• Information Systems Review
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• v.25 no.4
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• pp.27-45
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• 2023

The importance of information security has grown alongside the development of information and communication technology. However, companies struggle to select suitable countermeasures within their limited budgets. Sönmez and Kılıç (2021) proposed a model using AHP and mixed integer programming to determine the optimal investment combination for mitigating information security breaches. However, their model had limitations: 1) a lack of objective measurement for countermeasure efficacy against security threats, 2) unrealistic scenarios where risk reduction surpassed pre-investment levels, and 3) cost duplication when using a single countermeasure for multiple threats. This paper enhances the model by objectively quantifying countermeasure efficacy using the beta probability distribution. It also resolves unrealistic scenarios and the issue of duplicating investments for a single countermeasure. An empirical analysis was conducted on domestic SMEs to determine investment budgets and risk levels. The improved model outperformed Sönmez and Kılıç's (2021) optimization model. By employing the proposed effectiveness measurement approach, difficulty to evaluate countermeasures can be quantified. Utilizing the improved optimization model allows for deriving an optimal investment portfolio for each countermeasure within a fixed budget, considering information security costs, quantities, and effectiveness. This aids in securing the information security budget and effectively addressing information security threats.

• Journal of the Korean Regional Science Association
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• v.34 no.2
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• pp.3-19
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• 2018

The objective of this study is to observe the efficiency of clinical performance on the supply and demand of medical resources in Korea. For the empirical analysis, we constructed the dataset on age standardized mortality rate, the number of physician, specialist, surgery, medical institution, ratio of general hospitals of 16 provinces in Korea from 2006 to 2013. The panel probability frontier model is employed as an analysis method and considered heteroscedasticity and autocorrelation of the error in panel data. In addition, the demographic and socioeconomic characteristics of the 16 provinces, unemployment rate, elderly population ratio, GRDP per capita, and ratio of hospitals in comparison to the general hospitals are used to find the effect on the technical efficiency of clinical performance on supply and demand of medical resources. The results are as follows. First, for the clinical performance, the supply side of human resources such as doctors and specialists and the demand side factors such as chronic illness clinic per unit population have a significant influence, respectively. Second, the technical efficiency of clinical performance on the supply and demand of medical resources of each input component was 59-70% in terms of clinical efficiency in each region. Third. estimates of technical efficiency of inputs that affect clinical performance showed a slight increase in all regions during the analysis period, but the increase trend decreased slightly. Fourth, the ratio of the elderly population and GRDP per capita have a positive influence on the technical efficiency of clinical performance on the supply and demand of medical resources. The difference of each efficiency by region is due to the regional differences of the input medical resources and the combination of them and the demographic and socioeconomic characteristics of the region. It is understood that the differences in technological efficiency due to the complexity of supply and demand of medical resources, demographic structure and economic difference affecting clinical performance by region are different.

• Journal of Animal Science and Technology
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• v.53 no.1
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• pp.29-34
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• 2011

Using the materials collected from nine farms in a three-way cross system to produce commercial pigs produced from F1 sows (Landrace $\times$ Large White) $\times$ Duroc, the power of individual discrimination and parentage of the 13 microsatellite (MS) marker set that has been suggested for individual/brand identification (traceability) was empirically tested. Initially, genotypes of the parental population ($F_1$ sows and Duroc), and commercial pigs were determined and the genotype frequency and polymorphic index were estimated using the Cervus 2.0 program. The probability of identity among genotypes of random individuals, that random half sibs and that of full sib individuals, based on the genotypes from 91 $F_1$ sows and Duroc were expected to be $4.94{\times}10^{-34}$, $8.16{\times}10^{-23}$ and $2.01{\times}10^{-08}$, respectively, using the API-CALC version 1.0 program. When commercial pigs were included, the estimates increased to $3.74{\times}10^{-35}$, $5.48{\times}10^{-25}$ and $2.96{\times}10^{-11}$, respectively. For the empirical verification of the estimated powers of individual discrimination and parentage, the parentage test was performed for 452 commercial pigs using PAPA version 2.0, and individuals with the same genotype were investigated using the Cervus version 2.0 program. Parents for all commercial pigs were successfully estimated and no identical individual was identified in the pedigree. Although the individual discriminating power was not fully verified because of the lack of individuals corresponding with the theoretical power, the 100% efficiency of parentage test was clearly confirmed. Therefore, we believe that the 13 MS marker set in conjunction with management record/information for the pig production kept in a farm/brand should be useful in the pork traceability in a brand unit.

