• Journal of Korea Port Economic Association
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• v.32 no.1
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• pp.29-58
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• 2016

The purpose of this paper is to show the clustering trend and the comparison of empirical results and is to choose the clustering ports for 3 Korean ports(Busan, Incheon and Gwangyang Ports) by using the bootstrapped DEA(Data Envelopment Analysis) and game Cross-efficiency models for 38 Asian ports during the period 2003-2013 with 4 input variables(birth length, depth, total area, and number of cranes) and 1 output variable(container TEU). The main empirical results of this paper are as follows. First, bootstrapped DEA efficiency of SW and LT is 0.7660, 0.7341 respectively. Clustering results of the bootstrapped DEA analysis show that 3 Korean ports [ Busan (6.46%), Incheon (3.92%), and Gwangyang (2.78%)] can increase the efficiency in the SW model, but the LT model shows clustering values of -1.86%, -0.124%, and 2.11% for Busan, Gwangyang, and Incheon respectively. Second, the game cross-efficiency model suggests that Korean ports should be clustered with Hong Kong, Shanghi, Guangzhou, Ningbo, Port Klang, Singapore, Kaosiung, Keelong, and Bangkok ports. This clustering enhances the efficiency of Gwangyang by 0.131%, and decreases that of Busan by-1.08%, and that of Incheon by -0.009%. Third, the efficiency ranking comparison between the two models using the Wilcoxon Signed-rank Test was matched with the average level of SW (72.83 %) and LT (68.91%). The policy implication of this paper is that Korean port policy planners should introduce the bootstrapped DEA, and game cross-efficiency models when clustering is needed among Asian ports for enhancing the efficiency of inputs and outputs. Also, the results of SWOT(Strength, Weakness, Opportunity, and Threat) analysis among the clustering ports should be considered.

• Journal of Korea Port Economic Association
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• v.37 no.3
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• pp.75-91
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• 2021

Due to increased competition in supply side to reduce operational costs, port professionals have experienced extreme pressure, which demanded academicians to develop the model for efficient port operations from the industry perspective. Among many ports in the world, U.S. ports are our primary interest to analyze in our study for its high volume of cargoes transacted in the U.S. ports. We primarily employed DEA (Data Envelopment Analysis) technique to research the productivity of U.S. ports and applied the algorithm of double bootstrapped DEA proposed by Simar & Wilson (2007) to further investigate the driving forces of the performance of U.S. port operations. The external variables employed in our study comprise onDock Rail, Channel Depth, Location, Area, Acres, ForeignCargoRatio, and TEUChange, out of which onDock Rail, Acres, ForeignCargoRatio, and TEUChange were significant. In order to evaluate the effects of methodology selection, we conducted the same analysis applying the Censored model (Tobit) and contrasted the outcomes drawn from the two different techniques. Based on the findings from this work we proposed managerial implications and concluded.