• Korean journal of communication and information
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• v.43
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• pp.173-208
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• 2008

This study analyses the business performance according to the business diversification of Korean mass media enterprises from year 2003 to 2006. The conclusions drawn which could be divided into five main parts are the followings: First, newspaper companies pursue unrelated diversification in various industrial areas, in order to gain maximum profit while broadcasting companies exert themselves to provide better service by diversifying the major contents. Second, overall the interviewed companies display a constant decline in profit gained from their major business area thus establishing strategies to broaden their focus on diversification of any sort. Third, the researcher completed group analysis in regard of diversification measure resulting in division of three groups. The group which had the most immense diversification range gained the highest ROE, the lowest ROE volatility, and lesser probability of risk taking. The analysis adresses the companies broadening their business areas by researching and focusing on diversification are relatively stable in terms of the profit they gain. Fourth, the middle level group in terms of sales scale, debts, enterprise history, major share rate and high ROE group carry out diversification progressively. The sales scale affects positively to diversification, while the major share rate affects negatively to diversification. Fifth, in accordance to the research, diversification overall contributes to obtainance of successful outcome. Since there was not an immense amount of studies to be referred in the media area, the researcher interviewed and did panel discussion with numerous strategists and managers who are in charge of diversification of media companies. However, collection of only 4 years of data limits the research to be considered to be a generalized study, and does not reflect time gap between business diversification and business performance. Development is required in future studies to be established regarding the media companies' specificity different to other industries, classified the media companies into media types, and consider the time gap in the diversification activities and business performance.

• The KIPS Transactions:PartD
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• v.10D no.7
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• pp.1149-1154
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• 2003

Due to various requirements for the user access control to large databases in the hospitals and the banks, database security has been emphasized. There are many security models for database systems using wide variety of policy-based access control methods. However, they are not functionally enough to meet the requirements for the complicated and various types of access control. In this paper, we propose a database security system that can individually control user access to data groups of various sites and is suitable for the situation where the user's access privilege to arbitrary data is changed frequently. Data group(s) in different sixes d is defined by the table name(s), attribute(s) and/or record key(s), and the access privilege is defined by security levels, roles and polices. The proposed system operates in two phases. The first phase is composed of a modified MAC (Mandatory Access Control) model and RBAC (Role-Based Access Control) model. A user can access any data that has lower or equal security levels, and that is accessible by the roles to which the user is assigned. All types of access mode are controlled in this phase. In the second phase, a modified DAC(Discretionary Access Control) model is applied to re-control the 'read' mode by filtering out the non-accessible data from the result obtained at the first phase. For this purpose, we also defined the user group s that can be characterized by security levels, roles or any partition of users. The policies represented in the form of Block(s, d, r) were also defined and used to control access to any data or data group(s) that is not permitted in 'read ' mode. With this proposed security system, more complicated 'read' access to various data sizes for individual users can be flexibly controlled, while other access mode can be controlled as usual. An implementation example for a database system that manages specimen and clinical information is presented.

• Journal of Hospice and Palliative Care
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• v.13 no.3
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• pp.181-189
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• 2010

Purpose: Recently, health policy making is increasingly based on evidence. Therefore, Korean Terminal Cancer Patient Information System (KTCPIS) was developed to meet such need. We aimed to report its developmental process and statistics from 6 months data. Methods: Items for KTCPIS were developed through the consultation with practitioners. E-Velos web-based clinical trial management system was used as a technical platform. Data were collected for patients who were registered to 34 inpatient palliative care services, designated by Ministry of Health, Welfare, and Family Affairs, from $1^{st}$ of January to $30^{th}$ of June in 2009. Descriptive statistics were used for the analysis. Results: From the nationally representative set of 2,940 patients, we obtained the following results. Mean age was $64.8{\pm}12.9$ years, and 56.6% were male. Lung cancer (18.0%) was most common diagnosis. Only 50.3% of patients received the confirmation of terminal diagnosis by two or more physicians, and 69.7% had an insight of terminal diagnosis at the time of admission. About half of patients were admitted to the units on their own without any formal referral. Average and worst pain scores were significantly reduced after 1 week when compared to those at the time of admission. 73.4% faced death in the units, and home-discharge comprised only 13.3%. Mean length of stay per admission was $20.2{\pm}21.2$ days, with median value of 13. Conclusion: Nationally representative data on the characteristics of patients and their caregiver, and current practice of service delivery in palliative care units were obtained through the operation of KTCPIS.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.79-99
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• 2015

