• Korean Journal of Construction Engineering and Management
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• v.13 no.5
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• pp.94-102
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• 2012

Recently, the global financial crisis and the increasing number of unsold houses in Korea are construction companies to assess their efficiency. The most important factor in analyzing the efficiency of a company is the input-output variable. However, systematic stud the core input-output variables, which have a great influence on the efficiency analysis. Thus, to the core input-output variables for efficiency analysis of construction companies, this study propose a model that includes all combinations of input-output variables and to find the core input-output variables using the Data Envelopment Analysis(DEA) model and Principal Component Analysis(PCA). Existing research and theories were studied variables and 21 models were established to measure efficiency. were obtained that the core input and output variable in 2006 the number of employees and sales. For 2008, the core input variable was capital stock and the core output variable was quarterly net profit. For 2010, the core input variable was fixed asset and the core output variable was sales. Through obtaining the variables that greatly affect the efficiency of construction companies, it is considered that individual construction companies will be able to prepare a priority strategy to enhance efficiency.

• Journal of Korea Port Economic Association
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• v.30 no.1
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• pp.23-55
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• 2014

The purpose of this paper is to show the clustering trend and to choose the clustering ports for 3 Korean ports(Busan, Incheon and Gwangyang Ports) by using the self organizing maps based on neural network(SOM) and Tier models for 38 Asian ports during 11 years(2001-2011) with 4 input variables(birth length, depth, total area, and number of crane) and 1 output variable(container TEU). The main empirical results of this paper are as follows. First, clustering results by using SOM show that 3 Korean ports[Busan(26.5%), Incheon(13.05%), and Gwangyang(22.95%) each]can increase the efficiency. Second, according to Tier model, Busan(Hongkong, Sanghai, Manila, and Singapore), Incheon(Aden, Ningbo, Dabao, and Bangkog), and Gwangyang(Aden, Ningbo, Bangkog, Hipa, Dubai, and Guangzhou) should be clustered with those ports in parentheses. Third, when both SOM and Tier models are mixed, (1) efficiency improvement of Busan Port is greater than those of Incheon and Gwangyang ports. (2) Incheon port has shown the slow improvement during 2001-2007, but after 2008, improvement speed was high. (3) improvement level of Gwangyang port was high during 2001-2003, but after 2004, improvement level was constantly decreased. The policy implication of this paper is that Korean port policy planner should introduce the SOM, and Tier models with the mixed two models when clustering among the Asian ports for enhancing the efficiency of inputs and outputs.

• Journal of Korea Port Economic Association
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• v.33 no.2
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• pp.53-82
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• 2017

The purpose of this study is to show the clustering trend and compare empirical results, as well as to choose the clustering ports for 3 Korean ports (Busan, Incheon, and Gwangyang) by using meta-frontier DEA (Data Envelopment Analysis) and integer models on 38 Asian container ports over the period 2005-2014. The models consider 4 input variables (birth length, depth, total area, and number of cranes) and 1 output variable (container TEU). The main empirical results of the study are as follows. First, the meta-frontier DEA for Chinese seaports identifies as most efficient ports (in decreasing order) Shanghai, Hongkong, Ningbo, Qingdao, and Guangzhou, while efficient Korean seaports are Busan, Incheon, and Gwangyang. Second, the clustering results of the integer model show that the Busan port should cluster with Dubai, Hongkong, Shanghai, Guangzhou, Ningbo, Qingdao, Singapore, and Kaosiung, while Incheon and Gwangyang should cluster with Shahid Rajaee, Haifa, Khor Fakkan, Tanjung Perak, Osaka, Keelong, and Bangkok ports. Third, clustering through the integer model sharply increases the group efficiency of Incheon (401.84%) and Gwangyang (354.25%), but not that of the Busan port. Fourth, the efficiency ranking comparison between the two models before and after the clustering using the Wilcoxon signed-rank test is matched with the average level of group efficiency (57.88 %) and the technology gap ratio (80.93%). The policy implication of this study is that Korean port policy planners should employ meta-frontier DEA, as well as integer models when clustering is needed among Asian container ports for enhancing the efficiency. In addition Korean seaport managers and port authorities should introduce port development and management plans accounting for the reference and clustered seaports after careful analysis.

• Journal of Korea Port Economic Association
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• v.32 no.2
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• pp.123-135
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• 2016

This study uses Data Envelopment Analysis(DEA) to investigate the management efficiency of Korean shipping companies based on business administration costs such as corporate entertainment, advertisement, and labor costs. We analyze shipping enterprises listed on the Korean stock market of the period of 2010-2014. Corporate entertainment, advertisement and labor costs are used as input variables and sales and net income are used as output variables. We use technical efficiency, pure technical efficiency, scale efficiency and returns to scale to propose a plan to improve the efficiency of inefficiency decision-making units (DMUs). The results of the efficiency analysis show that six of the DMUs in the technical efficiency of CCR model and eight of the DMUs in the pure technical efficiency of BCC model are in efficient state. In terms of return to scale, six of the DMUs(24% of all DMUs) show increasing returns to scale, while 13 DMUs(52% of all DMUs) showdecreasing returns to scale. Because multiple efficient state for DMUs exist in the technical efficiency analysis, we conduct a super efficiency analysis. The results show that the efficient state of the twomost efficient DMUs are 1.314 and 1.243, respectively. This implies that these DMUs could maintain their current levels of the efficiency if they increase the amount spent on advertisements, corporate entertainment and labor costs by 31.4% and 24.3%. respectively. We conclude this study by providing the efficiency states of each DMU and target for improving the inefficiencies in each case.

