• Journal of the Society of Cosmetic Scientists of Korea
• /
• v.36 no.2
• /
• pp.157-165
• /
• 2010

In this study, the antioxidative effects, inhibitory effects on tyrosinase and elastase of Rhododendron brachycarpum D. Don extracts were investigated. And the moisturizing effect of cream containing R. brachycarpum D. Don extract were investigated by clinical trial. The ethyl acetate fraction of R. brachycarpum D. Don extract (1.83 ${\mu}g/mL$) showed the most prominent the free radical (1,1-diphenyl-2-picrylhydrazyl, DPPH) scavenging activity ($FSC_{50}$). Reactive oxygen species (ROS) scavenging activities ($OSC_{50}$) of R. brachycarpum D. Don extracts on ROS generated in $Fe^{3+}$-EDTA/$H_2O_2$ system were investigated using the luminol-dependent chemiluminescence assay. The 50 % extract fraction (0.064 ${\mu}g/mL$) showed the most prominent ROS scavenging activity. The protective effects of extract/fractions of R. brachycarpum D. Don on the rose-bengal sensitized photohemolysis of human erythrocytes were investigated. The R. brachycarpum D. Don extracts suppressed photohemolysis in a concentration dependent manner (1 ~ 10 ${\mu}g/mL$). The inhibitory effects ($IC_{50}$) of R. brachycarpum D. Don extracts on tyrosinase were determined with ethyl acetate fraction of R. brachycarpum D. Don extract (70.5 ${\mu}g/mL$) and aglycone fraction of extract (122.40 ${\mu}g/mL$). The inhibitory effects ($IC_{50}$) on elastase were determined with ethyl acetate of R. brachycarpum D. Don extract (43.50 ${\mu}g/mL$) and aglycone fraction of extract (20.73 ${\mu}g/mL$). The cream containing the ethyl acetate fraction of R. brachycarpum D. Don extracts was formulated for skin hydration effect and transepidermal water loss (TEWL). The cream containing R. brachycarpum D. Don extract was applied to the right lower arm. After 180 min, the water contents in skin were increased by 1 ~ 4 % than the placebo cream. And TEWL of parts was decreased as 7.7 $g/m^2h$ (experimental cream) and 8.9 $g/m^2h$ (placebo cream) respectively. These results indicate that extract/fractions of R. brachycarpum D. Don can function as antioxidants in biological systems, particularly skin exposed to UV radiation by scavenging $^1O_2$ and other ROS, and protect cellular membranes against ROS. And inhibitory activity on tyrosinase of the aglycone fraction could be applicable to new functional cosmetics for whitening and anti-wrinkle products. Also the increase of skin hydration of the cream containing extract could be applicable to new functional cosmetics for antiaging.

• Journal of Intelligence and Information Systems
• /
• v.17 no.2
• /
• pp.77-96
• /
• 2011

