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Comparison of domestic logistics performances of Turkey and European Union countries in 2018 with an integrated model

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PL
Porównanie krajowej działalności logistycznej w Turcji oraz krajach Unii Europejskiej w 2018 w stosunku do zintegrowanego modelu
Języki publikacji
EN
Abstrakty
EN
Background: The Logistics Performance Index (LPI), created by the World Bank, is a benchmark tool used to determine the threats and opportunities faced by countries in their logistics performances and to improve their performances. Countries aim to increase their LPI scores and rank higher on the LPI list while developing their strategies. Methods: In this study, it was aimed to compare the domestic logistics performances of Turkey and the European Union countries with an integrated model using the domestic logistics performance index data for 2018, which was recently published by the World Bank. In this direction, firstly, the importance levels of the criteria were determined with the CRITIC (Criteria Importance Through Intercritera Correlation) method, and then, using the importance levels of the criteria, the countries were ranked according to the domestic logistics performance score with the COPRAS (Complex Proportional Assessment) method. Results: As a result of the CRITIC method, the most important criterion in the ranking according to the importance levels of the criteria was “without physical examination”, which is the sub-criterion of the customs clearance period, while the Netherlands was the country with the best performance in the ranking performed by the COPRAS method, using the importance levels of the criteria determined by the CRITIC method. Conclusions: The study differs from current studies in the literature in that it is the first study to perform a domestic logistic performance comparison using CRITIC and COPRAS methods with an integrated model. The results of the current study can be compared with the results obtained by using different integrated models and different data in the studies to be conducted.
PL
Wstęp: Wskaźnik Logistics Performance Index (LPI), utworzony prze Bank Światowy, służy do benchmarkingu w określaniu zagrożeń i możliwości dla krajów w ich działalności logistycznej oraz dla działań w celu poprawy tej działalności. Państwa dążą do poprawy wartości swojego wskaźnika LPI poprzez ciągła poprawę swojej strategii działania. Metody: Celem pracy jest porównanie wskaźników krajowej działalności logistycznej Turcji oraz krajów Unii Europejskiej ze zintegrowanym modelem w oparciu dane za 2018 rok, opublikowane niedawno przez Bank Światowy. W tym celu wpierw określono ważność poszczególnych kryteriów przy pomocy metody CRITIC (Criteria Importance Through Intercritera Correlation), a następnie utworzono ranking krajów dotyczących ich działalności logistycznej przy użyciu metody COPRAS (Complex Proportional Assessment). Wyniki: Używając metodę CRITIC, ustalono, że najważniejszym kryterium w ranking było kryterium „bez badania fizycznego”, które jest podkryterium w okresie odpraw celnych. Holandia umiejscowiła się na pierwszym miejscu rankingu stworzonego przy użyciu metody COPRAS. Wnioski: Prezentowana praca różni się od prac obecnie publikowanych użyciem metody porównawczej, wykorzystującej metody CRITIC oraz COPRAS w odniesienie do zintegrowanego modelu. Jednak otrzymane wyniku mogą być porównywane z wynikami uzyskanymi przy zastosowaniu innych modeli zintegrowanych oraz na podstawie innego zestawu danych.
Czasopismo
Rocznik
Strony
193--204
Opis fizyczny
Bibliogr. 40 poz., tab.
Twórcy
autor
  • Niğde Ömer Halisdemir University, Faculty of Economics and Administrative Sciences, Department of International Trade and Logistics, Central Campus Bor Yolu 51240 Niğde, Turkey
Bibliografia
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  • 4. Bayır T., Yılmaz Z., 2017. Assesment of Logistic Performance Indexes of EU Countries By AHP And VIKOR Methods. Middle East Journal of Education (MEJE) 3(2), 73-92.
  • 5. Bozkurt C., Mermertaş F., 2019. Comparison of Turkey and the G8 Countries According to the Logistics Performance Index. Journal of Business and Economics Studies, 7(2), 107-117.
  • 6. Candan G., 2019. Integrated Approach of Fuzzy AHP and Grey Relational Analysis For Logistic Performance Evaluation. Anemon Muş Alparslan University Journal of Social Sciences, 7(5), 277-286, http://doi.org/10.18506/anemon.506769.
  • 7. Civelek M.E., Uca N., Cemberci M., 2015. The Mediator Effect of Logistics Performance Index on the Relation Between Global Competitiveness Index and Gross Domestic Product. European Scientific Journal 11 (3), 368–375.
  • 8. Çakır S., 2017. Measuring Logistics Performance of OECD Countries via Fuzzy Linear Regression. Journal of Multi-Criteria Decision Analysis, 24(3-4), 177-186, http://doi.org/10.1002/mcda.1601.
  • 9. Çatuk C., 2019. The Effect of Highway on Logistics Performance in International Trade. Al Farabi International Journal of Social Sciences, 3(4), 120-125.
  • 10. Diakoulaki D., Mavrotas G., Papayannakis L., 1995. Determining Objective Weights in Multiple Criteria Problems: The CRITIC Method. Computers and Operations Resarch, 22(7), 763-770, http://doi.org/10.1016/0305-0548(94)00059-H.
  • 11. Dijkman J., 2009. Germany Real Estate Yearbook 2009: Assets, Industry Trends and Market Players. Real Estate Publishers BV.
  • 12. Emanet H., 2017. Analysis of Logistics Performances of Central Asian Turkish Republics within the Context of Logistics Performance Index. International Conference on Eurasian Economies, Session 2C, 302-309.
  • 13. Erkan B., 2014. The Importance and Determinants of Logistics Performance of Selected Countries. Journal of Emerging Issues in Economics, Finance and Banking, 3(6), 1237-1254.
