• Journal of Intelligence and Information Systems
• /
• v.23 no.1
• /
• pp.23-46
• /
• 2017

Although there have been cases of evaluating the value of specific companies or projects which have centralized on developed countries in North America and Europe from the early 2000s, the system and methodology for estimating the economic value of individual technologies or patents has been activated on and on. Of course, there exist several online systems that qualitatively evaluate the technology's grade or the patent rating of the technology to be evaluated, as in 'KTRS' of the KIBO and 'SMART 3.1' of the Korea Invention Promotion Association. However, a web-based technology valuation system, referred to as 'STAR-Value system' that calculates the quantitative values of the subject technology for various purposes such as business feasibility analysis, investment attraction, tax/litigation, etc., has been officially opened and recently spreading. In this study, we introduce the type of methodology and evaluation model, reference information supporting these theories, and how database associated are utilized, focusing various modules and frameworks embedded in STAR-Value system. In particular, there are six valuation methods, including the discounted cash flow method (DCF), which is a representative one based on the income approach that anticipates future economic income to be valued at present, and the relief-from-royalty method, which calculates the present value of royalties' where we consider the contribution of the subject technology towards the business value created as the royalty rate. We look at how models and related support information (technology life, corporate (business) financial information, discount rate, industrial technology factors, etc.) can be used and linked in a intelligent manner. Based on the classification of information such as International Patent Classification (IPC) or Korea Standard Industry Classification (KSIC) for technology to be evaluated, the STAR-Value system automatically returns meta data such as technology cycle time (TCT), sales growth rate and profitability data of similar company or industry sector, weighted average cost of capital (WACC), indices of industrial technology factors, etc., and apply adjustment factors to them, so that the result of technology value calculation has high reliability and objectivity. Furthermore, if the information on the potential market size of the target technology and the market share of the commercialization subject refers to data-driven information, or if the estimated value range of similar technologies by industry sector is provided from the evaluation cases which are already completed and accumulated in database, the STAR-Value is anticipated that it will enable to present highly accurate value range in real time by intelligently linking various support modules. Including the explanation of the various valuation models and relevant primary variables as presented in this paper, the STAR-Value system intends to utilize more systematically and in a data-driven way by supporting the optimal model selection guideline module, intelligent technology value range reasoning module, and similar company selection based market share prediction module, etc. In addition, the research on the development and intelligence of the web-based STAR-Value system is significant in that it widely spread the web-based system that can be used in the validation and application to practices of the theoretical feasibility of the technology valuation field, and it is expected that it could be utilized in various fields of technology commercialization.

• Journal of Intelligence and Information Systems
• /
• v.21 no.4
• /
• pp.111-131
• /
• 2015

There are only a handful number of research conducted on pattern analysis of corporate distress as compared with research for bankruptcy prediction. The few that exists mainly focus on audited firms because financial data collection is easier for these firms. But in reality, corporate financial distress is a far more common and critical phenomenon for non-audited firms which are mainly comprised of small and medium sized firms. The purpose of this paper is to classify non-audited firms under distress according to their financial ratio using data mining; Self-Organizing Map (SOM). SOM is a type of artificial neural network that is trained using unsupervised learning to produce a lower dimensional discretized representation of the input space of the training samples, called a map. SOM is different from other artificial neural networks as it applies competitive learning as opposed to error-correction learning such as backpropagation with gradient descent, and in the sense that it uses a neighborhood function to preserve the topological properties of the input space. It is one of the popular and successful clustering algorithm. In this study, we classify types of financial distress firms, specially, non-audited firms. In the empirical test, we collect 10 financial ratios of 100 non-audited firms under distress in 2004 for the previous two years (2002 and 2003). Using these financial ratios and the SOM algorithm, five distinct patterns were distinguished. In pattern 1, financial distress was very serious in almost all financial ratios. 12% of the firms are included in these patterns. In pattern 2, financial distress was weak in almost financial ratios. 14% of the firms are included in pattern 2. In pattern 3, growth ratio was the worst among all patterns. It is speculated that the firms of this pattern may be under distress due to severe competition in their industries. Approximately 30% of the firms fell into this group. In pattern 4, the growth ratio was higher than any other pattern but the cash ratio and profitability ratio were not at the level of the growth ratio. It is concluded that the firms of this pattern were under distress in pursuit of expanding their business. About 25% of the firms were in this pattern. Last, pattern 5 encompassed very solvent firms. Perhaps firms of this pattern were distressed due to a bad short-term strategic decision or due to problems with the enterpriser of the firms. Approximately 18% of the firms were under this pattern. This study has the academic and empirical contribution. In the perspectives of the academic contribution, non-audited companies that tend to be easily bankrupt and have the unstructured or easily manipulated financial data are classified by the data mining technology (Self-Organizing Map) rather than big sized audited firms that have the well prepared and reliable financial data. In the perspectives of the empirical one, even though the financial data of the non-audited firms are conducted to analyze, it is useful for find out the first order symptom of financial distress, which makes us to forecast the prediction of bankruptcy of the firms and to manage the early warning and alert signal. These are the academic and empirical contribution of this study. The limitation of this research is to analyze only 100 corporates due to the difficulty of collecting the financial data of the non-audited firms, which make us to be hard to proceed to the analysis by the category or size difference. Also, non-financial qualitative data is crucial for the analysis of bankruptcy. Thus, the non-financial qualitative factor is taken into account for the next study. This study sheds some light on the non-audited small and medium sized firms' distress prediction in the future.

