• Proceedings of the Korea Contents Association Conference
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• 2014.11a
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• pp.429-430
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• 2014

본 연구는 DEA를 이용하여 국내 일반은행(13개)의 상대적 효율성을 분석하였다. 분석결과, CCR모형에 의한 기술효율성이 '1'인 은행은 5개, BCC모형에 의한 순수기술효율성이 '1'인 은행은 9개였으며, 규모효율성이 '1'인 은행은 5개였다. 시중은행과 지방은행의 효율성 평균 차이 분석에서는 기술효율성과 순수기술효율성에서는 유의한 차이가 없었으며, 규모효율성에서만 p<0.1에서 지방은행이 시중은행 보다 다소 높은 효율성을 보였다.

• The Korean Journal of Financial Management
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• v.19 no.1
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• pp.133-151
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• 2002

본 연구는 Malmquist지수를 이용하여 1997년 외환위기 때부터 2000년까지 국내은행의 생산성 변화를 순수기술효율성, 규모효율성 및 기술진보의 변화로 구분하여 측정함과 동시에 서울소재은행과 지방은행간의 생산성을 비교하였다. 1997년과 2000년간의 순수기술효율성의 변화는 감소하였지만, 규모효율성과 기술진보의 변화가 순수기술효율성의 하락 부분을 상쇄한 결과로 2000년에는 1997년의 생산성 수준을 회복하였다는 것을 확인하였다. $1997{\sim}2000$년의 서울소재 은행과 지방은행간을 비교한 결과에 의하면 서울소재은행의 생산성이 높게 나타났다. 두 집단간에 이러한 차이는 규모효율성으로부터 기인하였으며, 기술혁신을 의미하는 기술진보의 변화에는 두 집단간에 차이가 없었다. 특히 지방은행의 생산성은 2000년에도 1997년도 수준을 회복하지 못하고 있다.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.22 no.4
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• pp.301-315
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• 2011

DEA(Data Envelopment Analysis) is useful to measure the relative efficiency of organizational units where the presence of multiple inputs and outputs. This study applied DEA-CCR and DEA-BCC to evaluate the technical, pure technical, and scale efficiency of 29 university libraries. The input variables were number of books, print edition expenses, building space, staff, number of seats. As output variables we estimated: reader visits, number of borrowed items, number of visitors. It was found out that number of libraries with 100% relative efficiency among 29 libraries were 13. Also the results shows that main reason of inefficiency was from scale rather than from pure technical. Many inefficient libraries were operations of increasing return to scale.

• Journal of Korea Port Economic Association
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• v.22 no.3
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• pp.59-78
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• 2006

The purpose of this paper is to analyze the trend on scale efficiency of the Port of Gwangyang from 1994 to 2004 using CCR, BCC, and Malmquist index approaches. The main results are as follows. first, scale efficiency shows a 50% similar [5(94/95, 95/96, 97/98, 2001/2002, 2003/2004) out of 10] pattern to technical efficiency change. Second, total factor productivity increased at 48.57% rate of growth on average in 6 out of 10 periods except 96/97, 97/98, 99/2000, and 2000/2001. 2003/2004 period is the one period experiencing rapid total factor productivity changes, mainly due to technical progress. Third, the ranking order of accumulative indices is scale efficiency change, TFP change, efficiency change, technical change, and pure efficiency change. The main policy implication of this paper is that according to the CCR, BCC, and Malmquist results, the Port of Gwangyang should develop the plan for enhancing the 5 Malmquist indices with following the management way of benchmarking ports.

• The Journal of the Korea Contents Association
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• v.15 no.5
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• pp.475-487
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• 2015

This study measures the managerial efficiency of Korea's 14 public enterprises using bootstrap DEA in 2013. In addition, it examines the factors that affect on the bootstrap bias-corrected efficiency using truncated regression analysis. The results and implications of this study are as follows. First, using bootstrap DEA model analysis, the results showed that the mean technical efficiency was 0.3182, the mean pure technical efficiency was 0.4994 and the mean scale efficiency was 0.6585. The main cause of technical inefficiency was due to pure technical inefficiency. Second, rank test between technical efficiency of general DEA model and bootstrap DEA model was no significant difference under CRS and VRS assumption. Third, the main cause of the inefficiency in 11 DMUs among 14 DMUs were mainly due to the pure technology and three DMUs were because of the scale efficiency. Finally, in the truncated regression analysis, cost of labor, profit, sales, return of equity, and the number of employees appeared as factors affecting the scale efficiency at the 10% significance level.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2004.11a
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• pp.191-196
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• 2004

