• Title/Summary/Keyword: DEA-BCC 모델

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Determinants of Export Manufacturing Firm Efficiency: Focusing on R&D Intensity in a KOSDAQ-listed Firm (수출제조기업의 효율성 결정요인에 관한 분석: 코스닥 기업의 연구개발집약도를 중심으로)

  • Hwang, Kyung-Yun;Koo, Jong-Soon
    • International Area Studies Review
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    • v.20 no.2
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    • pp.63-83
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    • 2016
  • This paper examines the determinants of efficiency in a KOSDAQ-listed manufacturing firm. We use Data Envelopment Analysis (DEA) to estimate the efficiency of the export manufacturing firm. We employ two inputs (number of employees, equity) and one output (sales) in the DEA. The determinants of export manufacturing firm efficiency are estimated using the panel Tobit model. An analysis of 369 export manufacturing firms from 2013 to 2015 indicates the following results: First, the R&D intensity, the wage and salary intensity, total asset, and equity ratio each had a negative impact on both the CCR and BCC efficiency scores. However, export intensity had a negative impact on CCR efficiency scores in a KOSDAQ-listed total export manufacturing firm. Second, the R&D intensity had a positive impact on both the CCR and BCC efficiency scores, but export intensity, the wage and salary intensity, and equity ratio each had a negative impact on the CCR and BCC efficiency scores in a KOSDAQ-listed large export manufacturing firm. Third, the R&D intensity, the wage and salary intensity, total asset, and equity ratio each had a negative impact on both the CCR and BCC efficiency scores; respectively, in a KOSDAQ-listed small and medium export manufacturing firm.

A comparative analysis of terminal efficiency on Northeast Asia and America container ports (동북아 지역과 미국 주요 컨테이너항만간의 효율성 비교 - DEA 기법을 중심으로 -)

  • Ha, Myun-Shin
    • Journal of Korea Port Economic Association
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    • v.25 no.3
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    • pp.229-250
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    • 2009
  • This paper aims to implement an empirical research about the efficiency of America and Northeast Asia pots, and to suggest an effective strategy which can operate these ports more well. This study tries to apply the Data Envelopment Analysis(DEA) model to America and Northeast Asia ports. DEA is a methodology of comparing the relative efficiency of each decision making unit(DMU) by comparing it with other DMUs having similar input and output structure, and is specially very useful when a form of production function of each DMU such as a port is not known. DEA provides the extent of inefficiency of DMUs, which is practically useful information (like the efficiency score and reference sets) required to improve efficiency. This paper analyzed the relative efficiency of 35 ports in America and Northeast Asia for 3 years from 2005 to 2007 through DEA-CCR, DEA-BCC model and scale efficiency. Accordingly, this paper evaluates the efficiency of America and Northeast Asia ports, grasps the position at the present time, and suggests an advanced direction in future.

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An Exploratory Performance Analysis of The Forest Sector R&D Program (산림분야 기후변화대응 R&D사업에 대한 탐색적 성과분석 : 효율성 관점에서 DEA분석을 중심으로)

  • Moon, Kwan-sik;Lim, Chae-hong;Ahn, Kyung-sup
    • Journal of Digital Convergence
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    • v.14 no.8
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    • pp.47-56
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    • 2016
  • This study is on the actual analysis of R&D investment effect through DEA in terms of efficiency resulted from forestry sector climate change response R&D project. Namely, it is clarifying the mechanism of the scientific, technological, social performance result and output, depending on research development cost, employment size as same input. Also, it is in-depth analysis on which performance operates more efficiently in any detailed business. With the study result, we seek political implications to enhance investment effect and core element to consider R&D business project in the future.

