2012, Vol.7, No.4

Journal of Knowledge Information Technology and Systems 2023 KCI Impact Factor : 0.89

pISSN : 1975-7700 / eISSN : 2734-0570
https://journal.kci.go.kr/kkits

The increase of cargo volumes and vessel demand has led port authorities to improve infrastructure, enlarge area, and construct a hub port in Asia. This study applies the Data Envelopment Analysis(DEA) model to 12 Asia major ports. This study tries to apply the Data Envelopment Analysis(DEA) model to 12 Asia major ports. DEA is a methodology that compares the relative efficiency of each decision making unit(DMU) with similar input and output structure, and is specially very useful when a form of production function of each DMU such as a port is not known. DEA provides the extent of inefficiency of DMUs, which is practically useful information (like the efficiency score and reference sets) required to improve efficiency. This paper aims to analyze and compare the competitiveness of 3 major Korean ports, 3 major Chinese ports, 3 major Japanese ports, and 3 major Taipei using Data Envelopment Analysis(DEA) windows analysis based on 2006-2010 data from Containerization International Yearbooks. Through this study, we will investigate the efficiency of these major East Asian ports and demonstrate the trend of the efficiency.

This paper analyze the differences and problems of FDI statistical data in China. The FDI not only provide the capital, but also play an important role in the quality development. The over-evaluated and under-evaluated FDI statistical data could bring about unfavorable affects to real economy analysis and policy-making process. This paper indicates the statistical data differences and analyzes the causes from sixth aspects. First is the fixing of statistical data boundary. Second is the difference of the actually used FDI and the contracted FDI data. The contracted FDI data indicate the investment intention and potential power. Third is the difference of the green filed investment and the accumulated investment. We could compute the green field investment from the institutional channel, but hardly computing the accumulated investment because of the re-investment. Fourth is the difference of MOC and SAOFE, this is the most serious problem. Fifth is the difference of the central government and the local government. Sixth is the difference of host country data and the investment country data.

Business bankruptcy has had a lot of losses to various stakeholders. If we are able to predict bankruptcy or insolvency in advance, we can defend enterprises against bankruptcy or insolvency and minimize the loss. Various types of statistical and data mining models have been proposed for prediction of business bankruptcy. One of the main issues of the bankruptcy prediction models is to increase predictability. This research deals with logit, decision tree, neural networks, and SVM models those are mainly used to predict corporate bankruptcy. Sample companies were consisted of 49 bankrupt firms and 49 normal firms in KOSDAQ. Nine financial variables were selected by statistical test and five years of data were collected. In case of using SVM for preprocessing data, experimental results showed that classification models could improve the performance of the bankruptcy prediction.