The prediction of bankruptcy has been extensively studied in the accounting and finance field. It can have an important impact on lending decisions and the profitability of financial institutions in terms of risk management. Many researchers have focused on constructing a more robust bankruptcy prediction model. Early studies primarily used statistical techniques such as multiple discriminant analysis (MDA) and logit analysis for bankruptcy prediction. However, many studies have demonstrated that artificial intelligence (AI) approaches, such as artificial neural networks (ANN), decision trees, case-based reasoning (CBR), and support vector machine (SVM), have been outperforming statistical techniques since 1990s for business classification problems because statistical methods have some rigid assumptions in their application. In previous studies on corporate bankruptcy, many researchers have focused on developing a bankruptcy prediction model using financial ratios. However, there are few studies that suggest the specific types of bankruptcy. Previous bankruptcy prediction models have generally been interested in predicting whether or not firms will become bankrupt. Most of the studies on bankruptcy types have focused on reviewing the previous literature or performing a case study. Thus, this study develops a model using data mining techniques for predicting the specific types of bankruptcy as well as the occurrence of bankruptcy in Korean small- and medium-sized construction firms in terms of profitability, stability, and activity index. Thus, firms will be able to prevent it from occurring in advance. We propose a hybrid approach using two artificial neural networks (ANNs) for the prediction of bankruptcy types. The first is a back-propagation neural network (BPN) model using supervised learning for bankruptcy prediction and the second is a self-organizing map (SOM) model using unsupervised learning to classify bankruptcy data into several types. Based on the constructed model, we predict the bankruptcy of companies by applying the BPN model to a validation set that was not utilized in the development of the model. This allows for identifying the specific types of bankruptcy by using bankruptcy data predicted by the BPN model. We calculated the average of selected input variables through statistical test for each cluster to interpret characteristics of the derived clusters in the SOM model. Each cluster represents bankruptcy type classified through data of bankruptcy firms, and input variables indicate financial ratios in interpreting the meaning of each cluster. The experimental result shows that each of five bankruptcy types has different characteristics according to financial ratios. Type 1 (severe bankruptcy) has inferior financial statements except for EBITDA (earnings before interest, taxes, depreciation, and amortization) to sales based on the clustering results. Type 2 (lack of stability) has a low quick ratio, low stockholder's equity to total assets, and high total borrowings to total assets. Type 3 (lack of activity) has a slightly low total asset turnover and fixed asset turnover. Type 4 (lack of profitability) has low retained earnings to total assets and EBITDA to sales which represent the indices of profitability. Type 5 (recoverable bankruptcy) includes firms that have a relatively good financial condition as compared to other bankruptcy types even though they are bankrupt. Based on the findings, researchers and practitioners engaged in the credit evaluation field can obtain more useful information about the types of corporate bankruptcy. In this paper, we utilized the financial ratios of firms to classify bankruptcy types. It is important to select the input variables that correctly predict bankruptcy and meaningfully classify the type of bankruptcy. In a further study, we will include non-financial factors such as size, industry, and age of the firms. Thus, we can obtain realistic clustering results for bankruptcy types by combining qualitative factors and reflecting the domain knowledge of experts.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.157-175
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• 2012

