Abstract
This study selected container terminals of Gwangyang and Busan Ports to evaluate the influence of efficient container terminals. For the study, after data envelopment analysis (DEA) using the CCR and BCC models, the decision-making unit (DMU) system was used to define nodes; and with the use of a reference group in DEA (BCC model) and a lambda value, this study created a social network and analyzed the influences of efficient DMUs through a centrality analysis of eigenvectors. The results are presented as follows: First, as a result of the DEA, CCR efficiencies in PNC, HJNC, and HPNT container terminals of Busan Port were 1 and BCC efficiencies at Singamman Terminal, Wooam Terminal, PNC, HJNC, HPNT, and BNCT container terminals of Busan Port were 1. Second, as a result of undertaking social network analysis (SNA), according to an eigenvector centrality analysis, HJNC Terminal was referred to the most (influence score of 0.515), which indicates that it is the most influential as a container terminal. The influence of PNC Terminal was 0.512, while that of Wooam Terminal was 0.379. CJ Korea Express in Gwangyang Port was ranked fourth in influence, but its influence score of 0.256 indicates that it was the most influential of the container terminals at Gwangyang Port.
본 논문은 효율적인 컨테이너 터미널의 영향력을 평가하기 위해 광양항과 부산항 컨테이너 터미널을 분석대상으로 선정하였다. 연구방법은 DEA분석(CCR, BCC모형) 후 DMU를 노드로 하고, DEA(BCC모형)의 참조집단과 람다값을 이용하여 사회 네트워크를 생성하고 아이겐벡터 중심성 분석에 의해 효율적인 DMU들의 영향력을 분석하였다. 분석결과는 첫째, DEA분석 결과 CCR효율성은 부산항의 PNC, HJNC, HPNT 컨테이너 터미널이 효율성 1이고, BCC효율성은 부산항의 신감만부두, 우암부두, PNC, HJNC, HPNT, BNCT 컨테이너 터미널이 효율성 1이다. 둘째, SNA분석 결과 아이겐벡터 중심성 분석에 의하면 HJNC터미널이 0.515로 가장 많이 참조되고 있는 컨테이너 터미널로 영향력이 가장 높은 것으로 볼 수 있다. PNC터미널이 0.512, 우암부두가 0.379, 순이고 광양항의 CJ대한통운 전체 영향력에서는 4위이나, 광양항 컨테이너 터미널 중에서는 0.256으로 가장 영향력있는 컨테이너 터미널이다.