• Journal of Technology Innovation
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• v.11 no.1
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• pp.147-176
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• 2003

This Paper Presents a new measurement technique to derive the level of BSRC (Basic Science and Research Capacity) index by use of the factor analysis which is extended with the assumption of the standard normal probability distribution of the selected explanatory variables. The new measurement method is used to forecast the gap of Korea's BSRC level compared with those of major OECD countries in terms of time lag and to make their international comparison during the time period of 1981∼1999, based on the assumption that the BSRC progress function of each country takes the form of the logistic curve. The US BSRC index is estimated to be 0.9878 in 1981, 0.9996 in 1990 and 0.99991 in 1999, taking the 1st place. The US BSRC level has been consistently the top among the 16 selected variables, followed by Japan, Germany, France and the United Kingdom, in order. Korea's BSRC is estimated to be 0.2293 in 1981, taking the lowest place among the 16 OECD countries. However, Korea's BSRC indices are estimated to have been increased to 0.3216 (in 1990) and 0.44652 (in 1999) respectively, taking 10th place. Meanwhile, Korea's BSRC level in 1999 (0.44652) is estimated to reach those of the US and Japan in 2233 and 2101, respectively. This means that Korea falls 234 years behind USA and 102 years behind Japan, respectively. Korea is also estimated to lag 34 years behind Germany, 16 years behind France and the UK, 15 years behind Sweden, 11 years behind Canada, 7 years behind Finland, and 5 years behind the Netherlands. For the period of 1981∼1999, the BSRC development speed of the US is estimated to be 0.29700. Its rank is the top among the selected OECD countries, followed by Japan (0.12800), Korea (0.04443), and Germany (0.04029). the US BSRC development speed (0.2970) is estimated to be 2.3 times higher than that of Japan (0.1280), and 6.7 times higher than that of Korea. German BSRC development speed (0.04029) is estimated to be fastest in Europe, but it is 7.4 times slower than that of the US. The estimated BSRC development speeds of Belgium, Finland, Italy, Denmark and the UK stand between 0.01 and 0.02, which are very slow. Particularly, the BSRC development speed of Spain is estimated to be minus 0.0065, staying at the almost same level of BSRC over time (1981 ∼ 1999). Since Korea shows BSRC development speed much slower than those of the US and Japan but relative]y faster than those of other countries, the gaps in BSRC level between Korea and the other countries may get considerably narrower or even Korea will surpass possibly several countries in BSRC level, as time goes by. Korea's BSRC level had taken 10th place till 1993. However, it is estimated to be 6th place in 2010 by catching up the UK, Sweden, Finland and Holland, and 4th place in 2020 by catching up France and Canada. The empirical results are consistent with OECD (2001a)'s computation that Korea had the highest R&D expenditures growth during 1991∼1999 among all OECD countries ; and the value-added of ICT industries in total business sectors value added is 12% in Korea, but only 8% in Japan. And OECD (2001b) observed that Korea, together with the US, Sweden, and Finland, are already the four most knowledge-based countries. Hence, the rank of the knowledge-based country was measured by investment in knowledge which is defined as public and private spending on higher education, expenditures on R&D and investment in software.

• Journal of Global Scholars of Marketing Science
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• v.17 no.3
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• pp.1-19
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• 2007

