• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.93-107
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• 2014

In this paper, we report what we have observed with regard to user-perspective issue clustering based on multi-layered two-mode network analysis. This work is significant in the context of data collection by companies about customer needs. Most companies have failed to uncover such needs for products or services properly in terms of demographic data such as age, income levels, and purchase history. Because of excessive reliance on limited internal data, most recommendation systems do not provide decision makers with appropriate business information for current business circumstances. However, part of the problem is the increasing regulation of personal data gathering and privacy. This makes demographic or transaction data collection more difficult, and is a significant hurdle for traditional recommendation approaches because these systems demand a great deal of personal data or transaction logs. Our motivation for presenting this paper to academia is our strong belief, and evidence, that most customers' requirements for products can be effectively and efficiently analyzed from unstructured textual data such as Internet news text. In order to derive users' requirements from textual data obtained online, the proposed approach in this paper attempts to construct double two-mode networks, such as a user-news network and news-issue network, and to integrate these into one quasi-network as the input for issue clustering. One of the contributions of this research is the development of a methodology utilizing enormous amounts of unstructured textual data for user-oriented issue clustering by leveraging existing text mining and social network analysis. In order to build multi-layered two-mode networks of news logs, we need some tools such as text mining and topic analysis. We used not only SAS Enterprise Miner 12.1, which provides a text miner module and cluster module for textual data analysis, but also NetMiner 4 for network visualization and analysis. Our approach for user-perspective issue clustering is composed of six main phases: crawling, topic analysis, access pattern analysis, network merging, network conversion, and clustering. In the first phase, we collect visit logs for news sites by crawler. After gathering unstructured news article data, the topic analysis phase extracts issues from each news article in order to build an article-news network. For simplicity, 100 topics are extracted from 13,652 articles. In the third phase, a user-article network is constructed with access patterns derived from web transaction logs. The double two-mode networks are then merged into a quasi-network of user-issue. Finally, in the user-oriented issue-clustering phase, we classify issues through structural equivalence, and compare these with the clustering results from statistical tools and network analysis. An experiment with a large dataset was performed to build a multi-layer two-mode network. After that, we compared the results of issue clustering from SAS with that of network analysis. The experimental dataset was from a web site ranking site, and the biggest portal site in Korea. The sample dataset contains 150 million transaction logs and 13,652 news articles of 5,000 panels over one year. User-article and article-issue networks are constructed and merged into a user-issue quasi-network using Netminer. Our issue-clustering results applied the Partitioning Around Medoids (PAM) algorithm and Multidimensional Scaling (MDS), and are consistent with the results from SAS clustering. In spite of extensive efforts to provide user information with recommendation systems, most projects are successful only when companies have sufficient data about users and transactions. Our proposed methodology, user-perspective issue clustering, can provide practical support to decision-making in companies because it enhances user-related data from unstructured textual data. To overcome the problem of insufficient data from traditional approaches, our methodology infers customers' real interests by utilizing web transaction logs. In addition, we suggest topic analysis and issue clustering as a practical means of issue identification.

• Management & Information Systems Review
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• v.36 no.2
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• pp.277-306
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• 2017

Most past studies on the Critical Success Factors of Company Informatization focused on the completeness of Informatization and its financial effect, and there have not been enough studies on whether a company's management strategies can be supported by establishing Informatization direction. This implies that there must be verification on the followings; whether Informatization focuses on steering the implementation of management strategies, what correlation there are between major mechanism factors and Informatization performance. This also implies that there must be a new study to re-interpret the existing success factors of Informatization into strategic management paradigm. The purpose of this study is to empirically verify the influence of subject, environment, resource, and mechanism factors on informatization achievement, and to analyze the differences in influence of informatization success factors on informatization achievement depending on domestic large corporations and SMEs. This study presented the verification results for seven research hypotheses. It was confirmed through empirical analysis that securing resource factor was significant in informatization performance and that all sub-factors of learning mechanism and coordination mechanism were also significant in enterprise informatization achievement. In addition, it was confirmed through the control effect analysis depending on enterprise size that the differences in informatization performance of large corporations and SMEs are due to support environment factor, learning mechanism, and selection mechanism. The implications of this study are as follows: First, the significance of mechanism factors such as learning, internal coordination, and external coordination are relatively higher than other factors in informatization achievement. Secondly, informatization success factors that SMEs must focus on achieving are presented by analyzing the differences on informatization achievement of large corporations and SMEs. Third, since empirical research for informatization success mechanism factors not covered empirically in the prior research was directly progressed, it is thought that it could provide a comprehensive understanding for mechanism factors. In addition, this study is thought to provide a practical contribution that can be applied to other industrial areas and enterprises.

