• Journal of Service Research and Studies
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• v.12 no.2
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• pp.135-151
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• 2022

This study was conducted on a servicism basis as a study to build the management wisdom of Korea as an asset of human society. The case of the founding and development of the Republic of Korea, which developed into one of the world's leading powers with unprecedented rapid growth despite the devastating three-year war immediately after its founding, is an important subject of study. This study conducted a macroscopic analysis. In the process of carrying out the founding activities, it was confirmed that the original idea inherent in Koreans was the Taegeuk idea of the National Flag. The founders of the Republic of Korea consciously or unconsciously adopted the Taegeuk ideology as the founding ideology. It can be said that the Taegeuk ideology has led to greater success because it is based on the service philosophy, which is the common truth of mankind. In this study, the process of preparing for the founding of the Republic of Korea, which was founded by accepting a new maritime civilization from a country with a long continental civilization, the process of national foundation, and the process of operating the country after its founding were analyzed as a whole. In particular, it discovered the excellent wisdom of the founders who appropriately utilized the strengths of continental and maritime civilizations in necessary situations, and confirmed that the dynamic operating model of development through fierce competition between the two opponents was a success factor for high-speed national development. It requires great wisdom for Koreans with diverse ideological backgrounds and unique personalities to unite to establish and run a nation. This is because fierce competition and cooperation must be carried out at the same time. The great wisdom of the founding of the Republic of Korea was able to be demonstrated because the Taegeuk ideology was acting as a common basic ideology for Koreans. Taegeuk ideology, the source of wisdom that Korea has developed under constant tension, can be capitalized as human wisdom, and additional case studies are needed.

• Information Systems Review
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• v.15 no.2
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• pp.1-19
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• 2013

Smart working sparked by iPhone3 opens a revolution in smart ways of working at any time, regardless of location and environment. Also, It provide real-time information processing and analysis, rapid decision-making and the productivity of businesses, including through the timely response and the opportunity to increase the efficiency. As a result, every company are developing mobile information systems. But company data is accessed from the outside, it has problems to solve like security, hacking and information leakage. Also, Mobile devices such as smart phones belonging to the privately-owned asset can't be always controlled to archive company security policy. In the meantime, public smart phones owned by company was always applied security policy. But it can't not apply to privately-owned smart phones. Thus, this paper is focused to archive company security policy, but also enable the individual's free to use of smart phones when we use mobile information systems. So, when we use smart phone as individual purpose, the normal operation of all smart phone functions. But, when we use smart phone as company purpose like mobile information systems, the smart phone functions are blocked like screen capture, Wi-Fi, camera to protect company data. In this study, we suggest the design and implementation of real time control and management of mobile device using MDM(Mobile Device Management) solution. As a result, we can archive company security policy and individual using of smart phone and it is the optimal solution in the BYOD(Bring Your Own Device) era.

• KDI Journal of Economic Policy
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• v.30 no.1
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• pp.1-31
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• 2008

Various opinions have been suggested to explain the slump in equipment investment, such as increased government regulations, shareholder-oriented management by expanded foreign equity investment, response against M&A threats, conservative investment trends seen after a series of bankruptcy of large conglomerates (amidst crumbling myth of "Too Big to Fail"), and financial restructuring. Some also argued that the increased uncertainty in business environment is mainly responsible for conservative management, though there are few domestic studies made regarding the situation. But, in other countries, including the U.S., studies have shown that more volatility is seen now surrounding stock prices, profitability, and sales growth rate reflecting business performance. Also, there are other studies showing such expanded volatility have led to conservative management by businesses. In this regard, this study reviews the volatility conditions of business performance of Korean companies based on profitability, and then attempts to analyze the impact on investment brought on by increased volatility. Each company's profitability volatility used here is from the standard deviation of companies for the past five years. As a profitability indicator, the ROA (= operating profit/total asset) is used. According to the analysis, profitability volatility has remarkably increased from the mid 3% in 1994 to low 5% in 2005. Profitability volatility of the Korean companies has expanded to a great extent since the financial crisis. The crisis might have served to raise the volatility in the macroeconomic conditions. If increased volatility observed during the economic crisis had gradually declined after the crisis, the situation could be interpreted as a temporary phenomenon, not to be too concerned over. But, this was not the case for Korea. The volatility level, after the crisis, has not dropped back to its pre-crisis level. Hence, in the Korea's case, high volatility cannot be explained by the impact of financial crisis. Not only that, the fact that such expansion is seen in every industrial sector indicates that this phenomenon cannot be explained by the composition change of industries alone. An undergoing study shows that with a rapid spread of globalization, industries fiercely competing with China experience more volatility. Such increased volatility tends to contract investment, and since the crisis the impact of volatility on investment has slightly increased. It is noteworthy that this study only includes a part of 'uncertainty' that could be measured statistically. For instance, the profitability volatility indicator used in this study is unable to reflect all the effects that the tacit reduction of protection by the government or regulations might have made. So, the result here also indicates that other 'uncertain' factors not mentioned in this study may have served to contract investment sentiment. It would be impossible for policies to completely remove uncertainties measured by profitability volatility, but at least it is necessary to put effort to reduce the macroeconomic volatility in the future economic management. Stabilized macroeconomic management may not be enough to diminish all volatility that occurs within each company, but it would make a meaningful contribution in encouraging investment.

