• Korean Journal of Fisheries and Aquatic Sciences
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• v.34 no.3
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• pp.218-224
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• 2001

Olive flounder is one of the most important aquaculture species in Korea. Interest in the aquaculture of olive flounder has increased recently because of its good growth characteristics and high market price, However, the productivity of olive flounder aquaculture depends on economic inputs such as fuels, facilities, and labor, In this study, EMERGY concepts was used to compare the environment and economy of two olive flounder production methods, fishing fisheries and aquaculture, and to evaluate the sustainability of olive flounder production, EMERGY spelled with an 'm' is a universal measure of real wealth of the work of nature and society made on a common basis. Calculations of EMERGY production and storage provide a basis for making choices about environment and economy following. the general public policy to maximize real wealth, production and use. EMERGY flows from environment were $94.13\%$ for olive flounder fishing fisheries, and $2.20\%$ for aquaculture. EMERGY yield ratio, environmental loading ratio and sustainability index were 17.05, 1.02 and 274 for fishing fisheries and 0.06, 44.41 and 0.023 for aquaculture, respectively. These ratios indicate that the fishing fisheries will yield more net EMERGY, while the aquaculture requires a lower investment of EMERGY.

• Journal of Korea Technology Innovation Society
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• v.12 no.4
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• pp.687-714
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• 2009

In this paper, we study empirically the relations between financial constraints and dividend adjustment speed of innovative small and medium sized enterprises (SMEs) listed on Kosdaq Market of Korea Exchange. The main results of this study can be summarized as follows. Determinants suggested by the major theories of dividends, namely, residual dividend theory, dividend signaling theory, agency theory, catering theory, and transactions cost theory explain significantly the dividend payout policy of Kosdaq SMEs. Lintner's dividend adjustment model indicates that Kosdaq SMEs have long run target payout ratio, and that Kosdaq SMEs adjust partially the gap between actual and target payout ratio each year. In the core variables of Lintner (1956) dividend adjustment model, past DPS has more effect than current EPS. These results suggest that Kosdaq SMEs maintain stable dividend policy which maintain past DPS level without corporate special reasons. Dividend adjustment speed of innovative Kosdaq SMEs is more fast than that of uninnovative Kosdaq SMEs, and dividend adjustment speed of financial unconstrained innovative Kosdaq SMEs is faster than that of financial constrained innovative Kosdaq SMEs. Futhermore, dividend adjustment speed of innovative Kosdaq SMEs classified by Small and Medium Business Administration is faster than that of unclassified innovative Kosdaq SMEs. The former is linked with financial policies and services like credit guaranteed service, venture investment fund, insurance program, and so on. In conclusion, past DPS and current EPS suggested by the Lintner's dividend adjustment model explain mainly dividend adjustment speed, and financial constraints explain also partially. Therefore, if managers of innovative Kosdaq SMEs can properly understand of the effects of financial constraints on dividend smoothing, they can maintain constantly dividend policy. This is encouraging result for Korea government as it has implemented many policies to commit to innovative Kosdaq SMEs.

• The Journal of the Korean Institute of Forest Recreation
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• v.22 no.4
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• pp.11-22
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• 2018

This study analyzes the economic feasibility on the transition of production structure to increase income for a local forest village in Laos PDR. The study area was the Nongboua village in Sangthong district where the primary product is rice from rice paddy. Possible strategies were considered to increase the villagers' revenue, and Noni (Morinda citrifolia) was production in the short-term. We assumed that the project period was for 20 years for the analysis, and a total of 1,100 Noni tree was planted in 1 ha by $3m{\times}3m$ spacing. This study classified basic scenario one, scenario two, scenario three by the survival rate and purchase pirce of Noni. Generally Noni grows well. However, the seedlings' average survival rate (= production volume) was set up conservatively in this study to consider potential risks such as no production experience of Noni and tree disease. The scenario one assumed that the survival rate of Noni seedlings was 50% for 0-1 years, 60% for 0-2 years, and 70% for 3-20 years; the scenario two, 10% less, i.e., 40%, 50%, and 60%; and the scenario three, 10% less, i.e., 40%, 50%, 60% and purchase price 10% less, i.e., $0.29 to $0.26, respectively. Our analysis showed that all 3 scenarios resulted in economically-feasible IRR (internal rate of return) of 24.81%, 19.02%, and 16.30% of with a discounting rate of 10%. The B/C (benefit/cost) ratio for a unit area (1ha) was also analyzed for the three scenarios with a discounting rate of 10%, resutling in the B/C ratio of 1.71, 1.47, and 1.31. The study results showed that the Nongboua village would have a good opportunity to improve its low-income structure through planting and managing alternative crops such as Noni. Also the results can be used as useful decision-making information at a preliminary analysis level for planning other government and public investment projects for the Nonboua village.