• Journal of Intelligence and Information Systems
• /
• v.25 no.2
• /
• pp.123-139
• /
• 2019

The Korean NPL market was formed by the government and foreign capital shortly after the 1997 IMF crisis. However, this market is short-lived, as the bad debt has started to increase after the global financial crisis in 2009 due to the real economic recession. NPL has become a major investment in the market in recent years when the domestic capital market's investment capital began to enter the NPL market in earnest. Although the domestic NPL market has received considerable attention due to the overheating of the NPL market in recent years, research on the NPL market has been abrupt since the history of capital market investment in the domestic NPL market is short. In addition, decision-making through more scientific and systematic analysis is required due to the decline in profitability and the price fluctuation due to the fluctuation of the real estate business. In this study, we propose a prediction model that can determine the achievement of the benchmark yield by using the NPL market related data in accordance with the market demand. In order to build the model, we used Korean NPL data from December 2013 to December 2017 for about 4 years. The total number of things data was 2291. As independent variables, only the variables related to the dependent variable were selected for the 11 variables that indicate the characteristics of the real estate. In order to select the variables, one to one t-test and logistic regression stepwise and decision tree were performed. Seven independent variables (purchase year, SPC (Special Purpose Company), municipality, appraisal value, purchase cost, OPB (Outstanding Principle Balance), HP (Holding Period)). The dependent variable is a bivariate variable that indicates whether the benchmark rate is reached. This is because the accuracy of the model predicting the binomial variables is higher than the model predicting the continuous variables, and the accuracy of these models is directly related to the effectiveness of the model. In addition, in the case of a special purpose company, whether or not to purchase the property is the main concern. Therefore, whether or not to achieve a certain level of return is enough to make a decision. For the dependent variable, we constructed and compared the predictive model by calculating the dependent variable by adjusting the numerical value to ascertain whether 12%, which is the standard rate of return used in the industry, is a meaningful reference value. As a result, it was found that the hit ratio average of the predictive model constructed using the dependent variable calculated by the 12% standard rate of return was the best at 64.60%. In order to propose an optimal prediction model based on the determined dependent variables and 7 independent variables, we construct a prediction model by applying the five methodologies of discriminant analysis, logistic regression analysis, decision tree, artificial neural network, and genetic algorithm linear model we tried to compare them. To do this, 10 sets of training data and testing data were extracted using 10 fold validation method. After building the model using this data, the hit ratio of each set was averaged and the performance was compared. As a result, the hit ratio average of prediction models constructed by using discriminant analysis, logistic regression model, decision tree, artificial neural network, and genetic algorithm linear model were 64.40%, 65.12%, 63.54%, 67.40%, and 60.51%, respectively. It was confirmed that the model using the artificial neural network is the best. Through this study, it is proved that it is effective to utilize 7 independent variables and artificial neural network prediction model in the future NPL market. The proposed model predicts that the 12% return of new things will be achieved beforehand, which will help the special purpose companies make investment decisions. Furthermore, we anticipate that the NPL market will be liquidated as the transaction proceeds at an appropriate price.

