• Title/Summary/Keyword: 메타프론티어

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Comparison of Efficiency of Manufacturing Companies Listed on KOSPI Using Metafrontier: Focusing on ESG Ratings (메타프론티어를 이용하여 상장 제조업의 효율성 비교: ESG 등급을 중심으로)

  • Chanhi Cho;Hyoung-Yong Lee
    • Journal of Intelligence and Information Systems
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    • v.29 no.2
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    • pp.1-22
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    • 2023
  • Existing studies on mixed ratings that combine ESG ratings and credit ratings have been rare. Through meta-frontier analysis, this study examines the relationship between the prime and non-prime groups in ESG ratings, credit ratings, and mixed ratings that consider ESG ratings and credit ratings at the same time. Efficiency was compared. Meta-frontier analysis was used to compare the efficiency of 143 listed manufacturing companies in Korea between the prime and non-prime groups based on the ESG ratings assigned to them by KCGS and the credit ratings assigned by Korea's three major credit rating agencies. As a result of this study, first, the meta-efficiency of the prime mixed-grade group was statistically more efficient than the non-prime mixed-grade group under the variable return scale (VRS) assumption. Second, the prime ESG rating group had a relatively higher proportion of scale inefficiency than the non-prime ESG rating group. Third, in terms of economies of scale, the prime credit rating group had a higher proportion of diminishing returns to scale (DRS) than the non-prime credit rating group. This study will help companies interested in sustainability management to do ESG management.

An Empirical Comparison and Verification Study on the Seaport Clustering Measurement Using Meta-Frontier DEA and Integer Programming Models (메타프론티어 DEA모형과 정수계획모형을 이용한 항만클러스터링 측정에 대한 실증적 비교 및 검증연구)

  • Park, Ro-Kyung
    • 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.

A Brief Empirical Investigation of Seaport Clustering by Using Meta-Frontier and Cross-efficiency Models (메타프론티어와 교차효율성 모형을 통한 항만 클러스터링의 실증적 검증소고)

  • Park, Ro-Kyung
    • Korea Trade Review
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    • v.41 no.3
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    • pp.27-42
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    • 2016
  • This study is to investigate seaport clustering by using meta-frontier and cross-efficiency models. Data covers the 13 Asian ports during 2009, 2010 and 2013 with 3 inputs(depth, total area, and number of cranes) and 1 output(TEU). Correlations coefficient from cross-efficiency matrix are used for measuring clustering dendrogram. After that, meta-frontier analysis for investigating whether the clustering using cross-efficiency method increases the meta-efficiency. Empirical main results are as follows: First, group efficiencies of Busan, Incheon, and Gwangyang ports are increased. Second, meta and group efficiencies of China ports are greater than those of Korean ports. Third, distortion of technology gap of Gwangyang is lower than that of Busan and Incheon. Fourth, Gwangyang, clustering with Ningbo, Chingtao, Tokyo and Caosung ports in 2009 and with Dubai port in 2013 can increase the efficiency. Fifth, to enhance the efficiency, Busan port should be clustered to group 2 in 2010 and group 1 in 2013, and Incheon port clustered to group 2 in 2010 and 2013. Fifth, it is empirically investigated that Busan, Incheon and Gwangyang ports can increase the efficiency by using Cross-efficiency and Meta-frontier models. Port policy planner should promote the clustering policy for Busan with Hong Kong, Shanghai, and Singapore, Incheon and Gwangyang with Chingtao, Nagoya, Ningbo, Tokyo, and Kaoshung ports.

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A Comparison on Efficiency of Specialized Credit Finance Companies Using a Meta-Frontier (메타프론티어 분석을 이용한 여신전문금융회사의 효율성 비교)

  • Cho, Chanhi;Lee, Sangheun;Lee, Hyoung-Yong
    • Knowledge Management Research
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    • v.22 no.3
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    • pp.151-172
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    • 2021
  • The government's implementation of customer-friendly financial policies, such as lowering commission fees for credit card merchants and lowering the maximum interest rate, put the specialized credit finance companies in a crisis of lowering profitability. In this unfavorable situation, the efficiency study of specialized credit finance companies is meaningful. Accordingly, this study measured the efficiency of 34 specialized credit finance companies through Data Envelopment Analysis (DEA) and meta-frontier analysis. For meta-frontier analysis, specialized credit finance companies were divided into two groups (card companies and non-card companies) by industry or three groups (AA0 and above, AA-, and A+ or below) by credit rating. The results of the analysis will provide general insight into the efficiency of specialized credit finance companies. The results of this study are as follows. First, the average meta-efficiency of card companies was analyzed higher than that of non-card companies. Second, 80% of non-card's decision-making units (DMUs) were inefficient by pure technology rather than by scale. Third, decision-making units (DMUs), which account for 62.5% of the credit card company group and 80% of the 'AA-' credit rating group, are in non-economic areas of scale. Fourth, there was no statistically significant difference in meta-efficiency values (TE and PTE) by industry (card companies, non-card companies) and credit rating (AA0 or higher, AA-, A+ or lower). The contribution of this study will provide strategic initiatives for establishing management strategies to improve inefficiency by measuring the efficiency level of companies under an unfriendly business environment for specialized credit finance companies.

The Comparison of Productivity Change Gap of Public Hospitals and Private Hospitals in Korea (공공병원과 민간병원의 생산성 격차 비교)

  • Yang, Dong-Hyun
    • Journal of Digital Convergence
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    • v.11 no.10
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    • pp.203-215
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    • 2013
  • This study calculated meta Malmquist indices and their bootstraped estimates and then decomposed them into technical efficiency change(TEC), technology change(TC), pure technology catch up(PTCU), frontoer catch up(FCU), using annual data set of general hospitals from year 2007 to 2011 collected by Korean Hospital Association and then analyzed productivity change and technology gap of Korean general hospitals. The results and implications were as follows below. First, public general hospitals showed higher meta technical efficiencies than private general hospitals while exhibited lower technology gap ratio which meant a few large private general hospitals led the whole general hospitals. Second, group productivity of private general hospitals increased larger than public general hospitals due to the differences of PTCU rather than FCU. But, there was no statistically significant differences for technical efficiency, productivity change, technology gap. Thus, public general hospitals played the same role as the private general hospitals in terms of the number of patients treated. But, considering financial hardships of public general hospitals, public hospitals needed to share and learn medical and managerial skills of the best practice of private general hospitals.