• Journal of Korea Port Economic Association
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• v.37 no.4
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• pp.127-144
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• 2021

The purpose of this study is to derive service factors based on the "Rail Statistical Yearbook" data of railroad service providers from 1990 to 2019, and to analyze the effect of the service factors on the operating profit ratio(OPR), a representative management performance variable of railroad transport service providers. In particular, it has academic significance in terms of empirical research to evaluate whether the management innovation of the KoRail has changed in line with the purpose of establishing the corporation by dividing the research period into the first period (1990-2003) and the latter (2004-2019). The contents of this study investigated previous studies on the quality of railway passenger transportation service and analyzed the contents of government presentation data related to the management performance evaluation of the KoRail. As an empirical analysis model, a research model was constructed using OPR as a dependent variable and service factor variables of infrastructure, economy, safety, connectivity, and business diversity as explanatory variables based on the operation and management activity information during the analysis period 30 years. On the results of research analysis, OPR is that the infrastructure factor is improved by structural reform or efficiency improvement. And economic factors are the fact that operating profit ratio improves by reducing costs. The safety factor did not reveal the significant explanatory power of the regression coefficient, but the sign of influence was the same as the prediction. Connectivity factor reveals a influence on differences between first period and latter, but OPR impact direction is changed from negative in before to positive in late. This is an evironment in which connectivity is actually realized in later period. On diversity factor, there is no effect of investment share in subsidiaries and government subsidies on OPR.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.35-48
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• 2014

According to the 2013 construction market outlook report, the liquidation of construction companies is expected to continue due to the ongoing residential construction recession. Bankruptcies of construction companies have a greater social impact compared to other industries. However, due to the different nature of the capital structure and debt-to-equity ratio, it is more difficult to forecast construction companies' bankruptcies than that of companies in other industries. The construction industry operates on greater leverage, with high debt-to-equity ratios, and project cash flow focused on the second half. The economic cycle greatly influences construction companies. Therefore, downturns tend to rapidly increase the bankruptcy rates of construction companies. High leverage, coupled with increased bankruptcy rates, could lead to greater burdens on banks providing loans to construction companies. Nevertheless, the bankruptcy prediction model concentrated mainly on financial institutions, with rare construction-specific studies. The bankruptcy prediction model based on corporate finance data has been studied for some time in various ways. However, the model is intended for all companies in general, and it may not be appropriate for forecasting bankruptcies of construction companies, who typically have high liquidity risks. The construction industry is capital-intensive, operates on long timelines with large-scale investment projects, and has comparatively longer payback periods than in other industries. With its unique capital structure, it can be difficult to apply a model used to judge the financial risk of companies in general to those in the construction industry. Diverse studies of bankruptcy forecasting models based on a company's financial statements have been conducted for many years. The subjects of the model, however, were general firms, and the models may not be proper for accurately forecasting companies with disproportionately large liquidity risks, such as construction companies. The construction industry is capital-intensive, requiring significant investments in long-term projects, therefore to realize returns from the investment. The unique capital structure means that the same criteria used for other industries cannot be applied to effectively evaluate financial risk for construction firms. Altman Z-score was first published in 1968, and is commonly used as a bankruptcy forecasting model. It forecasts the likelihood of a company going bankrupt by using a simple formula, classifying the results into three categories, and evaluating the corporate status as dangerous, moderate, or safe. When a company falls into the "dangerous" category, it has a high likelihood of bankruptcy within two years, while those in the "safe" category have a low likelihood of bankruptcy. For companies in the "moderate" category, it is difficult to forecast the risk. Many of the construction firm cases in this study fell in the "moderate" category, which made it difficult to forecast their risk. Along with the development of machine learning using computers, recent studies of corporate bankruptcy forecasting have used this technology. Pattern recognition, a representative application area in machine learning, is applied to forecasting corporate bankruptcy, with patterns analyzed based on a company's financial information, and then judged as to whether the pattern belongs to the bankruptcy risk group or the safe group. The representative machine learning models previously used in bankruptcy forecasting are Artificial Neural Networks, Adaptive Boosting (AdaBoost) and, the Support Vector Machine (SVM). There are also many hybrid studies combining these models. Existing studies using the traditional Z-Score technique or bankruptcy prediction using machine learning focus on companies in non-specific industries. Therefore, the industry-specific characteristics of companies are not considered. In this paper, we confirm that adaptive boosting (AdaBoost) is the most appropriate forecasting model for construction companies by based on company size. We classified construction companies into three groups - large, medium, and small based on the company's capital. We analyzed the predictive ability of AdaBoost for each group of companies. The experimental results showed that AdaBoost has more predictive ability than the other models, especially for the group of large companies with capital of more than 50 billion won.

