• Journal of the Korean Society for Library and Information Science
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• v.38 no.2
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• pp.239-259
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• 2004

The purpose of this study is to review the established research practices and managerial methods on the range of Knowledge that have been independently studied from the conventional information system (libraries) and the managerial information system (MIS, DSS and EIS) within the quantitative and the non-quantitative perspective. The information systems were developed through their own purpose since the 1950s and these days the corporate environments have become integrated due to the rapid creation and expansion of information. Therefore, to make fast decisions in this situation it is appropriate that these two systems, Library and the managerial information system, should be dealt within the same category. In other words, not only the quantitative data that become main sources of DSS or EIS, but also the qualitative data such as the text documents, video and audio data, which have been managed in the libraries and information centers and not extracted from the former, can be used as the new knowledge source. Also BSS/EIS can provide the splendid infrastructure for Knowledge Management(KM) while libraries/information centers manage the comprehensive range of explicit and tacit knowledge, which can be a facilitator or main driver for KM.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.32 no.1
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• pp.391-413
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• 2021

Policy study information is the essential source of information in every step of decision making process to plan, execute and assess the national operation policy. The policy study subject of a national policy research center from study design the performance assessment on its practical effect is managed via thorough process to secure its effectiveness and efficiency. However, the directly exposed information to the practical user or the public who are in need of actual policy study information is the resource published in a form of policy study report, the final result. NKIS operated by the National Research Council for Economics, Humanities and Social Sciences under the Office for Government Policy Coordination, Prime Minister's Secretariat is a public information offering service that conduct integrated management on study reports from cooperative study among institutes along with policy outcome from 27 national policy research centers. This study aims to introduce the current status of operation and information management of NKIS, apprehend the management characteristics of policy study information resources of national policy research center, and deduce remarks that need to be considered for API with external service for the derivation of standardized sharing data element.

• The Korean Journal of Archival Studies
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• no.71
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• pp.49-89
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• 2022

This study suggests the necessity and domestic application plan a national authority database that promotes an integrated access, richer search, and understanding of historical information sources and archival resources distributed among cultural heritage institutions through the "Social Networks and Archive Context" project case. As the SNAC project was transformed into an international cooperative organization led by NARA, it was possible to secure a sustainable operating system and realize cooperative authority control. In addition, SNAC authority records have the characteristics of providing richer contextual information about life and history and social and intellectual network information compared to libraries. Through case analysis, First, like SNAC, a cooperative body led by the National Archives and having joint ownership of the National Library of Korea should lead the development and expand the scope of participating institutions. Second, in the cooperative method, take a structure in which divisions are made for each field with special strengths, but the main decision-making is made through the administrative team in which the two organizations participate. Third, development of scalable open source software that can collect technical information in various formats when constructing authority data, designing with the structure and elements of archival authority records, designing functions to control the quality of authority records, and building user-friendly interfaces and the need for a platform design reflecting content elements.

• Information Systems Review
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• v.20 no.4
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• pp.79-96
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• 2018

This study started by focusing on the internalization of the technology appraisal model into the credit rating model to increase the discriminative power of the credit rating model not only for SMEs but also for all companies, reflecting the items related to the financial stability of the enterprises among the technology appraisal items. Therefore, it is aimed to verify whether the technology appraisal model can be applied to identify high-stability SMEs in advance. We classified companies into industries (manufacturing vs. non-manufacturing) and the age of company (initial vs. non-initial), and defined as a high-stability company that has achieved an average debt ratio less than 1/2 of the group for three years. The C5.0 was applied to verify the discriminant power of the model. As a result of the analysis, there is a difference in importance according to the type of industry and the age of company at the sub-item level, but in the mid-item level the R&D capability was a key variable for discriminating high-stability SMEs. In the early stage of establishment, the funding capacity (diversification of funding methods, capital structure and capital cost which taking into account profitability) is an important variable in financial stability. However, we concluded that technology development infrastructure, which enables continuous performance as the age of company increase, becomes an important variable affecting financial stability. The classification accuracy of the model according to the age of company and industry is 71~91%, and it is confirmed that it is possible to identify high-stability SMEs by using technology appraisal items.

• Journal of Korean Society of Forest Science
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• v.95 no.3
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• pp.235-239
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• 2006

This study aimed to present that the understanding of the relational structure among stakeholders of a national park with the usage of 'social network analysis' can help more effectively to diagnose conflicts among stakeholders and to resolve them, Conducting a telephone-interview survey with 12 stakeholders of the Taean-Haean National Park, Republic of Korea, this study examined their perception of conflicts related to the park, and compared their social networks when their conflicts emerged with their networks when the conflicts did not appear. When the conflicts appeared, the more respondents tried to seek other information sources apart from the Korea National Park Service (KNPS) and the fewer interactions among the stakeholders occurred than when the conflicts were not present. Based on the findings, it was suggested that KNPS need not to directly distribute information to the stakeholders, and to create the third party to generate more interaction and cooperative relationship among the stakeholders, Thus, it can be said that the employment of social network analysis is worth being employed in the study of conflict management in national parks.

