• 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.

• 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.

• Journal of KIISE:Software and Applications
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• v.37 no.12
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• pp.909-918
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• 2010

To analyze the national competitiveness of Korea leads to the conclusion that global high-tech enterprises have been playing leading and pulling roles in making Korea in line with advanced countries even though the country is lacking in various natural resources. The characteristics of these companies above are as follows; Firstly, these enterprises continue to accumulate core technologies and know-how with highly competent human resources and well-organized management. Secondly, they are well structured and equipped with information technology infrastructures which are, for example, ERP, SCM, CRM, and PLM. Among them PLM is considered to be the principal core information technology infra in manufacturing industry. The urgent task of manufacturing industry recently is to develop new products to accept various needs of consumers, and to launch the products in time to market, which requires the manufactures to be equipped with product development infra and system to upgrade product fulfillment and mass production system in a short period. The introduction of PLM System is a solution of core strategy as a manufacturer for collaboration, global development, reengineering of manufacturing system, the innovation and efficiency of manufacturing process, and product quality improvement. The purpose of this study is to analyze the success factors of introducing PLM System and its practicing effectiveness. And the results of empirical study are as follows; (1) Technical success factors positively impact system quality and user satisfaction, (2) Organizational success factors positively impact system quality, but does not impact user satisfaction, (3) Environmental success factors positively impact system quality and user satisfaction, (4) System quality positively impacts user satisfaction, (5) User satisfaction positively impacts the effectiveness of implementing PLM systems, but system quality does not impact it.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.15 no.3
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• pp.191-206
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• 2020

Although several studies have been conducted on unification and the life of North Korean refugees, there have been few studies comparing the characteristics of North Korean refugees with South Korean workers, in terms of human resources in business administration. By considering the limitations of these prior studies, this study analyzed whether there are differences in factors affecting organizational commitment and turnover intention through a group comparative analysis between North Korean refugees and South Korean workers. For a comparative analysis between the two groups, we recruited 145 workers from North Korea and 213 South Korea-born workers. We found the following results with a multi-group structural equation model. We confirmed the measurement homogeneity by ensuring that both groups were equally aware of the measurement tools affecting organizational commitment and turnover intentions. As a result of testing the homogeneity of measurement, we also confirmed that there was a significant difference in optimism between the two groups; optimism affects organizational commitment and among social supports, affectionate support and interaction support affect organizational commitment. Analyzing the path between the two groups, we first were able to find that optimism influenced organizational commitment for both groups, in relation to positive psychological capital. However, in terms of degree, South Korean workers were found to be strongly affected. Second, with regard to social support, we were not able to find that affectionate support and interaction support have a significant impact on organizational commitment for South Korean workers, while for North Korean refugees, we confirmed that both supports have a significant impact. Third, we were unable to find any differences between the two group, in terms of other sub-components of positive psychological capital (self-efficacy, hope, and resiliency) or the sub-components of social support (informational support, tangible support). These results suggest that companies or managers employing North Korean refugee workers need to create an organizational environment that allows them to perceive social support, especially affectionate support and interaction support.

• Journal of Technology Innovation
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• v.25 no.3
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• pp.155-195
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• 2017

The goal of the present study is reviewing the literature on the scientific community and also on science, technology & society to increase interactions between innovation studies and social studies of science and technology. Up until now, various empirical studies on Korean scientists and engineers have been concentrated on researchers at universities, while they have paid inadequate attention to researchers at state-funded research institutes and private companies. In addition, these studies have tended to use concepts in Western academia to elucidate Korean cases. On the other hand, recent empirical researches on the effects of the evaluation systems in universities, PBS system, and the network of school ties suggest that these topics may reveal the unique characteristics of Korean scientific community. Empirical studies on the scientific community have also shown that Korean research institutes and researchers who are in charge of innovation in Korea have demonstrated a tendency to conform to the government's guidance due to long experiences of state-led R&D and nationalism. Research on science, technology and society has viewed the participation of citizens in science and technology as a way toward science and technology democracy, and tended to have a strong practical orientation. However, there has been a relatively small amount of research on how citizen participation influences the direction and content of technological innovation. Also, although, from the viewpoint of technological innovation, how participation of citizens in science and technology can contribute to knowledge production and innovation is a critical issue, relatively small numbers of case studies on this subject have been conducted. Therefore, as the scholars who have emphasized the democracy of science and technology have actually experimented with various ways of citizen participation, innovation researchers may have to design and implement citizen participation through which citizens' local knowledge can contribute to technological innovation.