It is difficult to evaluate the performance of process innovation of e-procurement which has large scale and complex processes. The existing evaluation methods for measuring the effects of process innovation have been mainly done with statistically quantitative methods by analyzing operational data or with qualitative methods by conducting surveys and interviews. However, these methods have some limitations to evaluate the effects because the performance evaluation of e-procurement process innovation should consider the interactions among participants who are active either directly or indirectly through the processes. This study considers the e-procurement process as a complex system and develops a simulation model based on MAS(Multi-Agent System) to evaluate the effects of e-procurement process innovation. Multi-agent based simulation allows observing interaction patterns of objects in virtual world through relationship among objects and their behavioral mechanism. Agent-based simulation is suitable especially for complex business problems. In this study, we used Netlogo Version 4.1.3 as a MAS simulation tool which was developed in Northwestern University. To do this, we developed a interaction model of agents in MAS environment. We defined process agents and task agents, and assigned their behavioral characteristics. The developed simulation model was applied to G2B system (KONEPS: Korea ON-line E-Procurement System) of Public Procurement Service (PPS) in Korea and used to evaluate the innovation effects of the G2B system. KONEPS is a successfully established e-procurement system started in the year 2002. KONEPS is a representative e-Procurement system which integrates characteristics of e-commerce into government for business procurement activities. KONEPS deserves the international recognition considering the annual transaction volume of 56 billion dollars, daily exchanges of electronic documents, users consisted of 121,000 suppliers and 37,000 public organizations, and the 4.5 billion dollars of cost saving. For the simulation, we analyzed the e-procurement of process of KONEPS into eight sub processes such as 'process 1: search products and acquisition of proposal', 'process 2 : review the methods of contracts and item features', 'process 3 : a notice of bid', 'process 4 : registration and confirmation of qualification', 'process 5 : bidding', 'process 6 : a screening test', 'process 7 : contracts', and 'process 8 : invoice and payment'. For the parameter settings of the agents behavior, we collected some data from the transactional database of PPS and some information by conducting a survey. The used data for the simulation are 'participants (government organizations, local government organizations and public institutions)', 'the number of bidding per year', 'the number of total contracts', 'the number of shopping mall transactions', 'the rate of contracts between bidding and shopping mall', 'the successful bidding ratio', and the estimated time for each process. The comparison was done for the difference of time consumption between 'before the innovation (As-was)' and 'after the innovation (As-is).' The results showed that there were productivity improvements in every eight sub processes. The decrease ratio of 'average number of task processing' was 92.7% and the decrease ratio of 'average time of task processing' was 95.4% in entire processes when we use G2B system comparing to the conventional method. Also, this study found that the process innovation effect will be enhanced if the task process related to the 'contract' can be improved. This study shows the usability and possibility of using MAS in process innovation evaluation and its modeling.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.99-122
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• 2019

According to the recent IDC (International Data Corporation) report, as from 2025, the total volume of data is estimated to reach ten times higher than that of 2016, corresponding to 163 zettabytes. then the main body of generating information is moving more toward corporations than consumers. So-called "the wave of Big-data" is arriving, and the following aftermath affects entire industries and firms, respectively and collectively. Therefore, effective management of vast amounts of data is more important than ever in terms of the firm. However, there have been no previous studies that measure the effects of big data investment, even though there are number of previous studies that quantitatively the effects of IT investment. Therefore, we quantitatively analyze the Big-data investment effects, which assists firm's investment decision making. This study applied the Event Study Methodology, which is based on the efficient market hypothesis as the theoretical basis, to measure the effect of the big data investment of firms on the response of market investors. In addition, five sub-variables were set to analyze this effect in more depth: the contents are firm size classification, industry classification (finance and ICT), investment completion classification, and vendor existence classification. To measure the impact of Big data investment announcements, Data from 91 announcements from 2010 to 2017 were used as data, and the effect of investment was more empirically observed by observing changes in corporate value immediately after the disclosure. This study collected data on Big Data Investment related to Naver 's' News' category, the largest portal site in Korea. In addition, when selecting the target companies, we extracted the disclosures of listed companies in the KOSPI and KOSDAQ market. During the collection process, the search keywords were searched through the keywords 'Big data construction', 'Big data introduction', 'Big data investment', 'Big data order', and 'Big data development'. The results of the empirically proved analysis are as follows. First, we found that the market value of 91 publicly listed firms, who announced Big-data investment, increased by 0.92%. In particular, we can see that the market value of finance firms, non-ICT firms, small-cap firms are significantly increased. This result can be interpreted as the market investors perceive positively the big data investment of the enterprise, allowing market investors to better understand the company's big data investment. Second, statistical demonstration that the market value of financial firms and non - ICT firms increases after Big data investment announcement is proved statistically. Third, this study measured the effect of big data investment by dividing by company size and classified it into the top 30% and the bottom 30% of company size standard (market capitalization) without measuring the median value. To maximize the difference. The analysis showed that the investment effect of small sample companies was greater, and the difference between the two groups was also clear. Fourth, one of the most significant features of this study is that the Big Data Investment announcements are classified and structured according to vendor status. We have shown that the investment effect of a group with vendor involvement (with or without a vendor) is very large, indicating that market investors are very positive about the involvement of big data specialist vendors. Lastly but not least, it is also interesting that market investors are evaluating investment more positively at the time of the Big data Investment announcement, which is scheduled to be built rather than completed. Applying this to the industry, it would be effective for a company to make a disclosure when it decided to invest in big data in terms of increasing the market value. Our study has an academic implication, as prior research looked for the impact of Big-data investment has been nonexistent. This study also has a practical implication in that it can be a practical reference material for business decision makers considering big data investment.

