Document Type : Original Article
Author
Department of Economics and Management, Naragh Branch, Islamic Azad University, Naragh, Iran
Abstract
Purpose: Business communities significantly promote free trade and trade security, so joining the business community is essential for long-term development. Therefore, the research aimed to form an international trade community based on network theory and resource dependence.
Methodology: Twenty countries were selected as a sample from the countries that engage in international trade based on the data available from the Comtrade database of the United Nations from 2005 to 2019.
Findings: The results showed: 1) the trading partner factor has a positive effect on the formation of international trading communities, that when a country cooperates with a large number of trading partners or has a dominant position in the international trade network, the probability of the country forms the same community with other countries is higher, 2) when a country considers itself dependent on the resources of other countries, the possibility of forming a similar community with other countries increases accordingly and 3) network position plays positive role in regulating the relationship between resource dependence and the international trade community.
Originality/Value: Countries that can boost resource trade based on the economic freedom and diversity of the importing country can reduce their dependence on other countries.
Keywords
- An, H., Gao, X., Fang, W., Ding, Y., & Zhong, W. (2014). Research on patterns in the fluctuation of the co-movement between crude oil futures and spot prices: a complex network approach. Applied energy, 136, 1067-1075. https://doi.org/10.1016/j.apenergy.2014.07.081
- Barber, M. J. (2007). Modularity and community detection in bipartite networks. Physical review E, 76(6), 066102. https://journals.aps.org/pre/abstract/10.1103/PhysRevE.76.066102
- Brada, J. C., & Mendez, J. A. (1985). Economic integration among developed, developing and centrally planned economies: a comparative analysis. The review of economics and statistics, 67(4), 549-556. https://www.jstor.org/stable/1924798
- Brandes, U. (2001). A faster algorithm for betweenness centrality. Journal of mathematical sociology, 25(2), 163-177. https://doi.org/10.1080/0022250X.2001.9990249
- Burt, R. S. (1992). Structural holes: the social structure of competition. Harvard University Press. https://www.hup.harvard.edu/catalog.php?isbn=9780674843714
- Chang, T. Y., Hsu, T. C., & Hong, Y. W. P. (2009). Exploiting data-dependent transmission control and MAC timing information for distributed detection in sensor networks. IEEE transactions on signal processing, 58(3), 1369-1382. https://ieeexplore.ieee.org/abstract/document/5313958
- Dong, D., An, H., & Huang, S. (2017). The transfer of embodied carbon in copper international trade: an industry chain perspective. Resources policy, 52, 173-180. https://doi.org/10.1016/j.resourpol.2017.02.009
- Fan, Y., Ren, S., Cai, H., & Cui, X. (2014). The state's role and position in international trade: a complex network perspective. Economic modelling, 39, 71-81. https://doi.org/10.1016/j.econmod.2014.02.027
- Freeman, L. C. (1977). A set of measures of centrality based on betweenness. Sociometry, 40(1), 35-41. https://doi.org/10.2307/3033543
- Gao, X., An, H., Fang, W., Li, H., & Sun, X. (2014). The transmission of fluctuant patterns of the forex burden based on international crude oil prices. Energy, 73, 380-386. https://doi.org/10.1016/j.energy.2014.06.028
- Zhong, W., An, H., Shen, L., Dai, T., Fang, W., Gao, X., & Dong, D. (2017). Global pattern of the international fossil fuel trade: the evolution of communities. Energy, 123, 260-270. https://ideas.repec.org/a/eee/energy/v123y2017icp260-270.html
- Garlaschelli, D., Di Matteo, T., Aste, T., Caldarelli, G., & Loffredo, M. I. (2007). Interplay between topology and dynamics in the world trade web. The European physical journal B, 57(2), 159-164. https://doi.org/10.1140/epjb/e2007-00131-6
- Guan, Q., An, H., Gao, X., Huang, S., & Li, H. (2016). Estimating potential trade links in the international crude oil trade: a link prediction approach. Energy, 102, 406-415. https://doi.org/10.1016/j.energy.2016.02.099
- Hillman, A. J., Withers, M. C., & Collins, B. J. (2009). Resource dependence theory: a review. Journal of management, 35(6), 1404-1427. https://doi.org/10.1177/0149206309343469
- Hinkelman, E. G., Shippey, K. C. (2002). Dictionary of international trade: handbook of the global trade community includes 19 key appendices. Novato, CA: World Trade Press.
- Zhong, W., An, H., Gao, X., & Sun, X. (2014). The evolution of communities in the international oil trade network. Physica a: statistical mechanics and its applications, 413, 42-52.
- Huang, S., An, H., Viglia, S., Buonocore, E., Fang, W., & Ulgiati, S. (2017). Revisiting China-Africa trade from an environmental perspective. Journal of cleaner production, 167, 553-570. https://doi.org/10.1016/j.jclepro.2017.08.171
- Brakeland, J. F., & Turner, V. (1997). Safeguard measures in international merchandise trade: a community perspective. Revue-marche commun et de l union europeenne, 454-469. (In Ferench).
