• Asia pacific journal of information systems
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• v.19 no.2
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• pp.157-178
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• 2009

Corporate credit rating is a very important factor in the market for corporate debt. Information concerning corporate operations is often disseminated to market participants through the changes in credit ratings that are published by professional rating agencies, such as Standard and Poor's (S&P) and Moody's Investor Service. Since these agencies generally require a large fee for the service, and the periodically provided ratings sometimes do not reflect the default risk of the company at the time, it may be advantageous for bond-market participants to be able to classify credit ratings before the agencies actually publish them. As a result, it is very important for companies (especially, financial companies) to develop a proper model of credit rating. From a technical perspective, the credit rating constitutes a typical, multiclass, classification problem because rating agencies generally have ten or more categories of ratings. For example, S&P's ratings range from AAA for the highest-quality bonds to D for the lowest-quality bonds. The professional rating agencies emphasize the importance of analysts' subjective judgments in the determination of credit ratings. However, in practice, a mathematical model that uses the financial variables of companies plays an important role in determining credit ratings, since it is convenient to apply and cost efficient. These financial variables include the ratios that represent a company's leverage status, liquidity status, and profitability status. Several statistical and artificial intelligence (AI) techniques have been applied as tools for predicting credit ratings. Among them, artificial neural networks are most prevalent in the area of finance because of their broad applicability to many business problems and their preeminent ability to adapt. However, artificial neural networks also have many defects, including the difficulty in determining the values of the control parameters and the number of processing elements in the layer as well as the risk of over-fitting. Of late, because of their robustness and high accuracy, support vector machines (SVMs) have become popular as a solution for problems with generating accurate prediction. An SVM's solution may be globally optimal because SVMs seek to minimize structural risk. On the other hand, artificial neural network models may tend to find locally optimal solutions because they seek to minimize empirical risk. In addition, no parameters need to be tuned in SVMs, barring the upper bound for non-separable cases in linear SVMs. Since SVMs were originally devised for binary classification, however they are not intrinsically geared for multiclass classifications as in credit ratings. Thus, researchers have tried to extend the original SVM to multiclass classification. Hitherto, a variety of techniques to extend standard SVMs to multiclass SVMs (MSVMs) has been proposed in the literature Only a few types of MSVM are, however, tested using prior studies that apply MSVMs to credit ratings studies. In this study, we examined six different techniques of MSVMs: (1) One-Against-One, (2) One-Against-AIL (3) DAGSVM, (4) ECOC, (5) Method of Weston and Watkins, and (6) Method of Crammer and Singer. In addition, we examined the prediction accuracy of some modified version of conventional MSVM techniques. To find the most appropriate technique of MSVMs for corporate bond rating, we applied all the techniques of MSVMs to a real-world case of credit rating in Korea. The best application is in corporate bond rating, which is the most frequently studied area of credit rating for specific debt issues or other financial obligations. For our study the research data were collected from National Information and Credit Evaluation, Inc., a major bond-rating company in Korea. The data set is comprised of the bond-ratings for the year 2002 and various financial variables for 1,295 companies from the manufacturing industry in Korea. We compared the results of these techniques with one another, and with those of traditional methods for credit ratings, such as multiple discriminant analysis (MDA), multinomial logistic regression (MLOGIT), and artificial neural networks (ANNs). As a result, we found that DAGSVM with an ordered list was the best approach for the prediction of bond rating. In addition, we found that the modified version of ECOC approach can yield higher prediction accuracy for the cases showing clear patterns.

• Asia pacific journal of information systems
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• v.20 no.3
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• pp.139-166
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• 2010