An exhibition is defined as market events for specific duration to present exhibitors' main products to business or private visitors, and it plays a key role as effective marketing channels. As the importance of exhibition is getting more and more, domestic exhibition industry has achieved such a great quantitative growth. But, In contrast to the quantitative growth of domestic exhibition industry, the qualitative growth of Exhibition has not achieved competent growth. In order to improve the quality of exhibition, we need to understand the preference or behavior characteristics of visitors and to increase the level of visitors' attention and satisfaction through the understanding of visitors. So, in this paper, we used the observation survey method which is a kind of field research to understand visitors and collect the real data for the analysis of behavior pattern. And this research proposed the following methodology framework consisting of three steps. First step is to select a suitable exhibition to apply for our method. Second step is to implement the observation survey method. And we collect the real data for further analysis. In this paper, we conducted the observation survey method to obtain the real data of the KIDS & EDU EXPO for Children in SETEC. Our methodology was conducted on 160 visitors and 78 booths from November 4th to 6th in 2010. And, the last step is to analyze the record data through observation. In this step, we analyze the feature of exhibition using Demographic Characteristics collected by observation survey method at first. And then we analyze the individual booth features by the records of visited booth. Through the analysis of individual booth features, we can figure out what kind of events attract the attention of visitors and what kind of marketing activities affect the behavior pattern of visitors. But, since previous research considered only individual features influenced by exhibition, the research about the correlation among features is not performed much. So, in this research, additional analysis is carried out to supplement the existing research with data mining techniques. And we analyze the relation among booths using data mining techniques to know behavior patterns of visitors. Among data mining techniques, we make use of two data mining techniques, such as clustering analysis and ARM(Association Rule Mining) analysis. In clustering analysis, we use K-means algorithm to figure out the correlation among booths. Through data mining techniques, we figure out that there are two important features to affect visitors' behavior patterns in exhibition. One is the geographical features of booths. The other is the exhibit contents of booths. Those features are considered when the organizer of exhibition plans next exhibition. Therefore, the results of our analysis are expected to provide guideline to understanding visitors and some valuable insights for the exhibition from the earlier phases of exhibition planning. Also, this research would be a good way to increase the quality of visitor satisfaction. Visitors' movement paths, booth location, and distances between each booth are considered to plan next exhibition in advance. This research was conducted at the KIDS & EDU EXPO for Children in SETEC(Seoul Trade Exhibition & Convention), but it has some constraints to be applied directly to other exhibitions. Also, the results were derived from a limited number of data samples. In order to obtain more accurate and reliable results, it is necessary to conduct more experiments based on larger data samples and exhibitions on a variety of genres.

• Journal of Intelligence and Information Systems
• /
• v.20 no.3
• /
• pp.77-92
• /
• 2014

Recently, numerous documents including unstructured data and text have been created due to the rapid increase in the usage of social media and the Internet. Each document is usually provided with a specific category for the convenience of the users. In the past, the categorization was performed manually. However, in the case of manual categorization, not only can the accuracy of the categorization be not guaranteed but the categorization also requires a large amount of time and huge costs. Many studies have been conducted towards the automatic creation of categories to solve the limitations of manual categorization. Unfortunately, most of these methods cannot be applied to categorizing complex documents with multiple topics because the methods work by assuming that one document can be categorized into one category only. In order to overcome this limitation, some studies have attempted to categorize each document into multiple categories. However, they are also limited in that their learning process involves training using a multi-categorized document set. These methods therefore cannot be applied to multi-categorization of most documents unless multi-categorized training sets are provided. To overcome the limitation of the requirement of a multi-categorized training set by traditional multi-categorization algorithms, we propose a new methodology that can extend a category of a single-categorized document to multiple categorizes by analyzing relationships among categories, topics, and documents. First, we attempt to find the relationship between documents and topics by using the result of topic analysis for single-categorized documents. Second, we construct a correspondence table between topics and categories by investigating the relationship between them. Finally, we calculate the matching scores for each document to multiple categories. The results imply that a document can be classified into a certain category if and only if the matching score is higher than the predefined threshold. For example, we can classify a certain document into three categories that have larger matching scores than the predefined threshold. The main contribution of our study is that our methodology can improve the applicability of traditional multi-category classifiers by generating multi-categorized documents from single-categorized documents. Additionally, we propose a module for verifying the accuracy of the proposed methodology. For performance evaluation, we performed intensive experiments with news articles. News articles are clearly categorized based on the theme, whereas the use of vulgar language and slang is smaller than other usual text document. We collected news articles from July 2012 to June 2013. The articles exhibit large variations in terms of the number of types of categories. This is because readers have different levels of interest in each category. Additionally, the result is also attributed to the differences in the frequency of the events in each category. In order to minimize the distortion of the result from the number of articles in different categories, we extracted 3,000 articles equally from each of the eight categories. Therefore, the total number of articles used in our experiments was 24,000. The eight categories were "IT Science," "Economy," "Society," "Life and Culture," "World," "Sports," "Entertainment," and "Politics." By using the news articles that we collected, we calculated the document/category correspondence scores by utilizing topic/category and document/topics correspondence scores. The document/category correspondence score can be said to indicate the degree of correspondence of each document to a certain category. As a result, we could present two additional categories for each of the 23,089 documents. Precision, recall, and F-score were revealed to be 0.605, 0.629, and 0.617 respectively when only the top 1 predicted category was evaluated, whereas they were revealed to be 0.838, 0.290, and 0.431 when the top 1 - 3 predicted categories were considered. It was very interesting to find a large variation between the scores of the eight categories on precision, recall, and F-score.