• Journal of radiological science and technology
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• v.36 no.2
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• pp.99-110
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• 2013

In the medical field, the necessity of education growth for the professional Radiation Technologists has been emphasized to become experts on radiation and the radiation field is important of the society. Also, in hospitals and companies, important on thesis is getting higher in order to active and cope with rapidly changing internal and external environment and a more in-depth expert training, the necessity of new teaching and learning model that can cope with changes in a more proactive has become. Thesis writing classes brought limits to the in-depth learning as to start a semester and rely on only specific programs besides, inevitable on passive participation. In addition, it does not have a variety opportunity to present, an actual opportunity that can be written and discussed does not provide much caused by instructor-led classes. As well as, it has had a direct impact on the quality of the thesis, furthermore, having the opportunity to participate in various conferences showed the limitations. In order to solve these problems, in this study, writing thesis has organized training operations as a consistent gradual deepening of learning, at the same time, the operational idea was proposed based on the connectivity integrated operating and effective training program & instructional tool for improving the ability to perform the written actual thesis. The development of teaching and learning model consisted of 4 system modeling, scaffolding, articulation, exploration. Depending on the nature of the course, consisting team following the personal interest and the topic allow for connection subject, based on this, promote research capacity through a step-by-step evaluation and feedback and, fundamentally strengthen problem-solving skills through the journal studies, help not only solving the real-time problem by taking wiki-space but also efficient use of time, increase the quality of the thesis by activating cooperation through mentoring, as a result, it was to promote a positive partnership with the academic. Support system in three stages planning subject, progress & writing, writing thesis & presentation and based on cognitive apprenticeship. The ongoing Coaching and Reflection of professor and expert was applied in order to maintain these activities smoothly. The results of this study will introduce actively, voluntarily and substantially join to learners, by doing so, culture the enhancement of creativity, originality and the ability to co-work and by enhance the expertise of based-knowledge, it is considered to be help to improve the comprehensive ability.

• Journal of Intelligence and Information Systems
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• v.22 no.2
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• pp.33-56
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• 2016

Many researchers have focused on developing bankruptcy prediction models using modeling techniques, such as statistical methods including multiple discriminant analysis (MDA) and logit analysis or artificial intelligence techniques containing artificial neural networks (ANN), decision trees, and support vector machines (SVM), to secure enhanced performance. Most of the bankruptcy prediction models in academic studies have used financial ratios as main input variables. The bankruptcy of firms is associated with firm's financial states and the external economic situation. However, the inclusion of qualitative information, such as the economic atmosphere, has not been actively discussed despite the fact that exploiting only financial ratios has some drawbacks. Accounting information, such as financial ratios, is based on past data, and it is usually determined one year before bankruptcy. Thus, a time lag exists between the point of closing financial statements and the point of credit evaluation. In addition, financial ratios do not contain environmental factors, such as external economic situations. Therefore, using only financial ratios may be insufficient in constructing a bankruptcy prediction model, because they essentially reflect past corporate internal accounting information while neglecting recent information. Thus, qualitative information must be added to the conventional bankruptcy prediction model to supplement accounting information. Due to the lack of an analytic mechanism for obtaining and processing qualitative information from various information sources, previous studies have only used qualitative information. However, recently, big data analytics, such as text mining techniques, have been drawing much attention in academia and industry, with an increasing amount of unstructured text data available on the web. A few previous studies have sought to adopt big data analytics in business prediction modeling. Nevertheless, the use of qualitative information on the web for business prediction modeling is still deemed to be in the primary stage, restricted to limited applications, such as stock prediction and movie revenue prediction applications. Thus, it is necessary to apply big data analytics techniques, such as text mining, to various business prediction problems, including credit risk evaluation. Analytic methods are required for processing qualitative information represented in unstructured text form due to the complexity of managing and processing unstructured text data. This study proposes a bankruptcy prediction model for Korean small- and medium-sized construction firms using both quantitative information, such as financial ratios, and qualitative information acquired from economic news articles. The performance of the proposed method depends on how well information types are transformed from qualitative into quantitative information that is suitable for incorporating into the bankruptcy prediction model. We employ big data analytics techniques, especially text mining, as a mechanism for processing qualitative information. The sentiment index is provided at the industry level by extracting from a large amount of text data to quantify the external economic atmosphere represented in the media. The proposed method involves keyword-based sentiment analysis using a domain-specific sentiment lexicon to extract sentiment from economic news articles. The generated sentiment lexicon is designed to represent sentiment for the construction business by considering the relationship between the occurring term and the actual situation with respect to the economic condition of the industry rather than the inherent semantics of the term. The experimental results proved that incorporating qualitative information based on big data analytics into the traditional bankruptcy prediction model based on accounting information is effective for enhancing the predictive performance. The sentiment variable extracted from economic news articles had an impact on corporate bankruptcy. In particular, a negative sentiment variable improved the accuracy of corporate bankruptcy prediction because the corporate bankruptcy of construction firms is sensitive to poor economic conditions. The bankruptcy prediction model using qualitative information based on big data analytics contributes to the field, in that it reflects not only relatively recent information but also environmental factors, such as external economic conditions.

