Gelişmekte Olan Ülkelerin Lojistik Pazar Gelişimi Bakımından Bulanık Kümeleme ve Diskriminant Analizleriyle Kümelenmesi

Yıl 2023, Cilt: 18 Sayı: 69, 19 - 40, 03.02.2023

Karahan Kara

https://doi.org/10.19168/jyasar.1149970

Öz

Ülkelerin pazar gelişmişlik düzeylerinin belirlenmesinde lojistik performans göstergeleri önem arz etmektedir. Özellikle gelişmekte olan ülkelerin lojistik pazarları ülke ekonomi ve ticari faaliyet hacimlerinin artmasında etkin rol oynamaktadır. Bu araştırmada gelişmekte olan ülkelerin 2022 yılı lojistik pazar gelişmişlik düzeylerine göre kümelenmesi amaçlanmıştır. Bu nedenle araştırmada bulanık kümeleme ve diskriminant analizleri uygulanmıştır. Araştırmanın örneklem alanını 50 gelişmekte olan ülke oluşturmaktadır. Araştırmaya ait veriler The Agility Emerging Markets Logistics Index raporlarından alınmıştır. Araştırma iki safhada gerçekleştirilmiştir. Birinci safhada gelişmekte olan ülkeler bulanık kümeleme analiziyle sınıflandırılmıştır. Analiz bulgularına göre yüksek ve düşük lojistik pazar gelişmişlik kümesi olmak üzere 2 küme elde edilmiştir. Araştırmanın ikinci safhasında kümelenmiş ülkelerin küme üyeliklerinin test edilmesi amacıyla diskriminant analizi yapılmıştır. Diskriminant analizi bulgularına göre küme üyeliklerinin tamamı doğrulanmıştır. Araştırma sonucunda ülkelerin küme üyelik durumları, değişkenlere göre küme merkezleri tespit edilmiş ve elde edilen çıkarımlar paylaşılmıştır.

Anahtar Kelimeler

Lojistik pazar performansı, Bulanık kümeleme analizi, Diskriminant analizi, Gelişmekte olan ülkeler

Clustering of Developing Countries in Terms of Logistics Market Development with Fuzzy Clustering and Discriminant Analysis

Öz

Logistics performance indicators are important in determining the market development levels of countries. Especially the logistics markets of developing countries play an active role in increasing the country's economy and trade volumes. In this research, it is aimed to cluster the developing countries according to their level of logistics market development in 2022. For this reason, fuzzy clustering and discriminant analyzes have been applied in the research. The sample area of the study consists of 50 developing countries. The data of the research have been taken from The Agility Emerging Markets Logistics Index reports. The research has been carried out in two phases. In the first phase, developing countries are classified by fuzzy cluster analysis. According to the analysis findings, 2 clusters have been obtained as high and low logistics market development cluster. In the second phase, discriminant analysis has been conducted to test the cluster membership of clustered countries. According to the discriminant analysis findings, all cluster memberships have been confirmed. As a result of the research, the cluster membership status of the developing countries and cluster centers according to the variables have been determined and the obtained implications have been presented.

Anahtar Kelimeler

Logistics market performance, Fuzzy cluster analysis, Discriminant analysis, Developing countries