• Journal of dental hygiene science
• /
• v.18 no.6
• /
• pp.327-339
• /
• 2018

This study aimed to provide basic data for establishing the clinical basis for dental hygienist-led dental hygiene process of care by identifying multiple risk factors for self-support program participants in Gangneung city; we also compared oral health status and behavioral changes through customized oral health care. Four dental hygienists who were evaluated for degree of conformity provided dental hygiene process of care to eight self-support program participants who were selected as having an oral health risk among people in the self-support center. The clinical indicators measured during dental hygiene assessment and evaluation and behavioral changes due to dental hygiene intervention were compared and analyzed. With respect to clinical indicators, at the time of probe, the retention rate of patients with gingival bleeding decreased from 61.4% to 14.7% after intervention (p=0.004). Furthermore, the retention rate of patients with a periodontal pocket >4 mm decreased from 15.6% to 5.8% (p=0.001). The average modified O'Leary index of the patients improved from 23 to 40 (p=0.002). Previously, all eight subjects used the vertical or horizontal method of brushing; after dental hygiene care interventions regarding method and frequency of toothbrushing, use of oral care products, and individual interventions, they started using the rolling or Bass method of toothbrushing. Four of eight subjects reported using interdental toothbrushes after intervention. As a result of applying the change model to the transtheoretical behavior change of the subject, the result of strengthening the health behavior was confirmed. For promotion of oral health by the prevention-centered incremental oral health care system, dental hygienist-led dental hygiene management and maintenance is essential. It is thought that continuous research, such as for feasibility evaluation, cost benefit analysis, and preparation of legal systems, is needed to establish and activate dental hygiene management.

• Journal of Intelligence and Information Systems
• /
• v.25 no.2
• /
• pp.99-122
• /
• 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 the Korean Geographical Society
• /
• v.30 no.3
• /
• pp.242-254
• /
• 1995

The Hadano Basin is located at a distance of about 70kms and 60kms from Tokyo and Yokohama and lies in the south-west part of the Kanto region in Japan. The basin area, which correspoends to the catchment of the Kaname River, is about areal size of 60.7$\textrm{km}^2$ and extends about length of 8kms in E-W direction and about width of 5kms in N-S direction (Fig.1). The Hadano basin is filled with thick pile of the alluvum from deposits composed of volcanic materials, mostly came from the Hakone Volcano and overlain by Fuji Volcanic ashes. Fluvial deposits form the good aquifer, therefore water resources of Handano City has been largely depending upon the eroundwater. Urbanization and industrialization of the basin has been rapid in the last thirty years, after activation of "Factory Attraction Policy of Hadano City" in 1956. Growth in population and number of factory due to urbanization changed the land-use pattern of the basin rapidly and increased the water demands. Therefore, Hadano City exploited a new source of water supply, and have introduced the prefectureal waterworks since 1976. On the other hand, the rapid urbanization has brought about the pollution of streams in the basin by domestic sewage and industrial waste water. Diffusion rate of sewerage systems in Hadano City is 38% in 1993. In ordcr to examine the impact of anthropogenic factors on river environments, the author took up the change of land-use and diffusion area of sewerage as parameters, and performed field surveys on water discharge and quality. The survey has been made at upstream and downstream of the main stream regularly per month, to get informati ons about the variation of discharge and water quality aiong the stream and its diurnal fluctuation. Annual variation has been analyzed based the data from Hadano City Office. The results are summarized as follows. 1. Stream discharge has been increasing by urbanization (Fig.3). Water quality (C $l^{-10}$ , N $H^{+}$$_{ 4}$-N, BOD) has been improving gradually after the application of sewerage service, yet water pollution load at the lower station has increased than that at the upper one because of the larger anthropogenic discharge volumes (Fig.4). 2. Corrclation coefficient of discharges between upper and lower was 0.81-0.92. Pollutant loads of the R. Kamame after the confluence with R. Kuzuha grew up by 2.4-3.7 times as compared with its upper reaches, and it increased to 3.7-6.9 times after the confluence with the R. Muro (Fig.5). 3. The changes of water quality along the stream can be divided into two groups (Fig.6a). First: water quality of the R. Kaname and R. Shijuhachisse is becoming worse towards the lower reaches because the water from branches are polluted. Second: water quality are improved in the lower where spring and small branch streams supply clear water, for example R. Mizunashi, R. Muro and R. Kuzuha. 4. Measured discharge at the upper station in the R. Shijuhachisse is 0.153㎥/sec, and about 55% of this is recharged until it reaches to the lower point. The R. Mizunashi has a discharge of 1.155㎥/sec at the upper point, is recharged 0.24㎥/sec until the midstream and groundwater spring 0.2㎥/sec at the lower reaches. R. Kuzuha recharged all the mountain runoff (0.2㎥/sec) at the upper reaches. The R. Muro is supplied by many springs and the estimated discharge of spring was 0.47㎥/sec (Fig.6b). 5. Diurmal variations in discharge and water quality are influenced clearly by domestic and industrial waste waters (Fig.7, 8).ed clearly by domestic and industrial waste waters (Fig.7, 8).