• Korean Journal of Psychosomatic Medicine
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• v.24 no.2
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• pp.227-235
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• 2016

Objectives : The aims of this study were to know the frequency of cognitive dysfunction among patients with autoimmune thyroid disorders, and to reveal influencing factors on it, especially to clarify association with autoimmune thyroid antibodies. Methods : From sixty-five female patients with autoimmune thyroid disorders, demographic data were obtained by structured interview. Their cognitive funtions were measured using the MMSE-K and the MoCA-K tests. Depression was evaluated by the K-HDRS. Results : 1) Among patients with autoimmune thyroid disorders, 7.69% of them were below 24 on the MMSE-K, while 10.77% were below 22 on the MoCA-K. The frequency of cognitive deficit was not significantly different according to having positivity to antimicrosomal antibodies or not. 2) The antimicrosomal antibody-positive patients had significantly higher antithyroglobulin antibody titers, antimicrosomal antibody titers, and TSH concentration, while had significantly lower free T4 levels(p<0.05, respectively). 3) The total scores of the MMSE-K and the MoCA-K had significant correlation with age, marital status, antithyroglobulin antibody titers and K-HDRS(p<0.05, respectively). 4) The regression analysis revealed that variables such as age, education, autoimmune thyroid antibodies, thyroid function and depression did not influence on cognitive function of patients with autoimmune thyroid disorders. Conclusions : Our results could not support that cognitive function of patients with autoimmune thyroid disorders had correlation with autoimmune thyroid antibodies.

• Journal of The Korean Association For Science Education
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• v.41 no.6
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• pp.471-481
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• 2021

The importance of science education for cultivating the competencies required by an intelligent information society is gradually being strengthened. The government's roles and responsibilities for science education are stipulated by laws and policies in Korea. In order to systematically support science education, continuous monitoring of related policies is essential. This study aims to develop indicators that can be used to systematically and continuously monitor the national policies on science education in Korea. To achieve this goal, we first derive the framework for the indicators that has two dimensions (learner and science education context) and three categories (input, process, and outcome) from literature reviews. In order to derive the components and subcomponents of the indicators, the contents of science education-related indicators developed in Korea or abroad were reviewed. In order to verify the suitability and validity of the framework and components of the initial indicators, a two-round Delphi method was conducted with 25 expert participants with five different professions in science education. Finally, three components of the 'input' category (student characteristics, teacher characteristics, and educational infrastructure), three components of the 'process' category (science curriculum implementation, science educational contents and programs implementation, and teacher professional development program implementation), and five components of the 'outcome' category (science competency, participation and action, affective achievement, cognitive achievement, and satisfaction) were derived. An instrument to collect data from students, teachers, and institutions was developed based on the components and subcomponents, and content validity and internal consistency of the instrument were analyzed. Korea's Science Education Indicators developed in this study can comprehensively measure the current status of science education and is expected to contribute to a more efficient and effective science education policy planning and implementation.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.16 no.4
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• pp.115-133
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• 2021

