• Journal of the Korea Academia-Industrial cooperation Society
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• v.5 no.2
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• pp.191-196
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• 2004

Allocative efficiency measures the extents to which the technically efficient units falls shorts of achieving minimal cost. By using this measure manager can make decision about how to redistribute organizational resources to improve price efficiency. Allocative and overall efficiency are derived on the basis of budget line and cost minimization concept. The purpose of this study is to introduce the concept of allocative efficiency and propose two modified DEA models. Examples are provided to illustrate the similarities and the application procedure of the two model. By providing example and tracing the data application procedure, we found the same results but some cautions are needed to interpret the valuation.

• Korean Management Science Review
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• v.31 no.3
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• pp.1-12
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• 2014

This study suggests the two types DEA models such as DEA CCR model and Super Efficiency model to evaluate the value of a company and to apply them for the investments. 14 kinds of real data of companies such as EV/EBITDA, EPS growth rate, PCR, PER, dividend yield, PBR, stock price/net current asset, debt ratio, current ratio, ROE, operating margin, inventory turnover, accounts receivable turnover, and sales growth ratio were used as input variables of DEA models. 12 year data from December 30, 2000 up to December 30, 2012 were collected, and the data with negative, missing and 0 values were removed reflecting the characteristics of the DEA. In order to verify the effectiveness of the models, we compared the historical variability and rate of return of both models those of the market. Study results are as follows. First, two DEA models are more stable than market in terms of rate of return because the historical variability of both models are less than that of market. Second, Super Efficiency model is more stable than CCR model. Lastly, the cumulative rate of return of Super Efficiency model (434%) is greater than that of the CCR model (420%) and that of the market (269%).

• The Journal of Fisheries Business Administration
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• v.47 no.1
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• pp.63-86
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• 2016

The purpose of this study is to investigate production efficiency of Oceans and Fisheries Human Resources Development Programs Efficiency using Bootstrap-DEA. The study extracts 33 officials curriculum, 11 fisheries managers curriculum for its analytical. First, the study estimates technical, pure technical, and scale efficiency of each curriculums based on traditional DEA under the assumption of CRS and VRS. 8(official 7, managers 1) curriculums are identified as efficient DMUs under the CCR-model, and 13(official 10, managers 3) under the BCC-model. We provide inputs that allow inefficient curriculum to be efficient DMUs on a production frontier, and a reference set for their bench-marking. Second, rank test, Wilcoxon-Mann-Whitney test to find a statistical significance of heterogeneity existing in efficiences between Bootstrap-DEA tenical vs Bootstrap-DEA pure technical was no significant difference. We have identified that G10, 11, 12 13, 25, 31, 33, 39 curriculums are the most efficiently produced in the technical and pure technical efficiency. Also we managed to measure the inefficiency which exists in efficiently produced curriculums when estimating the bias corrected efficiency scores. In Technical efficiency, Operation and facility was significant at the 10%. In Pure technical efficiency, facility was significant at the 10%.

• Journal of Information Management
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• v.40 no.3
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• pp.41-60
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• 2009

This study utilizes DEA/Window analysis in order to investigate the relative efficiency levels of korea national university libraries during the period of transition 2004-2008. The empirical results show the following findings. Firstly, a technical efficiency was not efficient which was a scale efficiency rather than a pure technical efficiency in 2008. Secondly, a technical and pure technical efficiency has increased steadily from 2004 to 2008(but a technical, pure technical efficiency has decreased since in 2008). As a result, a technical efficiency increase can be explained by a pure technical efficiency increase.

• Journal of Korean Society for Quality Management
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• v.37 no.4
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• pp.61-70
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• 2009

This paper introduces quantitative tools for evaluating the relative efficiency of Career & Counseling Jobs in universities. As tools, it uses Data Envelopment Analysis (DEA) developed by Charnes and Cooper. It finally selects 29 DMUs which are listed on the Ministry Of Education, Science And Technology(http://academyinfo.go.kr). We measures the technical efficiency of each DMU with the use of DEA-CRS, rather then DEA-VRS because DEA-CRS not only compares relative efficiencies but also implicitly considers economies of scale based on the assumption of linearity. We run a linear programming model Frontier Analyst Program for the estimation of the relative efficiencies of each DMU. The model also indicates the precise amount of inefficiencies for each input, which mean how much inputs are wasted for a given output and how much the university is inefficiently operated. This analysis helps to give guideline for the organization to construct a futureoriented operational strategy and also to show clear picture of contents of mismanagement for the past. The details of mismanagement are to be identified, analysed and finally corrected.

• The Journal of the Korea Contents Association
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• v.20 no.10
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• pp.496-509
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• 2020

This study analyzes 5-year efficiency of the game industry, from 2014 to 2018 which is aimed at 25 online and mobile game companies, that are emerging as a new growth engine of a national economy to come and as a core areas of late entertainment industry. The DEA is used for static efficiency analyze and the DEA-Window is used for dynamic efficiency analyze. This study uses assets, the number of employees and costs as input variables and it also uses operating profits and sales as output variables. The main results show that scale efficiency presents a resonable result over 0.85 on a total average except 2014. However, there has not been a year that is over 0.80 of the whole period in technical efficiency. Also, in terms of business scale, there is a huge efficiency gap between high rank companies and low rank companies and the average trend of efficiency has been increased from 2014 to 2016 but it has been decreased since 2017.