The supply chain management (SCM) systems have emerged as strong managerial tools for manufacturing firms in enhancing competitive strength. Despite of large investments in the SCM systems, many companies are not fully realizing the promised benefits from the systems. A review of literature on adoption, implementation and success factor of IOS (inter-organization systems), EDI (electronic data interchange) systems, shows that this issue has been examined from multiple theoretic perspectives. And many researchers have attempted to identify the factors which influence the success of system implementation. However, the existing studies have two drawbacks in revealing the determinants of systems implementation success. First, previous researches raise questions as to the appropriateness of research subjects selected. Most SCM systems are operating in the form of private industrial networks, where the participants of the systems consist of two distinct groups: focus companies and vendors. The focus companies are the primary actors in developing and operating the systems, while vendors are passive participants which are connected to the system in order to supply raw materials and parts to the focus companies. Under the circumstance, there are three ways in selecting the research subjects; focus companies only, vendors only, or two parties grouped together. It is hard to find researches that use the focus companies exclusively as the subjects probably due to the insufficient sample size for statistic analysis. Most researches have been conducted using the data collected from both groups. We argue that the SCM success factors cannot be correctly indentified in this case. The focus companies and the vendors are in different positions in many areas regarding the system implementation: firm size, managerial resources, bargaining power, organizational maturity, and etc. There are no obvious reasons to believe that the success factors of the two groups are identical. Grouping the two groups also raises questions on measuring the system success. The benefits from utilizing the systems may not be commonly distributed to the two groups. One group's benefits might be realized at the expenses of the other group considering the situation where vendors participating in SCM systems are under continuous pressures from the focus companies with respect to prices, quality, and delivery time. Therefore, by combining the system outcomes of both groups we cannot measure the system benefits obtained by each group correctly. Second, the measures of system success adopted in the previous researches have shortcoming in measuring the SCM success. User satisfaction, system utilization, and user attitudes toward the systems are most commonly used success measures in the existing studies. These measures have been developed as proxy variables in the studies of decision support systems (DSS) where the contribution of the systems to the organization performance is very difficult to measure. Unlike the DSS, the SCM systems have more specific goals, such as cost saving, inventory reduction, quality improvement, rapid time, and higher customer service. We maintain that more specific measures can be developed instead of proxy variables in order to measure the system benefits correctly. The purpose of this study is to find the determinants of SCM systems success in the perspective of vendor companies. In developing the research model, we have focused on selecting the success factors appropriate for the vendors through reviewing past researches and on developing more accurate success measures. The variables can be classified into following: technological, organizational, and environmental factors on the basis of TOE (Technology-Organization-Environment) framework. The model consists of three independent variables (competition intensity, top management support, and information system maturity), one mediating variable (collaboration), one moderating variable (government support), and a dependent variable (system success). The systems success measures have been developed to reflect the operational benefits of the SCM systems; improvement in planning and analysis capabilities, faster throughput, cost reduction, task integration, and improved product and customer service. The model has been validated using the survey data collected from 122 vendors participating in the SCM systems in Korea. To test for mediation, one should estimate the hierarchical regression analysis on the collaboration. And moderating effect analysis should estimate the moderated multiple regression, examines the effect of the government support. The result shows that information system maturity and top management support are the most important determinants of SCM system success. Supply chain technologies that standardize data formats and enhance information sharing may be adopted by supply chain leader organization because of the influence of focal company in the private industrial networks in order to streamline transactions and improve inter-organization communication. Specially, the need to develop and sustain an information system maturity will provide the focus and purpose to successfully overcome information system obstacles and resistance to innovation diffusion within the supply chain network organization. The support of top management will help focus efforts toward the realization of inter-organizational benefits and lend credibility to functional managers responsible for its implementation. The active involvement, vision, and direction of high level executives provide the impetus needed to sustain the implementation of SCM. The quality of collaboration relationships also is positively related to outcome variable. Collaboration variable is found to have a mediation effect between on influencing factors and implementation success. Higher levels of inter-organizational collaboration behaviors such as shared planning and flexibility in coordinating activities were found to be strongly linked to the vendors trust in the supply chain network. Government support moderates the effect of the IS maturity, competitive intensity, top management support on collaboration and implementation success of SCM. In general, the vendor companies face substantially greater risks in SCM implementation than the larger companies do because of severe constraints on financial and human resources and limited education on SCM systems. Besides resources, Vendors generally lack computer experience and do not have sufficient internal SCM expertise. For these reasons, government supports may establish requirements for firms doing business with the government or provide incentives to adopt, implementation SCM or practices. Government support provides significant improvements in implementation success of SCM when IS maturity, competitive intensity, top management support and collaboration are low. The environmental characteristic of competition intensity has no direct effect on vendor perspective of SCM system success. But, vendors facing above average competition intensity will have a greater need for changing technology. This suggests that companies trying to implement SCM systems should set up compatible supply chain networks and a high-quality collaboration relationship for implementation and performance.