  • 14. Erturgut R., Gürler H.E., 2019. The Relationship Between Civilization and Logistics Performance of the Countries in the Logistics Performance Index: The Example of Denmark-Austria. In 8th Eurasian Conference on Language and Social Sciences, 252, http://doi.org/10.35578/eclss.52775.
  • 15. Görgün M.R., 2020. The Situation of Turkish Logistics Sector in Logistics Performance Criteria. EKEV Academy Journal, 24(81), 229-246.
  • 16. İmamoğlu İ.K., 2019. Comparison by Turkey and Logistics Performance of The Shanghai Cooperation Organization (Sco) Member Countries. The Journal, 12(68), 1143-1154, http://doi.org/10.17719/jisr.2019.3901.
  • 17. 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.
  • 18. Kaklauskas A., Zavadskas E.K., Naimavicienė J., Krutinis M., Plakys V., Venskus D., 2010. Model for a Complex Analysis of Intelligent Built Environment. Automation in Construction, (19), 326-340, http://doi.org/10.1016/j.autcon.2009.12.006
  • 19. Karaköy Ç., Ölmez U., 2019. Evaluation of the LPI Values of Balkan Countries. SETSCI Conference Proceedings 4 (8), 178-180, https://doi.org/10.36287/setsci.4.8.031.
  • 20. Kılınç E., Fidan O., Mutlu H.M., 2019. Comparison of Turkey, China and Russian Federation According to the Logistics Performance Index. International Journal of Economic Studies, 5(2), 17-34.
  • 21. Kısa A.C.G., Ayçin E., 2019. Evaluation of the Logistics Performance of OECD Countries with EDAS Method Based on SWARA. Çankırı Karatekin University Faculty of Economics and Administrative Sciences Journal, 9(1), 301-325, http://doi.org/10.18074/ckuiibfd.500320.
  • 22. Madic M., Radovanović M., 2015. Ranking of Some Most Commonly Used Nontraditional Machining Processes Using ROV and CRITIC Methods. UPB Sci. Bull., Series D, 77(2), 193-204.
  • 23. Martí L., Puertas R., García L., 2014. The Importance of the Logistics Performance Index in International Trade. Applied Economics 46 (24), 2982-2992, http://doi.org/10.1080/00036846.2014.916394.
  • 24. Martí L., Martín J.C., Puertas R., 2017. A DEA-Logistics Performance Index. Journal of Applied Economics, 20(1), 169-192, http://doi.org/10.1016/S1514-0326(17)30008-9.
  • 25. Mercangoz B. A., Yildirim B., 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.
  • 26. Oğuz S., Alkan G., Yılmaz B., 2019. Evaluation of Logistics Performance of Selected Asian Countries’ by TOPSIS Method. IBAD Journal of Social Sciences, (Special Issue), 497-507, http://doi.org/10.21733/ibad.613421.
  • 27. 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, http://doi.org/10.31590/ejosat.657693.
  • 28. Ozmen M., 2019. Logistics Competitiveness of OECD Countries Using an Improved TODIM Method. Sādhanā, 44(5), 108, http://doi.org/10.1007/s12046-019-1088-y.
  • 29. Özbek A., 2017. Performance Evaluation of Turkey Diyanet Foundation By SAW, COPRAS and TOPSIS Method. Journal of Management and Economics Studies, 15(1), 66-84, http://doi.org/10.11611/yead.277484.
  • 30. Rashidi K., Cullinane K., 2019. Evaluating the Sustainability of National Logistics Performance Using Data Envelopment Analysis. Transport Policy, (74), 35-46, http://doi.org/10.1016/j.tranpol.2018.11.014
  • 31. 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, http://doi.org/10.1016/j.tranpol.2018.05.007
  • 32. Sofyalıoğlu Ç., Kartal B., 2013. A Comparison and Some Suggestions for Turkey’s and Eurasian Economic Community Countries’ Logistic Performance Index Scores. In International Conference on Eurasian Economies, Session 7B, 524-531, http://doi.org/10.36880/C04.00766.
  • 33. 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), 86-93, http://doi.org/10.17740/eas.econ.2015-V1-04.
  • 34. Ulutaş A., Karaköy Ç., 2019a. The Measurement of Logistics Performance Index of G-20 Countries with Multi-Criteria Decision Making Model. Cumhuriyet University Journal of Economic and Administrative Sciences, 20(2), 71-84.
  • 35. Ulutaş A., Karaköy Ç., 2019b. An Analysis of the Logistics Performance Index of EU Countries with an Integrated MCDM Model. Economics and Business Review, 5(4), 49-69, http://doi.org/10.18559/ebr.2019.4.3.
  • 36. Yangınlar G., 2019. The Comparison Logistics Performance and GDP Between G7 Countries and Turkey. V. European Congress on Economic Issues 2019 Proceedings Book, 68-80.
  • 37. Yapraklı T.Ş., Ünalan M., 2017. The Global Logistics Performance Index and Analysis of The Last Ten Years Logistics Performance of Turkey. Journal of Economic and Administrative Sciences, 31(3), 589-606.
  • 38. Yildirim B.F., Mercangoz B.A., 2020. Evaluating the Logistics Performance of OECD Countries by Using Fuzzy AHP and ARAS-G. Eurasian Economic Review, 10(1), 27-45, http://doi.org/10.1007/s40822-019-00131-3.
  • 39. Yıldız A., Aydoğan K., Kartum G., 2020. An Investigation of Turkey’s Position in International Logistics Performance Index By Cluster Analysis. Turkish Studies - Social, 15(3), 1659-1679, http://doi.org/10.29228/TurkishStudies.41640.
  • 40. Zavadskas E., Kaklauskas, 1996. A Multiple Criteria Evaluation of Buildings; Technika: Vilnius, Lithuania
Uwagi
PL
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
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