• The Journal of Society for e-Business Studies
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• v.17 no.1
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• pp.75-90
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• 2012

The aim of this study is to investigate the preferred website's design factors. Social computing is driving a dramatic evolution of the Web these days, and a number of users are increasing every day. But many website designers are just focusing on functional aspects of website. Also, there are few studies regarding the social computing website's emotional design. Proper designs of social computing websites could be designed through investigating the websites design factors preferred by users. Empirical study was conducted in order to investigate websites design factors preferred by users. Website design and user emotion of social computing websites were measured by the questionnaire and 254 people participated. Also, Website design and user emotion of non-social computing websites were measured by same participants, and then comparing results each other. Five design factors and eight emotion factors were derived, and only four out of design factors and three out of emotion factors were found as having significant effects on the satisfaction of social computing website. In addition, different factors in determining user satisfaction when using social computing websites and non-social computing website.

• The Journal of Society for e-Business Studies
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• v.21 no.2
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• pp.151-161
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• 2016

In the era of the Web 2.0, characterized by the openness, sharing and participation, it is easy for internet users to produce and share the data. The amount of the unstructured data which occupies most of the digital world's data has increased exponentially. One of the kinds of the unstructured data called personal online product reviews is necessary for both the company that produces those products and the potential customers who are interested in those products. In order to extract useful information from lots of scattered review data, the process of collecting data, storing, preprocessing, analyzing, and drawing a conclusion is needed. Therefore we introduce the text-mining methodology for applying the natural language process technology to the text format data like product review in order to carry out extracting structured data by using R programming. Also, we introduce the data-mining to derive the purpose-specific customized information from the structured review information drawn by the text-mining.

• Convergence Security Journal
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• v.7 no.3
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• pp.79-86
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• 2007

B2B e-business is an economic transaction formed between companies through various networks including internet. At present, e-business between companies partly applies information distribution between companies, but many enterprises expect that a corporate basic system will be gradually changed into XML basis if web service is earnestly materialized, so they are competing with each other in developing XML DBMS. Existing XML DBMS studies used XML DTD in order to represent the structure of XML document. Such XML DTD defines the expression of a simple type, so there are many difficulties in defining the structure of XML document. To cope with this, in this paper, the author will develop database schema generator utilizing relational database generally used in storing contents of data, on the basis of XML schema selected as a standard of W3C. Also, to store XML data, the author proposed the automatic conversion method of relational database schema that used XML schema.

• The Journal of Society for e-Business Studies
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• v.18 no.2
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• pp.153-173
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• 2013

The ontology has been gaining increasing interests by recent arise of the semantic web and related technologies. The focus is mostly on inference query processing that requires high-level techniques for storage and searching ontologies efficiently, and it has been actively studied in the area of semantic-based searching. W3C's recommendation is to use RDFS and OWL for representing ontologies. However memory-based editors, inference engines, and triple storages all store ontology as a simple set of triplets. Naturally the performance is limited, especially when a large-scale ontology needs to be processed. A variety of researches on proposing algorithms for efficient inference query processing has been conducted, and many of them are based on using proven relational database technology. However, none of them had been successful in obtaining the complete set of inference results which reflects the five characteristics of the ontology properties. In this paper, we propose a new index structure called hyper cube index to efficiently process inference queries. Our approach is based on an intuition that an index can speed up the query processing when extensive inferencing is required.

• Journal of the Korea Convergence Society
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• v.5 no.4
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• pp.9-13
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• 2014

The environment of IT is, currently, on its developing process to the period of cloud computing, big data, and business intelligence which not only enable computer and internet to be utilized like the water or the air, but also be a new motivating force for its advance. In the respect of various interactions and Infographics, however, it is requiring more demands from its users, and additional functions which cannot be provided by the Web Browser. In this study, therefore, it will be suggested a design of N-screen convergence presentation tier by using infographics based on N-Tier platform.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2002.05a
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• pp.222-229
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• 2002

A value net is a business design that uses digital supply chain concepts to achieve both superior customer satisfaction and company profitability. In order to implement the value net model, information processing and distribution needs to occur in real time. Software agent technology is becoming popular due to the inherent characteristics of autonomy, distributedness and modularity. In this paper, we adopt agent technology to handle all real time decision process, making the value net model a complex multi-agent network of decision makers. For the agents to properly coordinate their respective activities we develop MAVN model, a Web-based multi-agent language grounded in the XML and Java.