• Culinary science and hospitality research
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• v.21 no.3
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• pp.68-79
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• 2015

The purpose of this study is to provide preliminary information relating to the applicability of Chinese soybean as functional food material. This study compared the manufacturing characteristics, phenolic compounds contents, and antioxidative activities of soymilk prepared with Korean(Jinpumkong) and Chinese Heinong 48) soybean. Antioxidative activities were measured by in vitro models such as 1,1-diphenyl-2-crylhydrazyl DPPH) radical scavenging activity, and 2,2'-azino-bis(3-ethylbenzothiazoline-6-ulfonic acid ABTS) radical scavenging activity. The physicochemical properties(solid contents, pH, and color) and suspension stability of soymilk were not significantly different between the types of raw soybean. Total phenolic and flavonoid contents of soymilk prepared with Chinese cultivar($20.71{\pm}0.34GAE\;mg/g\;dw$ and $6.31{\pm}0.11QE\;mg/g\;dw$, respectively) were significantly higher than those of soymilk prepared with Korean cultivar $^{***}p<.001$ and $^{**}p<0.01$, respectively). Total tannin content of soymilk prepared with Korean cultivar($2.29{\pm}.22TAE\;mg/g\;dw$) was significantly higher than that of soymilk prepared with Chinese cultivar($^{***}p<0.01$). The electron donating ability(EDA) of soymilk was significantly increased in a dose dependent manner(p<0.05); the soymilk prepared with Chinese cultivar showed significantly higher EDA on the concentration of 2.5 mg/mL(p<0.01) and 10.0 mg/mL(p<0.05) than that of soymilk prepared with Korean cultivar. The antioxidative activities of soymilk were significantly increased in a dose dependent manner on ABTS radical scavenging(p<0.05), and there was no significant difference between the types of raw soybean. These results suggest that Chinese soybean, which contains plenty of phenolic compounds and has superior antioxidant activity, may have great potential as a raw material for functional beverage preparation.

• The Korean Journal of Financial Management
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• v.23 no.2
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• pp.109-141
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• 2006

The central factor in the pecking order theory of financial structure is the asymmetric distribution of information between managers and less-informed outside investors. Myers and Majluf (1984) show that this asymmetry leads managers to prefer internal funds to external funds. Funds are raised through equity issues only after the capacity to issue debt has been exhausted. In contrast, according to static tradeoff theory, an optimum financial structure exists by the tradeoff between tax saving by debt and bankruptcy costs. This study examines the recent changes of Korean firms' financial structure and financing behavior and the determinants of financial structure. The sample of firms comes from the period of $1996{\sim}2004$, and the number of firms is 32,003. The major findings are as follows. First, in contrast with previous studies using US firms as sample, Korean firms have been using debt financing as their major financing instrument. Especially, the firms in the fund deficit situation relies much more on $long{\sim}term$ and $short{\sim}term$ debts rather than on equity issues. Second, as is the case with previous studies using US firms sample indicates, the financing deficit variable can not explain perfectly the net debt issue. However, compared with net equity issue variable, net debt issue variable is more closely related to the financing deficit variable. Third, when financing deficit variable is added to the current list of explanatory variables of financial structure determinants model, it has a significant and positive explanatory power. In addition, the coefficients of determinants are much improved. Thus, it is concluded that although pecking order theory is not perfect, it appears to be more useful compared to static tradeoff theory, at least in explaining the recent financing behavior of Korean manufacturing firms.

• Journal of Korean Society of Environmental Engineers
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• v.38 no.1
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• pp.14-24
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• 2016

Information on the lists of pollutants from industrial wastewater discharge are essential not only to specify the key pollutants to be managed in permission process but to design the treatment facilities by the dischargers. In this study, wastewater quality analysis was conducted for three industrial categories including the specified hazardous water pollutants. The general description of the wastewater occurrence, major sources, treatment facilities are also investigated to obtain integrated database on the pollutant inventories for the industrial categories. In addition Based on the analysis of raw wastewater and final effluent, the detected pollutant items are confirmed by analyzing their presence in the raw or supplement materials, the potential of formation as byproducts, and the possibility of inclusion as impurities. The three industrial categories include petrochemical basic compounds, basic organic compounds, and thermal power generation. The water pollutants emitted from petrochemical basic compound manufacturing facilities are 31 items including 16 specified hazardous water pollutants. Basic organic compound manufacturing facilities discharge 30 kinds of pollutants including 14 specified hazardous water pollutants. Thermal power generation facilities emit 20 pollutants, 8 specified hazardous water pollutants among them. These substances were decided as emission inventories of water pollutants finally through the probability evaluation. The compounds detected for each categories are screened through investigation on the possible causes of their occurrence and confirmed as the final water pollutant inventories.

