ (Access date: 17.05.2023)
  • 2. Ali, T., Chiu, Y. R., Aghaloo, K., Nahian, A. J., & Ma, H. (2020). Prioritizing the Existing Power Generation Technologies in Bangladesh’s Clean Energy Scheme Using A Hybrid Multi-Criteria Decision Making Model. Journal of Cleaner Production, 267, 121901. https://doi.org/10.1016/j.jclepro.2020.121901
  • 3. Alinezhad, A., & Khalili, J. (2019). New Methods and Applications in Multiple Attribute Decision Making (MADM) (Vol. 277). Cham: Springer. https://doi.org/10.1007/978-3-030-15009-9
  • 4. Arıkan Kargı, V. S. (2022). Evaluation of Logistics Performance of The OECD Member Countries with Integrated Entropy and Waspas Method. Journal of Management and Economics, 29(4), 801-811. https://doi.org/10.18657/yonveek.1067480
  • 5. Candan, G. (2019). Integrated Approach of Fuzzy AHP and Grey Relational Analysis for Logistic Performance Evaluation. Journal of Social Sciences of Mus Alparslan University, 7(5), 277-286. https://doi.org/10.18506/anemon.506769
  • 6. 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
  • 7. 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
  • 8. 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
  • 9. Gergin, R. E., & Baki, B. (2015). Evaluation by Integrated AHP and TOPSIS Method of Logistics Performance in Turkey's Regions. Business and economics research Journal, 6(4), 115.
  • 10. GiGroup. (2022). Logistics Global HR Trends. Available from: https://uk.gigroup.com/logistics-global-hr-trends-2022/ (Access date: 05.05.2023)
  • 11. Ighravwe, D., & Babatunde, M. (2018). Selection of a Mini-grid Business Model for Developing Countries Using CRITIC-TOPSIS with Interval Type-2 Fuzzy Sets. Decision Science Letters, 7(4), 427-442. https://doi.org/10.5267/j.dsl.2018.1.004
  • 12. 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. http://doi.org/10.17270/J.LOG.2020.504
  • 13. Kara, K., & Yalçın, G. C. (2022). Digital Logistics Market Performance of Developing Countries. International Journal of Academic Accumulation, 5(5). http://doi.org/10.53001/uluabd.2022.38
  • 14. 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
  • 15. Keršuliene, V., Zavadskas, E. K., & Turskis, Z. (2010). Selection of Rational Dispute Resolution Method by Applying New Step‐Wise Weight Assessment Ratio Analysis (SWARA). Journal of Business Economics and Management, 11(2), 243-258. https://doi.org/10.3846/jbem.2010.12
  • 16. Keshavarz Ghorabaee, M., Amiri, M., Kazimieras Zavadskas, E., & Antuchevičienė, J. (2017). Assessment of Third-Party Logistics Providers Using A CRITIC–WASPAS Approach with Interval Type-2 Fuzzy Sets. Transport, 32(1), 66-78. https://doi.org/10.3846/16484142.2017.1282381
  • 17. Mercangoz, B. A., Yildirim, B. F., & Yildirim, S. K. (2020). Time Period Based COPRAS-G Method: Application on the Logistics Performance Index. LogForum, 16(2). http://doi.org/10.17270/J.LOG.2020.432
  • 18. 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
  • 19. Orhan, M. (2019). Comparison of the Logistics Performance between Turkey and European Union Member Countries with Entropy Weighted Edas Method. European Journal of Science and Technology, (17), 1222-1238. https://doi.org/10.31590/ejosat.657693
  • 20. Ozmen, M. (2019). Logistics Competitiveness of OECD Countries Using an Improved TODIM
  • 21. Method. Sādhanā, 44, 1-11. https://doi.org/10.1007/s12046-019-1088-y
  • 22. Placek, M. (2023). Logistics Industry Worldwide – Statistics & Facts. Available from: https://www.statista.com/topics/5691/logistics-industry-worldwide/#topicOverview (Access date: 29.05.2023)
  • 24. Senir, G. (2021). Comparison of Domestic Logistics Performances of Turkey and European Union Countries in 2018 with an Integrated Model. LogForum, 17(2). http://doi.org/10.17270/J.LOG.2021.576
  • 25. Stević, Ž., Erceg, Ž., & Kovačević, B. (2022). The Impact of Sensitivity Analysis on the Evaluation of the Logistics Performance Index. Matrix, 2(2), 2-2. http://doi.org/10.7251/NOEEN2231041S
  • 26. Ulutaş, A., & Karaköy, Ç. (2019). An Analysis of the Logistics Performance Index of EU Countries with an Integrated MCDM Model. Economics and Business Review, 5(4), 49-69. https://doi.org/10.18559/ebr.2019.4.3
  • 27. 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
  • 28. 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
Typ dokumentu
Bibliografia
Identyfikator YADDA
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