• Asia Marketing Journal
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• v.12 no.4
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• pp.79-111
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• 2011

Digital economy has grown rapidly so that the new business area called 'Internet business' has been dramatically extended as time goes on. However, in the case of Internet business, market shares of individual companies seem to fluctuate very extremely. Thus marketing managers who operate the Internet sites have seriously observed the competition structure of the Internet business market and carefully analyzed the competitors' behavior in order to achieve their own business goals in the market. The newly created Internet business might differ from the offline ones in management styles, because it has totally different business circumstances when compared with the existing offline businesses. Thus, there should be a lot of researches for finding the solutions about what the features of Internet business are and how the management style of those Internet business companies should be changed. Most marketing literatures related to the Internet business have focused on individual business markets. Specifically, many researchers have studied the Internet portal sites and the Internet shopping mall sites, which are the most general forms of Internet business. On the other hand, this study focuses on the entire Internet business industry to understand the competitive circumstance of online market. This approach makes it possible not only to have a broader view to comprehend overall e-business industry, but also to understand the differences in competition structures among Internet business markets. We used time-series data of Internet connection rates by consumers as the basic data to figure out the competition patterns in the Internet business markets. Specifically, the data for this research was obtained from one of Internet ranking sites, 'Fian'. The Internet business ranking data is obtained based on web surfing record of some pre-selected sample group where the possibility of double-count for page-views is controlled by method of same IP check. The ranking site offers several data which are very useful for comparison and analysis of competitive sites. The Fian site divides the Internet business areas into 34 area and offers market shares of big 5 sites which are on high rank in each category daily. We collected the daily market share data about Internet sites on each area from April 22, 2008 to August 5, 2008, where some errors of data was found and 30 business area data were finally used for our research after the data purification. This study performed several empirical analyses in focusing on market shares of each site to understand the competition among sites in Internet business of Korea. We tried to perform more statistically precise analysis for looking into business fields with similar competitive structures by applying the cluster analysis to the data. The research results are as follows. First, the leading sites in each area were classified into three groups based on averages and standard deviations of daily market shares. The first group includes the sites with the lowest market shares, which give more increased convenience to consumers by offering the Internet sites as complimentary services for existing offline services. The second group includes sites with medium level of market shares, where the site users are limited to specific small group. The third group includes sites with the highest market shares, which usually require online registration in advance and have difficulty in switching to another site. Second, we analyzed the second place sites in each business area because it may help us understand the competitive power of the strongest competitor against the leading site. The second place sites in each business area were classified into four groups based on averages and standard deviations of daily market shares. The four groups are the sites showing consistent inferiority compared to the leading sites, the sites with relatively high volatility and medium level of shares, the sites with relatively low volatility and medium level of shares, the sites with relatively low volatility and high level of shares whose gaps are not big compared to the leading sites. Except 'web agency' area, these second place sites show relatively stable shares below 0.1 point of standard deviation. Third, we also classified the types of relative strength between leading sites and the second place sites by applying the cluster analysis to the gap values of market shares between two sites. They were also classified into four groups, the sites with the relatively lowest gaps even though the values of standard deviation are various, the sites with under the average level of gaps, the sites with over the average level of gaps, the sites with the relatively higher gaps and lower volatility. Then we also found that while the areas with relatively bigger gap values usually have smaller standard deviation values, the areas with very small differences between the first and the second sites have a wider range of standard deviation values. The practical and theoretical implications of this study are as follows. First, the result of this study might provide the current market participants with the useful information to understand the competitive circumstance of the market and build the effective new business strategy for the market success. Also it might be useful to help new potential companies find a new business area and set up successful competitive strategies. Second, it might help Internet marketing researchers take a macro view of the overall Internet market so that make possible to begin the new studies on overall Internet market beyond individual Internet market studies.

