• Journal of Global Scholars of Marketing Science
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• v.9
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• pp.203-219
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• 2002

The purpose of the study is to analyze environmental changes in market, customer and information technology of Internet fashion industry, to present fashion company's current situation and strategy for confronting business environmental changes, and to propose the direction of the future research in this fashion field. First, the importance of Internet has been increased due to the rapid growth in number of Internet user and the size of the e-commerce. Second, to satisfy the customers requiring reasonable price and differentiated product and service, e-Branding strategy implementation, customized service development and enhancement on Web-site credibility and loyalty are demanding in the market. Third, development on the mobile technology and the increase in the Internet user using mobile communication device requires preparing mobile consuming environment, new business platform. Fashion industry's current situation and competitive strategy developed by the environmental change analysis were used for developing future research direction, that is, the m-business, CRM, and e-Branding. Through the understanding of environmental changes in Internet fashion industry and proposed research direction, research on Internet fashion marketing is hoped to be vitalized. Results of this study were expected to be utilized the strategy development.

• The Journal of the Korea Contents Association
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• v.13 no.1
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• pp.73-82
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• 2013

The purpose of this study is to explore attributes of trusted blog product-reviews and to examine the weight of each attribute. First, the attributes of trusted blog product-reviews were collected through consumer interviews. Second, the trust attributes were examined in terms of their relative importance. The results are: 1) Thirty-five of trust attributes were discovered and categorized into 'popularity', 'presence', 'attractiveness', 'trustworthiness', and 'expertise'. 2) In general, attributes reflecting usefulness, trustworthiness and attractiveness seemed the most important trust factors. 3) 'presence', which have not been highlighted so far in trust research, was emerged as an important trust factor in the web blog context. Theoretical and practical implications were discussed.

• Journal of Digital Contents Society
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• v.12 no.1
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• pp.107-114
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• 2011

Recently, interests for recruiting and the rates of using smart phones are growing fast. The paper proposes Recruid, which is a smart phone application based on android platform, that helps real-time communication matching between users. They can use every services of Recruid anywhere and anytime which reflects mobility characteristics of smart phones, and also use additional services on web pages. Recruid provides three main services. First, it provides mobile subscription and submission functionality on high mobility and convenience. Second, it provides group service that user can make their own groups. Last, it also provides reliability evaluation mechanism of activity data in Recruid thus, Recruid provides high credibility to users. The paper presents implementation of Recruid and shows the snapshots of the presented system.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.11
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• pp.730-737
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• 2018

With the increasing number of health consumers aiming for a happy quality of life, the O2O medical marketing market is activated by choosing reliable health care facilities and receiving high quality medical services based on the medical information distributed on web's blog. Because unstructured text data used on the Internet, mobile, and social networks directly or indirectly reflects authors' interests, preferences, and expectations in addition to their expertise, it is difficult to guarantee credibility of medical information. In this study, we propose a blog reading system that provides users with a higher quality medical information service by classifying medical information blogs (medical blog, ad blog) using bigdata and MLP processing. We collect and analyze many domestic medical information blogs on the Internet based on the proposed big data and machine learning technology, and develop a personalized health information recommendation system for each disease. It is expected that the user will be able to maintain his / her health condition by continuously checking his / her health problems and taking the most appropriate measures.

• International conference on construction engineering and project management
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• 2011.02a
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• pp.213-220
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• 2011

In the current construction industry where various stakeholders take part, BIM Data exchange using standard format can provide a more efficient working environment for related staffs during the life-cycle of the building. Currently, the formats used to exchange the data from 3D-CAD application to structure energy analysis at the design stages are IFC, the international standard format provided by IAI, and gbXML, developed by Autodesk. However, because of insufficient data compatibility, the BIM data produced in the 3D-CAD application cannot be directly used in the energy analysis, thus there needs to be additional data entry. The reasons for this are as follows: First, an IFC file cannot contain all the data required for energy simulation. Second, architects sometimes write material names on the drawings that are not matching to those in the standard material library used in energy analysis tools. DOE-2.2 and Energy Plus are the most popular energy analysis engines. And both engines have their own material libraries. However, our investigation revealed that the two libraries are not compatible. First, the types and unit of properties were different. Second, material names used in the library and the codes of the materials were different. Furthermore, there is no material library in Korean language. Thus, by comparing the basic library of DOE-2, the most commonly used energy analysis engine worldwide, and EnergyPlus regarding construction materials; this study will analyze the material data required for energy analysis and propose a way to effectively enter these using semantic web's ontology. This study is meaningful as it enhances the objective credibility of the analysis result when analyzing the energy, and as a conceptual study on the usage of ontology in the construction industry.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.199-219
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• 2018