This study examines the concept and related technologies of the 4th industrial revolution that has been mixed so far and examines the socio-economic changes and influences resulting from it, and the cases of responding to the 4th industrial revolution in major countries. Based on this, by deriving SWOT factors and calculating the importance of each factor for Korean venture companies to prepare for the forth industrial revolution, it was intended to help the government and policymakers in suggesting directions for establishing related policies. Furthermore, the purpose of this study was to suggest a direction for securing global competitiveness to Korean venture entrepreneurs and to help with basic and systematic analysis for further academic in-depth research. For this study, a total of 21 items derived through extensive literature research and data research to understand what are the necessary competency factors for internal and external environmental changes in order for Korean venture companies to have global competitiveness in the era of the 4th Industrial Revolution. After reviewing SWOT factors by three expert groups and confirming them through Delphi survey, the importance of each item was analyzed by using AHP, a systematic decision-making technique. As a result of the analysis, it was shown that Strength(48%), Opportunity(25%), Threat(16%), Weakness(11%) were considered important in order. In terms of sub-items, 'quick and flexible commercialization capability', 'platform/big data/non-face-to-face service activation', and 'ICT infrastructure and it's utilization' were shown to be of the comparatively high importance. On the other hand, in the lower three items, 'macro-economic stability and social infrastructure', 'difficulty in entering overseas markets due to global protectionism', and 'absolutely inferior in foreign investment' were found to have low priority. As a result of the correlation verification by item to see differences in opinions by industry, academia, and policy expert groups, there was no significant difference of opinion, as industry and academic experts showed a high correlation and industry experts and policy experts showed a moderate correlation. The correlation between the academic and policy experts was not statistically significant (p<0.01), so it was analyzed that there was a difference of opinion on importance. This was due to the fact that policy experts highly valued 'quick and flexible commercialization', which are strengths, and 'excellent educational system and high-quality manpower' and 'creation of new markets' which are opportunity items, while academic experts placed great importance on 'support part of government policy', which are strengths. The implication of this study is that in order for Korean venture companies to secure competitiveness in the field of the 4th industrial revolution, it is necessary to have a policy that preferentially supports the relevant items of strengths and opportunity factors. The difference in the details of strength factors and opportunity factors, which shows a high level of variability, suggests that it is necessary to actively review it and reflect it in the policy.

• The Korean Journal of Archival Studies
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• no.22
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• pp.77-114
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• 2009

The right to know is not satisfied merely by making or improving laws or systems. The right to know is a matter of culture rather than system. Nevertheless, consistent system improvement measures are required. There are many laws relating to the right to know. In particular, at the core are the Official Information Disclosure Act, the Record and Archives Management Act, and the Presidential Record Management Act. The fact that systems relating to official record management and presidential record management are related to the right to know is understood by the promotion of records and archives management reform after the year 2004, as a result of which the national archives management innovation road map was established. Reflecting the many opinions of the "Information Disclosure System Improvement Task Force" composed with participation of the government and the press after the participatory government's announcement of "Measures to Advance the Support System for News Coverage," amendments to the Information Disclosure Act have come forward with system improvement measures in connection with issues that had arisen until then. Such improvement measures have not resulted in actual improvements. This thesis proposes several system improvement measures, focusing on those that have arisen until now but have not been reflected in discussion, such as converting the concept of information non-disclosure into disclosure postponement, preparing and disclosing particular information disclosure standards, specifying personal information for non-disclosure, specifying and strictly applying any information that has not been disclosed for purposes of internal review, deleting non-disclosure items in stenographic records that do not have a reason to exist, and establishing limits and terms of non-disclosure. Of the most remarkable system improvement measures that have been made until now is our recognition that the right to know is not limited to the information disclosure system but that the "cause" of archive management should be systematic and scientific. In other words, the right to know is understood to establish not just accidential factors, such as with a whistle-blower, but the inevitable factors of systemization of production, distribution, preservation, and use of archives. Much more study should be pursued regarding disclosure of archives information. In particular, difficult issues to be resolved regarding reading records at permanent archives management institutions, such as the National Archives of Korea, or copyrights that arise in the process, require constant study from academia and relevant institutions.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.1-23
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• 2019