• Journal of Technology Innovation
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• v.23 no.4
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• pp.1-31
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• 2015

Korea has continuously increased investment on R&D to improve global technology competitiveness through technology innovation. Korea's R&D expenditure as a percent of GDP is world's No. 1 as 4.15 and it accomplished 1 trillion won trade volume for 4 consecutive years. However, despite these efforts, technology balance of payment, which is an important factor that can measure nation's technology competitiveness is in a state of chronic deficit and the lowest level among OECD countries. In this paper, we studied methods to improve Korea's technology balance of payment We figured out concept and current state of technology trade and examined the importance of technology trade through making a comparison between commodity trade and technology trade. There have been studies regarding technology trade, but there was no study which tried to figure out cognition on technology trade from the point of view of companies which plays an important role in technology trade. For this, this study distinguished companies with experience in technology trade and which have not and conducted a survey to figure out cognition and current state of companies. The survey result showed noticeable difference on cognition of top decision makers between companies with experience in technology trade and which have not and there are serious shortage in department and staff which is exclusively responsible for technology trade. Also, despite their needs for education regarding technology trade, the ratio of employees who received education is below 10 % of the total respondents. This study suggested improvement methods such as reforming survey methods of technology trade statistics, enhancing social cognition, supporting to vitalize technology export, building infrastructure regarding technology trade, and opening education programs for cultivating experts based on preceding research and industry survey.

• Microbiology and Biotechnology Letters
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• v.36 no.1
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• pp.61-66
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• 2008

The proteins in whey are separated and used as food additives. The remains (mainly lactose) are spray-dried to produce sweet whey powder, which is widely used as an additive for animal feed. Sweet whey powder is also used as a carbon source for the production of valuable products such as polysaccharides. Glucose, fructose, galactose, and sucrose as asupplemental carbon source were evaluated for the production of PS-7 from Azotobacter indicus var. myxogenes L3 grown on whey based MSM media. Productions of PS-7 with 2% (w/v) fructose and sucrose were 2.05 and 2.31g/L, respectively. The highest production of PS-7 was 2.82g/L when 2% (w/v) glucose was used as the carbon source. Galactose showed low production of PS-7 among the carbon sources tested. The effects of various carbon sources addition to whey based MSM medium showed that glucose could be the best candidate for the enhancement of PS-7 production using whey based MSM medium. To evaluate the effect of glucose addition to whey based media on PS-7 production, fermentations with whey and glucose mixture (whey 1, 2, 3%; whey 1% + glucose 1%, whey 1% + glucose 2% and glucose 2%, w/v) were carried out. Significant enhancement of PS-7 production with addition of 1% (w/v) and 2% (w/v) glucose in 1% (w/v) whey media was observed. The PS-7 concentration of 2% glucose added whey lactose based medium was higher than that of 1% glucose addition, however, the product yield $Y_{p/s}$ was higher in 1% glucose added whey lactose based MSM medium. Therefore, the optimal condition for the PS-7 production from the Azotobacter indicus var.myxogenes L3, was 1% glucose addition to 1% whey lactose MSM medium.

