E-ISSN : 2288-2766
Purpose – The purpose of this study is to establish the government's logistics policy by calculating the logistics cost of the company and grasping the management status, to reduce the logistics cost of the related companies and to provide basic statistical data necessary for the management strategy. This work examines some associations between reasons for reducing corporate logistics costs (RCLC) and corporate classification such as industry and sales size. Research design, data, and methodology – The survey was conducted in 2018 for 2,000 companies based on the business of mining, manufacturing and wholesale and retail industries since 2010. The survey population is 94,976, of which 92,708 are small and medium enterprises and 2,268 are large corporations. The association among factors may be statistically and visually explored by using chi-squared test and correspondence analysis. Result – This study reveals the association between reasons for RCLC and corporate classification and properties and closeness that exist between the categories of each factor can be mined. Conclusion – As a task to reduce logistics costs of industrial products, expansion and operation of joint logistics business, establishment of cooperative logistics network, and establishment of ordinance on support for smart distribution logistics can be proposed.
Agresti, A. (2002). Categorical data analysis (2nd ed.). Hoboken, New Jersey: John Wiley & Sons Inc.
Benzercri, J. P. (1992). Correspondence analysis handbook. New York: Marcel Decker.
Brigitte, Le R. (2009). Multiple correspondence analysis. Thousand Oaks, CA: Sage Publications.
Chung, H. J. (2012). A study on the management condition of corporate logistics costs. Review of Accounting and Policy Studies, 17(3), 431-453.
Clausen, S. E. (1988). Applied correspondence analysis: an introduction. Thousand Oaks, CA: Sage Publications.
Doey, L., & Kurta, J. (2011). Correspondence analysis applied to psychological research. Tutorials in Quantitative Methods for Psychology, 7(1), 5-14.
Greenacre, M. J. (1984). Theory and applications of correspondence analysis. New York: Academic Press.
Greenacre, M. J. (2007). Correspondence analysis in practice. Boca Raton, Florida: Taylor and Francis Group.
Hair, J. F., Black, B., Babin, B., Anderson, R. E., & Tatham, R. L. (2007). Multivariate data analysis. Toronto: Prentice Hall.
Hoffman, D. L., & Franke, G. R. (1986). Correspondence analysis: graphical representation of categorical data in marketing research. Journal of Marketing Research, 23(3), 213-227.
Jeon, H. J., & Kim, Y. M. (2015). Determinant Factors on Business Performance of the Logistics Firms in Korea. Korea International Commercial review, 30(2), 109-131.
Lee, S. C., Park, J. W., & Kim, H. G. (2011). A study on the asymmetric behavior of logistic costs. Korean Journal of Logistics, 19(1), 75-96.
Park, B. J. (2013). The effect of level and change of logistic costs in manufacturing on firm value. Korean International Accounting Review, 48(2013,4), 29-54.
Ryu, S. Y., & Park, B. J. (2013). Effect of logistics costs reduction on value relevance of accounting information. Korea Logistics Review, 23(5), 189-211.
Steven, J. P. (2009). Applied multivariate statistics for the social sciences. New York: Lawrence Erlbaum Associates Inc.
Yang, B. H. (2013). Understanding multivariate analysis. Seoul: Communication books.