• Journal of Korean Society of Forest Science
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• v.32 no.1
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• pp.29-35
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• 1976

In order to estimate sawmill productivities, rates of technical change and optimum combination of production factors, Cobb-Douglas production functions have been derived using data obtained from 96 sample mills in Busan-Incheon, southwestern and northeastern areas. The results may be summarized as follows: 1. There is a tendency of expanding average sawmill size in the areas. The horse-power holdings per mill have been increased at the rates of 91 percent in Busan-Incheon, 7.7 percent in southwestern and 16.9 percent in northeastern areas. This implies that the mills around log-importing ports have made rapid development, compared with those in forest regions. 2. The regression coefficients (production elasticities) of the functions for the year of 1967 in the above three areas are much similar each other, but significant differencies are found in the production functions of 1975. In other words, sawmill productivity was mainly restricted by capital deficiencies in all areas in 1967, but this situation was succeeded only by N-E area in 1975. The range of sum of regression coefficients is 1.0437-1.4214, this indicates increasing rates of return to scale. 3. The annual rates of technical changes in B-I, S-W and N-E areas for the observed period are 17.6, 7.6 and 2.2 percents respectively. Busan-Incheon is the only area where labor productivity is higher than that of capital. 4. The best combination of production factors for maximizing firm's profit is subject to the changes of input and output prices. With some assumptions of prices and costs, the optimum levels of power and labor input in B-I, S-W and N-E areas are 57:17, 427:94 and 192:27.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.7
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• pp.2358-2370
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• 2010

This research aims to empirically analyze the factors that affect the success of technology marketing by Korean universities. The total of 207 universities which successfully made technology transfers from 2006 to 2008 was examined to test the nine hypotheses. For the purpose of testing the hypotheses, technology infrastructure (research costs and the number of SCIE papers), the compensation system for the patents (application and registration), the number of patents (application and registration), TLO staff (the number of people in charge of technology transfer and the job experience in industries), the compensation system for technology transfers (researchers and contributors), and attitudes of university management and industries were analyzed with structural equation methods to figure out their effects on the revenues of technology transfer. The results of this research are summarized as follows. First, technology infrastructures of universities were found to have positive effects on securing patents. As the university research costs in the field of science and technology are increases, the research capabilities are enhanced and this a larger number of researchers are conducted. Second, this research shows that compensation systems for patent application and registration in universities have motivated researchers to take out patents for the outputs of their research. Third, the number of patents universities possess was found to have a positive effect on technology transfer. An increase in the number of patents universities possess implies an increase in the diversity and excellence of the target technologies for transfer. Fourth, the number of patents universities possess turned out to have a positive effect on TLO staff. The number of experts in charge of technology transfer including technology dealers, valuation analysis and patent attorneys should be increased as target technologies for transfer increase according to the increase of patents possessed. Because the technologies are transferee from universities to businesses, businesses (job) experience of TLO staff in industries are also important. This research is meaningful because it has identified the factors affecting the results of technology transfer by employing structural equation methods. In particular, an official governmental survey data for the academic-industrial cooperation were analyzed systematically in terms of technology infrastructure, compensation systems related to patents, the number of patents, TLO staff, compensation systems for technology transfer, and attitudes of university management and industries. All these facts might could differentiate this study from the previous studies.

• Journal of Korean Society of Transportation
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• v.21 no.4
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• pp.113-132
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• 2003

Using nonradial data envelopment analysis(DEA) under assumptions of strong disposability and variable returns scale, this paper annually estimates productive. technical and allocative efficiencies of three publicly-owned rail transit properties which are different in terms of organizational type: Seoul Subway Corporation(SSC, local public corporation), the Seoul Metropolitan Electrified Railways sector (SMESRS) of Korea National Railroad(the national railway operator controlled by the Ministry of Construction and Transportation(MOCT)), and Busan Urban Transit Authority (BUTA, the national authority controlled by MOCT). Using the estimation results of Tobit regression analysis. the paper next computes their true productive, true technical and true allocative efficiencies, which reflect only the impacts of internal factors such as production activity by removing the impacts of external factors such as an organizational type and a track utilization rate. And the paper also computes an organizational efficiency and annually gross efficiencies for each property. The paper then conceptualized that the property produces a single output(car-kilometers) using four inputs(labor, electricity, car & maintenance and track) and uses unbalanced panel data consisted of annual observations on SSC, SMESRS and BUTA. The results obtained from DEA show that, on an average, SSC is the most efficient property on the productive and allocative sides, while SMESRS is the most technically-efficient one. On the other hand. BUTA is the most efficient one on the truly-productive and allocative sides, while SMESRS on the truly-technical side. Another important result is that the differences in true efficiency estimates among the three properties are considerably smaller than those in efficiency estimates. Besides. the most cost-efficient organizational type appears to be a local public corporation represented by SSC, which is also the most grossly-efficient property. These results suggest that a measure to sort out the impacts of external factors on the efficiency of rail transit properties is required to assess fairly it, and that a measure to restructure (establish) an existing(a new) rail transit property into a local public corporation(or authority) is required to improve its cost efficiency.