• Journal of Distribution Science
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• v.9 no.2
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• pp.49-59
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• 2011

A franchise can be said to be the main method of distribution and marketing. It appears to be the future of the retail industry and is one of the world's fastest growing businesses sectors, as many policy reports and research results have acknowledged. Korea's franchise industry began in the 1970s, spread out into many areas (including food services, retail, and the service industry), and has grown by over 10% each year ever since. The industry's influence on the national economy becomes ever greater. Although the size of the franchise industry is expected to grow as it spreads and as the government expands its support, it has not yet attracted much academic interest. Research has so far been very fragmented. The main interest has been the relationship and conflicts between the head offices and the affiliates. No study has yet occurred on whether the concepts of satisfaction and intent to conclude a contract directly affect the success or failure of the affiliates. Few studies have empirically inquired into the demographic characteristics and abilities of the affiliates that significantly affect their results. Domestic franchise industries must prepare to leap from quantitative to qualitative growth. Most important is the need for affiliate headquarters and affiliates to build confidence between them. A friendly and reliable relationship between affiliate headquarters and affiliates will eliminate distrust from the franchise and maintain a healthy franchise system. This study suggests that current and prospective heads of affiliation should concentrate not on attracting affiliates but on investment and techniques of affiliate support. They should work on the reinforcement of brand power, the appropriate affiliate business environment, systematic education/training, taking burdens off the affiliate business persons, consolidating the relationship with the affiliate business persons, marketing mix factors (e.g. products, price conditions, logistics and shipping services, promotion, supervising and supervisor, operation procedures/processes, and material evidence); these all greatly affect the success or failure of the affiliate business. Supporting the affiliates is an important factor that enhances their results and satisfaction and consequently increases the positive recommendations to others and the ratio of recurrent conclusions of contracts, which ultimately generate the growth of the franchises. In addition, it is suggested that prospective franchise founders should make every effort to choose a good head office since the characteristics of the head office greatly influence the success of the affiliates. This study is significant in that it grasps the characteristics of the head office of affiliation and of the affiliates that influence affiliate results in ways not yet academically attempted.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.3 no.3
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• pp.45-76
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• 2000

The purposes of this study were to investigate and understand the present status of various types of "deserts", such as sand desert, gravel desert, rock desert, earth desert, salt desert, desert, rocky desert, gobi desert, sandy desert, clay desert, etc., and the general countermeasures for the combating "desertification" "desertization", and to develop the technologies on the revegetation and restoration for the combating desertification in China. The methods of this study were mainly composed of field surveys on the several experimental sites and research institutes related to combating desertification in China, and examinations on the various technologies for the combating desertification at the Daxing Experimental Station of Beijing Forestry University. The conclusion from this study may be summarized as follows; 1. Status and tendency of desertification in China : China is one of the countries seriously threatened by desertification. Desertification affected areas in China are mainly distributed in arid, semi-arid and dry sub-humid areas in China, covering the most regions of the Northeast China (eastern region of Inner-Mongolia), the northern part of the North China (middle and western region of Inner-Mongolia, Shaanxi, Ningsha, Gansu) and the western part of the Northwest China (Xinzang, Qinghai, Xizang). The total area affected by desertification in China is approximately 2.622 million $km^2$. It covers 27.3% of the total territory of China. Until recently, it is estimated that the annual spreading ratio of desertification in China is 2,460 $km^2$. Therefore, desertification is mostly serious problems facing to the Chinese people. 2. The causes and environmental effect of desertification : The desertification in China is mainly caused by compound factors, including natural condition and human activities. In China, the desertification is started by the decrease of precipitation, continuous dry and drought, strong wind, wind and water erosion, land degradation and loss of natural vegetation caused by climate variation, and accelerated by the human activities, such as over-cultivating, over-grazing, over-cutting of woods, irrational use of water resources. Because desertification has affected the geographical features, soil nutrients contents, salinity, vegetation coverage and the functions of ecosystem, the environmental deteriorations in the desertification affected areas are very seriously. 3. The fundamental strategies of combating desertification in China are the increase of education and awareness of people through various mass media, the revision of laws to guarantee operation of Desertification Combating Law and to improve many relating laws and regulations, the application of advanced technologies and training of experts, the establishment of discriminative policies, and increasing arrangement of budget-investment, and so on. China, as a signed country in UNCCD, has made efforts for the combating desertification. Korea is also signed country in UNCCD, so we should play an important role in the desertification combating projects of China for the northest asia and global environmental conservation as well as environmental conservation of Korea.