• Journal of Climate Change Research
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• v.5 no.2
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• pp.139-151
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• 2014

It is increasingly supported by scientific evidence that greenhouse gas caused by human activities is changing the global climate. In particular, the changing climate has affected human health, directly or indirectly, and its adverse impacts are estimated to increase in the future. In response, many countries have established and implemented a variety of mitigation and adaptation measures. However, it is significant to note that climate change will continue over the next few centuries and its impacts on human health should be tackled urgently. The purpose of this paper is to examine domestic policies and research in health sector in adaptation to climate change. It further aims to recommend future research directions for enhanced response to climate change in public health sector, by reviewing a series of adaptation policies in the selected countries and taking into account the general features of health adaptation policies. In this regard, this study first evaluates the current adaptation policies in public health sector by examining the National Climate Change Adaptation Master Plan(2011~2015) and Comprehensive Plan for Environment and Health(2011~2020) and reviewing research to date of the government and relevant institutions. For the literature review, two information service systems are used: namely, the National Science and Technology Information Service(NTIS) and the Policy Research Information Service & Management(PRISM). Secondly, a series of foreign adaptation policies are selected based on the global research priorities set by WHO (2009) and reviewed in order to draw implications for domestic research. Finally, the barriers or constraints in establishing and implementing health adaptation policies are analyzed qualitatively, considering the general characteristics of adaptation in the health sector to climate change, which include uncertainty, finance, technology, institutions, and public awareness. This study provides four major recommendations: to mainstream health sector in the field of adaptation policy and research; to integrate cross-sectoral adaptation measures with an aim to the improvement of health and well-being of the society; to enhance the adaptation measures based on evidence and cost-effectiveness analysis; and to facilitate systemization in health adaptation through setting the key players and the agenda.

• Journal of Food Hygiene and Safety
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• v.36 no.1
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• pp.68-76
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• 2021

Foodborne outbreaks frequently occur worldwide and result in huge economic losses. It is the therefore important to estimate the costs associated with foodborne diseases to minimize the economic damage. At the same time, it is difficult to accurately estimate the economic loss from foodborne disease due to a wide variety of cost components. In Korea, there are a limited number of analytical studies attempting to estimate such costs. In this study we investigated the components of economic cost used in foreign countries to better estimate the cost of foodborne disease in Korea. Seven recent studies investigated the cost components used to estimate the cost of foodborne disease in humans. This study categorized the economic loss into four types of cost: direct costs, indirect costs, food business costs, and government administration costs. The healthcare costs most often included were medical (outpatient) and hospital costs (inpatient). However, these cost components should be selected according to the systems and budgets of medical services by country. For non-healthcare costs, several other studies considered transportation costs to the hospital as an exception to the cost of inpatient care. So, further discussion is needed on whether to consider inpatient care costs. Among the indirect costs, premature mortality, lost productivity, lost leisure time, and lost quality of life/pain, grief and suffering costs were considered, but the opportunity costs for hospital visits were not considered in any of the above studies. As with healthcare costs, government administration costs should also be considered appropriate cost components due to the difference in government budget systems, for example. Our findings will provide fundamental information for economic analysis associated with foodborne diseases to improve food safety policy in Korea.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.1
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• pp.91-104
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• 2023