• Journal of the Korean Society of Food Culture
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• v.9 no.4
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• pp.379-394
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• 1994

The purpose of this study was to identify the correlation between the factors influencing on housewives' decision-making processes of organic foods and the relating variables, and the 5 stages of decision-making processes of the EBK model is utilized in this study. The sample was selected from 411 housewives living in Seoul from 1st of September through 20th of September, 1993. Frequency, Percentage, Mean, Factor analysis, One-way ANOVA, Duncan's multiple range test, t-Test, Correlation, Multiple regression analysis and Path analysis were measured. Major results are as follows: 1. Purchasing motivation of the organic foods were in order of the health care, nutritive value and taste care. 2. The major informations source for the knowledge of organic foods were in order of TV/radio, newspaper/magazine, recommendations informations and advice through a family/friends/acquaintances. 3. Evalution criteria in shopping of organic foods, the total degree of consideration over the purchasing factors of organic foods was fairly high level: consumers thought much of the sanitation/freshness, nutritive value and the food safety. In this regard opinion leaders was dominantly mass media. Consumers have a tendency to purchase organic foods in consideration of their children and husband. 4. Major place to purchase organic foods are super markets and department stores. And When shopping organic foods, housewives by all means confirm the check points in their own mind, which were expiry date, manufactured date and packing condition, but unexpectedly manufactured company was out of concern. 5. Housewives usually satisfy with decision after purchasing organic foods, while they were fairly unsatisfied with the price, quality, incomplete description for ingredients and manufactured date. 6. The variables influencing to the sincerity when selecting the most desired organic foods is how be cares about the natural freshness of the foods and the types of residents in order. Another interesting tendency is the richer they are very considerate to decide. It is to say the people who cares more about the natural freshness is the sincerer when making decision and also the class who lives in the apartment house enjoying high income do not easily accept the product quality.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.85-109
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• 2018

Recommender system recommends the items expected to be purchased by a customer in the future according to his or her previous purchase behaviors. It has been served as a tool for realizing one-to-one personalization for an e-commerce service company. Traditional recommender systems, especially the recommender systems based on collaborative filtering (CF), which is the most popular recommendation algorithm in both academy and industry, are designed to generate the items list for recommendation by using 'overall rating' - a single criterion. However, it has critical limitations in understanding the customers' preferences in detail. Recently, to mitigate these limitations, some leading e-commerce companies have begun to get feedback from their customers in a form of 'multicritera ratings'. Multicriteria ratings enable the companies to understand their customers' preferences from the multidimensional viewpoints. Moreover, it is easy to handle and analyze the multidimensional ratings because they are quantitative. But, the recommendation using multicritera ratings also has limitation that it may omit detail information on a user's preference because it only considers three-to-five predetermined criteria in most cases. Under this background, this study proposes a novel hybrid recommendation system, which selectively uses the results from 'traditional CF' and 'CF using multicriteria ratings'. Our proposed system is based on the premise that some people have holistic preference scheme, whereas others have composite preference scheme. Thus, our system is designed to use traditional CF using overall rating for the users with holistic preference, and to use CF using multicriteria ratings for the users with composite preference. To validate the usefulness of the proposed system, we applied it to a real-world dataset regarding the recommendation for POI (point-of-interests). Providing personalized POI recommendation is getting more attentions as the popularity of the location-based services such as Yelp and Foursquare increases. The dataset was collected from university students via a Web-based online survey system. Using the survey system, we collected the overall ratings as well as the ratings for each criterion for 48 POIs that are located near K university in Seoul, South Korea. The criteria include 'food or taste', 'price' and 'service or mood'. As a result, we obtain 2,878 valid ratings from 112 users. Among 48 items, 38 items (80%) are used as training dataset, and the remaining 10 items (20%) are used as validation dataset. To examine the effectiveness of the proposed system (i.e. hybrid selective model), we compared its performance to the performances of two comparison models - the traditional CF and the CF with multicriteria ratings. The performances of recommender systems were evaluated by using two metrics - average MAE(mean absolute error) and precision-in-top-N. Precision-in-top-N represents the percentage of truly high overall ratings among those that the model predicted would be the N most relevant items for each user. The experimental system was developed using Microsoft Visual Basic for Applications (VBA). The experimental results showed that our proposed system (avg. MAE = 0.584) outperformed traditional CF (avg. MAE = 0.591) as well as multicriteria CF (avg. AVE = 0.608). We also found that multicriteria CF showed worse performance compared to traditional CF in our data set, which is contradictory to the results in the most previous studies. This result supports the premise of our study that people have two different types of preference schemes - holistic and composite. Besides MAE, the proposed system outperformed all the comparison models in precision-in-top-3, precision-in-top-5, and precision-in-top-7. The results from the paired samples t-test presented that our proposed system outperformed traditional CF with 10% statistical significance level, and multicriteria CF with 1% statistical significance level from the perspective of average MAE. The proposed system sheds light on how to understand and utilize user's preference schemes in recommender systems domain.