• Industry Promotion Research
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• v.7 no.4
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• pp.97-108
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• 2022

This paper examined the impact of financing structure on the innovation efficiency of SMEs by constructing an econometric model using panel data of SMEs listed on the SME board from 2010 to 2020 as the research sample. The innovation efficiency of SMEs was measured by the Stochastic Frontier Analysis (SFA), the relationship between financing structure and innovation efficiency of SMEs was examined with the help of the Tobit model, and the corresponding heterogeneity analysis was conducted. Finally, the robustness of the model was tested. It was concluded that the effects of debt and equity financing on the quantitative efficiency of innovation were non-linear and mainly showed an inverted "U" shaped relationship. For innovation quality efficiency, bond financing could positively contribute, while equity financing negatively inhibits. Finally, the corresponding advice was given.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.8
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• pp.130-139
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• 2020

The defense industry is a future-oriented industry, in which high-tech technologies are concentrated. In addition, it is a key industry for maintaining national security, and the R&D of the defense industry is very important. With the introduction of a competitive system by defense industry companies, the necessity of improving the efficiency and productivity of defense industry companies is emerging. In this study, the efficiency of the defense industry was measured using the CCR and BCC model. In addition, super efficiency was derived using the Super-SBM model. For this study, the 2019 defense industry management analysis data of the Korea Defense Industry Association (KDIA) was used, and the analysis was performed by setting the number of researchers and investments of domestic defense industry companies as the input variables and sales as the output variables. Through this study, it is expected that the defense industry's R&D efficiency will be grasped, which will help establish a policy for fostering the defense industry in the future.

• Journal of the Korean Society for Library and Information Science
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• v.41 no.2
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• pp.237-256
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• 2007

This study examined DEA(Data Envelopment Analysis) and how it measures the efficiency of library units. DEA is a useful nonparametric method to evaluate the relative efficiency of a set of decision making units(DMUs) with multiple inputs and outputs. This study evaluated 102 different public libraries utilizing 4 inputs and 4 outputs focussing on the year 2005. For inefficient libraries, the study analysed the potential improvement and the source of inefficiency comparing the peer groups. The result of this study shows that efficiency of public libraries varies in different localities and forms of operation.

• Journal of Korea Port Economic Association
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• v.25 no.2
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• pp.57-72
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• 2009

The purpose of this paper is to show the empirical measurement way of operation risk evaluation in domestic seaports for overcoming the limitations which the traditional DEA method has by using 13 Korean ports in 2003 for 4 inputs(birthing capacity, cargo handling capacity, number of coastal guard vessel, number o f coastal special guard vessel ) and 5 outputs(Export and Import Quantity, Number of Ship Calls, number of coastal accident, number of coastal crime, number of coastal pollution). Because traditional DEA method has produced the limited set of information, negative DEA mixed with tier, stratification and layering methods should be adopted. The goal of negative DEA is to set up DEA models that will place the poor operating ports on or close to the empirical frontier. The core empirical results of this paper are as follows. First, Donghae ports should benchmark the operation way of Yeasu, Busan, Woolsan ports in terms of the middle and longterm base. Second, 5 ports(ports of Taean, Yeasu, Tongyoung, Busan, Sokcho) which were revealed as the poor operating ports in Negative DEA analysis should benchmark Incheon, Woolsan, Pohan, and Donhae ports. The policy implication to the Korean seaports and planners is that Korean seaports should introduce the new methods like Negative DEA of this paper for predicting the poor operating in the ports.

• Environmental and Resource Economics Review
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• v.22 no.2
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• pp.329-365
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• 2013

The purpose of this paper is to measure economy and environment efficiencies under fossil fuel and environment regulation by countries for 2000-2009. Distinguishing 83 countries with three groups of OECD, upper-middle, and low countries, we compare four models such as environment oriented, economy-oriented, environment-economy oriented, and two-stage types, which include a desirable output, GDP and an undesirable output, pollutant together in the production possibility set. OECD countries relatively showed high economy efficiency and low environment efficiency, whereas Non-OECD countries showed high environment efficiency and low economy efficiency. OECD countries reported a higher possibility to reduce fossil fuel and $CO_2$ emission.

• Journal of Korea Port Economic Association
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• v.37 no.1
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• pp.31-70
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• 2021

The purpose of this paper is to measure the clustering change and analyze empirical results, and choose the clustering ports for Busan, Incheon, and Gwangyang ports by using Hierarchical clustering(single, complete, average, and centroid), Silhouette, and 2SCE[the Second Stage(Type II) cross-efficiency] matrix clustering models on Asian container ports over the period 2009-2018. The models have chosen number of cranes, depth, birth length, and total area as inputs and container TEU as output. The main empirical results are as follows. First, ranking order according to the efficiency increasing ratio during the 10 years analysis shows Silhouette(0.4052 up), Hierarchical clustering(0.3097 up), and 2SCE(0.1057 up). Second, according to empirical verification of the Silhouette and 2SCE models, 3 Korean ports should be clustered with ports like Busan Port[ Dubai, Hong Kong, and Tanjung Priok], and Incheon Port and Gwangyang Port are required to cluster with most ports. Third, in terms of the ASEAN, it would be good to cluster like Busan (Singapore), Incheon Port (Tanjung Priok, Tanjung Perak, Manila, Tanjung Pelpas, Leam Chanbang, and Bangkok), and Gwangyang Port(Tanjung Priok, Tanjung Perak, Port Kang, Tanjung Pelpas, Leam Chanbang, and Bangkok). Third, Wilcoxon's signed-ranks test of models shows that all P values are significant at an average level of 0.852. It means that the average efficiency figures and ranking orders of the models are matched each other. The policy implication is that port policy makers and port operation managers should select benchmarking ports by introducing the models used in this study into the clustering of ports, compare and analyze the port development and operation plans of their ports, and introduce and implement the parts which required benchmarking quickly.