• Asia pacific journal of information systems
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• v.19 no.2
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• pp.139-155
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• 2009

For the last few decades, many studies have tried to explore and unveil venture companies' success factors and unique features in order to identify the sources of such companies' competitive advantages over their rivals. Such venture companies have shown tendency to give high returns for investors generally making the best use of information technology. For this reason, many venture companies are keen on attracting avid investors' attention. Investors generally make their investment decisions by carefully examining the evaluation criteria of the alternatives. To them, credit rating information provided by international rating agencies, such as Standard and Poor's, Moody's and Fitch is crucial source as to such pivotal concerns as companies stability, growth, and risk status. But these types of information are generated only for the companies issuing corporate bonds, not venture companies. Therefore, this study proposes a method for evaluating venture businesses by presenting our recent empirical results using financial data of Korean venture companies listed on KOSDAQ in Korea exchange. In addition, this paper used multi-class SVM for the prediction of DEA-based efficiency rating for venture businesses, which was derived from our proposed method. Our approach sheds light on ways to locate efficient companies generating high level of profits. Above all, in determining effective ways to evaluate a venture firm's efficiency, it is important to understand the major contributing factors of such efficiency. Therefore, this paper is constructed on the basis of following two ideas to classify which companies are more efficient venture companies: i) making DEA based multi-class rating for sample companies and ii) developing multi-class SVM-based efficiency prediction model for classifying all companies. First, the Data Envelopment Analysis(DEA) is a non-parametric multiple input-output efficiency technique that measures the relative efficiency of decision making units(DMUs) using a linear programming based model. It is non-parametric because it requires no assumption on the shape or parameters of the underlying production function. DEA has been already widely applied for evaluating the relative efficiency of DMUs. Recently, a number of DEA based studies have evaluated the efficiency of various types of companies, such as internet companies and venture companies. It has been also applied to corporate credit ratings. In this study we utilized DEA for sorting venture companies by efficiency based ratings. The Support Vector Machine(SVM), on the other hand, is a popular technique for solving data classification problems. In this paper, we employed SVM to classify the efficiency ratings in IT venture companies according to the results of DEA. The SVM method was first developed by Vapnik (1995). As one of many machine learning techniques, SVM is based on a statistical theory. Thus far, the method has shown good performances especially in generalizing capacity in classification tasks, resulting in numerous applications in many areas of business, SVM is basically the algorithm that finds the maximum margin hyperplane, which is the maximum separation between classes. According to this method, support vectors are the closest to the maximum margin hyperplane. If it is impossible to classify, we can use the kernel function. In the case of nonlinear class boundaries, we can transform the inputs into a high-dimensional feature space, This is the original input space and is mapped into a high-dimensional dot-product space. Many studies applied SVM to the prediction of bankruptcy, the forecast a financial time series, and the problem of estimating credit rating, In this study we employed SVM for developing data mining-based efficiency prediction model. We used the Gaussian radial function as a kernel function of SVM. In multi-class SVM, we adopted one-against-one approach between binary classification method and two all-together methods, proposed by Weston and Watkins(1999) and Crammer and Singer(2000), respectively. In this research, we used corporate information of 154 companies listed on KOSDAQ market in Korea exchange. We obtained companies' financial information of 2005 from the KIS(Korea Information Service, Inc.). Using this data, we made multi-class rating with DEA efficiency and built multi-class prediction model based data mining. Among three manners of multi-classification, the hit ratio of the Weston and Watkins method is the best in the test data set. In multi classification problems as efficiency ratings of venture business, it is very useful for investors to know the class with errors, one class difference, when it is difficult to find out the accurate class in the actual market. So we presented accuracy results within 1-class errors, and the Weston and Watkins method showed 85.7% accuracy in our test samples. We conclude that the DEA based multi-class approach in venture business generates more information than the binary classification problem, notwithstanding its efficiency level. We believe this model can help investors in decision making as it provides a reliably tool to evaluate venture companies in the financial domain. For the future research, we perceive the need to enhance such areas as the variable selection process, the parameter selection of kernel function, the generalization, and the sample size of multi-class.