• Journal of Digital Convergence
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• v.18 no.2
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• pp.235-247
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• 2020

The Mutual Financial Cooperatives(MFCs) in Korea need to make efforts to increase efficiency and productivity in order to secure stable and sustainable growth and competitiveness. Therefore, this study analyzes the efficiency and productivity of MFCs from 2012 to 2018 and suggests some implications. The methodology employed is a Dynamic-Network Slacks-Based Measure(DNSBM) Model. The findings from an empirical study include that first, on average efficiency scores of the institutions, NH(0.225) showed the highest overall efficiency, and followed by SH(0.128) and MG(0.126). After 2015, most of the MFCs' efficiency scores had risen until to 2018. Second, in divisional analysis, the inefficiency in creating the high profitability-stage had been greater than establishing-funds-stage. Third, in projection analysis of Division 2, the inefficiency of the output factors such as interest income and operating income was severe. Fourth, the results from the Malmquist Productivity Index analysis of Division 1 of the fist-stage illustrate that all three MFCs showed minus catch-up effects. Also, a soundness from reducing bad loans and expansion of loans in combination with generating various ways of creating profits besides the interest income is urgently needed for Korean MFCs.

• Journal of Energy Engineering
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• v.25 no.3
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• pp.1-8
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• 2016

North Korea is currently undergoing an economic crisis of industrial productivity reduction, which resulted from decreased energy production and economic sanctions due to conflicts with the international society. This paper examined the technological status of North Korea's natural gas and coal industries which are essential sectors for recovery of the economy and North-South cooperation on energy industry. This paper also analyzed investment strategies in North Korean energy industries and calculated the size of economic ripple effect of the investment on North and South Korea. In order to analyze the effect of the investment on North Korean economy, we constructed an inter-industry relation table of North Korea for year 2014 and used an input-output model. The ripple effect of the investment in natural gas and coal industries turned out to be 1.012 billion dollars and 2.742 billion dollars respectively. In order to analyze the ripple effect of the investment on South Korean economy, we constructed an inter-industry relation table of South Korea for year 2013 and used a demand-driven model for inter-industry analysis. As a result, production, added-value and employment inducement coefficients of the investment were calculated as 2.02073, 0.62697 and 8.99409 for the natural gas industry and 2.02130, 0.62701 and 9.00413 for the coal industry respectively.

• Journal of the Korean Institute of Gas
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• v.20 no.4
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• pp.7-14
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• 2016

The economic crisis in North Korea has reduced its capacity to invest in the energy industries. The country is going through a vicious cycle of decreased investment in the energy industries and reduced energy production. This suggests that the energy industries would come to the top priority of investment once the economy improves. This paper calculated the economic ripple effect of the investment on North and South Korean economies based on the assumption that 390 billion won was invested in the construction of a natural gas combined-cycle power plant in Gaesong Industrial Complex. In order to analyze the economic ripple effect of the investment on North Korean economy, we constructed the inter-industry relation table of North Korea for year 2014 and used the input-output model. The ripple effect of the investment in the natural gas industry turned out to be 1.012 billion dollars. In order to analyze the effect of the investment on South Korean economy, we constructed the inter-industry relation table of South Korea for year 2013 and used the demand-driven model for inter-industry analysis. As a result, production, added-value and employment inducement coefficients of the investment in the natural gas industry were calculated as 2.02073, 0.62697 and 8.99409 respectively.

• Korean Journal of Construction Engineering and Management
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• v.14 no.3
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• pp.78-87
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• 2013

The modern society has been drastically changed from the industrial economic society to the knowledge based society, to catch up with the knowledge and the change of technology required for the modern people, the people can not live in the modern society without the continued study or education. In case of architectural design firm, it is concentrating on the productivity of enterprise by cultivating the working level through the self education focused on the improvement of inner capacity. In connection with this, the efficiency of enterprises are analyzed by carrying out the Data Envelopment Analysis(DEA) utilizing the financial ratio index in the various field of industries recently, the analysis study for the efficiency utilizing DEA is increased in the construction industries as well. However, in case of construction industries, the study focused on the efficiency of administration only has been progressed, it is the real situation that the approach for the analysis of education efficiency of each enterprise is very insufficient. Therefore, this study analyzed the education efficiency of architectural design firm after the selection of input and output variables by utilizing the DEA model and utilizing the AHP analysis technique by deducting the variables through the preceding study in relation to the education efficiency and the interview with the specialists.