• Journal of Intelligence and Information Systems
• /
• v.24 no.3
• /
• pp.177-198
• /
• 2018

Recently, as economic property it has become necessary to acquire and utilize the framework for water resource measurement and performance management as the property of water resources changes to hold "public property". To date, the evaluation of water technology has been carried out by feasibility study analysis or technology assessment based on net present value (NPV) or benefit-to-cost (B/C) effect, however it is not yet systemized in terms of valuation models to objectively assess an economic value of technology-based business to receive diffusion and feedback of research outcomes. Therefore, K-water (known as a government-supported public company in Korea) company feels the necessity to establish a technology valuation framework suitable for technical characteristics of water resources fields in charge and verify an exemplified case applied to the technology. The K-water evaluation technology applied to this study, as a public interest goods, can be used as a tool to measure the value and achievement contributed to society and to manage them. Therefore, by calculating the value in which the subject technology contributed to the entire society as a public resource, we make use of it as a basis information for the advertising medium of performance on the influence effect of the benefits or the necessity of cost input, and then secure the legitimacy for large-scale R&D cost input in terms of the characteristics of public technology. Hence, K-water company, one of the public corporation in Korea which deals with public goods of 'water resources', will be able to establish a commercialization strategy for business operation and prepare for a basis for the performance calculation of input R&D cost. In this study, K-water has developed a web-based technology valuation model for public interest type water resources based on the technology evaluation system that is suitable for the characteristics of a technology in water resources fields. In particular, by utilizing the evaluation methodology of the Institute of Advanced Industrial Science and Technology (AIST) in Japan to match the expense items to the expense accounts based on the related benefit items, we proposed the so-called 'K-water's proprietary model' which involves the 'cost-benefit' approach and the FCF (Free Cash Flow), and ultimately led to build a pipeline on the K-water research performance management system and then verify the practical case of a technology related to "desalination". We analyze the embedded design logic and evaluation process of web-based valuation system that reflects characteristics of water resources technology, reference information and database(D/B)-associated logic for each model to calculate public interest-based and profit-based technology values in technology integrated management system. We review the hybrid evaluation module that reflects the quantitative index of the qualitative evaluation indices reflecting the unique characteristics of water resources and the visualized user-interface (UI) of the actual web-based evaluation, which both are appended for calculating the business value based on financial data to the existing web-based technology valuation systems in other fields. K-water's technology valuation model is evaluated by distinguishing between public-interest type and profitable-type water technology. First, evaluation modules in profit-type technology valuation model are designed based on 'profitability of technology'. For example, the technology inventory K-water holds has a number of profit-oriented technologies such as water treatment membranes. On the other hand, the public interest-type technology valuation is designed to evaluate the public-interest oriented technology such as the dam, which reflects the characteristics of public benefits and costs. In order to examine the appropriateness of the cost-benefit based public utility valuation model (i.e. K-water specific technology valuation model) presented in this study, we applied to practical cases from calculation of benefit-to-cost analysis on water resource technology with 20 years of lifetime. In future we will additionally conduct verifying the K-water public utility-based valuation model by each business model which reflects various business environmental characteristics.

• Journal of Technology Innovation
• /
• v.19 no.1
• /
• pp.111-144
• /
• 2011

This study is to confirm the core factors of innovative capabilities and technological entrepreneurship affecting the performance of technology management and business management of small and medium-sized enterprises (SMEs). Through the consideration about the complex natures of technological innovation affecting by multidimensional factors, this study designs the research model that innovative capabilities, the performances of technology and business management are arranged in accordance with the innovation process; input-output-outcome. To meet this research purpose, the hypothesis are set up based on the previous research studies and the research samples are selected from members of the Innovative Business (INNO-BIZ) Association, located in Seoul and Geyonggi province. As a result of regression analysis to the responses gathered from 360 firms, the performance of business management is influenced positively by the technology superiority, market growth and business profitability which are the dominant factors of performance of technology management. In addition, three sub-variables of innovative capabilities such as R&D, strategic planning and learning capability, have positive effects on both the managerial performances. Innovativeness and progressiveness of technological entrepreneurship affect both the performances positively. Moreover, the co-relation between technological entrepreneurship of an innovation leader and innovative capabilities of organizational members are identified. Lastly, technological entrepreneurship has the mediating effect on the path of leading innovative capabilities to the managerial performances. In conclusion, the research results imply that technological innovation-type firms should periodically evaluate the performance of technology management which are the output of technological innovations and the reinvestment for ultimate business success. And improving and developing innovative capabilities and technological entrepreneurship is required to continuously and consistently investing and supporting resources on technological innovations at the firm-and government-level. It is considered that these are the crucial methods for securing the technologically competitive advantage of SMEs with less resources and narrow innovation range.