• Korean Journal of Social Welfare
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• v.59 no.2
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• pp.5-38
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• 2007

This study attempts to develop a theory on the evolving process of wealthy major donors' sharing lives in Korea through a grounded theory approach. To conduct this study, the researchers have in-depth interviews with 11 exemplary wealthy major donors who have more than one million US dollars in his or her own asset and donate more than ten thousand US dollars annually. In data analysis, this study identifies 161 concepts on the evolving process of wealthy major donors' sharing lives; and the concepts are categorized with 33 sub-categories and 14 categories. In the paradigm model on the evolving process of wealthy major donors' sharing lives, it is identified that the central phenomenon, 'practicing sharing lives as noblesse oblige', is related with the causal conditions such as 'learning through memories and observation', 'realizing my duties', and 'emphasizing'; and the central phenomenon is related with the contingent conditions such as 'being sensitive to external evaluation', 'having limited information on giving', 'distrusting donation related environments'. The action/interactional sequences such as 'utilizing relationships' and 'strengthening active participation' are accomplished by moderating conditions such as 'having internal and external supports' and 'guiding by firm conviction'. It reveals that as a result, wealthy major donors enjoy the feeling of becoming a ideal and true wealthy person, establish sharing lives as firm and major parts of overall lives, and experience strong desires for better future and society. In this study, 'generous sharing that shares personal heritages and social benefits' is analyzed as a core category; it shows that sharing of wealthy major donors is related to the characteristics of generosity practice based on moral self-benefiting rather than complete altruistic characteristics or self-sacrificial characteristics. The process analysis reveals that it has the following stages: first, initial giving by exposure to causes or requests; second, routine practice of giving; third, evolution of practice of giving with gradual expansion in quantities and qualities; and fourth, living with giving. In the process, the following four types are identified: devoted wealthy donors for sharing, wealthy donors practicing sharing in daily life, wealthy donors practicing sharing with learning on external stimulus, and wealthy donors practicing sharing on empathy. Finally, this study discusses both meanings of identifying and developing a theory on the evolving process of wealthy major donors' sharing lives and implications of the research results in cultivating and developing potential wealthy major donors in Korea.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.123-136
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• 2014