The records system is believed to have started in Italy in the 14th century in line with trade developments in Europe. In 1491, Luca Pacioli, a mathematician, and an Italian Franciscan monk wrote the first book that described double-entry accounting processes. In many countries, including Korea, the government accounting standards used single-entry bookkeeping rather than double-entry bookkeeping that can be aggregated by account subject. The cash-based and single-entry bookkeeping used by the government in the past had limitations in providing clear information on financial status and establishing a performance-oriented financial management system. Accordingly, the National Accounting Act (promulgated in October 2007) stipulated the introduction of double-entry bookkeeping and accrual accounting systems in the government sector from January 1, 2009. Furthermore, the Korean government has also introduced International Financial Reporting Standards (IFRS), and the System of National Accounts (SNA). Since 2014, Korea owned five national accounts. In Korea, valuation began with the 1968 National Wealth Statistics Survey. The academic origins of the valuation of national wealth statistics which had been investigated by due diligence every 10 years since 1968 are based on the 'Engineering Valuation' of professor Marston in the Department of Industrial Engineering at Iowa State University in the 1930s. This field has spread to economics, etc. In economics, it became the basis of capital stock estimation for positive economics such as econometrics. The valuation by the National Wealth Statistics Survey contributed greatly to converting the book value of accounting data into vintage data. And in 2000 National Statistical Office collected actual disposal data for the 1-digit asset class and obtained the ASL(average service life) by Iowa curve. Then, with the data on fixed capital formation centered on the National B/S Team of the Bank of Korea, the national wealth statistics were prepared by the Permanent Inventory Method(PIM). The asset classification was also classified into 59 types, including 2 types of residential buildings, 4 types of non-residential buildings, 14 types of structures, 9 types of transportation equipment, 28 types of machinery, and 2 types of intangible fixed assets. Tables of useful lives of tangible fixed assets published by the Korea Appraisal Board in 1999 and 2013 were made by the Iowa curve method. In Korea, the Iowa curve method has been adopted as a method of ASL estimation. There are three types of the Iowa curve method. The retirement rate method of the three types is the best because it is based on the collection and compilation of the data of all properties in service during a period of recent years, both properties retired and that are still in service. We hope the retirement rate method instead of the individual unit method is used in the estimation of ASL. Recently Korean government's accounting system has been developed. When revenue expenditure and capital expenditure were mixed in the past single-entry bookkeeping we would like to suggest that BOK and National Statistical Office have accumulated knowledge of a rational difference between revenue expenditure and capital expenditure. In particular, it is important when it is estimated capital stock by PIM. Korea also needs an empirical study on economic depreciation like Hulten & Wykoff Catalog A of the US BEA.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.175-191
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• 2011