• Journal of Intelligence and Information Systems
• /
• v.24 no.3
• /
• pp.21-44
• /
• 2018

In recent years, the rapid development of internet technology and the popularization of smart devices have resulted in massive amounts of text data. Those text data were produced and distributed through various media platforms such as World Wide Web, Internet news feeds, microblog, and social media. However, this enormous amount of easily obtained information is lack of organization. Therefore, this problem has raised the interest of many researchers in order to manage this huge amount of information. Further, this problem also required professionals that are capable of classifying relevant information and hence text classification is introduced. Text classification is a challenging task in modern data analysis, which it needs to assign a text document into one or more predefined categories or classes. In text classification field, there are different kinds of techniques available such as K-Nearest Neighbor, Naïve Bayes Algorithm, Support Vector Machine, Decision Tree, and Artificial Neural Network. However, while dealing with huge amount of text data, model performance and accuracy becomes a challenge. According to the type of words used in the corpus and type of features created for classification, the performance of a text classification model can be varied. Most of the attempts are been made based on proposing a new algorithm or modifying an existing algorithm. This kind of research can be said already reached their certain limitations for further improvements. In this study, aside from proposing a new algorithm or modifying the algorithm, we focus on searching a way to modify the use of data. It is widely known that classifier performance is influenced by the quality of training data upon which this classifier is built. The real world datasets in most of the time contain noise, or in other words noisy data, these can actually affect the decision made by the classifiers built from these data. In this study, we consider that the data from different domains, which is heterogeneous data might have the characteristics of noise which can be utilized in the classification process. In order to build the classifier, machine learning algorithm is performed based on the assumption that the characteristics of training data and target data are the same or very similar to each other. However, in the case of unstructured data such as text, the features are determined according to the vocabularies included in the document. If the viewpoints of the learning data and target data are different, the features may be appearing different between these two data. In this study, we attempt to improve the classification accuracy by strengthening the robustness of the document classifier through artificially injecting the noise into the process of constructing the document classifier. With data coming from various kind of sources, these data are likely formatted differently. These cause difficulties for traditional machine learning algorithms because they are not developed to recognize different type of data representation at one time and to put them together in same generalization. Therefore, in order to utilize heterogeneous data in the learning process of document classifier, we apply semi-supervised learning in our study. However, unlabeled data might have the possibility to degrade the performance of the document classifier. Therefore, we further proposed a method called Rule Selection-Based Ensemble Semi-Supervised Learning Algorithm (RSESLA) to select only the documents that contributing to the accuracy improvement of the classifier. RSESLA creates multiple views by manipulating the features using different types of classification models and different types of heterogeneous data. The most confident classification rules will be selected and applied for the final decision making. In this paper, three different types of real-world data sources were used, which are news, twitter and blogs.