• Korea Journal of Hospital Management
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• v.13 no.3
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• pp.69-93
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• 2008

Rising healthcare cost is a global phenomenon that justifies governments' introduction of 'incentive regulation' plan for the improvement of hospital efficiency. A number of previous studies tried to evaluate the efficiency of healthcare organization by using Data Envelopment Analysis(DEA), a common efficiency benchmarking method. However, there is a concern that this kind of efficiency evaluation could induce "quantity-quality trade-off". Moreover, as quality aspect is especially important in terms of 'effectiveness' of health care, it should be considered in efficiency evaluation of healthcare organization. A number of different models were tried so far to incorporate quality aspect into DEA, however, none is universally recognized as a standard. Thus, in this study, previous quality-incorporating DEA models were categorized into 6 types according to the way of incorporating quality aspect, and strengths and limitations of each type were reviewed with a set of artificial data as an example. Based on this review, a new quality-incorporating efficiency evaluation model, named Quality-adjusted output DEA(QAO-DEA), was suggested. As an exploratory empirical analysis, technical efficiency of human resource were measured with different quality-incorporating DEA models, using 2004 data from National University Hospitals. In conclusion, Quality-adjusted output DEA(QAO-DEA) model seems to be one of the most desirable alternatives to incorporate quality aspect in efficiency evaluation of hospital, and deserves the consideration as a policy tool to induce simultaneous improvement of both efficiency and quality.

• Journal of Korea Port Economic Association
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• v.17 no.1
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• pp.27-51
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• 2001

The purpose of this paper is to investigate the productive efficiency of world container ports by using the DEA (Data Envelopment Analysis) method and raw data from previous research in measuring the international competitiveness of world container ports. Ports have to cope with rapid changes in shipping environments. In order for a port to compete in the global market, it must provide port services promptly and accurately. Basically, there are two approaches to measuring the international competitiveness of a container port. First, there is the traditional productivity analysis method, which analyzes productivity based on the container port's facilities (efficiency, selectivity, land availability), and by its general capacity (handling ability, storage capacity, terminal productivity). Second there is multi-attribute utility analysis, which considers several elements including the reasons for selecting particular container ports and factors determining international competitiveness. This paper follows the first method (traditional productivity analysis) and extends the limitation of previous studies by using the DEA method newly, and suggesting: the relative productive efficiency of container ports. The main results of this paper are as follows: First, the results of the DEA analysis in terms of world container ports matches that of a previous study (Jun et al., 1993) at a level of 35%. The low ratio is due to the constrained set of input-output elements, the result of only twenty container ports being analyzed in this paper. Second, the result of the DEA analysis in terms of North-East Asia's container ports matches with that of a previous study (Ha, 1996) at a level of 100 percent. Therefore we can conclude that the DEA analysis is the best measurement method for international competitiveness. Policy implications for this study are as follows: First, when port authorities want to measure the international competition power of container ports and enhance their productive efficiency, they should consider the traditional method and newly introduce the DEA method. Second, according to the analysis results of the DEA method, pen authorities should recommend benchmarking ports as reference ports in order to enhance the productive efficiency of container ports that show an efficiency score of below 1.

• The Journal of the Korea Contents Association
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• v.10 no.12
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• pp.290-299
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• 2010

This study aims to evaluate the efficiency of superior appraisal corporations using DEA(Data Envelopment Analysis). DEA is known as a method for evaluating relative efficiency of organizations with multiple inputs and outputs. We used CCR-O and BCC-O DEA models to evaluate relative efficiency of superior appraisal corporations. Input variable is number of appraisers, output variables are total sales and net income. Total of 13 appraisal corporations in Korea were selected for this study, and the data were collected from financial reports for 2008 fiscal year. The result of this study is summarized as follows. First, the average of superior appraisal corporation's technical efficiency score is about 88.3% by applying CCR-O model. Second, the average of superior appraisal corporation's pure technical efficiency score is about 90% and scale efficiency score is about 98.2% by applying BCC-O model. According to the result of DEA, the cause of inefficiency is pure technical efficiency.

• Journal of Korean Institute of Industrial Engineers
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• v.40 no.1
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• pp.84-99
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• 2014

In this study, determinant input-output variables are identified for calculating Data Envelopment Analysis (DEA) efficiency scores relating to evaluating the efficiency of government-sponsored research and development (R&D) projects. In particular, this study proposes a systematic framework of design and analysis of experiments, called "all possible DEAs", for pinpointing DEA determinant input-output variables. In addition to correlation analyses, two modified measures of time series analysis are developed in order to check the similarities between a DEA complete data structure (CDS) versus the rest of incomplete data structures (IDSs). In this empirical analysis, a few DEA determinant input-output variables are found to be associated with a typical public R&D performance evaluation logic model, especially oriented to a mid- and long-term performance perspective. Among four variables, only two determinants are identified : "R&D manpower" ($x_2$) and "Sales revenue" ($y_1$). However, it should be pointed out that the input variable "R&D funds" ($x_1$) is insignificant for calculating DEA efficiency score even if it is a critical input for measuring efficiency of a government-sonsored R&D project from a practical point of view a priori. In this context, if practitioners' top priority is to see the efficiency between "R&D funds" ($x_1$) and "Sales revenue" ($y_1$), the DEA efficiency score cannot properly meet their expectations. Therefore, meticulous attention is required when using the DEA application for public R&D performance evaluation, considering that discrepancies can occur between practitioners' expectations and DEA efficiency scores.