1. Purpose Rural tourism is made by individuals with different characteristics, needs and wants. It is important to have information on the characteristics and preferences of the consumers of the different types of existing rural accommodation. The stud aims to identify the determinants of consumer preference by type of accommodations. 2. Methodology 2.1 Sample Data were collected from 1000 people by telephone survey with three-stage stratified random sampling in seven metropolitan areas in Korea. Respondents were chosen by sampling internal on telephone book published in 2006. We surveyed from four to ten-thirty 0'clock afternoon so as to systematic sampling considering respondents' life cycle. 2.2 Two-step cluster Analysis Our study is accomplished through the use of a two-step cluster method to classify the accommodation in a reduced number of groups, so that each group constitutes a type. This method had been suggested as appropriate in clustering large data sets with mixed attributes. The method is based on a distance measure that enables data with both continuous and categorical attributes to be clustered. This is derived from a probabilistic model in which the distance between two clusters in equivalent to the decrease in log-likelihood function as a result of merging. 2.3 Multinomial Logit Analysis The estimation of a Multionmial Logit model determines the characteristics of tourist who is most likely to opt for each type of accommodation. The Multinomial Logit model constitutes an appropriate framework to explore and explain choice process where the choice set consists of more than two alternatives. Due to its ease and quick estimation of parameters, the Multinomial Logit model has been used for many empirical studies of choice in tourism. 3. Findings The auto-clustering algorithm indicated that a five-cluster solution was the best model, because it minimized the BIC value and the change in them between adjacent numbers of clusters. The accommodation establishments can be classified into five types: Traditional House, Typical Farmhouse, Farmstay house for group Tour, Log Cabin for Family, and Log Cabin for Individuals. Group 1 (Traditional House) includes mainly the large accommodation establishments, i.e. those with ondoll style room providing meals and one shower room on family tourist, of original construction style house. Group 2 (Typical Farmhouse) encompasses accommodation establishments of Ondoll rooms and each bathroom providing meals. It includes, in other words, the tourist accommodations Known as "rural houses." Group 3 (Farmstay House for Group) has accommodation establishments of Ondoll rooms not providing meals and self cooking facilities, large room size over five persons. Group 4 (Log Cabin for Family) includes mainly the popular accommodation establishments, i.e. those with Ondoll style room with on shower room on family tourist, of western styled log house. While the accommodations in this group are not defined as regards type of construction, the group does include all the original Korean style construction, Finally, group 5 (Log Cabin for Individuals)includes those accommodations that are bedroom western styled wooden house with each bathroom. First Multinomial Logit model is estimated including all the explicative variables considered and taking accommodation group 2 as base alternative. The results show that the variables and the estimated values of the parameters for the model giving the probability of each of the five different types of accommodation available in rural tourism village in Korea, according to the socio-economic and trip related characteristics of the individuals. An initial observation of the analysis reveals that none of variables income, the number of journey, distance, and residential style of house is explicative in the choice of rural accommodation. The age and accompany variables are significant for accommodation establishment of group 1. The education and rural residential experience variables are significant for accommodation establishment of groups 4 and 5. The expenditure and marital status variables are significant for accommodation establishment of group 4. The gender and occupation variable are significant for accommodation establishment of group 3. The loyalty variable is significant for accommodation establishment of groups 3 and 4. The study indicates that significant differences exist among the individuals who choose each type of accommodation at a destination. From this investigation is evident that several profiles of tourists can be attracted by a rural destination according to the types of existing accommodations at this destination. Besides, the tourist profiles may be used as the basis for investment policy and promotion for each type of accommodation, making use in each case of the variables that indicate a greater likelihood of influencing the tourist choice of accommodation.

• Asia pacific journal of information systems
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• v.20 no.2
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• pp.125-155
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• 2010