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.1-18
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• 2015

Collaborative filtering(CF) algorithm has been popularly used for recommender systems in both academic and practical applications. A general CF system compares users based on how similar they are, and creates recommendation results with the items favored by other people with similar tastes. Thus, it is very important for CF to measure the similarities between users because the recommendation quality depends on it. In most cases, users' explicit numeric ratings of items(i.e. quantitative information) have only been used to calculate the similarities between users in CF. However, several studies indicated that qualitative information such as user's reviews on the items may contribute to measure these similarities more accurately. Considering that a lot of people are likely to share their honest opinion on the items they purchased recently due to the advent of the Web 2.0, user's reviews can be regarded as the informative source for identifying user's preference with accuracy. Under this background, this study proposes a new hybrid recommender system that combines with users' review mining. Our proposed system is based on conventional memory-based CF, but it is designed to use both user's numeric ratings and his/her text reviews on the items when calculating similarities between users. In specific, our system creates not only user-item rating matrix, but also user-item review term matrix. Then, it calculates rating similarity and review similarity from each matrix, and calculates the final user-to-user similarity based on these two similarities(i.e. rating and review similarities). As the methods for calculating review similarity between users, we proposed two alternatives - one is to use the frequency of the commonly used terms, and the other one is to use the sum of the importance weights of the commonly used terms in users' review. In the case of the importance weights of terms, we proposed the use of average TF-IDF(Term Frequency - Inverse Document Frequency) weights. To validate the applicability of the proposed system, we applied it to the implementation of a recommender system for smartphone applications (hereafter, app). At present, over a million apps are offered in each app stores operated by Google and Apple. Due to this information overload, users have difficulty in selecting proper apps that they really want. Furthermore, app store operators like Google and Apple have cumulated huge amount of users' reviews on apps until now. Thus, we chose smartphone app stores as the application domain of our system. In order to collect the experimental data set, we built and operated a Web-based data collection system for about two weeks. As a result, we could obtain 1,246 valid responses(ratings and reviews) from 78 users. The experimental system was implemented using Microsoft Visual Basic for Applications(VBA) and SAS Text Miner. And, to avoid distortion due to human intervention, we did not adopt any refining works by human during the user's review mining process. To examine the effectiveness of the proposed system, we compared its performance to the performance of conventional CF system. The performances of recommender systems were evaluated by using average MAE(mean absolute error). The experimental results showed that our proposed system(MAE = 0.7867 ~ 0.7881) slightly outperformed a conventional CF system(MAE = 0.7939). Also, they showed that the calculation of review similarity between users based on the TF-IDF weights(MAE = 0.7867) leaded to better recommendation accuracy than the calculation based on the frequency of the commonly used terms in reviews(MAE = 0.7881). The results from paired samples t-test presented that our proposed system with review similarity calculation using the frequency of the commonly used terms outperformed conventional CF system with 10% statistical significance level. Our study sheds a light on the application of users' review information for facilitating electronic commerce by recommending proper items to users.

• The Journal of Society for e-Business Studies
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• v.17 no.3
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• pp.73-84
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• 2012

With transition to Knowledge society and introduction of information industry, there are many companies which have higher stock price than the suggested value from its financial information. To explain similar cases in capital markets, many researchers focus on non-financial information such as Web Traffic data or intangible assets such as intellectual property rights rather than traditional financial analysis. Besides, the relationships between financial and non-financial information with firm value are changed according to industry lifecycle. As Industry grows, financial information of company is more important for firm valuation in Capital market. We'd like to review the changes of relationships between financial information and firm valuation in Capital market especially for "Software", "Digital Contents", and "Internet" companies listed in Kosdaq market during 2000~2011. The result of data analysis shows the financial information gets more important after 2007. Inversely, it provides analytical bases that related industry gets mature. Also we show that intangible properties are more relevant to stock price of those technical based companies than others.

• The Journal of Society for e-Business Studies
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• v.14 no.4
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• pp.229-253
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• 2009

The effect of knowledge sharing and its importance have been reported in the information systems literature. However, little has been learned about the impact of knowledgeexchange web sites. This study explores relationships between individual characteristics such as personal innovativeness in the domain of information technology (PIIT), computer selfefficacy (CSE), and computer anxiety (CA), user involvement (UI), and knowledge sharing in a knowledge.exchange website. In order to examine our research model, we adopted a survey research design based on the Structural Equation Modeling method. By analyzing 241 samples collected, we conclude that UI and CA are valuable to discuss in terms of their theoretical and practical implications. This study not only extends the research on personal innovativeness and knowledge sharing, but also suggests a need for focused research efforts to investigate real practices on knowledge sharing. This study initiates this kind of efforts to make a relevant tie between design of knowledge sharing applications and the success case in the commercial field to encourage users to share their knowledge.