The Mediating Effect of the Logistics Performance Index on the Relationship Between the Enabling Trade Index and Foreign Trade Volume of Countries
Öz
The main purpose of this study is to detect the mediation effect of the logistic performance index in the relationship between the enabling trade index and the volume of foreign trade. the simple mediation model was created by using the enabling trade index published by the World Economic Forum, the logistics performance index published by the World Bank, and the foreign trade volume data of the countries. The study used 94 countries’ data for the years 2016, 2014, and 2012, compiled 282 samples in total. In the application part of the study, the correlation coefficients between variables were determined with IBM SPSS 20 Package Program, and then a simple mediation analysis was performed using PROCESS 3.5 macro developed by Andrew F. Hayes. According to the findings obtained as a result of the analysis, it has been determined that the logistics performance index mediates the relationship between the enabling trade index and the foreign trade volume. The fully standardized effect size of the mediation effect (K2) was determined as .5564, and it was revealed that the mediating role was at a high effect level. In addition, the indirect effect value (a. b) is determined as 6.670.
Anahtar Kelimeler
Foreign trade volume, Enabling trade index, Logistic performance index, PROCESS, Mediation analysis
Kaynakça
- Addey, K., Yeboah, O., & Shaik, S. (2017). Role of Trade Agreements and Enabling Trade Indexes on Trade Creation or Trade Diversion of US state Corn, Soybeans and Wheat (No. 1377-2016-109867), https://ageconsearch.umn.edu/record/252791/
- Aktaş, İ. (2019). Analysis of The Effect of Logistics Performance Index and Economic Freedom Index on Global Facilitating Trade (Tez No. 556892) [Doktora tezi, Maltepe Üniversitesi]. Yükseköğretim Kurulu Ulusal Tez Merkezi.
- Coto-Millán, P., Fernández, X. L., Pesquera, M. Á., & Agüeros, M. (2016). Impact of logistics on technical efficiency of world production (2007–2012). Networks and Spatial Economics, 16(4), 981-995. https://doi.org/10.1007/s11067-015-9306-6
- Fritz, M. S., & MacKinnon, D. P. (2007). Required sample size to detect the mediated effect. Psychological science, 18(3), 233-239. https://doi.org/10.1111/j.1467-9280.2007.01882.x
- Hayes, A. F. (2018). Introduction To Mediation, Moderation, And Conditional Process Analysis: A Regression-Based Approach (2. Ed.). The Guilford.
- Hayes, A. F., & Rockwood, N. J. (2017). Regression-based statistical mediation and moderation analysis in clinical research: Observations, recommendations, and implementation. Behavior research and therapy, 98, 39-57. https://doi.org/10.1016/j.brat.2016.11.001
- Host, A., Pavlic Skender, H., & Zaninovic, P. A. (2019). Trade Logistics – The Gravity Model Approach. Journal Zbornik Radova Ekonomskog Fakulteta U Rijeci / Proceedings of Rijeka Faculty of Economics, 37(1), 327–342. https://doi.org/10.18045/zbefri.2019.1.327
- Gurbuz, S. (2019). Mediator, regulatory and situational impact analysis in social sciences. Seçkin. L. Gurbuz, S., & Bayik, M. E. (2018, 2-3 November). The Modern Approach to Analysis of Mediation Models: Should the Baron and Kenny Method be Abandoned Now, 6. Proceedıngs of Organizational Behavior Congress, Turkey-Isparta.
- MacKinnon, D. P., Lockwood, C. M., & Williams, J. (2004). Confidence limits for the indirect effect: Distribution of the product and resampling methods. Multivariate behavioral research, 39(1), 99-128, https://doi.org/10.1207/s15327906mbr3901_4
- Preacher, K. J., & Selig, J. P. (2012). Advantages of Monte Carlo confidence intervals for indirect effects. Communication Methods and Measures, 6(2), 77-98, https://doi.org/10.1080/19312458.2012.679848