Ontologies have been adopted in various business and scientific communities as a key component of the Semantic Web. Despite the increasing importance of ontologies, ontology developers still perceive construction tasks as a challenge. A clearly defined and well-structured methodology can reduce the time required to develop an ontology and increase the probability of success of a project. However, no reliable knowledge-engineering methodology for ontology development currently exists; every methodology has been tailored toward the development of a particular ontology. In this study, we developed a Graduation Screen Ontology (GSO). The graduation screen domain was chosen for the several reasons. First, the graduation screen process is a complicated task requiring a complex reasoning process. Second, GSO may be reused for other universities because the graduation screen process is similar for most universities. Finally, GSO can be built within a given period because the size of the selected domain is reasonable. No standard ontology development methodology exists; thus, one of the existing ontology development methodologies had to be chosen. The most important considerations for selecting the ontology development methodology of GSO included whether it can be applied to a new domain; whether it covers a broader set of development tasks; and whether it gives sufficient explanation of each development task. We evaluated various ontology development methodologies based on the evaluation framework proposed by G$\acute{o}$mez-P$\acute{e}$rez et al. We concluded that METHONTOLOGY was the most applicable to the building of GSO for this study. METHONTOLOGY was derived from the experience of developing Chemical Ontology at the Polytechnic University of Madrid by Fern$\acute{a}$ndez-L$\acute{o}$pez et al. and is regarded as the most mature ontology development methodology. METHONTOLOGY describes a very detailed approach for building an ontology under a centralized development environment at the conceptual level. This methodology consists of three broad processes, with each process containing specific sub-processes: management (scheduling, control, and quality assurance); development (specification, conceptualization, formalization, implementation, and maintenance); and support process (knowledge acquisition, evaluation, documentation, configuration management, and integration). An ontology development language and ontology development tool for GSO construction also had to be selected. We adopted OWL-DL as the ontology development language. OWL was selected because of its computational quality of consistency in checking and classification, which is crucial in developing coherent and useful ontological models for very complex domains. In addition, Protege-OWL was chosen for an ontology development tool because it is supported by METHONTOLOGY and is widely used because of its platform-independent characteristics. Based on the GSO development experience of the researchers, some issues relating to the METHONTOLOGY, OWL-DL, and Prot$\acute{e}$g$\acute{e}$-OWL were identified. We focused on presenting drawbacks of METHONTOLOGY and discussing how each weakness could be addressed. First, METHONTOLOGY insists that domain experts who do not have ontology construction experience can easily build ontologies. However, it is still difficult for these domain experts to develop a sophisticated ontology, especially if they have insufficient background knowledge related to the ontology. Second, METHONTOLOGY does not include a development stage called the "feasibility study." This pre-development stage helps developers ensure not only that a planned ontology is necessary and sufficiently valuable to begin an ontology building project, but also to determine whether the project will be successful. Third, METHONTOLOGY excludes an explanation on the use and integration of existing ontologies. If an additional stage for considering reuse is introduced, developers might share benefits of reuse. Fourth, METHONTOLOGY fails to address the importance of collaboration. This methodology needs to explain the allocation of specific tasks to different developer groups, and how to combine these tasks once specific given jobs are completed. Fifth, METHONTOLOGY fails to suggest the methods and techniques applied in the conceptualization stage sufficiently. Introducing methods of concept extraction from multiple informal sources or methods of identifying relations may enhance the quality of ontologies. Sixth, METHONTOLOGY does not provide an evaluation process to confirm whether WebODE perfectly transforms a conceptual ontology into a formal ontology. It also does not guarantee whether the outcomes of the conceptualization stage are completely reflected in the implementation stage. Seventh, METHONTOLOGY needs to add criteria for user evaluation of the actual use of the constructed ontology under user environments. Eighth, although METHONTOLOGY allows continual knowledge acquisition while working on the ontology development process, consistent updates can be difficult for developers. Ninth, METHONTOLOGY demands that developers complete various documents during the conceptualization stage; thus, it can be considered a heavy methodology. Adopting an agile methodology will result in reinforcing active communication among developers and reducing the burden of documentation completion. Finally, this study concludes with contributions and practical implications. No previous research has addressed issues related to METHONTOLOGY from empirical experiences; this study is an initial attempt. In addition, several lessons learned from the development experience are discussed. This study also affords some insights for ontology methodology researchers who want to design a more advanced ontology development methodology.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.5 no.4
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• pp.1-34
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• 2010