• Journal of Nutrition and Health
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• v.53 no.5
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• pp.488-502
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• 2020

Purpose: This study examined the factors influencing Nutrition Quotient for adults (NQ-A), focusing on workers in the manufacturing industry. Methods: The participants were 738 industrial workers in Gwangju. Their dietary information was analyzed using a questionnaire of NQ-A, which consisted of 21 checklist items and the general characteristics of the subjects. The scores of NQ-A and its 4 factors (balance, moderation, diversity, and dietary behavior) were calculated according to the general characteristics of subjects. The data were analyzed using a χ² test, t-test, correlation, and multiple regression using SPSS 21.0 program. Results: The mean NQ-A score was 45.3 for the total subjects, indicating a low grade. Non-shift workers showed significantly higher scores for balance, diversity, and moderation factors than shift workers. Forty-three (8.7%) shift workers and 123 (50.8%) non-shift workers had good NQ-A scores, whereas 453 (91.3%) shift workers and 119 (49.2%) non-shift workers required monitoring for nutrition. The significant influencing factors of NQ-A were the working pattern (p < 0.001), gender (p < 0.001), family composition (p < 0.001), age (p < 0.001), and household income (p < 0.001). As a result of multiple regression analysis, there was a significant difference in the NQ-A score for moderation (F = 141.930, p < 0.001), diversity (F = 98.998, p < 0.001), balance (F = 52.329, p < 0.001), and dietary behavior (F = 12.895, p < 0.001). As a result, shift work and gender had the greatest influence on the balance (β = -0.372, p < 0.001), diversity (β = -0.316, p < 0.001), and moderation (β = -0.507, p < 0.001) factors in NQ-A. Gender had the most influence on the dietary behavior in NQ-A. Conclusion: Shift work and gender were significant factors affecting NQ-A. To manage workers' health better, it will be necessary to prepare a nutrition education program according to the type of working pattern and gender.

• Archives of design research
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• v.13 no.1
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• pp.209-218
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• 2000

The purpose of this study was to improve the curriculum for industrial design department in the junior colleges. In order to achieve the purpose, two methodologies were carried out. First is job analysis of the industrial designers who have worked in the small & medium manufacturing companies, second is survey for the opinions of professors in the junior colleges. Some results were as follows: 1. The period of junior college for industrial designers is 2 years according to present. But selectively 1 year of advanced course can be established. 2. The practice subjects same as computational formative techniques needed to product development have to be increased. In addition kinds of selection subjects same as foreign language, manufacturing process, new product information and consumer behavior investigation have to be extended. 3. The next subjects need to adjust the title, contents and hours. (1) The need of 3.D related subjects same as computer modeling, computer rendering, 3.D modeling was high. The use of computer is required to design presentation subjects. (2)The need of advertising and sale related subjects same as printing, merchandise, package, typography, photography was low, the need of presentation techniques of new product development was high. (3) The need of field practice, special lecture on practice and reading original texts related subjects was same as at present, but these are not attached importance to form. As the designers feel keenly the necessity of using foreign language, the need of language subject was high.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.1
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• pp.253-270
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• 2023

