Determining the logistics market performance of developing countries by entropy and MABAC methods

• Autorzy: Kara Karahan , Bentyn Zbigniew , Yalçın Galip Cihan

Identyfikatory

DOI

10.17270/J.LOG.2022.752

• Języki publikacji: EN

Abstrakty

EN

Background: The levels of logistics market performance of developing countries are published with Agility Emerging Markets Logistics Index (AEMLI) reports. The main purpose of this research is to propose a new model to determine the logistics market performance of developing countries in 2022 and to reorder the developing countries according to their logistics market performance. Methods: AEMLI indicators have been accepted as the basic criteria for determining the logistics market performance. The importance levels of these criteria have been determined by the Entropy technique. The logistics market performance rankings of developing countries according to the criteria were determined using the Multi-Attributive Border Approximation Area Comparison (MABAC) technique. The data set of 50 developing countries included in the 2022 AEMLI report has been used in the investigation. Results: According to the proposed new model, the weights of the criteria and logistics market performance rankings of developing countries have been determined. The importance levels of the criteria have been determined as Business Fundamentals (BF), Digital Readiness (DR), International Logistics Opportunities (ILO), and Domestic Logistics Opportunities (DLO), respectively. The ranking based on the new model was compared with the rankings in the 2022 AEMLI report. 21 of the 50 developing countries have improved their rankings. The ranking of 20 countries has been dropped. There is no change in the ranking of 9 countries. Additionally, according to AEMLI, the country with the highest logistics market performance is China, while the country with the best logistics market performance according to the proposed model is the United Arab Emirates (UAE). Conclusions: Contrary to the literature, Entropy and MABAC techniques were used to rank the logistics market performances of developing countries by making use of AEMLI reports. The issues that countries should focus on in the development of their logistics market performance are shown.

Słowa kluczowe

EN

logistics market performance; developing countries; entropy; MABAC; MCDM

PL

rynek logistyczny; kraje rozwijające się; entropia; MABAC; MCDM

• Wydawca: Wyższa Szkoła Logistyki
• Czasopismo: LogForum
• Rocznik: 2022
• Tom: Vol. 18, no. 4
• Strony: 421--434
• Opis fizyczny: Bibliogr. 35 poz., tab.

Twórcy

autor

Kara Karahan

• Business Management and Organization, Artvin Çoruh University Hopa, Artvin, Turkey

autor

Bentyn Zbigniew

• Poznań University of Economics and Business, Department of Logistics, Poznań, Poland

• https://orcid.org/0000-0002-2061-2616

autor

Yalçın Galip Cihan

• Kırıkkale University Institute of Science and Technology, Department of Mathematics, Yahşihan, Kirikkale, Turkey

