• 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 Energy Engineering
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• v.23 no.3
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• pp.42-50
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• 2014

Korean government has been investing more than 400 billion KRW in R&D on renewable energy. This paper aims to measure the R&D efficiency of national R&D program in the field of renewable energy, and to identify the sources of inefficiency. 4,213 R&D projects supported by Korean government during 2009-2011 are analyzed by using Data Envelopment Analysis and statistical tests. Results implies as follows. First, hydrogen, bio, fuel cell, photovoltaic have higher R&D efficiency than other renewable energies. Second, universities conducted national R&D program more efficiently than firms did, and small and medium sized enterprises are more efficient than large sized enterprises. Third, R&D inefficiency is mainly caused by the lacks of patent performance rather than excessive R&D investment or academic paper performance.

• 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.

• Knowledge Management Research
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• v.17 no.1
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• pp.49-72
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• 2016

Extant research on firm innovation productivity is limited in measuring the innovation productivity, in which they measured firm innovation productivity by using either inputs or outputs of innovation. The present study complemented the extant research by employing Data Envelopment Analysis (DEA) approach to measure firm innovation productivity. Furthermore, this paper examined the effects of firms' external knowledge search, as one of open innovation practices, on firm innovation productivity, for open innovation activities are regarded as an influencing factor on firm innovation productivity in the previous literatures. Using the data of the Korean Innovation Survey (KIS) of manufacturing industries conducted in 2008, this study developed hypotheses in which we considered not only two dimensions of external knowledge search (breadth and depth) but also two subtypes of external knowledge search (market-driven and science-driven). The results found that searching deeply and market-driven search are positively related to firm innovation productivity, but science-driven search is somewhat negatively related to firm innovation productivity. Furthermore, market-driven search can mitigate the negative effect of science-driven search on innovation productivity.

• International Journal of Advanced Culture Technology
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• v.12 no.2
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• pp.167-179
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• 2024

This study addresses the challenge of objectively evaluating the performance of early-stage startups amidst limited information and uncertainty. Focusing on companies selected by TIPS, a leading private sector-driven startup support policy in Korea, the research develops a new indicator to assess technological efficiency. By analyzing various input and output variables collected from Crunchbase and KIND (Korea Investor's Network for Disclosure System) databases, including technology use metrics, patents, and Crunchbase rankings, the study derives technological efficiency for TIPS-selected startups. A prediction model is then developed utilizing machine learning techniques such as Random Forest and boosting (XGBoost) to classify startups into efficiency percentiles (10th, 30th, and 50th). The results indicate that prediction accuracy improves with higher percentiles based on the technical efficiency index, providing valuable insights for evaluating and predicting startup performance in early markets characterized by information scarcity and uncertainty. Future research directions should focus on assessing growth potential and sustainability using the developed classification and prediction models, aiding investors in making data-driven investment decisions and contributing to the development of the early startup ecosystem.

• Journal of Korea Port Economic Association
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• v.20 no.2
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• pp.35-52
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• 2004

The purpose of this paper is to measure the competition power of administration service in Korean Seaports by using the scores of customer satisfaction for administration service investigated yearly from 2000 to 2003 by Ministry of Maritime Affairs & Fisheries. And also, this paper shows the competition power of Korean seaports in terms of efficiency by using DEA(data envelopment analysis) method after measuring the change of productive efficiency scores subject to including and excluding the scores of customer satisfaction for administration service as output variable. The empirical main results of this paper are as follows: First, the efficiency scores of the Ports of Donghae, Gunsan, Jeju, Yeosu, Masan, and Pohang have worsened if the customer satisfaction score is excluded as output variable. Therefore these ports have been influenced by the score of customer satisfaction more positively. Second, the changes of the ranking order by measuring the average efficiency scores of each ports subject to including and excluding the scores of customer satisfaction for administration service as output variable are as follows: Busan(9-->7), Incheon(6-->6), Yeosu(1-->4), Gwangyang(4-->3), Masan (10-->9), Ulsan(5-->5), Donghae(8-->11), Gusan(12-->12), Mogpo(3-->2), Pohang(11-->10), Jeju(7-->8), Daesan(2-->1).

• The Korean Journal of Financial Studies
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• v.6 no.1
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• pp.47-65
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• 2000

효율성은 산출물의 가치와 그 산출물을 창출해 내기 위해 생산과정에서 소비한 투입물 가치의 비율로 나타낸다 투입물이나 산출물의 시장가격이 존재하는 경우 이 값을 가중치로 이용하여 산출물과 투입물의 가치를 계산할 수 있다. 그러나 투입물과 산출물의 종류가 다양한 경우에는 투입물의 가치를 적절히 평가하기가 쉽지 않다. Data Envelopment Analysis(DEA)모형은 효율성을 여러 가지 투입물의 가중평균에 대한 여러 가지 산출요소의 가중평균의 비율로 표시하며, 특정 의사결정단위의 효율성 정도는 유사한 투입 산출구조를 가지는 준거집단과 비교하여 상대적으로 측정하고자 하는 방법이다. 본 논문에서는 DEA모형의 구조와 이론적 근거, 그리고 적용상의 장단점에 대해 알아 본 뒤 국내 일선우체국의 운영자료를 토대로 하여 공공적인 성격을 띠는 기관의 운영효율성 측정에의 적용사례를 제시하였다. 투입자료로는 '98년 우정사업자료를 중심으로 공통영업비, 우편영업비, 금융영업비, 직원수, 관할가구수, 관할면적, 고정자산 등 7개 변수와 우편영업수익, 금융영업수익, 보험수지차, 배달 및 중계 우편물량, 현금출납 취급건수, 연평잔실적의 6개 변수를 각각 투입물과 산출물 변수로 설정하여 모형을 구축하였다. 분석대상으로 삼은 64개 우체국 전체의 효율성 평균은 82.14%으로 나타났으며 DEA모형의 효율성결과와 기존에 이미 발표된 정보통신부 평가결과와의 상관관계는 0.46291로 강하지는 않지만 두 변수간에는 정(正)의 상관관계가 있음을 알 수 있었다.