Research on investment determinants of accelerators, which are attracting attention by greatly improving the survival rate of startups by providing professional incubation and investment to startups at the same time, is gradually expanding. However, previous studies do not have a theoretical basis in developing investment determinants in the early stages, and they use factors of angel investors or venture capital, which are similar investors, and are still in the stage of analyzing importance and priority through empirical research. Therefore, this study verified for the first time in Korea the discrimination and effectiveness of investment determinants using accelerator investment determinants developed based on the business model innovation framework in previous studies. To this end, we first set the criteria for success and failure of startup investment based on scale-up theory and conducted a survey of 22 investment experts from 14 accelerators in Korea, and secured valid data on a total of 97 startups, including 52 successful scale-up startups and 45 failed scale-up startups, were obtained and an independent sample t-test was conducted to verify the mean difference between these two groups by accelerator investment determinants. As a result of the analysis, it was confirmed that the investment determinants of accelerators based on business model innovation framework have considerable discrimination in finding successful startups and making investment decisions. In addition, as a result of analyzing manufacturing-related startups and service-related startups considering the characteristics of innovation by industry, manufacturing-related startups differed in business model, strategy, and dynamic capability factors, while service-related startups differed in dynamic capabilities. This study has great academic implications in that it verified the practical effectiveness of accelerator investment determinants derived based on business model innovation framework for the first time in Korea, and it has high practical value in that it can make effective investments by providing theoretical grounds and detailed information for investment decisions.

• Journal of Internet Computing and Services
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• v.25 no.1
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• pp.99-107
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• 2024

Along with economic growth and industrial development, there is an increasing demand for various electronic components and device production of semiconductor, SMT component, and electrical battery products. However, these products may contain foreign substances coming from manufacturing process such as iron, aluminum, plastic and so on, which could lead to serious problems or malfunctioning of the product, and fire on the electric vehicle. To solve these problems, it is necessary to determine whether there are foreign materials inside the product, and may tests have been done by means of non-destructive testing methodology such as ultrasound ot X-ray. Nevertheless, there are technical challenges and limitation in acquiring X-ray images and determining the presence of foreign materials. In particular Small-sized or low-density foreign materials may not be visible even when X-ray equipment is used, and noise can also make it difficult to detect foreign objects. Moreover, in order to meet the manufacturing speed requirement, the x-ray acquisition time should be reduced, which can result in the very low signal- to-noise ratio(SNR) lowering the foreign material detection accuracy. Therefore, in this paper, we propose a five-step approach to overcome the limitations of low resolution, which make it challenging to detect foreign substances. Firstly, global contrast of X-ray images are increased through histogram stretching methodology. Second, to strengthen the high frequency signal and local contrast, we applied local contrast enhancement technique. Third, to improve the edge clearness, Unsharp masking is applied to enhance edges, making objects more visible. Forth, the super-resolution method of the Residual Dense Block (RDB) is used for noise reduction and image enhancement. Last, the Yolov5 algorithm is employed to train and detect foreign objects after learning. Using the proposed method in this study, experimental results show an improvement of more than 10% in performance metrics such as precision compared to low-density images.

• Journal of the Korean Society of Food Science and Nutrition
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• v.42 no.10
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• pp.1701-1706
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• 2013

Domestic and overseas information with regard to harmful substances are analyzed. From the results, environmental-derived hazard chemicals, which show relatively low recognition, and hazard chemicals that occur unavoidably in food manufacturing process are selected as target harmful substances. Thus, educational leaflet contents were developed based on these substances. To find the effects of education with the above contents, this study surveyed 120 female college students living in Seoul. The purpose of the survey is to analyze the change in recognition, attitude and behavior on hazard chemicals in foods. The survey found that the recognition on harmful substance in foods increased; from 31.5~78.0% before education to 98.8% after education. It also indicates that vague anxiety in which the harmful substances may damage their health decreased by approx. 25.0%; from 77.8% before education to 52.8% after education. For the question of what they would do when government promotes to reduce harmful substances in foods, 12.3% of respondents said that they would actively follow the suggestions and 73.5% of them said that they would do their best before an education. However, 56.1% of them said that they would actively follow the suggestions after the education. It indicates that the ability to recognize harmful substances changed after the education. With regard to consumer behavior, when they knew about the harmful substances in foods, 49.6% of them said that they would select foods after investigating relevant information before the education, while 77.4% of them said that after the education; which is an increase of 27.8%. Further, 45.4% of them said that they would not purchase relevant foods before the education, while 20.9% of them said that after the education; which is a decrease of 24.5%. Therefore, it is considered that vague anxiety of consumers can be eliminated by providing persuasive information on harmful substances. To expand on the communication channel with consumers for food safety, contents development and educational promotion should be enhanced for providing food safety related information.