• Journal of Distribution Science
• /
• v.10 no.1
• /
• pp.5-10
• /
• 2012

Currently, the rewards programs offered by many companies to strengthen customer relationships have been working quite well. In addition, many companies' rewards programs, designed for stabilizing revenue, are recognized to be effective. However, these rewards programs are not significantly differentiated between companies and there are no accurate conclusions currently, which can be made about their effects. Because of this, a company with a customer rewards program may not comprehend the true level of active participation. In this environment some companies' rewards programs inadvertently hinder business profitability as a side effect while attempting to increase customer loyalty. In fact, airline and oil companies pass on the financial cost of their programs to the customer, and as a result, they have been criticized publicly. The result of this is that the corporations with bad rewards programs tend to get a bad image. In this study of stores' rewards programs, we centered our focus on the design of the program. The main problem in this study is to recognize the financial value of the rewards program and whether it can create a competitive edge for the companies despite the cost issues experienced by them. Customers receiving financial rewards for their business may be just as satisfied with a particular company or store versus those who are not, and the program, perhaps, does not form a distinctive competitive advantage. When the customer is deciding between competing companies to secure their product needs with, we wanted to figure out how much of an affect a valuable reward program had on their decision making. To evaluate this, we set the first hypothesis as, "based on the level of involvement of the customers, there is a difference between customers' preferences for rewards programs." In the results of Experiment 1 we saw that in a financial compensation program for high-involvement groups and low-involvement groups, significant differences appeared and Hypothesis 1 was partially supported. As for the second hypothesis that "customers will have different preferences between a financial rewards programs (SE) and a joint rewards programs (JE)," the analysis showed that the preference for JE was significantly higher than that for other programs. In addition, through Experiment 2, we were able to find meaningful results, which revealed that consumers have shown a significant difference in their preferences between SE and JE. The purpose of these experiments was to enable the designing of a rewards program by learning how to enhance service information distribution and strengthen customer relationships. From the results, there should be a great amount of value for future service-related endeavors and academic research programs. The research is significant, because the results can be found to have a positive effect on reward program designs however, it does have the following limitations. First, this study was performed using an experiment, and all experiments have limitations. Second, although there was an individual evaluation and a joint evaluation, setting a proper evaluation criteria was difficult. In this study, 1,000 Korean won (KRW) in the individual evaluation had a value of 2 points, and, in the joint evaluation, 1,000 KRW had a value of 1 point. There may have been alternative ways to differentiate the evaluations to obtain the proper results. In this study, since there was no funding, the experiments were performed orally however, this was complementary to the study. Third, the subjects who participated in this experiment were students. Conducting this study through experimentation was unavoidable for us, and future research should be conducted using an actual program with the target customers.

• Management & Information Systems Review
• /
• v.29 no.2
• /
• pp.1-25
• /
• 2010

This study attempted to empirically test the determinants of stock returns in Korean stock market applying multi-factor model proposed by Haugen and Baker(1996). Regression models were developed using 16 variables related to liquidity, risk, historical price, price level, and profitability as independent variables and 690 stock monthly returns as dependent variable. For the statistical analysis, the data were collected from the Kis Value database and the tests of forecasting power in this study minimized various possible bias discussed in the literature as possible. The statistical results indicated that: 1) Liquidity, one-month excess return, three-month excess return, PER, ROE, and volatility of total return affect stock returns simultaneously. 2) Liquidity, one-month excess return, three-month excess return, six-month excess return, PSR, PBR, ROE, and EPS have an antecedent influence on stock returns. Meanwhile, realized returns of decile portfolios increase in proportion to predicted returns. This results supported previous study by Haugen and Baker(1996) and indicated that firm-characteristic model can better predict stock returns than CAPM. 3) The firm-characteristic model has better predictive power than Fama-French three-factor model, which indicates that a portfolio constructed based on this model can achieve excess return. This study found that expected return factor models are accurate, which is consistent with other countries' results. There exists a surprising degree of commonality in the factors that are most important in determining the expected returns among different stocks.

