Browse > Article
http://dx.doi.org/10.6106/KJCEM.2019.20.6.107

Predicting Cooperative Relationships between Engineering Companies in World Bank's ODA Projects  

Yu, Youngsu (Department of Civil Engineering, Seoul National University of Science and Technology)
Koo, Bonsang (Department of Civil Engineering, Seoul National University of Science and Technology)
Lee, Kwanhoon (Department of Computer Science and Engineering, Korea University)
Han, Seungheon (Department of Civil and Environmental Engineering, Yonsei University)
Publication Information
Korean Journal of Construction Engineering and Management / v.20, no.6, 2019 , pp. 107-116 More about this Journal
Abstract
Korean construction engineering firms want to pave the way for expansion of overseas markets through the World Bank's Official Development Assistance (ODA) projects as a way to improve their overseas project performance. However, since the World Bank project competes with global companies for limited projects, building partnerships with suitable business partners is essential to gain an upper hand in bidding competition and meet the institutional conditions of the recipient country. In this regard, many network studies have been conducted in the past through Social Network Analysis (SNA), but few have been analyzed based on the process of changes in the network. So, This study collected winning data from the three Southeast Asian countries that ended after the World Bank's ODA project performed smoothly, and established a learning-based link prediction model that reflected the dynamic nature of the network. As a result, the 11 main variables acting on building a cooperative relationship between winning companies were derived and the effect of each variables on the probability value of cooperation between individual links was identified.
Keywords
International Cooperative Strategies; World Bank ODA; Link Prediction; XGBoost;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
1 Altman, D.G., and Bland, J.M. (1994). "Diagnostic tests. 1: Sensitivity and specificity." BMJ: British Medical Journal, 308(6943), p. 1552.   DOI
2 Bastian M., Heymann S., and Jacomy M. (2009). Gephi: an open source software for exploring and manipulating networks. International AAAI Conference on Weblogs and Social Media.
3 Chawla, N.V., Bowyer, K.W., Hall, L.O., and Kegelmeyer, W.P. (2002). "SMOTE: synthetic minority over-sampling technique." Journal of artificial intelligence research, 16, pp. 321-357.   DOI
4 Frederickson, H.G., and LaPorte, T.R. (2002). Airport security, high reliability, and the problem of rationality. Public Administration Review, 62, pp. 33-43.   DOI
5 KENCA (2019). Engineering Insight. Korea Engineering & Consulting Association. pp. 1-23.
6 Kim, T.Y. (2017). Python Deep Learning Keras with Block. Digital Books, pp. 10-340.
7 Klinkman, M.S., Coyne, J.C., Gallo, S., and Schwenk, T.L. (1998). "False positives, false negatives, and the validity of the diagnosis of major depression in primary care." Archives of Family Medicine, 7(5), pp. 451-461.   DOI
8 KNA (2018). WB Bid Guidelines, Korea Energy Agency, pp. 3-25.
9 Koo, B.S., Shin, B.J., Yu, Y.S., and Jung, J.W. (2017). "Formulating International Entry Strategies for World Bank Consulting Projects Through Country-level Competitive Analysis: A Vietnam Case Study." Korean Journal of Construction Engineering and Management, KICEM, 18(4), pp. 57-66.   DOI
10 Lee, J.S., Lee, J.H., Han, S.H., and Kang, S.Y. (2018). "Partnering Strategy for Bidding Success in World Bank's Vietnam Consulting Project." Journal of The Korean Society of Civil Engineers, 38(6), pp. 1021-1028.   DOI
11 Liu, L., Han, C., and Xu, W. (2015). "Evolutionary analysis of the collaboration networks within National Quality Award Projects of China." International Journal of Project Management, 33(3), pp. 599-609.   DOI
12 Yap, B.W., Rani, K.A., Rahman, H.A.A., Fong, S., Khairudin, Z., and Abdullah, N.N. (2014). "An application of oversampling, undersampling, bagging and boosting in handling imbalanced datasets." In Proceedings of the first international conference on advanced data and information engineering (DaEng-2013), pp. 13-22. Springer, Singapore.
13 Chen, T., and Guestrin, C. (2016). "Xgboost: A scalable tree boosting system." In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, pp. 785-794.
14 Fessler, J.A., and Sutton, B.P. (2003). "Nonuniform fast Fourier transforms using min-max interpolation." IEEE transactions on signal processing, 51(2), pp. 560-574.   DOI
15 Mori, J., Kajikawa, Y., Kashima, H., and Sakata, I. (2012). "Machine learning approach for finding business partners and building reciprocal relationships." Expert Systems with Applications, 39(12), pp. 10402-10407.   DOI
16 Seo. H.B. (2017). A Deep Learning based Approach to Prediction of Technological Convergence, Seoul National Univ. of Science & Technology Master's Thesis, pp. 1-50.
17 Seo. H.B., and Lee. H.Y. (2018). "Predicting the Technological Convergence between Manufacturing and Service based on SVM-based Link Prediction." Journal of the Korean Institute of Industrial Engineers, 44(2), pp. 141-152.   DOI
18 Wang, P., Xu, B., Wu, Y., and Zhou, X. (2015). "Link prediction in social networks: the state-of-the-art." Science China Information Sciences, 58(1), pp. 1-38.
19 World Bank (2018). IBRD/IDA/IFC/MIGA Guidance Country Engagement. World Bank, pp. 3-46.
20 Zheng, X., Le, Y., Chan, A.P., Hu, Y., and Li, Y. (2016). "Review of the application of social network analysis (SNA) in construction project management research." International Journal of project management, 34(7), pp. 1214-1225.   DOI