With the everyday use of the Internet and the spread of various smart devices, users have been able to communicate in real time and the existing communication style has changed. Due to the change of the information subject by the Internet, data became more massive and caused the very large information called big data. These Big Data are seen as a new opportunity to understand social issues. In particular, text mining explores patterns using unstructured text data to find meaningful information. Since text data exists in various places such as newspaper, book, and web, the amount of data is very diverse and large, so it is suitable for understanding social reality. In recent years, there has been an increasing number of attempts to analyze texts from web such as SNS and blogs where the public can communicate freely. It is recognized as a useful method to grasp public opinion immediately so it can be used for political, social and cultural issue research. Text mining has received much attention in order to investigate the public's reputation for candidates, and to predict the voting rate instead of the polling. This is because many people question the credibility of the survey. Also, People tend to refuse or reveal their real intention when they are asked to respond to the poll. This study collected comments from the largest Internet portal site in Korea and conducted research on the 19th Korean presidential election in 2017. We collected 226,447 comments from April 29, 2017 to May 7, 2017, which includes the prohibition period of public opinion polls just prior to the presidential election day. We analyzed frequencies, associative emotional words, topic emotions, and candidate voting rates. By frequency analysis, we identified the words that are the most important issues per day. Particularly, according to the result of the presidential debate, it was seen that the candidate who became an issue was located at the top of the frequency analysis. By the analysis of associative emotional words, we were able to identify issues most relevant to each candidate. The topic emotion analysis was used to identify each candidate's topic and to express the emotions of the public on the topics. Finally, we estimated the voting rate by combining the volume of comments and sentiment score. By doing above, we explored the issues for each candidate and predicted the voting rate. The analysis showed that news comments is an effective tool for tracking the issue of presidential candidates and for predicting the voting rate. Particularly, this study showed issues per day and quantitative index for sentiment. Also it predicted voting rate for each candidate and precisely matched the ranking of the top five candidates. Each candidate will be able to objectively grasp public opinion and reflect it to the election strategy. Candidates can use positive issues more actively on election strategies, and try to correct negative issues. Particularly, candidates should be aware that they can get severe damage to their reputation if they face a moral problem. Voters can objectively look at issues and public opinion about each candidate and make more informed decisions when voting. If they refer to the results of this study before voting, they will be able to see the opinions of the public from the Big Data, and vote for a candidate with a more objective perspective. If the candidates have a campaign with reference to Big Data Analysis, the public will be more active on the web, recognizing that their wants are being reflected. The way of expressing their political views can be done in various web places. This can contribute to the act of political participation by the people.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.10
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• pp.2966-2973
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• 2009

In this article, I propose effective strategies for improving the Predictive Health Care. The results of qualitative study on health information show the following order from the highest score: whether health information is scientifically sound ($3.7\pm0.5$), whether people can easily understand health information ($3.6\pm0.5$), and whether health information reflects the public'sconcerns (($3.5\pm0.5$), and whether health information includes enough information to satisfy the public ($2.9\pm0.6$). The most pressing reforms for the effective Predictive Health Care areto provide enough health information and regularly collection of information because the Predictive Health Care has not provided enough information, authoritative information has rarely been offered, and methodological limitations on producing and applying predictive information have not been addressed. Although the Predictive Health Care provides online services like web-based epidemic reporting system, it needs to extend services from the epidemic information to general health information because of lack of promoting the Predictive Health Care and of credibility of information offered so far. Lastly, the Predictive Health Care needs to strengthen efforts to collect information, form common grounds between information and the public's concerns, clarify classification system of information, and offer an easy way for the public to use information.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.63-82
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• 2016

Individuals gather information online to resolve problems in their daily lives and make various decisions about the purchase of products or services. With the revolutionary development of information technology, Web 2.0 has allowed more people to easily generate and use online reviews such that the volume of information is rapidly increasing, and the usefulness and significance of analyzing the unstructured data have also increased. This paper presents an analysis on the lexical features of expert product reviews to determine their influence on consumers' purchasing decisions. The focus was on how unstructured data can be organized and used in diverse contexts through text mining. In addition, diverse lexical features of expert reviews of contents provided by a third-party review site were extracted and defined. Expert reviews are defined as evaluations by people who have expert knowledge about specific products or services in newspapers or magazines; this type of review is also called a critic review. Consumers who purchased products before the widespread use of the Internet were able to access expert reviews through newspapers or magazines; thus, they were not able to access many of them. Recently, however, major media also now provide online services so that people can more easily and affordably access expert reviews compared to the past. The reason why diverse reviews from experts in several fields are important is that there is an information asymmetry where some information is not shared among consumers and sellers. The information asymmetry can be resolved with information provided by third parties with expertise to consumers. Then, consumers can read expert reviews and make purchasing decisions by considering the abundant information on products or services. Therefore, expert reviews play an important role in consumers' purchasing decisions and the performance of companies across diverse industries. If the influence of qualitative data such as reviews or assessment after the purchase of products can be separately identified from the quantitative data resources, such as the actual quality of products or price, it is possible to identify which aspects of product reviews hamper or promote product sales. Previous studies have focused on the characteristics of the experts themselves, such as the expertise and credibility of sources regarding expert reviews; however, these studies did not suggest the influence of the linguistic features of experts' product reviews on consumers' overall evaluation. However, this study focused on experts' recommendations and evaluations to reveal the lexical features of expert reviews and whether such features influence consumers' overall evaluations and purchasing decisions. Real expert product reviews were analyzed based on the suggested methodology, and five lexical features of expert reviews were ultimately determined. Specifically, the "review depth" (i.e., degree of detail of the expert's product analysis), and "lack of assurance" (i.e., degree of confidence that the expert has in the evaluation) have statistically significant effects on consumers' product evaluations. In contrast, the "positive polarity" (i.e., the degree of positivity of an expert's evaluations) has an insignificant effect, while the "negative polarity" (i.e., the degree of negativity of an expert's evaluations) has a significant negative effect on consumers' product evaluations. Finally, the "social orientation" (i.e., the degree of how many social expressions experts include in their reviews) does not have a significant effect on consumers' product evaluations. In summary, the lexical properties of the product reviews were defined according to each relevant factor. Then, the influence of each linguistic factor of expert reviews on the consumers' final evaluations was tested. In addition, a test was performed on whether each linguistic factor influencing consumers' product evaluations differs depending on the lexical features. The results of these analyses should provide guidelines on how individuals process massive volumes of unstructured data depending on lexical features in various contexts and how companies can use this mechanism from their perspective. This paper provides several theoretical and practical contributions, such as the proposal of a new methodology and its application to real data.