• Culinary science and hospitality research
• /
• v.22 no.7
• /
• pp.267-280
• /
• 2016

Customer Experience Management (CEM) appeared as a complementary solution to overcome CRM limitations. CEM enhances profitability through building long-term relations with customers by understanding their experiences. This study aims at investigating the impact of customer experience quality on the willingness to pay more through customer satisfaction in the hotel businesses. The survey for this study was carried out on customers who had domestic hotel experience s within the last 6 months. Out of the 306 questionnaires retrieved, 225 valid responses were used for the empirical analysis that utilizied the statistical package programs SPSS 18.0 and AMOS 18.0. The research findings may be summarized as follows. First, as an outcome of the research hypothesis that each component of customer experience management would influence satisfaction, 'the peace of mind' & 'the moment of truth' were shown to have a significantly positive (+) impact on it. On the other hand, 'the product experience' was shown not to significantly influence it in a positive (+) way. Second, as an outcome of the research hypothesis that satisfaction would influence willingness to pay more. From the findings of the study, theoretical implications are as follows. It can be predicted that customer experience management will likely make customers more profitable because customers are willing to pay more with a sense of loyalty built through satisfaction of the hotel industry. In the practical implications, the dimension of experience quality examined by the study can be used as an index to measure and manage customer experience in the hotel industry.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
• /
• v.5 no.1
• /
• pp.45-67
• /
• 2010

On July 21st in 2005, People's Bank of China (PBOC) turned the currency peg against the U.S. dollar into managed currency system based on a basket of unnamed currencies under China's exchanged rate regime. This change means that China's enterprises are not free from currency fluctuations. The purpose of this study is to analyze the relations between Renminbi fluctuations in the floating exchange rate and financial results of venture companies. The process and outcomes of this study are as follows, First, in order to measure the financial results of venture companies, I choose venture companies in Shandong Province listed on the Shanghai Stock Exchange (SSE) at random and several quarter financial sheets according to safety ratios, profitability ratios, growth ratios, activity ratios. Second, I arrange the daily Renminbi exchange rate data announced from July 21st, 2005 to December 31st, 2008 by PBOC into the quarterly data. Third, in order to confirm the relations between Renminbi fluctuations and financial results of venture companies, I carry out Pearson's correlation analysis. As a result, the revaluation of the Chinese Renminbi has weakly negative effects on debt ratio, total assets turnover ratio and equity turnover ratio in statistics. But the revaluation of the Chinese Renminbi is not related to other financial index in statistics. The result of this study is that the revaluation of the Chinese Renminbi has little influence on the export and import of Chinese venture companies and certifies the fact that Chinese venture companies have much foreign currency assets. In addition to avoid the currency exposure risk, this study shows the effective method about currency exposure risk which adjusts proportion of Renminbi to foreign currency.

• Information Systems Review
• /
• v.11 no.1
• /
• pp.161-179
• /
• 2009

Nowadays an increasing number of Internet users are running individual websites as Blog or Cyworld. As this type of personal media has a great influence on communication among people, business comes to care about Network Effect, Network Software, and Social Network. For instance, Cyworld created the web service called 'Minihompy' for individual web-logs, and acquired 2.4milion users in 2007. Although many people assumed that the popularity of Minihompy, or Blog would be a passing fad, Cyworld has improved its service, and expanded its Network with various contents. This kind of expansion reflects survival efforts from infinite competitions among ISPs (Internet Service Provider) with focus on enhancing usability to users. However, Cyworld's Network Effect is gradually diminished in these days. Both of low production cost of service vendors and the low searching/conversing costs of users combine to make ISPs hard to keep their market share sustainable. To overcome this lackluster trend, Cyworld has adopted new strategies and try to lock their users in their service. Various efforts to improve the continuance and expansion of Network effect remain unclear and uncertain. If we understand beforehand how a service would improve Network effect, and which service could bring more effect, ISPs can get substantial help in launching their new business strategy. Regardless many diverse ideas to increase their user's duration online ISPs cannot guarantee 'how the new service strategies will end up in profitability. Therefore, this research studies about Network effect of Cyworld's 'Minihompy' using System-Dynamics method which could analyze dynamic relation between users and ISPs. Furthermore, the research aims to predict changes of Network Effect based on the strategy of new service. 'Page View' and 'Duration Time' can be enhanced for the short tenn because they enhance the service functionality. However, these services cannot increase the Network in the long-run. Limitations of this research include that we predict the future merely based on the limited data. We also limit the independent variables over Network Effect only to the following two issues: Increasing the number of users and increasing the Service Functionality. Despite of some limitations, this study perhaps gives some insights to the policy makers or others facing the stiff competition in the network business.