- Ji, Q., Zhang, H. Y., & Fan, Y. (2014). Identification of global oil trade patterns: an empirical research based on complex network theory. Energy conversion and management, 85, 856-865.
- Kuznets, S. (1955). Economic growth and income inequality. The American economic review, 45(1), 1-28. https://www.jstor.org/stable/1811581
- Leicht, E. A., & Newman, M. E. (2008). Community structure in directed networks. Physical review letters, 100(11), 118703. https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.100.118703
- Li, H., An, H., Wang, Y., Huang, J., & Gao, X. (2016). Evolutionary features of academic articles co-keyword network and keywords co-occurrence network: based on two-mode affiliation network. Physica a: statistical mechanics and its applications, 450, 657-669. https://doi.org/10.1016/j.physa.2016.01.017
- Linnemann, H. (1966). An econometric study of international trade flows(No. 42). Amsterdam, North-Holland. https://academic.oup.com/ej/article/77/306/366/5235645
- Blondel, V. D., Guillaume, J. L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of statistical mechanics: theory and experiment, 2008(10), P10008. DOI:1088/1742-5468/2008/10/P10008
- Milo, R., Shen-Orr, S., Itzkovitz, S., Kashtan, N., Chklovskii, D., & Alon, U. (2002). Network motifs: simple building blocks of complex networks. Science, 298(5594), 824-827.
- Newman, M. E. (2004). Fast algorithm for detecting community structure in networks. Physical review E, 69(6), 066133. https://journals.aps.org/pre/abstract/10.1103/PhysRevE.69.066133
- Ozmel, U., Reuer, J. J., & Gulati, R. (2013). Signals across multiple networks: how venture capital and alliance networks affect interorganizational collaboration. Academy of management journal, 56(3), 852-866. https://journals.aom.org/doi/abs/10.5465/amj.2009.0549
- Pfeffer, J., & Salancik, G. R. (2003). The external control of organizations: a resource dependence perspective. Stanford University Press.
- Qin, Z., Zhang, J., & Wang, J. (2010). Enhanced reliable transmission control protocol for spatial information networks. International conference on space information technology 2009 (Vol. 7651, pp. 388-395). SPIE. https://doi.org/10.1117/12.855405
- Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: uses and interpretations. Neuroimage, 52(3), 1059-1069. https://doi.org/10.1016/j.neuroimage.2009.10.003
- Sabidussi, G. (1966). The centrality index of a graph. Psychometrika, 31(4), 581-603.
- Schwarz, A. J., Gozzi, A., & Bifone, A. (2008). Community structure and modularity in networks of correlated brain activity. Magnetic resonance imaging, 26(7), 914-920. https://doi.org/10.1016/j.mri.2008.01.048
- Shipilov, A. V. (2009). Firm scope experience, historic multimarket contact with partners, centrality, and the relationship between structural holes and performance. Organization science, 20(1), 85-106.
- Sonora, R. J. (2008). On the impacts of economic freedom on international trade flows: asymmetries and freedom components. EFZG working paper series, (05), 1-31. https://hrcak.srce.hr/file/202163
- Tinbergen, J. (1962). Shaping the world economy: suggestions for an international economic policy. Journal of farm economics, 46(1), 271-273. https://onlinelibrary.wiley.com/doi/abs/10.2307/1236502
- Trojan, C. (1986). Milk policy and the role of the community in the international trade. Molkerei-Zeitung.
- Tzekina, I., Danthi, K., & Rockmore, D. N. (2008). Evolution of community structure in the world trade web. The European physical journal B, 63(4), 541-545.
- Vick-Majors, T. J., Priscu, J. C., & Amaral-Zettler, L. A. (2014). Modular community structure suggests metabolic plasticity during the transition to polar night in ice-covered antarctic lakes. The ISME journal, 8(4), 778-789. DOI: 1038/ismej.2013.190
- Westphal, J. D., Boivie, S., & Ming Chng, D. H. (2006). The strategic impetus for social network ties: reconstituting broken CEO friendship ties. Strategic management journal, 27(5), 425-445.
- Xia, J., Wang, Y., Lin, Y., Yang, H., & Li, S. (2018). Alliance formation in the midst of market and network: insights from resource dependence and network perspectives. Journal of management, 44(5), 1899-1925.
- Zhang, H. Y., Ji, Q., & Fan, Y. (2014). Competition, transmission and pattern evolution: a network analysis of global oil trade. Energy policy, 73, 312-322. https://doi.org/10.1016/j.enpol.2014.06.020
- Zheng, Y., & Xia, J. (2018). Resource dependence and network relations: a test of venture capital investment termination in China. Journal of management studies, 55(2), 295-319. https://doi.org/10.1111/joms.12255