• Journal of Intelligence and Information Systems
• /
• v.24 no.3
• /
• pp.157-176
• /
• 2018

The purpose of this research is to develop a detection model for companies designated as administrative issue in KOSDAQ market using financial data. Administration issue designates the companies with high potential for delisting, which gives them time to overcome the reasons for the delisting under certain restrictions of the Korean stock market. It acts as an alarm to inform investors and market participants of which companies are likely to be delisted and warns them to make safe investments. Despite this importance, there are relatively few studies on administration issues prediction model in comparison with the lots of studies on bankruptcy prediction model. Therefore, this study develops and verifies the detection model of the companies designated as administrative issue using financial data of KOSDAQ companies. In this study, logistic regression and decision tree are proposed as the data mining models for detecting administrative issues. According to the results of the analysis, the logistic regression model predicted the companies designated as administrative issue using three variables - ROE(Earnings before tax), Cash flows/Shareholder's equity, and Asset turnover ratio, and its overall accuracy was 86% for the validation dataset. The decision tree (Classification and Regression Trees, CART) model applied the classification rules using Cash flows/Total assets and ROA(Net income), and the overall accuracy reached 87%. Implications of the financial indictors selected in our logistic regression and decision tree models are as follows. First, ROE(Earnings before tax) in the logistic detection model shows the profit and loss of the business segment that will continue without including the revenue and expenses of the discontinued business. Therefore, the weakening of the variable means that the competitiveness of the core business is weakened. If a large part of the profits is generated from one-off profit, it is very likely that the deterioration of business management is further intensified. As the ROE of a KOSDAQ company decreases significantly, it is highly likely that the company can be delisted. Second, cash flows to shareholder's equity represents that the firm's ability to generate cash flow under the condition that the financial condition of the subsidiary company is excluded. In other words, the weakening of the management capacity of the parent company, excluding the subsidiary's competence, can be a main reason for the increase of the possibility of administrative issue designation. Third, low asset turnover ratio means that current assets and non-current assets are ineffectively used by corporation, or that asset investment by corporation is excessive. If the asset turnover ratio of a KOSDAQ-listed company decreases, it is necessary to examine in detail corporate activities from various perspectives such as weakening sales or increasing or decreasing inventories of company. Cash flow / total assets, a variable selected by the decision tree detection model, is a key indicator of the company's cash condition and its ability to generate cash from operating activities. Cash flow indicates whether a firm can perform its main activities(maintaining its operating ability, repaying debts, paying dividends and making new investments) without relying on external financial resources. Therefore, if the index of the variable is negative(-), it indicates the possibility that a company has serious problems in business activities. If the cash flow from operating activities of a specific company is smaller than the net profit, it means that the net profit has not been cashed, indicating that there is a serious problem in managing the trade receivables and inventory assets of the company. Therefore, it can be understood that as the cash flows / total assets decrease, the probability of administrative issue designation and the probability of delisting are increased. In summary, the logistic regression-based detection model in this study was found to be affected by the company's financial activities including ROE(Earnings before tax). However, decision tree-based detection model predicts the designation based on the cash flows of the company.

• Journal of Korean Academy of Nursing
• /
• v.28 no.4
• /
• pp.1047-1059
• /
• 1998