• Journal of Veterinary Clinics
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• v.23 no.2
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• pp.105-110
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• 2006

A three-layer, feed-forward artificial neural network (ANN) with sixteen input neurons, three hidden neurons, and one output neuron was developed to identify the presence of infectious bronchitis (IB) infection as early as possible in laying hen flocks. Retrospective data from flocks that enrolled IB surveillance program between May 2003 and November 2005 were used to build the ANN. Data set of 86 flocks was divided randomly into two sets: 77 cases for training set and 9 cases for testing set. Input factors were 16 epidemiological findings including characteristics of the layer house, management practice, flock size, and the output was either presence or absence of IB. ANN was trained using training set with a back-propagation algorithm and test set was used to determine the network's capability to predict outcomes that it has never seen. Diagnostic performance of the trained network was evaluated by constructing receiver operating characteristic (ROC) curve with the area under the curve (AUC), which were also used to determine the best positivity criterion for the model. Several different ANNs with different structures were created. The best-fitted trained network, IBV_D1, was able to predict IB in 73 cases out of 77 (diagnostic accuracy 94.8%) in the training set. Sensitivity and specificity of the trained neural network was 95.5% (42/44, 95% CI, 84.5-99.4) and 93.9% (31/33, 95% CI, 79.8-99.3), respectively. For testing set, AVC of the ROC curve for the IBV_D1 network was 0.948 (SE=0.086, 95% CI 0.592-0.961) in recognizing IB infection status accurately. At a criterion of 0.7149, the diagnostic accuracy was the highest with a 88.9% with the highest sensitivity of 100%. With this value of sensitivity and specificity together with assumed 44% of IB prevalence, IBV_D1 network showed a PPV of 80% and an NPV of 100%. Based on these findings, the authors conclude that neural network can be successfully applied to the development of a screening model for identifying IB infection in laying hen flocks.

• Environmental and Resource Economics Review
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• v.22 no.4
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• pp.817-841
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• 2013

The purpose of this study is to examine feasibility of the 'Master Plan of 2013 Slow Food Expo(2013 AsiO Gusto), Korea' and to analyze the following economic effect. To this end, we used existing data and statistics, and estimated the demand by means of survey for people's traveling and questionnaires for ordinary Koreans. For examining financial feasibility for hosting the Expo, BC ratio (Benefit-Cost Ratio) and NPV (Net Present Value) was applied. For estimating the economic effect following the Expo, the effect on all over the country and the Gyeong-gi province was analyzed, using the MRIO (Model of Regional Input-Output). Specifically, with the net effect of Expo, the economic feasibility test shows 1.04~2.15 BC ratio with 10% free admission, and 1.02~2.27 BC ratio in Finance analysis. Furthermore, the Expo feeds through Gyeong-gi (including Nam-yang-ku) regional economies with production induction effect, value-added induction effect, and employment induction effect. The amounts of regional effects are 373.6~738.7 billion won, 166.2~327.4 billion won, and 1,971~2,009 persons, respectively. Also, the "2013 Slow Food Expo, Korea" was analyzed profitable in general. Residents in Nam-yang-ju expects the Expo to bring vitalities into their hometown. The Expo is highly related to the positive economic effectiveness of Nam-yang-ju.

