- 권한신청
- P-ISSN1738-3110
- E-ISSN2093-7717
- SCOPUS, ESCI
중국 주요 50개 도시의 전자상거래 발전성과에 대한 평가
Evaluation on Development Performances of E-Commerce for 50 Major Cities in China
왕강 (Wonju National University)
Abstract
Purpose - In this paper, the degree of similarity and dissimilarity between pairs of 50 major cities in China can be shown on the basis of three evaluation variables(internet businessman index, internet shopping index and e-commerce development index). Dissimilarity distance matrix is used to analyze both similarity and dissimilarity between each fifty city in China by calculating dissimilarity as distance. Higher value signifies higher degree of dissimilarity between two cities. Cluster analysis is exploited to classify 50 cities into a number of different groups such that similar cities are placed in the same group. In addition, multidimensional scaling(MDS) technique can obtain visual representation for exploring the pattern of proximities among 50 major cities in China based on three development performance attributes. Research design, data, and methodology - This research is performed by the 2013 report provided with AliResearch in China(1/1/2013~11/30/2013) and utilized multivariate methods such as dissimilarity distance matrix, cluster analysis and MDS by using CLUSTER, KMEANS, PROXIMITIES and ALSCAL procedures in SPSS 21.0. Results - This research applies two types of cluster analysis and MDS on three development performances based on the 2013 report of Aliresearch. As a result, it is confirmed that grouping is possible by categorizing the types into four clusters which share similar characteristics. MDS is exploited to carry out positioning of both grouped locations of cluster and 50 major cities belonging to each cluster. Since all the values corresponding to Shenzhen, Guangzhou and Hangzhou(which belong to cluster 1 among 50 major cities) are very large, these cities are superior to other cities in all three evaluation attributes. Twelve cities(Beijing, ShangHai, Jinghua, ZhuHai, XiaMen, SuZhou, NanJing, DongWan, ZhangShan, JiaXing, NingBo and FoShan), which belong to cluster 3, are inferior to those of cluster 1 in terms of all three attributes, but they can be expected to be the next e-commerce revolution. The rest of major cities, in particular, which belong to cluster 4 are relatively inferior in all three attributes, so that this automatically evokes creative innovation, which leads to e-commerce development as a whole in China. In terms of internet businessman index, on the other hand, Tainan, Taizhong, and Gaoxiong(which belong to cluster 2) are situated superior to others. However, these three cities are inferior to others in an internet shopping index sense. The rest of major cities, in particular, which belong to cluster 4 are relatively inferior in all three evaluation attributes, so that this automatically evokes innovation and entrepreneurship, which leads to e-commerce development as a whole in China. Conclusions - This study suggests the implications to help e-governmental officers and companies make strategies in both Korea and China. This is expected to give some useful information in understanding the recent situation of e-commerce in China, by looking over development performances of 50 major cities. Therefore, we should develop marketing, branding and communication relevant to online Chinese consumers. One of these efforts will be incentives like loyalty points and coupons that can encourage consumers and building in-house logistics networks.
- keywords
- Development Performances, Dissimilarity Distance Matrix, Multidimensional Scaling, Cluster Analysis, STRESS
참고문헌
Aldenderfer, M. S., & Blashfield, R. K. (1985). Cluster analysis. Los Angeles, CA: Sage Publications.
Borg, I., & Groenen, P. J. F.(2005). Modern multidimensional scaling(2nd ed.). New York, NY: Springer-Verag.
Carroll, J. D., & Chang. J. J. (1970). Generalization of the singular value (Eckart-Young) decomposition to N-way tables. Psychometrika, 35, 238-319.
Han, J. P., Park, J. H., & Kim, Y. S. (2013). A study on the problem and improvements of the B2C E-commerce logistics services in China. Electronic trading Reviews, 11(1), 1-25.
Jeong, D. B. (2013). A study on universities positioning using multidimensional scaling. Korean Business Education Review, 28(2), 1-15.
Jeong, D. B. (2014). Evaluation of research performances for 28national universities. Journal of Korean Data &Information Science Society, 25(6), 1241-1251.
Jeong, D. B. (2015). A study on cluster and positioning of domestic electronic commerce based on purchasing motivation. Journal of Korean Data & Information Science Society, 29(4), 841-856.
Jeong, I. H. (2012). A study on the causal relationship of educational performance factors for university educational competence - focus on Malcolm Baldrige national quality award model-. Korean Business Education Review, 27, 207-239.
Jhun, I. H. (2014). The Recent development status and prospective of E-commerce in China. Oversea Economic Focus, 2014(30), 1-14.
Kaufman, L., & Rousseeuw, P. (2005). Finding groups in data –An introduction to cluster analysis (2nd ed.). Hoboken, New Jersey: Hohn Wiley & Sons.
Kim, H., & Seo, S. S. (2012). A study on the performance of online community of purchasing motivation by smart phone. Journal of Korean Electronic Commerce Research Association, 13, 41-57.
Kruskal, J. B. (1964). Major MDS based on a firm numerical analysis foundation. Psychometrika, 29, 1-27.
Kruskal, J. B., & Wish, M. (1977). Multidimensional Scaling. Beverly Hills, CA: Sage Publications.
Lee, G. H., & Kim, P. S. (2013). Effect of savings rate by adolescent consumption habit and business/economic education. Korean Business Education Review, 28, 363-377.
Lee, K. Y., Lee, H. J., Park, D. H., & Kim, S. K. (2014). Online shopping patterns and new change in China. KPMG International, April, 1-36.
Lee, L. J. (2011). A study on the purchase Intent of Internet Shopping Mall. Journal of Korean Electronic Commerce Research Association, 12, 93-106.
Lee, J. Y. (2013). The current online shopping market and its prospect based on internet and mobile. KISDI, 25, 96-108.
Savaresi, S. M., & Boley, D. (2004). A comparative analysis on the bisecting k-means and PDDP clustering algorithm. Intelligent Data Analysis, 8, 345-362.
Shanghai Assetplus (2015). E-commerce industry in China (pp.1-10). Retrieved March 10, 2015, from https://www.chinawindow.co.kr/download.php?category=spt6...pd
Sheng, Z., Chen, L., & Zhang, R. (2014). Report on 2013 development performances of e-commerce for 50 major cities in China (pp. 25-26). Retrieved June 4, 2014, from http://www.aliresearch.com
Shepard, R. N. (1962). Nonmetric algorithm. Psychometrika, 27, 219-246.
Takane, Y., Young. F. W., & DeLeeuw, J, (1977). Combined all previous major MDS developments into a single unified algorithm. Psychometrika, 42, 7-67.
Torgerson. W. S. (1952). Multidimensional scaling: 1. Theory and method. Psychometrika, 17, 401-419.
Yang, B. H. (2013). Understanding multivariate analysis. Seoul, Korea : Communication books.
- 다운로드 수
- 조회수
- 0KCI 피인용수
- 0WOS 피인용수