Senile Dementia is one of the dispositional mental disorder which has been known to the world since Hippocratic age. It has become a wide-spread social problem all over the world because of chronic disease processes and the demands of dependent care for several years as well as improbability of treatment of it at the causal level. Essentially, life styles of the older generation differ from those of the younger generation. While the fomer is used to the patriarchal system and the spirit of filial piet and respect, the latter is pragmatized and individualized under the effects of the Western material civilization. These differences between the two generations cause conflict between family members. In particular, the pain and conflict of care-givers who take care of a totally dependent dementia patient not only is inciting to the collapse of the family union, but is expanding into a serious social problem. According to this practical difficulty, this study has tried to compare dementia care-givers' experiences inter-culturally and to help set up more proper nursing interventions, describing and explaining them through ethnographies by participant observation and in-depth interviews that enable seeing them in a more close, honest and certain way. It also tries to provide a theoetical model of nusing care for dementia patients which is proper to Korean culture. This study is composed of 12 participants (4 males, 8 females) whose ages range from 37-71 years. The relations of patients are 5 spouses(3 husbands, 2 wives), 4 daughters-in-law, 2 daughters, and 1 son-in-law. The following are the care-givers' meaning of experiences that results of the study shows. The first is "psychological conflict". It contains the minds of getting angry, reproaching, being driven to dispair, blaming oneself, giving up lives, and being afraid, hopeless, and resigned. The second is "physical, social and psychological pressure" . At this stage, care-givers are shown to be under stress of both body and soul for the lack of freedom and tiredness. They also feel constraint because they hardly cope with the care and live through others' eyes. The third is "isolation". It makes the relationship of patient care-giver to be estranged, without understanding each other. They, also, experience indifference such as being upset and left alone. The forth is "acceptance" They gradually have compassion, bear up and then adapt themselves to the circumstances they are in. The fifth is "love". Now they learn to reward the other with love. It is also shown that this stage contains the process of winning others' recognition. The final is "hope". In this stage they really want situations to go smoothly and hope everything will be O.K. These consequences enable us to summarize the principles of cue experience such as, in the early stage, negative response such as physical·psychological confusion, pain and conflict are primary. Then the stage of acceptance emerges. It is an initial positive response phase when care-givers may admit their situations. As time passes by a positive response stage emerges. At last they have love and hope. Three stages we noted above : however, there are never consistent situations. Rather it gradually comes into the stage of acceptance, repeating continuous conflict, pressure and isolation. If any interest and understanding of families or the support of surrounding society lack, it will again be converted to negative responses sooner or later. Otherwise, positive responses like hope and love can be encouraged if the family and the surroundings give active aids and understanding. After all, the principles of dementia care experiences neither stay at any stage, nor develop from negative stages to positive stages steadily. They are cycling systems in which negative responses and positive responses are constantly being converted. I would like to suggest the following based on the above conclusions : First, the systematic and planned education of dementia should be performed in order to enhance public relations. Second, a special medical treatment center which deals with dementia, under government's charge, should be managed. Third, the various studies approaching dementia care experiences result in the development of more reasonable and useful nursing guidelines.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
• /
• v.7 no.2
• /
• pp.113-127
• /
• 2012

From the end of 1st quarter of 2012, Korean mandatory firms had started releasing financial reports conforming to the K-IFRS(Korean adopted International Financial Reporting Standards). Major characteristics of IFRS, such as 'principles based' features, consolidated reporting, 'fair value' measurement, increased pressure for non-financial disclosures have resulted in brief and various disclosure practices regarding the main body of each statements and vast amount of note description requirements. Meanwhile, a host of previous studies on IFRS disclosures have incorporated regulatory and/or 'compete information' perspectives, mainly focusing on suggesting further enforcement of strengthened requirements and providing guidelines for specific treatments. Thus, as an extension of prior findings and suggestions this study had explored to conduct an integrative approach embracing views of the reporting entities and the users of financial information. In spite of all the state-driven efforts for faithful representation and comparability of corporate financial reports, an overhaul of disclosure practices of fiscal year 2010 and 2011 had revealed numerous cases of insufficiency and discordance in terms of mandatory norms and market expectations. As to the causes of such shortcomings, this study identified several factors from the corporate side and the users of the information; some inherent aspects of IFRS, industry/corporate-specific context, expenditures related to internalizing IFRS system, reduced time frame for presentation. lack of clarity and details to meet the quality of information - understandability, comparability etc. - commonly requested by the user group. In order to improve current disclosure practices, dual approach had been suggested; Firstly, to encourage and facilitate implementation, (1) further segmentation and differentiation of mandates among companies, (2) redefining the scope and depth of note descriptions, (3) diversification and coordination of reporting periods, (4) providing support for equipping disclosure systems and granting incentives for best practices had been discussed. Secondly, as for the hard measures, (5) regularizing active involvement of corporate and user group delegations in the establishment and amendment process of K-IFRS (6) enforcing detailed and standardized disclosure on reporting entities had been recommended.