• Journal of Digital Convergence
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• v.18 no.1
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• pp.35-44
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• 2020

The purpose of this paper is to investigate various factors affecting the efficiency of China IC industry, reflecting the resource utilization and overall development level of China IC industry. In this paper, three input indicators (R&D Expenditure, Cash paid to and for employees, Total fixed assets) and two output indicators (Net profit, Sales revenue) were selected for 17 listed companies in the IC industry with operating profit of less than 1 billion yuan in 2018 and applied the technology efficiency theory and DEA analysis method to analyze the relative efficiency of the output base. The study found that one of the 17 companies analyzed was efficient, two were inefficient on an inadequate scale, and the rest were inefficient due to insufficient production technology, scale and management level. This study argues that enterprises should reduce resource input appropriately and strengthen scientific and technological innovation management to maximize resource utilization efficiency. Therefore, this study has important practical significance for improving the efficiency of IC enterprises. The Malmquist method will be used to study the total factor productivity of small and Medium-sized companies in IC industry.

• The Korean Journal of Financial Management
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• v.25 no.4
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• pp.183-212
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• 2008

The purpose of this paper is to analyze the efficiency change and determinants of the korean non-life insurance companies. we use DEA (Data Envelopment Analysis) model to measure company efficiency change and use GLS, Tobit model, FIixed effect model, Random effect model, GMM to measure efficiency determinants. we utilize ten non-life insurance companies in korea and the panel data for five from 2001 to 2005. The empirical results show the following findings. First, technical efficiency shows that approximately 15.5% of inefficiency exists on the non-life insurance companies and it reveals that the cause for technical inefficiency is due to scale inefficiency. Second, Dea Window results show that the stable dissimilarity by standard deviation, LDP of CCR. Third, the results of efficiency determinants show that increase efficiency is depend on the premium income and real estates.

• Korea Journal of Hospital Management
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• v.16 no.4
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• pp.35-66
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• 2011

This study analyzed efficiency by utilizing DEA analytical technique centering on materials for 2009 of 20 major university hospitals in capital area. Input variables were utilized professor & full-time doctor, resident, nurse & number of bed hospitals. Output variables were analyzed by dividing number of annual outpatients & number of annual inpatients, and annually total outpatient profit & inpatient profit into a model of the standard for number of patients and the standard for medical profit. DEA analysis was elicited efficiency score by applying CCR, BCC, BFG, scale profit, and SE model. Through t-test after eliciting efficiency score, the implications were suggested by comparing efficiency between DMU in Seoul and DMU in capital area, by comparing between high-class general hospitals and general hospitals, and by comparing between high-class general hospitals in Seoul and 5 big hospitals. As a result of analysis, the major university hospitals in capital area showed high efficiency as a whole close to "1," but indicated low efficiency relatively in CCR field. Thus, the expansion in scale within capital area was indicated to reach the limit. Second, in a model of analyzing the standard for number of patients, the medical institutions, which are being operated efficiently, were indicated to be 10 DMUs. In the standard for medical profit, 12 DMUs were analyzed to be operated efficiently. Third, the efficiency in general hospital was higher than high-class general hospital. Thus, the efficiency of operation was indicated to be more important than scale. Also, large high-class hospitals(big 5) where are located in downtown Seoul showed the higher efficiency than other general high-class general hospitals, but were indicating very low efficiency in some DMUs. Fourth, as a result of generalizing and evaluating the number of patients and the medical profit, the efficient DMU was indicated to be more when analyzing on the basis of medical profit than the standard for number of patients. Thus, major university hospitals in capital area were indicated to make more effort for section in medical profit. Based on the analytical results of efficiency, a strategy for reinforcing efficiency in inefficient DMU was indicated to be needed a strategy of creating customers for promoting number of patients and a strategy for making operation efficient for increasing profitability.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.271-271
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• 2015

The Ecuadorian coast has two different climate regions. One is humid region where the annual rainfall is above 2000 mm and rain falls in almost all months of the year, and the other is dry region where the annual rainfall can fall below 50 mm and rainfall can be very seasonal. The agriculture is frequently limited by the seasons during the year and the availability of rainfall amounts. The corn fields in Ecuador are cultivated during the rainy season, due to this reason. The weather conditions for optimum development of corn growth require a monthly average rainfall of 120 mm to 140 mm and a temperature range of $22^{\circ}C{\sim}32^{\circ}C$ for the dry region, and a monthly average rainfall of 200 mm to 400 mm and a temperature range of $25^{\circ}C{\sim}30^{\circ}C$ for the humid area. The objective of this study is to predict how the weather conditions are going to change in corn fields of the coastal region of Ecuador in the future decades. For this purpose, this study selected six General Circulation Models (GCM) including BCC-CSM1-1, IPSL-CM5A-MR, MIROC5, MIROC-ESM, MIROC-ESM-CHEM, MRIC-CGC3 with different climate scenarios of the RCP 4.5, RCP 6.0, and RCP 8.5, and applied for the period from 2011 to 2100. The climate variables information was obtained from the INAMHI (National Institute of Meteorology and Hydrology) in Ecuador for the a base line period from 1986 to 2012. The results indicates that two regions would experience significant changes in rainfall and temperature compared to the historical data. In the case of temperature, an increment of $1^{\circ}C{\sim}1.2^{\circ}C$ in 2025s, $1.6^{\circ}C{\sim}2.2^{\circ}C$ in 2055s, $2.1^{\circ}C{\sim}3.5^{\circ}C$ in 2085s were obtained from the dry region while less increment were shown from the humid region with having an increment of $1^{\circ}C$ in 2025s, $1.4^{\circ}C{\sim}1.8^{\circ}C$ in 2055s, $1.9^{\circ}C{\sim}3.2^{\circ}C$ in 2085s. Significant changes in rainfall are also projected. The rainfall projections showed an increment of 8%~11% in 2025s, 21%~33% in 2055s, and 34%~70% in 2085s for the dry region, and an increment of 2%~10%, 14%~30% and 23%~57% in 2025s, 2055s and 2085s decade respectively for humid region.

• Journal of Korea Port Economic Association
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• v.32 no.3
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• pp.1-20
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• 2016

The objective of this study was to select world top 20 major container terminals that belong to Korea and China as comparison units to analyze their efficiency and productivity trend. Super Efficiency Data Envelopment Analysis(SE-DEA) and Malmquist Productivity Index(MPI) models were applied to this study. Compared with previous studies, we narrowed down the scope of the comparison units to terminals and conducted more detailed analysis to present more meaningful implications to the actual operation. The different result from existing studies, SE-DEA analysis implied that the major terminals of Busan New Port showed similar efficiency and productivity with China's terminals. In MPI analysis, the average index of Korean and China's terminals is 1.051 and 1.049. The analysis indicates that the productivity was improved 5.1% and 4.9% annually. From 2012 to 2015, the average terminal TCI index of South Korea and China were 1.032 and 1.0318-the main factor which influences MPI. This means improving productivity mainly rely on investment in new equipment and technology upgrades rather than improve operational efficiency over the past few years. Future management decisions should consider more aggressive marketing to increase the volume and improve operational efficiency to enhance productivity. Further research should apply the overall efficiency of the methodology considering the financial diversification, terminal capacity, service levels, and other factors.