With the MICE(Meeting, Incentive travel, Convention, Exhibition) industry coming into the spotlight, there has been a growing interest in the domestic exhibition industry. Accordingly, in Korea, various studies of the industry are being conducted to enhance exhibition performance as in the United States or Europe. Some studies are focusing particularly on analyzing visiting patterns of exhibition visitors using intelligent information technology in consideration of the variations in effects of watching exhibitions according to the exhibitory environment or technique, thereby understanding visitors and, furthermore, drawing the correlations between exhibiting businesses and improving exhibition performance. However, previous studies related to booth recommendation systems only discussed the accuracy of recommendation in the aspect of a system rather than determining changes in visitors' behavior or perception by recommendation. A booth recommendation system enables visitors to visit unplanned exhibition booths by recommending visitors suitable ones based on information about visitors' visits. Meanwhile, some visitors may be satisfied with their unplanned visits, while others may consider the recommending process to be cumbersome or obstructive to their free observation. In the latter case, the exhibition is likely to produce worse results compared to when visitors are allowed to freely observe the exhibition. Thus, in order to apply a booth recommendation system to exhibition halls, the factors affecting the performance of the system should be generally examined, and the effects of the system on visitors' unplanned visiting behavior should be carefully studied. As such, this study aims to determine the factors that affect the performance of a booth recommendation system by reviewing theories and literature and to examine the effects of visitors' perceived performance of the system on their satisfaction of unplanned behavior and intention to reuse the system. Toward this end, the unplanned behavior theory was adopted as the theoretical framework. Unplanned behavior can be defined as "behavior that is done by consumers without any prearranged plan". Thus far, consumers' unplanned behavior has been studied in various fields. The field of marketing, in particular, has focused on unplanned purchasing among various types of unplanned behavior, which has been often confused with impulsive purchasing. Nevertheless, the two are different from each other; while impulsive purchasing means strong, continuous urges to purchase things, unplanned purchasing is behavior with purchasing decisions that are made inside a store, not before going into one. In other words, all impulsive purchases are unplanned, but not all unplanned purchases are impulsive. Then why do consumers engage in unplanned behavior? Regarding this question, many scholars have made many suggestions, but there has been a consensus that it is because consumers have enough flexibility to change their plans in the middle instead of developing plans thoroughly. In other words, if unplanned behavior costs much, it will be difficult for consumers to change their prearranged plans. In the case of the exhibition hall examined in this study, visitors learn the programs of the hall and plan which booth to visit in advance. This is because it is practically impossible for visitors to visit all of the various booths that an exhibition operates due to their limited time. Therefore, if the booth recommendation system proposed in this study recommends visitors booths that they may like, they can change their plans and visit the recommended booths. Such visiting behavior can be regarded similarly to consumers' visit to a store or tourists' unplanned behavior in a tourist spot and can be understand in the same context as the recent increase in tourism consumers' unplanned behavior influenced by information devices. Thus, the following research model was established. This research model uses visitors' perceived performance of a booth recommendation system as the parameter, and the factors affecting the performance include trust in the system, exhibition visitors' knowledge levels, expected personalization of the system, and the system's threat to freedom. In addition, the causal relation between visitors' satisfaction of their perceived performance of the system and unplanned behavior and their intention to reuse the system was determined. While doing so, trust in the booth recommendation system consisted of 2nd order factors such as competence, benevolence, and integrity, while the other factors consisted of 1st order factors. In order to verify this model, a booth recommendation system was developed to be tested in 2011 DMC Culture Open, and 101 visitors were empirically studied and analyzed. The results are as follows. First, visitors' trust was the most important factor in the booth recommendation system, and the visitors who used the system perceived its performance as a success based on their trust. Second, visitors' knowledge levels also had significant effects on the performance of the system, which indicates that the performance of a recommendation system requires an advance understanding. In other words, visitors with higher levels of understanding of the exhibition hall learned better the usefulness of the booth recommendation system. Third, expected personalization did not have significant effects, which is a different result from previous studies' results. This is presumably because the booth recommendation system used in this study did not provide enough personalized services. Fourth, the recommendation information provided by the booth recommendation system was not considered to threaten or restrict one's freedom, which means it is valuable in terms of usefulness. Lastly, high performance of the booth recommendation system led to visitors' high satisfaction levels of unplanned behavior and intention to reuse the system. To sum up, in order to analyze the effects of a booth recommendation system on visitors' unplanned visits to a booth, empirical data were examined based on the unplanned behavior theory and, accordingly, useful suggestions for the establishment and design of future booth recommendation systems were made. In the future, further examination should be conducted through elaborate survey questions and survey objects.