This paper is to measure and ealuates the technical efficiency, pure technical efficiency and scale efficiency with three inputs and two outputs with the use of DEA(data envelopment analysis) in Korean RCC(Rescue Co-ordination Center/RSC(Rescue Sub-Center). Several conclusion emerge. first the average efficiency of overall technical efficiency measure about $91.03\%$ and pure technical efficiency $96.80\%$ is much large then scale efficiency $93.83\%$. It means that inefficiency has much more to do whit the inefficient utilization of resources rather then the scale of production. second, DRS(decreasing return to scale) is Tongyeong and IRS(increasing return to scale) is Incheon, Taean, Gunsan, Yeosu, Ulsan, Donghae in RCC/RSC. finally, inefficiency RCC/RSC. have to benchmarking with reference sets.

• Journal of Korean Society of Forest Science
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• v.102 no.3
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• pp.390-397
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• 2013

The study, utilizing a data envelopment analysis (DEA) which is one of the nonparametric estimation methods, aims to evaluate the management efficiency of chestnut tree cultivators in such provinces in Chungchungnam-do as Cheong-yang, Gong-ju, Bu-yeo and so on. The analysis data of this study is based on inputs and outputs of 20 forestry households surveyed in the 2012 survey titled 'A Study on Current Level and Condition of Chestnut Cultivation and Management', which was conducted from March 2012 to October 2012. The elements of inputs are composed of management cost, harvesting cost, material cost, non-operation expenses and cultivation area, while the element of output is a gross margin only. Then the study analyzes a technical efficiency, a puretechnical efficiency and a scale efficiency using CCR and BCC model among DEA methods. Based on that, it also provides improvement methods for forestry households that turned out to be inefficient. In order to verify the result of DEA analysis, the study additionally compares a result of this efficiency study with that of chestnuts management standard diagnostic table. According to the result, the average value of technical efficiency analyzed was 0.667, proving to be inefficient in general. Given that the average value of pure-technical efficiency was 0.944 and that of scale efficiency was 0.703, it can be inferred that inefficiency exists in the field of scale, not in the field of cultivation techniques. As for forestry households with the efficiency score of 1, it is shown that there were 6 households that recorded 1 in the technical efficiency field and 13 households that recorded 1 in the pure technical efficiency. Meanwhile, there were 6 households that recorded 1 in all of the three aspects. In the comparison with the scores from chestnuts management standard diagnostic table, there were 5 households made a high score of over 80, among which are 3 households with score 1 in the technical efficiency. Also, the results of this study and the chestnuts management standard diagnostic table are proved to have the same result, both of them showing the same households that recorded the highest score and the lowest score. This means the management efficiency evaluation using DEA can be applied to the fieldwork along with the chestnuts management standard diagnostic table.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.10
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• pp.6823-6831
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• 2015

The purpose of this study is to analyse cucumber farms managing efficiency and to draw some factors needed to maximize farms production efficiency. After reviewing and analysing some cucumber farms main management index in detail, we found that, compared to scale improvement, technical efficiency has more potential to increase cucumber farms income. Moreover There is a positive correlation between cucumber production and farms pure technical efficiency slightly, but because of the degree, we suggested to reduce input elements for enhancing pure technical efficiency under certain conditions.

• Journal of Technology Innovation
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• v.29 no.3
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• pp.33-62
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• 2021

This study compares and analyzes the efficiency of R&D activities of technology-innovative small and medium-sized enterprises(SMEs) between industries and proposes ways to improve efficiency. The research samples are 6,708 technology-innovative SMEs, which have received a guarantee by the KIBO from 2008 to 2011. Input variables are the level of R&D personnel, R&D investment, and output variables are patent applications, prototype. Efficiency is measured by the DEA model, and indirect comparisons that are individually measured by industry are performed. As a result of the analysis, the CCR for determining the optimal returns to scale is 0.19, the BCC for determining the optimal input distribution is 0.70, and the SE for determining the optimal output is 0.30. By industry type, the medium and low-tech industries have high CCR and BCC, while the high-end and high-tech industries have high SE. R&D activities need to be operated on an optimal scale through managing R&D performance because there is the inefficiency of scale across the industry. The contribution of the study is to analyze the R&D efficiency of each industry of technology-innovative SMEs by the technology evaluation data of the KIBO.

• 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.