Big data refers to data that is difficult to store, manage, and analyze by existing software. As the lifestyle changes of consumers increase the size and types of needs that consumers desire, they are investing a lot of time and money to understand the needs of consumers. Companies in various industries utilize Big Data to improve their products and services to meet their needs, analyze unstructured data, and respond to real-time responses to products and services. The financial industry operates a decision support system that uses financial data to develop financial products and manage customer risks. The use of big data by financial institutions can effectively create added value of the value chain, and it is possible to develop a more advanced customer relationship management strategy. Financial institutions can utilize the purchase data and unstructured data generated by the credit card, and it becomes possible to confirm and satisfy the customer's desire. CRM has a granular process that can be measured in real time as it grows with information knowledge systems. With the development of information service and CRM, the platform has change and it has become possible to meet consumer needs in various environments. Recently, as the needs of consumers have diversified, more companies are providing systematic marketing services using data mining and advanced CRM (Customer Relationship Management) techniques. KB Kookmin Card, which started as a credit card business in 1980, introduced early stabilization of processes and computer systems, and actively participated in introducing new technologies and systems. In 2011, the bank and credit card companies separated, leading the 'Hye-dam Card' and 'One Card' markets, which were deviated from the existing concept. In 2017, the total use of domestic credit cards and check cards grew by 5.6% year-on-year to 886 trillion won. In 2018, we received a long-term rating of AA + as a result of our credit card evaluation. We confirmed that our credit rating was at the top of the list through effective marketing strategies and services. At present, Kookmin Card emphasizes strategies to meet the individual needs of customers and to maximize the lifetime value of consumers by utilizing payment data of customers. KB Kookmin Card combines internal and external big data and conducts marketing in real time or builds a system for monitoring. KB Kookmin Card has built a marketing system that detects realtime behavior using big data such as visiting the homepage and purchasing history by using the customer card information. It is designed to enable customers to capture action events in real time and execute marketing by utilizing the stores, locations, amounts, usage pattern, etc. of the card transactions. We have created more than 280 different scenarios based on the customer's life cycle and are conducting marketing plans to accommodate various customer groups in real time. We operate a smart offering system, which is a highly efficient marketing management system that detects customers' card usage, customer behavior, and location information in real time, and provides further refinement services by combining with various apps. This study aims to identify the traditional CRM to the current CRM strategy through the process of changing the CRM strategy. Finally, I will confirm the current CRM strategy through KB Kookmin card's big data utilization strategy and marketing activities and propose a marketing plan for KB Kookmin card's future CRM strategy. KB Kookmin Card should invest in securing ICT technology and human resources, which are becoming more sophisticated for the success and continuous growth of smart offering system. It is necessary to establish a strategy for securing profit from a long-term perspective and systematically proceed. Especially, in the current situation where privacy violation and personal information leakage issues are being addressed, efforts should be made to induce customers' recognition of marketing using customer information and to form corporate image emphasizing security.

• Journal of the Korean Society of Earth Science Education
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• v.12 no.1
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• pp.82-93
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• 2019

The purpose of this study is to analyze the results of satisfaction for learning eco-system on the teacher training program conducted at the IS-Geo (International School for Geoscience Resources) which is KIGAM (Korea Institute of Geoscience and Mineral Resources), and to determine the satisfaction and educational effects of the teacher training programs on elementary and secondary teachers. And then, to suggest improvement points in the future operation of the teacher training program at the IS-Geo. Therefore, we conducted questionnaire of satisfaction for learning eco-system based on the data collected by a survey of 98 elementary and secondary teachers who participated in the teacher training program at the IS-Geo, from July 2017 to August 2018. The research results are as follows. First, the results of satisfaction for learning eco-system showed high values of 4.58 or higher in both the elementary and secondary programs, and the teacher training program conducted by the IS-Geo had a positive effect on the training participants. Second, internal factors indicating learning motivation and learning development were elementary teacher training 4.70 and secondary teacher training 4.64, and it is necessary to develop training contents and programs by classifying them into majors other than the earth science department. Third, intermediate factors indicating contents of education and learning curriculum were 4.67 for an elementary teacher training program and 4.72 for secondary teacher training program. In addition, in order to operate the teacher training program according to the purpose of science and technology culture, it is necessary to develop a teaching-learning model and to improve the quality of teaching. Fourth, external factors indicating learner support and quality of instructors were 4.83 for an elementary teacher training program and 4.72 for a secondary teacher training program. In particular, it is necessary to develop teaching materials that can be used immediately in school classes and can generate interest.