• Asia pacific journal of information systems
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• v.19 no.4
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• pp.177-200
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• 2009

AIn the advanced - economy, the services industry hasbecome a dominant sector. Evidently, the services sector has grown at a much faster rate than any other. For instance, in such developed countries as the U.S., the proportion of the services sector in its GDP is greater than 75%. Even in the developing countries including India and China, the magnitude of the services sector in their GDPs is rapidly growing. The increasing dependence on service gives rise to new initiatives including service science and service-dominant logic. These new initiatives propose a new theoretical prism to promote the better understanding of the changing economic structure. From the new perspectives, service is no longer regarded as a transaction or exchange, but rather co-creation of value through the interaction among service users, providers, and other stakeholders including partners, external environments, and customer communities. The purpose of this study is the following. First, we review previous literature on service, service innovation, and service systems and integrate the studies based on service dominant logic. Second, we categorize the ten propositions of service dominant logic into conceptual propositions and the ones that are directly related to service provision. Conceptual propositions are left out to form the research model. With the selected propositions, we define the research constructs for this study. Third, we develop measurement items for the new service concepts including service provider network, customer network, value co-creation, and convergence of service with product. We then propose a research model to explain the relationship among the factors that affect the value creation mechanism. Finally, we empirically investigate the effects of the factors on firm performance. Through the process of this research study, we want to show the value creation mechanism of service systems in which various participants in service provision interact with related parties in a joint effort to create values. To test the proposed hypotheses, we developed measurement items and distributed survey questionnaires to domestic companies. 500 survey questionnaires were distributed and 180 were returned among which 171 were usable. The results of the empirical test can be summarized as the following. First, service providers' network which is to help offer required services to customers is found to affect customer network, while it does not have a significant effect on value co-creation and product-service convergence. Second, customer network, on the other hand, appears to influence both value co-creation and product-service convergence. Third, value co-creation accomplished through the collaboration of service providers and customers is found to have a significant effect on both product-service convergence and firm performance. Finally, product-service convergence appears to affect firm performance. To interpret the results from the value creation mechanism perspective, service provider network well established to support customer network is found to have significant effect on customer network which in turn facilitates value co-creation in service provision and product-service convergence to lead to greater firm performance. The results have some enlightening implications for practitioners. If companies want to transform themselves into service-centered business enterprises, they have to consider the four factors suggested in this study: service provider network, customer network, value co-creation, and product-service convergence. That is, companies becoming a service-oriented organization need to understand what the four factors are and how the factors interact with one another in their business context. They then may want to devise a better tool to analyze the value creation mechanism and apply the four factors to their own environment. This research study contributes to the literature in following ways. First, this study is one of the very first empirical studies on the service dominant logic as it has categorized the fundamental propositions into conceptual and empirically testable ones and tested the proposed hypotheses against the data collected through the survey method. Most of the propositions are found to work as Vargo and Lusch have suggested. Second, by providing a testable set of relationships among the research variables, this study may provide policy makers and decision makers with some theoretical grounds for their decision making on what to do with service innovation and management. Finally, this study incorporates the concepts of value co-creation through the interaction between customers and service providers into the proposed research model and empirically tests the validity of the concepts. The results of this study will help establish a value creation mechanism in the service-based economy, which can be used to develop and implement new service provision.

• Journal of Korea Port Economic Association
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• v.34 no.4
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• pp.191-208
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• 2018

This paper focuses on the integration aspect of operators to determine an improvement strategy for the operating system to enhance competitiveness of Busan Port. This Study proposes the following alternatives: valuation standards for the integration of operators, the road map for the integration period, the scope and role setting of integrated operators' participation of Busan Port Authority(BPA), and the separation and linkage North Port and the New Port operators. First, the valuation standards for operator integration should be based on international standards. Additionally quantitative factors such as financial situation, business performance and participating companies' profitability, and the qualitative factors such as management ability, technology, and labor relations should be considered. Second, the timing of North Port's operator integration should be prioritized in the short term in conjunction with the commencement of its phase 2-4, 2-5, and 2-6. The integration of New Port operators should provide a road map for a relatively long-term perspective. Third, the participation of BPA' integrated operators should be considered in terms of publicity as a policy coordinator between terminals and by pursuing the profitability of entering into overseas business by fostering Korean global terminal operators. The scope and role of participation ensures that the experience and technology of the terminal operation business is maximized. Fourth, because physically intergrating the North Port' operator into a single corporate form is difficult, initially establishing a special purpose company to maximize the effect of the integrated operation is necessary. Then, the operators decided to convert to a holding company given the termination of the lease term contract with the State or BPA, and ultimately proposed a merger into a single corporation.