Recently, failure rates of Kosdaq IPO firms are increasing and their survival rates tend to be very low, and when these firms do fail, often times backed by a number of governmental financial supports, they may inflict severe financial damage to investors, let alone economy as a whole. To ensure investors' confidence in Kosdaq and foster promising and healthy businesses, it is necessary to precisely assess their intrinsic values and survivability. This study investigates what contributed to the failure of IPO firms and analyzed how these elements are factored into corresponding firms' stock returns. Failure risks are assessed at the time of IPO. This paper considers factors reflecting IPO characteristics, a firm's underwriter prestige, auditor's quality, IPO offer price, firm's age, and IPO proceeds. The study further went on to examine how, if at all, these failure risks involved during IPO led to post-IPO stock prices. Sample firms used in this study include 98 Kosdaq firms that have failed and 569 healthy firms that are classified into the same business categories, and Logit models are used in estimate the probability of failure. Empirical results indicate that auditor's quality, IPO offer price, firm's age, and IPO proceeds shown significant relevance to failure risks at the time of IPO. Of other variables, firm's size and ROA, previously deemed significantly related to failure risks, in fact do not show significant relevance to those risks, whereas financial leverage does. This illustrates the efficacy of a model that appropriately reflects the attributes of IPO firms. Also, even though R&D expenditures were believed to be value relevant by previous studies, this study reveals that R&D is not a significant factor related to failure risks. In examing the relation between failure risks and stock prices, this study finds that failure risks are negatively related to 1 or 2 year size-adjusted abnormal returns after IPO. The results of this study may provide useful knowledge for government regulatory officials in contemplating pertinent policy and for credit analysts in their proper evaluation of a firm's credit standing.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.157-173
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• 2011

As the Internet use explodes recently, the malicious attacks and hacking for a system connected to network occur frequently. This means the fatal damage can be caused by these intrusions in the government agency, public office, and company operating various systems. For such reasons, there are growing interests and demand about the intrusion detection systems (IDS)-the security systems for detecting, identifying and responding to unauthorized or abnormal activities appropriately. The intrusion detection models that have been applied in conventional IDS are generally designed by modeling the experts' implicit knowledge on the network intrusions or the hackers' abnormal behaviors. These kinds of intrusion detection models perform well under the normal situations. However, they show poor performance when they meet a new or unknown pattern of the network attacks. For this reason, several recent studies try to adopt various artificial intelligence techniques, which can proactively respond to the unknown threats. Especially, artificial neural networks (ANNs) have popularly been applied in the prior studies because of its superior prediction accuracy. However, ANNs have some intrinsic limitations such as the risk of overfitting, the requirement of the large sample size, and the lack of understanding the prediction process (i.e. black box theory). As a result, the most recent studies on IDS have started to adopt support vector machine (SVM), the classification technique that is more stable and powerful compared to ANNs. SVM is known as a relatively high predictive power and generalization capability. Under this background, this study proposes a novel intelligent intrusion detection model that uses SVM as the classification model in order to improve the predictive ability of IDS. Also, our model is designed to consider the asymmetric error cost by optimizing the classification threshold. Generally, there are two common forms of errors in intrusion detection. The first error type is the False-Positive Error (FPE). In the case of FPE, the wrong judgment on it may result in the unnecessary fixation. The second error type is the False-Negative Error (FNE) that mainly misjudges the malware of the program as normal. Compared to FPE, FNE is more fatal. Thus, when considering total cost of misclassification in IDS, it is more reasonable to assign heavier weights on FNE rather than FPE. Therefore, we designed our proposed intrusion detection model to optimize the classification threshold in order to minimize the total misclassification cost. In this case, conventional SVM cannot be applied because it is designed to generate discrete output (i.e. a class). To resolve this problem, we used the revised SVM technique proposed by Platt(2000), which is able to generate the probability estimate. To validate the practical applicability of our model, we applied it to the real-world dataset for network intrusion detection. The experimental dataset was collected from the IDS sensor of an official institution in Korea from January to June 2010. We collected 15,000 log data in total, and selected 1,000 samples from them by using random sampling method. In addition, the SVM model was compared with the logistic regression (LOGIT), decision trees (DT), and ANN to confirm the superiority of the proposed model. LOGIT and DT was experimented using PASW Statistics v18.0, and ANN was experimented using Neuroshell 4.0. For SVM, LIBSVM v2.90-a freeware for training SVM classifier-was used. Empirical results showed that our proposed model based on SVM outperformed all the other comparative models in detecting network intrusions from the accuracy perspective. They also showed that our model reduced the total misclassification cost compared to the ANN-based intrusion detection model. As a result, it is expected that the intrusion detection model proposed in this paper would not only enhance the performance of IDS, but also lead to better management of FNE.