Analyzing the Effect of COVID-19 on the Operational Efficiency of Asia's Major Container Ports: A Data Envelopment Analysis (COVID-19 위기가 아시아 주요 컨테이너항만의 운영효율성에 미치는 영향)

  • KIM, Tae-Hyung;CHOI, Sang-Duk
    • Journal of the Korean Society of Marine Environment & Safety
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    • v.27 no.6
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    • pp.763-774
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    • 2021
  • The COVID-19 virus has generated major shockwaves in all spheres of human life since its outbreak. Maritime transport (both cargo and passenger) is one of the industries most heavily affected, yet over 80% of the world cargo is transported by sea. This study analyzes maritime port operational efficiencies before and after the start of the COVID-19 pandemic to determine whether the pandemic has caused major differences in the operational efficiencies of many leading Asian maritime container ports via data envelopment analysis (DEA). The results of both the CCR and BCC models reveal that overall, efficiency during the COVID-19 pandemic has been higher than before the pandemic despite a few inefficiencies. This implies that the pandemic has so far not has major consequences for the operational efficiency of maritime ports. However, two ports (Busan and Guangzhou) should adjust the scale sizes and technical capacities of their operations to improve performance.

우리나라 항만배후물류기업의 경영 효율성 분석에 관한 연구

  • Park, Sang-Jun;Ryu, Dong-Geun
    • Proceedings of the Korean Institute of Navigation and Port Research Conference
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    • 2015.07a
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    • pp.194-196
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    • 2015
  • 우리나라 항만배후물류단지는 선진항만에 비해 활성화가 미흡하고 다양한 비즈니스모델 개발이 필요하다. DEA모형을 이용하여 우리나라 항만배후물류기업의 경영 효율성을 비교, 분석함으로써 항만배후물류기업의 효율성 수준을 파악하고, 비효율적인 항만배후물류기업의 효율성 개선방안을 제시하고자 하였다.

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A Study on the Efficiency Analysis for the Automotive Parts Manufacturer Using Data Envelopment Analysis (DEA를 활용한 자동차부품 기업의 효율성 평가에 관한 연구)

  • Cho, Hyung-Kook;Lee, Cheol-Gyu;Yoo, Wang-Jin
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.15 no.2
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    • pp.609-615
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    • 2014
  • Due to the recent global recession, the car industry demand levels have plummeted which led to a crisis in the automotive parts industry for the first time in history. Since the fourth quarter of 2008, the automotive parts manufacturers in America have faced a record loss and those in Japan and Europe who also had a strong track record are facing a weak economy. In addition, the domestic automotive parts industry is also affected by the global economic crisis. This research is that the relative efficiency analysis utilizing the DEA has done on the object of 25 small and medium-sized automotive parts manufacturers publicly listed, As the efficiency analysis result 6 of 25 manufacturers are efficient in CCR model and 12 manufactures have shown efficiency in BCC model, the efficiency analysis in consideration of the manufacturer size. The manufacturers with efficiency 1 in 25 manufacturers are DMU 1, 5, 7, 10, 18, 24 and the relatively benchmarking objects in other manufactures are DMU 1, 10, 24, Based on the results of this research, a direction to the domestic automotive parts manufacturers as well as a significant information will be provided in managing the companies in the future by the improvement of management efficiency through the practical efficiency analysis.

A Study on Urbanization Efficiency analysis of China's 31 provinces and cities (중국 31개 성 및 직할시의 도시화 효율성 분석에 관한 연구)

  • Zhou, Yi Xi;Jeon, Jun-Woo;Kim, Hyung-Ho
    • Journal of Digital Convergence
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    • v.17 no.12
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    • pp.147-157
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    • 2019
  • The purpose of this study is to analyze the efficiency of urbanization in 31 provinces and cities in China, including both desirable and non-expected outputs produced during the urbanization process. Efficiency was analyzed by applying the SBM-DEA model using the urbanization calculation data of 2017 in 31 provinces and cities in China. The results show that the urbanization efficiency of eastern region is the highest, followed by central region and northeast region, and the urbanization efficiency of western region is the lowest. This study is meaningful in that it analyzes the efficiency of urbanization in 31 provinces and cities in China and suggests the direction of continuous urbanization policy. This study is limited in that it does not reflect the past trend only by conducting cross-sectional analysis for one year in 2017, and it is necessary to comprehensively evaluate urbanization efficiency by conducting additional longitudinal area analysis in the future.