• The Journal of the Korea Contents Association
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• v.18 no.2
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• pp.216-230
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• 2018

In this paper, we analyze the economic ripple effects of technology related to the unmanned aerial vehicle industry by applying industry association analysis. Specifically, the effects of employment creation, value added inducement, sensitivity coefficient, and influence coefficient can be calculated, and implications for the analysis result are presented. As a result, the employment inducement effect was confirmed to be 10.017 persons per 1 billion won of investment. The value added inducement effect was much higher than the other manufacturing industry average (employment inducement coefficient: 2.285, value added inducement coefficient: 0.581) when the 1 won budget was added, resulting in 0.9771 won added value. In the unmanned aerial vehicle industry, the coefficient of sensitivity, which means the front chain effect, is 0.7870, which is lower than the manufacturing average (sensitivity coefficient 1.125), and the coefficient of influence, which means the backward chain effect, is 1.161, which is higher than the manufacturing average (influence coefficient: 1.116). Therefore, it is classified as the final demand manufacturing industry. This means that the unmanned aerial vehicle industry is an industry that is less affected by economic fluctuations and can be interpreted as an industry with a greater economic impact than other sectors. Based on these data, it can be used to establish the R&D investment direction policy of the unmanned aerospace industry.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.7
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• pp.517-524
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• 2020

In line with the recent wave of the 4th Industrial Revolution, the environment for defense R&D is transforming into a center of high-tech military technology. In particular, developed countries are strengthening control of technology exports and technology transfer to protect advanced defense science and technology. For this reason, the budget demand for securing the ability to develop independently high-tech weapons and core technologies suitable for the future battlefield environment is increasing, and increasing efficiency in R&D investment has been highlighted for efficient distribution of limited budgets. This study examined the efficiency of the defense basic R&D project using the non-parametric approach, DEA. The R&D budget, R&D researcher, and R&D period were selected as the input variables, and the number of papers and patents were used as output variables. The efficiency of basic R&D projects was analyzed through CCR, BCC models, and SE. Lastly, based on the efficiency measurements, the cause of the inefficiency of R&D projects was suggested, and ways to improve efficiency were suggested. This study is expected to be used as useful information that can be applied to project performance management through efficiency analysis of basic defense R&D projects and be reflected in the project planning stage through feedback.

• Journal of the Korean association of regional geographers
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• v.19 no.2
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• pp.246-258
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• 2013

The relocation of the public institutions such as the provincial government attracts much attentions since it results in substantial regional economic impacts. In this context this paper analyzes the effects of relocation of Gyeongsangbuk-Do provincial government from Daegu city to Gyeongsangbuk-Do, which is scheduled for 2014, on Daegyeong economic region. Based on the interregional input-output model and I-O data which were provided by the Bank of Korea, this paper examines the multiplier effects of the relocation in terms of production, value added, and employment on Daegyeong economic region and other regions, except for the construction effects of the provincial capital. According to the analysis, the relocation is expected to reduce the production by 290million won, value added by 709million won, and employment by 571 persons in Daegyeong economic region. Also, the relocation turns out to decrease the production by 1,179million won and value added by 123million won of other regions. This paper discusses some policy implications of the analysis.

• Journal of Navigation and Port Research
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• v.41 no.3
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• pp.127-136
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• 2017

The aim of this study is using DEA-CCR, BCC, and Malmquist analysis to determine the efficiency and productivity of Korean automobile ports. We analyzed eight Korean automobile port terminals, using the number of workers and size of ports as input variables and the number of processed car as output variables. An efficiency analysis of the eight automobile port terminals for the four-year span from 2013 to 2016 revealed efficiency levels of 1 for the CCR, BCC, and the scales for Ulsan port and the Gwangyang port terminal, indicating efficient operation of the terminals. As a result of benchmarking analysis, Gunsan port 1, 2 terminal, Incheon port, Pyeongtaek Dangjin 2 terminal should benchmark Busan Port and Ulsan Port. Conversely, Malmquist analysis showed a slight increase in the production volume from 2013 to 2015, but a decline to 1 or less from 2015 to 2016. In the case of TECI the technology was confirmed as effective at 1 or more from 2014 to 2015. The TCI value was 0.87 for the period from 2015 to 2016. During this period, the TCI index of all terminals was less than 1.