Recently, online shopping has further developed as the use of the Internet and a variety of smart mobile devices becomes more prevalent. The increase in the scale of such shopping has led to the creation of many Internet shopping malls. Consequently, there is a tendency for increasingly fierce competition among online retailers, and as a result, many Internet shopping malls are making significant attempts to attract online users to their sites. One such attempt is keyword marketing, whereby a retail site pays a fee to expose its link to potential customers when they insert a specific keyword on an Internet portal site. The price related to each keyword is generally estimated by the keyword's frequency of appearance. However, it is widely accepted that the price of keywords cannot be based solely on their frequency because many keywords may appear frequently but have little relationship to shopping. This implies that it is unreasonable for an online shopping mall to spend a great deal on some keywords simply because people frequently use them. Therefore, from the perspective of shopping malls, a specialized process is required to extract meaningful keywords. Further, the demand for automating this extraction process is increasing because of the drive to improve online sales performance. In this study, we propose a methodology that can automatically extract only shopping-related keywords from the entire set of search keywords used on portal sites. We define a shopping-related keyword as a keyword that is used directly before shopping behaviors. In other words, only search keywords that direct the search results page to shopping-related pages are extracted from among the entire set of search keywords. A comparison is then made between the extracted keywords' rankings and the rankings of the entire set of search keywords. Two types of data are used in our study's experiment: web browsing history from July 1, 2012 to June 30, 2013, and site information. The experimental dataset was from a web site ranking site, and the biggest portal site in Korea. The original sample dataset contains 150 million transaction logs. First, portal sites are selected, and search keywords in those sites are extracted. Search keywords can be easily extracted by simple parsing. The extracted keywords are ranked according to their frequency. The experiment uses approximately 3.9 million search results from Korea's largest search portal site. As a result, a total of 344,822 search keywords were extracted. Next, by using web browsing history and site information, the shopping-related keywords were taken from the entire set of search keywords. As a result, we obtained 4,709 shopping-related keywords. For performance evaluation, we compared the hit ratios of all the search keywords with the shopping-related keywords. To achieve this, we extracted 80,298 search keywords from several Internet shopping malls and then chose the top 1,000 keywords as a set of true shopping keywords. We measured precision, recall, and F-scores of the entire amount of keywords and the shopping-related keywords. The F-Score was formulated by calculating the harmonic mean of precision and recall. The precision, recall, and F-score of shopping-related keywords derived by the proposed methodology were revealed to be higher than those of the entire number of keywords. This study proposes a scheme that is able to obtain shopping-related keywords in a relatively simple manner. We could easily extract shopping-related keywords simply by examining transactions whose next visit is a shopping mall. The resultant shopping-related keyword set is expected to be a useful asset for many shopping malls that participate in keyword marketing. Moreover, the proposed methodology can be easily applied to the construction of special area-related keywords as well as shopping-related ones.

• Journal of the Korean Institute of Landscape Architecture
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• v.49 no.6
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• pp.64-79
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• 2021

In Korea, mountains constitute 60 percent of the total land area. For a long time, mountains have been deeply involved in people's daily lives, acting as a significant layer of culture. For those reasons, it would be meaningful to shed light on the values of the mountain culture of Korea and seek various ways to utilize them. Therefore, this study aims to explore Korea's mountain enjoyment culture, considering the mountain leisure in the Joseon Dynasty period, when the heritage of mountain enjoying culture was prevalent, and that of the present era. For the analysis, hiking records of the Joseon Dynasty and present-day hiking blog posts related to three famous mountains in Korea were examined. Findings show that people stayed in the mountains for a long time in the Joseon Dynasty, concentrating on the landscape deeply, and various academic and artistic cultures flourished there. In contrast, contemporary people regard the mountain merely as a space to access the peak and climb down quickly within a day. Therefore, the landscape of the mountain cannot be used as a cultural asset beyond natural scenery. However, there are several positive aspects to today's climbing culture. For example, it is easy to obtain information on climbing and feasible ways to conduct various sizes and concepts of hiking due to the development of technology and transportation. In order to develop Korea's unique mountain enjoyment culture in the future, we should propose a 'leisurely hike' that allows people to enjoy the mountain scenery fully and sublimating it into culture, rather than being hiking that is limited to climbing. In addition, it is essential to create suitable spaces, arranging them appropriately to utilize the history and humanities context of the mountain, and connect local facilities and the workforce, thereby causing the development of various mountain enjoyment cultures.

• Journal of Venture Innovation
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• v.5 no.3
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• pp.71-80
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• 2022

The real estate market is an important part of a country's economy and plays a major role in economic growth through the growth of many related industries. Changes in interest rates affect asset prices and have a significant impact on housing prices. This study analyzed housing prices by dividing them into nationwide, local, and Seoul housing prices in order to analyze whether the effect of changes in interest rates on housing prices shows regional differences. The analysis was conducted from the first quarter of 2011 to the fourth quarter of 2021, and was analyzed using the DOLS model. The main analysis results are as follows. First, interest rates were found to have a significant negative effect on national housing prices, and a drop in interest rates significantly increased national housing prices and an increase in interest rates significantly lowered national housing prices. The consumer price index and loan growth rate also had a positive effect on housing prices nationwide, but statistical significance was not high. Second, interest rates had a negative effect on local housing prices, unlike national housing prices, but were not statistically significant. On the other hand, it was found that the consumer price index and loan growth rate had a larger and significant positive effect on local housing prices compared to national housing prices. Finally, it was found that the interest rate had the only significant negative effect on housing prices in Seoul. And this effect was greater and more significant than the effect on national and local housing prices. In the end, it was found that the effect of interest rates on Korean housing prices differs locally. Interest rates have a significant negative effect on national housing prices, and local housing prices, but they are not statistically significant. In addition, the interest rate was found to have the largest and most significant negative effect on housing prices in Seoul. In addition, it was found that there was a difference in the effect of macroeconomic variables on housing prices. This means that there are differences between regions with different factors influencing local and Seoul housing prices, and this point should be considered when drafting and implementing real estate policies.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.107-122
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• 2017