• Asia pacific journal of information systems
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• v.12 no.3
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• pp.1-20
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• 2002

For the last decade, some leading groups of corporations in Korea have integrated and managed their Information Systems(IS) functions. Each group established a separate System Management(SM) company to manage the IS and tried to get a synergy effect from the integration. These attempts, however, were not initiated by any one company. Rather they were group efforts. Moreover, the previous measuring tools evaluated IS with the scope of technical performance or quantitative user satisfaction using an absolute scale. Obscure criteria were used in an attempt to present improvements in IS function which were qualitatively weak. This study evaluates whether integration has been efficient and successful. For this purpose, we evaluate the performance efficiency of IS functions with Data Envelopment Analysis(DEA) methodology. In comparison with prior methods, DEA presents the rate of relative efficiency, the efficiency frontier for improving inefficiency, the degree of improvement(slack), and the guideline to construct any benchmark(reference set). For our DEA evaluation, this study selected a leading group of 23 companies in Korea. Our experimental results are as follows. First, efficiency was rated low on average. It also demonstrates that the motivation of performance efficiency of IS functions is deficient. Second, the result of the test to find the existence of economy of scale and scope shows that the growth of an organization and industrial characteristics do not affect IS performance efficiency from the perspective of user satisfaction. Finally, the comparison with other evaluation approaches informs us that DEA can be a complementary evaluation method which supports other measuring tools.

• Journal of Korean Society of Transportation
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• v.22 no.3 s.74
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• pp.17-26
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• 2004

항공운송산업에서 항공화물이 차지하는 비중이 점차적으로 확대되고 있으며 향후 2020년(보잉은 2022년)까지의 성장률도 보잉과 에어버스에서는 여객 수요보다 화물수요가 각 1.3%, 0.8%의 높은 성장을 거둘 것이라는 전망을 하고 있다. 특히 에어버스에서는 아시아 태평양 지역 역내와 중국 발 유럽행의 항공화물이 평균 7.0%의 높은 성장을 할 것으로 전망하고 있다. 이러한 높은 성장 전망 외에도 항공화물이 항공운송산업 혹은 세계경제의 선행지표로도 사용되고 있다. 이렇듯 항공운송산업에서 항공화물 부문의 역할이 점차적으로 증대되고 있어 본 연구에서는 항공화물 사업부문에 많은 활동을 하고 있는 항공사의 효율성이 그렇지 않은 항공사의 효율성을 비교하는 연구를 하였다. 먼저 항공 화물 매출액 기준 상위 10개사(2002년 기준)의 효율성을 자료포락 분석(DEA, Data Envelopment Analysis)을 이용 분석하였다. 그리고 이를 이용하여 항공사 전체 매출액 상위 10개사(화물 매출액 상위 10개사를 제외), 미국의 9개 항공사(상위 50대 항공사 중), 기타 10개사를 선정하여 각각의 효율성 비교를 통하여 항공화물 사업을 활발히 하는 항공사와 그렇지 않은 항공사와의 효율성에 대해 상대적 비교를 하였다. 이를 통해 항공화물 사업 부문이 항공사의 경영 효율성에 미치는 영향에 대해 간접 비교를 시도하였다. 분석 결과 항공운송사업중 항공화물 부문이 상위 10대 항공사 효율성이 다른 그룹의 항공사 보다 높게 제시되었다. 이는 항공사의 운송 사업을 화물 운송과 여객 운송 부문의 공동 네트워크의 활용을 통한 시너지 효과를 통해 항공사 효율성을 높일 수 있음을 의미한다.

• Journal of Korean Society for Quality Management
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• v.38 no.3
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• pp.463-474
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• 2010

In this study, we evaluate the efficiency of Construction Industry using Data Envelopment Analysis(DEA). Since the Construction Industry has been traditionally operated through competition, it is important to measure the efficiency. In this paper, we empirically analyze the Efficiency of the 50 Korean Construction Industry. In detail, we used the scale of efficiency in order that efficiency cannot be affected by the total technical efficiency of each company and the scale of DMU by applying CCR or BBC model. Also, we analyzed the changes of measurement DEA model score. we adopted the basic DEA, RTS Region and MPSS(Most Productive Scale Size) method which are combined with efficiency measurement model in order to analyze the operational status. Furthermore, by complementing the shortfalls of the scale efficiency value of the DEA Model, RTS Region Model can be recommended to be appropriate in the evaluation of ideal input/output Quantity. In particular, input variables are total assets, construction capacity, the technical staff and output variables are sales volume, operating income. The result of RTS Region and MPSS shows that 9 DMUs of the efficiency frontier in the Construction Industry are analyzed to be relatively efficient DMUs, and 41 DMUs are analyzed to be inefficient DMUs, and finally inefficient DMUs are separated with Region 1 and Region 6.