• https://orcid.org/0000-0001-9348-0709

Bibliografia

• 1. AEMLI, 2022, Agility Emerging Markets Logistics Index 2022, available from https://www.agility.com/en/emerging-markets-logistics-index/, access date: 08.21.2022.
• 2. Beysenbaev, R., 2018, The importance of country-level logistics efficiency assessment to the development of international trade, British Journal for Social and Economic Research, 3(6), 13-20.
• 3. 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
• 4. Božanić, D. I., Pamučar, D. S., Karović, S. M., 2016, Application the MABAC method in support of decision-making on the use of force in a defensive operation, Tehnika, 71(1), 129-136. https://doi.org/10.5937/tehnika1601129B
• 5. 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
• 6. Çelebi, Ü., Civelek, M. E., Çemberci, M., 2015, The mediator effect of foreign direct investments on the relation between logistics performance and economic growth, Journal of Global Strategic Management, 17. https://ssrn.com/abstract=3338308
• 7. Çemberci, M., Civelek, M. E., Canbolat, N., 2015, The moderator effect of global competitiveness index on dimensions of logistics performance index, Procedia-social and behavioral sciences, 195, 1514-1524. https://doi.org/10.1016/j.sbspro.2015.06.453
• 8. Doll, A., Friebel, D., Rückriegel, M., Schwarzmüller, C., 2014, Global logistics markets. Munich: Roland Berger Strategy Consultants.
• 9. Erol, I., Ferrell Jr, W. G., 2009, Integrated approach for reorganizing purchasing: Theory and a case analysis on a Turkish company, Computers & Industrial Engineering, 56(4), 1192-1204. https://doi.org/10.1016/j.cie.2008.07.011
• 10. García, L., Martí, L., Martín, J. C., Puertas, R., 2015, A DEA-Logistic Performance Index. In European Transport Conference 2015Association for European Transport (AET). https://aetransport.org/past-etc-papers/conference-papers-2015
• 11. 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. https://doi.org/10.1016/j.renene.2016.11.057
• 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., 2022, Relationship Between Domestic Logistics Opportunity Efficiency and International Logistics Opportunity Efficiency Based on Market Potential: Empirical Research on Developing Countries, Journal of Management Marketing and Logistics, 9(2), 79-89. https://doi.org/10.17261/Pressacademia.2022.1555
• 14. 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
• 15. 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
• 16. 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
• 17. Mercangöz, B. A., Yildirim, B. F., Yildirim, S. K., 2020, Time period based COPRAS-G method: application on the Logistics Performance Index, LogForum, 16(2), 239-250. 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-Innovative And Economics Research Journal, 10(1), 13-34. https://doi.org/10.2478/eoik-2022-0004
• 19. Oğuz, S. Alkan, G., Yilmaz, B., 2019, Seçilmiş Asya ülkelerinin lojistik performanslarının TOPSİS yöntemi ile değerlendirilmesi, IBAD Sosyal Bilimler Dergisi, 497-507. https://doi.org/10.21733/ibad.613421
• 20. Ozmen, M., 2019, Logistics competitiveness of OECD countries using an improved TODIM method, Sādhanā, 44(5), 1-11. https://doi.org/10.1007/s12046-019-1088-y
• 21. Özdağoğlu, A., Yakut, E., Bahar, S., 2017, Machine selection in a dairy product company with entropy and SAW methods integration, Dokuz Eylül Üniversitesi İktisadi İdari Bilimler Fakültesi Dergisi, 32(1), 341-359. https://dergipark.org.tr/en/download/article-file/627664
• 22. 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. https://doi.org/10.1016/j.eswa.2017.08.042
• 23. 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
• 24. 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
• 25. Senir, G., 2021, Comparison of domestic logistics performances of Turkey and European Union countries in 2018 with an integrated model, LogForum, 17(2), 193-204. http://doi.org/10.17270/J.LOG.2021.576
• 26. Shestak, V., Konstantinov, V., Govorov, V., Budko, E., Volodin, O., 2021, Harmonization of Russian supply chain management standards with EU requirements, Regional Science Policy & Practice. https://doi.org/10.1111/rsp3.12423
• 27. Uca, N., Civelek, M. E., Çemberci, M., 2015, The Effect of The Components of Logistics Performance Index on Gross Domestic Product: Conceptual Model Proposal, Eurasian Business & Economics Journal, 1(1), 86-93. https://dx.doi.org/10.17740/eas.econ.2015-V1-04
• 28. Uca N., Ince H., Sumen H., 2016. The Mediator Effect of Logistics Performance Index on the Relation Between Corruption Perception Index and Foreign Trade Volume, European Scientific Journal 12(25) 37- 45. https://hdl.handle.net/11467/1573
• 29. Ulutaş, A., Karaköy, Ç., 2019a, 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
• 30. Ulutaş, A., Karaköy, Ç., 2019b, G-20 Ülkelerinin lojistik performans endeksinin çok kriterli karar verme modeli ile ölçümü, Cumhuriyet Üniversitesi İktisadi ve İdari Bilimler Dergisi, 20(2), 71-84. http://esjournal.cumhuriyet.edu.tr/tr/pub/issue/50375/615882
• 31. Wang, T. C., Lee, H. D., 2009, Developing a fuzzy TOPSIS approach based on subjective weights and objective weights, Expert systems with applications, 36(5), 8980-8985. https://doi.org/10.1016/j.eswa.2008.11.035
• 32. Yalçin, B., Ayvaz, B., 2020, Çok Kriterli Karar Verme Teknikleri İle Lojistik Performansin Değerlendirilmesi. İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi, 19(38), 117-138. https://dergipark.org.tr/tr/pub/ticaretfbd/issue/58122/847231
• 33. Yildirim, B. F., Adiguzel Mercangöz, 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
• 34. Zaninović, P. A., Zaninović, V., Skender, H. P., 2021, The effects of logistics performance on international trade: EU15 vs CEMS, Economic Research-Ekonomska Istraživanja, 34(1), 1566-1582. https://doi.org/10.1080/1331677X.2020.1844582
• 35. Zhang, H., Gu, C. L., Gu, L. W., Zhang, Y., 2011, The evaluation of tourism destination competitiveness by TOPSIS & information entropy - A case in the Yangtze River Delta of China, Tourism Management, 32(2), 443-451. https://doi.org/10.1016/j.tourman.2010.02.007

Uwagi

PL

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).