Volatility in the stock market returns is a measure of investment risk. It plays a central role in portfolio optimization, asset pricing and risk management as well as most theoretical financial models. Engle(1982) presented a pioneering paper on the stock market volatility that explains the time-variant characteristics embedded in the stock market return volatility. His model, Autoregressive Conditional Heteroscedasticity (ARCH), was generalized by Bollerslev(1986) as GARCH models. Empirical studies have shown that GARCH models describes well the fat-tailed return distributions and volatility clustering phenomenon appearing in stock prices. The parameters of the GARCH models are generally estimated by the maximum likelihood estimation (MLE) based on the standard normal density. But, since 1987 Black Monday, the stock market prices have become very complex and shown a lot of noisy terms. Recent studies start to apply artificial intelligent approach in estimating the GARCH parameters as a substitute for the MLE. The paper presents SVR-based GARCH process and compares with MLE-based GARCH process to estimate the parameters of GARCH models which are known to well forecast stock market volatility. Kernel functions used in SVR estimation process are linear, polynomial and radial. We analyzed the suggested models with KOSPI 200 Index. This index is constituted by 200 blue chip stocks listed in the Korea Exchange. We sampled KOSPI 200 daily closing values from 2010 to 2015. Sample observations are 1487 days. We used 1187 days to train the suggested GARCH models and the remaining 300 days were used as testing data. First, symmetric and asymmetric GARCH models are estimated by MLE. We forecasted KOSPI 200 Index return volatility and the statistical metric MSE shows better results for the asymmetric GARCH models such as E-GARCH or GJR-GARCH. This is consistent with the documented non-normal return distribution characteristics with fat-tail and leptokurtosis. Compared with MLE estimation process, SVR-based GARCH models outperform the MLE methodology in KOSPI 200 Index return volatility forecasting. Polynomial kernel function shows exceptionally lower forecasting accuracy. We suggested Intelligent Volatility Trading System (IVTS) that utilizes the forecasted volatility results. IVTS entry rules are as follows. If forecasted tomorrow volatility will increase then buy volatility today. If forecasted tomorrow volatility will decrease then sell volatility today. If forecasted volatility direction does not change we hold the existing buy or sell positions. IVTS is assumed to buy and sell historical volatility values. This is somewhat unreal because we cannot trade historical volatility values themselves. But our simulation results are meaningful since the Korea Exchange introduced volatility futures contract that traders can trade since November 2014. The trading systems with SVR-based GARCH models show higher returns than MLE-based GARCH in the testing period. And trading profitable percentages of MLE-based GARCH IVTS models range from 47.5% to 50.0%, trading profitable percentages of SVR-based GARCH IVTS models range from 51.8% to 59.7%. MLE-based symmetric S-GARCH shows +150.2% return and SVR-based symmetric S-GARCH shows +526.4% return. MLE-based asymmetric E-GARCH shows -72% return and SVR-based asymmetric E-GARCH shows +245.6% return. MLE-based asymmetric GJR-GARCH shows -98.7% return and SVR-based asymmetric GJR-GARCH shows +126.3% return. Linear kernel function shows higher trading returns than radial kernel function. Best performance of SVR-based IVTS is +526.4% and that of MLE-based IVTS is +150.2%. SVR-based GARCH IVTS shows higher trading frequency. This study has some limitations. Our models are solely based on SVR. Other artificial intelligence models are needed to search for better performance. We do not consider costs incurred in the trading process including brokerage commissions and slippage costs. IVTS trading performance is unreal since we use historical volatility values as trading objects. The exact forecasting of stock market volatility is essential in the real trading as well as asset pricing models. Further studies on other machine learning-based GARCH models can give better information for the stock market investors.