Kaynakça

• Aboul-Dahab, K., & Ibrahim, M. A. (2020). Investigating the efficiency of the logistics performance index (LPI) weighting system using the technique for order of preference by similarity to ideal solution (TOPSIS) method. International Journal of Science and Research, 9, 269-277.
• AEMLI (2022), Agility Emerging Markets Logistics Index 2022, available from https://www.agility.com/en/emerging-markets-logistics index/#:~:text=They%20offer%20insights%20into%20strategic,business%20conditions%20and%20digital%20readiness, access date: 12.05.2022.
• Alyoubi, B. A. (2021). Clustering Analysis of Logistics Performance in Saudi Arabia: A Roadmap to Cloud Computing and IoT & Blockchain Solutions. International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies, 12(7), 1-14.
• Anuşlu, M. D., & Fırat, S. Ü. (2019). Clustering analysis application on Industry 4.0-driven global indexes. Procedia Computer Science, 158, 145-152.
• Ari, E., & Yildiz, A. (2018). OECD ülkelerinin göç istatistikleri bakimindan bulanik kümeleme analizi ile incelenmesi. Pamukkale University Journal of Social Sciences Institute/Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 33, 17-28.
• 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.
• Beysenbaev, R., & Dus, Y. (2020). Proposals for improving the logistics performance index. The Asian Journal of Shipping and Logistics, 36(1), 34-42.
• Burmaoglu, S., & Sesen, H. (2011). Analyzing the dependency between national logistics performance and competitiveness: Which logistics competence is core for national strategy?. Journal of competitiveness, 3(4), 4-21.
• Danaci, T., & Nacar, R. (2017). Comparing the Foreign Trade and Logistic Performance of Turkey and EU Members with Cluster Analysis. PressAcademia Procedia, 3(1), 31-36.
• Ekici, Ş. Ö., Kabak, Ö., & Ülengin, F. (2019). Improving logistics performance by reforming the pillars of Global Competitiveness Index. Transport Policy, 81, 197-207.
• Eren, H., & Ömürbek, N. (2021). OECD ülkelerinin lojistik performanslari açisindan kümelenmesi. Suleyman Demirel University Journal of Faculty of Economics & Administrative Sciences, 26(2), 153-166.
• Faria, R. N. D., Souza, C. S. D., & Vieira, J. G. V. (2015). Evaluation of logistic performance indexes of brazil in the international trade. RAM. Revista de Administração Mackenzie, 16, 213-235.
• Hartigan, J. A. (1975). Clustering algorithms. John Wiley & Sons, Inc..
• Kálmán, B., & Tóth, A. (2021). Links between the economy competitiveness and logistics performance in the Visegrád Group countries: Empirical evidence for the years 2007-2018. Entrepreneurial Business and Economics Review, 9(3), 169-190.
• 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.
• Kaufman L., Rousseeuw P. (1990). Finding Groups in Data: An Introduction to Cluster Analysis. John Wiley, New York
• Li, K. X., Jin, M., Qi, G., Shi, W., & Ng, A. K. (2018). Logistics as a driving force for development under the belt and road initiative–the Chinese model for developing countries. Transport Reviews, 38(4), 457-478.
• Liao, T. W. (2005). Clustering of time series data—a survey. Pattern recognition, 38(11), 1857-1874.
• Martí, L., Puertas, R., & García, L. (2014a). The importance of the logistics performance index in international trade. Applied Economics, 46(24), 2982-2992.
• Marti, L., Puertas, R., & García, L. (2014b). Relevance of trade facilitation in emerging countries' exports. The Journal of International Trade & Economic Development, 23(2), 202-222.
• 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.
• Polat, M., Kara, K., & Yalcin, G. C. (2022). Clustering Countries on Logistics Performance and Carbon Dioxide (CO 2) Emission Efficiency: An Empirical Analysis. Business & Economics Research Journal, 13(2).
• Puertas, R., Martí, L., & García, L. (2014). Logistics performance and export competitiveness: European experience. Empirica, 41(3), 467-480.
• Roy, V., Mitra, S. K., Chattopadhyay, M., & Sahay, B. S. (2018). Facilitating the extraction of extended insights on logistics performance from the logistics performance index dataset: A two-stage methodological framework and its application. Research in Transportation Business & Management, 28, 23-32.
• Saxena, A., Prasad, M., Gupta, A., Bharill, N., Patel, O. P., Tiwari, A., ... & Lin, C. T. (2017). A review of clustering techniques and developments. Neurocomputing, 267, 664-681.
• Şahin, M., ve Hamarat, B. (2002). G10 - Avrupa Birliği ve OECD ülkelerinin sosyo-ekonomik benzerliklerinin fuzzy kümeleme analizi ile belirlenmesi. ODTÜ Uluslararası Ekonomi Kongresi VI. Ankara. 11-14 Eylül, s. 1-19.
• Tabachnick, B. G., Fidell, L. S., & Ullman, J. B. (2007). Using multivariate statistics. Boston, MA: pearson.
• Trauwaert, E., Kaufman, L., & Rousseeuw, P. (1991). Fuzzy clustering algorithms based on the maximum likelihood priciple. Fuzzy Sets and Systems, 42(2), 213-227.
• Wang, M. L., & Choi, C. H. (2018). How logistics performance promote the international trade volume? A comparative analysis of developing and developed countries. International Journal of Logistics Economics and Globalisation, 7(1), 49-70.
• Yilanci, A. G. V. (2010). Bulanik kümeleme analizi ile türkiye’deki illerin sosyoekonomik açidan siniflandirilmasi. Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 15(3), 453-470.