• KDI Journal of Economic Policy
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• v.30 no.2
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• pp.99-128
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• 2008

Nearly all Koreans are insured through National Health Insurance(NHI). While NHI coverage is nearly universal, it is not complete. Coverage is largely limited to minimal level of hospital and physician expenses, and copayments are required in each case. As a result, Korea's public insurance system covers roughly 50% of overall individual health expenditures, and the remaining 50% consists of copayments for basic services, spending on services that are either not covered or poorly covered by the public system. In response to these gaps in the public system, 64% of the Korean population has supplemental private health insurance. Expansion of private health insurance raises negative externality issue. Like public financing schemes in other countries, the Korean system imposes cost-sharing on patients as a strategy for controlling utilization. Because most insurance policies reimburse patients for their out-of-pocket payments, supplemental insurance is likely to negate the impact of the policy, raising both total and public sector health spending. So far, most empirical analysis of supplemental health insurance to date has focused on the US Medigap programme. It is found that those with supplements apparently consume more health care. Two reasons for higher health care consumption by those with supplements suggest themselves. One is the moral hazard effect: by eliminating copayments and deductibles, supplements reduce the marginal price of care and induce additional consumption. The other explanation is that supplements are purchased by those who anticipate high health expenditures - adverse effect. The main issue addressed has been the separation of the moral hazard effect from the adverse selection one. The general conclusion is that the evidence on adverse selection based on observable variables is mixed. This article investigates the extent to which private supplementary insurance affect use of health care services by public health insurance enrollees, using Korean administrative data and private supplements related data collected through all relevant private insurance companies. I applied a multivariate two-part model to analyze the effects of various types of supplements on the likelihood and level of public health insurance spending and estimated marginal effects of supplements. Separate models were estimated for inpatients and outpatients in public insurance spending. The first part of the model estimated the likelihood of positive spending using probit regression, and the second part estimated the log of spending for those with positive spending. Use of a detailed information of individuals' public health insurance from administration data and of private insurance status from insurance companies made it possible to control for health status, the types of supplemental insurance owned by theses individuals, and other factors that explain spending variations across supplemental insurance categories in isolating the effects of supplemental insurance. Data from 2004 to 2006 were used, and this study found that private insurance increased the probability of a physician visit by less than 1 percent and a hospital admission by about 1 percent. However, supplemental insurance was not found to be associated with a bigger health care service utilization. Two-part models of health care utilization and expenditures showed that those without supplemental insurance had higher inpatient and outpatient expenditures than those with supplements, even after controlling for observable differences.

• 한국사회정책
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• v.20 no.3
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• pp.77-115
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• 2013

The purpose of this study is to present empirical evidence for discussion of financing social welfare via estimating optimal tax burden in the main member countries of the OECD by using Hausman-Taylor method considering endogeneity of explanatory variables. Also, the author produced an international tax comparison index reflecting theoretical hypotheses on revenue-expenditure nexus within a model to compare real tax burden by countries and to examine feasibility of tax increase in Korea. As a result of the analysis, the higher the level of tax burden was, the higher the level of welfare expenditure was, indicating the connection between high burden and high welfare from the aspect of scale. The results also indicated that the subject countries recently entered into the state of low tax burden. Meanwhile, Korea had maintained low burden until the late 1990s but the tax burden soared up since the financial crisis related to the IMF. However, due to the impact of foreign economy and the tax reduction policy, it reentered into the low-burden state after 2009. On the other hand, the degree of social welfare expenditure's reducing tax burden has been gradually enhanced since the crisis. In this context, the current optimal tax burden ratio of Korea as of 2010 may be 25.8%~26.5% of GDP based on input of welfare expenditure variables, a percent that Korea was investigated to be a 'high tax burden-low ITC' country whose tax increase of 0.7~1.4%p may be feasible and that the success of tax system reform for tax increase might be higher probability when compare to others. However, measures of increasing social security contributions and consumption tax were analyzed to be improper from the aspect of managing finance when compared to increase in other tax items, considering the relatively higher ITC. Tax increase is not necessarily required though there may be room for tax increase; the optimal tax burden ratio can be understood as the level that may be achieved on average when compared to other nations, not as the "proper" level. Thus, discussion of tax increase should be accompanied with comprehensive understanding of models of economic developmental difference from nations and institutional & historical attributes included in specific tax mix.