• Journal of Technology Innovation
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• v.24 no.4
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• pp.277-307
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• 2016

Since 2011, DCF(Discounted Cash Flow) method has been used initiatively for valuating R&D technology assets in the agricultural food industry and recently technology valuation based on royalties comparison among technology transfer transactions has been also carried out in parallel when evaluating the technology assets such as new seed development technologies. Since the DCF method which has been known until now has many input variables to be estimated, sophisticated estimation has been demanded at the time of technology valuation. In addition, considering more similar trading cases when applying sales transaction comparison or industry norm method based on information of technology transfer royalty, it is an important issue that should be taken into account in the same way in the Agri-Food industry. The main input variables used for technology valuation in the Agri-Food industry are life cycle of technology asset, the financial information related to the Agri-Food industry, discount rate, and technology contribution rate. The latest infrastructure building and data updating related to technology valuation has been carried out on a regular basis in the evaluation organization of the Agri-Food segment. This study verifies the key variables that give the most important impact on the results for the existing technology valuation in the Agri-Food industry and clarifies the difference between the existing valuation result and the outcome by referring the support information that is derived through the latest input information applied in DCF method. In addition, while presenting the scheme to complement fragment information which the latest input data just influence result of technology valuation, we tried to perform comparative analysis between the existing valuation results and the evaluated outcome after the latest of reference data for making a decision the input values to be estimated in DCF. To perform these analyzes, it was first selected the representative cases evaluated past in the Agri-Food industry, applied a sensitivity analysis for input variables based on these selected cases, and then executed a simulation analysis utilizing the key input variables derived from sensitivity analysis. The results of this study is to provide the information which there are the need for modernization of the data related to the input variables that are utilized during valuating technology assets in the Agri-Food sector and for building the infrastructure of the key input variables in DCF. Therefore it is expected to provide more fruitful information about the results of valuation.

• KDI Journal of Economic Policy
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• v.31 no.1
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• pp.239-276
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• 2009

The paper investigates the possible relationship between earnings prediction by security analysts and special ownership ties that link security companies those analysts belong to and firms under analysis. "Security analysts" are known best for their role as information producers in stock markets where imperfect information is prevalent and transaction costs are high. In such a market, changes in the fundamental value of a company are not spontaneously reflected in the stock price, and the security analysts actively produce and distribute the relevant information crucial for the price mechanism to operate efficiently. Therefore, securing the fairness and accuracy of information they provide is very important for efficiencyof resource allocation as well as protection of investors who are excluded from the special relationship. Evidence of systematic distortion of information by the special tie naturally calls for regulatory intervention, if found. However, one cannot presuppose the existence of distorted information based on the common ownership between the appraiser and the appraisee. Reputation effect is especially cherished by security firms and among analysts as indispensable intangible asset in the industry, and the incentive to maintain good reputation by providing accurate earnings prediction may overweigh the incentive to offer favorable rating or stock recommendation for the firms that are affiliated by common ownership. This study shares the theme of existing literature concerning the effect of conflict of interests on the accuracy of analyst's predictions. This study, however, focuses on the potential conflict of interest situation that may originate from the Korea-specific ownership structure of large conglomerates. Utilizing an extensive database of analysts' reports provided by WiseFn(R) in Korea, we perform empirical analysis of potential relationship between earnings prediction and common ownership. We first analyzed the prediction bias index which tells how optimistic or friendly the analyst's prediction is compared to the realized earnings. It is shown that there exists no statistically significant relationship between the prediction bias and common ownership. This is a rather surprising result since it is observed that the frequency of positive prediction bias is higher with such ownership tie. Next, we analyzed the prediction accuracy index which shows how accurate the analyst's prediction is compared to the realized earnings regardless of its sign. It is also concluded that there is no significant association between the accuracy ofearnings prediction and special relationship. We interpret the results implying that market discipline based on reputation effect is working in Korean stock market in the sense that security companies do not seem to be influenced by an incentive to offer distorted information on affiliated firms. While many of the existing studies confirm the relationship between the ability of the analystand the accuracy of the analyst's prediction, these factors cannot be controlled in the above analysis due to the lack of relevant data. As an indirect way to examine the possibility that such relationship might have distorted the result, we perform an additional but identical analysis based on a sub-sample consisting only of reports by best analysts. The result also confirms the earlier conclusion that the common ownership structure does not affect the accuracy and bias of earnings prediction by the analyst.