• Proceedings of the Korea Society of Information Technology Applications Conference
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• 2007.05a
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• pp.234-245
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• 2007

The empirical studies on the implementation of data warehousing systems (DWS) are lacking while there exist a number of studies on the implementation of IS. This study intends to examine the factors affecting the implementation success of DWS. The study adopts the empirical analysis of the sample of 112 responses from DWS practitioners. The study results suggest several implications for researchers and practitioners. First, when the support from top management becomes great, the implementation success of DWS in organizational aspects is more likely. When the support from top management exists, users are more likely to be encouraged to use DWS, and organizational resistance to use DWS is well coped with increasing the possibility of implementation success of DWS. The support of resource increases the implementation success of DWS in project aspects while it is not significantly related to the implementation success of DWS in organizational aspects. The support of funds, human resources, and other efforts enhances the possibility of successful implementation of project; the project does not exceed the time and resource budgets and meet the functional requirements. The effect of resource support, however, is not significantly related to the organizational success. The user involvement in systems implementation affects the implementation success of DWS in organizational and project aspects. The success of DWS implementation is significantly related to the users' commitment to the project and the proactive involvement in the implementation tasks. users' task. The observation of the behaviors of competitors which possibly increases data quality does not affect the implementation success of DWS. This indicates that the quality of data such as data consistency and accuracy is not ensured through the understanding of the behaviors of competitors, and this does not affect the data integration and the successful implementation of DWS projects. The prototyping for the DWS implementation positively affects the implementation success of DWS. This indicates that the extent of understanding requirements and the communication among project members increases the implementation success of DWS. Developing the prototypes for DWS ensures the acquirement of accurate or integrated data, the flexible processing of data, and the adaptation into new organizational conditions. The extent of consulting activities in DWS projects increases the implementation success of DWS in project aspects. The continuous support for consulting activities and technology transfer enhances the adherence to the project schedule preventing the exceeding use of project budget and ensuring the implementation of intended system functions; this ultimately leads to the successful implementation of DWS projects. The research hypothesis that the capability of project teams affects the implementation success of DWS is rejected. The technical ability of team members and human relationship skills themselves do not affect the successful implementation of DWS projects. The quality of the system which provided data to DWS affects the implementation success of DWS in technical aspects. The standardization of data definition and the commitment to the technical standard increase the possibility of overcoming the technical problems of DWS. Further, the development technology of DWS affects the implementation success of DWS. The hardware, software, implementation methodology, and implementation tools contribute to effective integration and classification of data in various forms. In addition, the implementation success of DWS in organizational and project aspects increases the data quality and system quality of DWS while the implementation success of DWS in technical aspects does not affect the data quality and system quality of DWS. The data and systems quality increases the effective processing of individual tasks, and reduces the decision making times and efforts enhancing the perceived benefits of DWS.

• Asia pacific journal of information systems
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• v.17 no.4
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• pp.207-228
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• 2007

Measuring Information Technology(IT) organizations' activities have been limited to mainly measure financial indicators for a long time. However, according to the multifarious functions of Information System, a number of researches have been done for the new trends on measurement methodologies that come with financial measurement as well as new measurement methods. Especially, the researches on IT Balanced Scorecard(BSC), concept from BSC measuring IT activities have been done as well in recent years. BSC provides more advantages than only integration of non-financial measures in a performance measurement system. The core of BSC rests on the cause-and-effect relationships between measures to allow prediction of value chain performance measures to allow prediction of value chain performance measures, communication, and realization of the corporate strategy and incentive controlled actions. More recently, BSC proponents have focused on the need to tie measures together into a causal chain of performance, and to test the validity of these hypothesized effects to guide the development of strategy. Kaplan and Norton[2001] argue that one of the primary benefits of the balanced scorecard is its use in gauging the success of strategy. Norreklit[2000] insist that the cause-and-effect chain is central to the balanced scorecard. The cause-and-effect chain is also central to the IT BSC. However, prior researches on relationship between information system and enterprise strategies as well as connection between various IT performance measurement indicators are not so much studied. Ittner et al.[2003] report that 77% of all surveyed companies with an implemented BSC place no or only little interest on soundly modeled cause-and-effect relationships despite of the importance of cause-and-effect chains as an integral part of BSC. This shortcoming can be explained with one theoretical and one practical reason[Blumenberg and Hinz, 2006]. From a theoretical point of view, causalities within the BSC method and their application are only vaguely described by Kaplan and Norton. From a practical consideration, modeling corporate causalities is a complex task due to tedious data acquisition and following reliability maintenance. However, cause-and effect relationships are an essential part of BSCs because they differentiate performance measurement systems like BSCs from simple key performance indicator(KPI) lists. KPI lists present an ad-hoc collection of measures to managers but do not allow for a comprehensive view on corporate performance. Instead, performance measurement system like BSCs tries to model the relationships of the underlying value chain in cause-and-effect relationships. Therefore, to overcome the deficiencies of causal modeling in IT BSC, sound and robust causal modeling approaches are required in theory as well as in practice for offering a solution. The propose of this study is to suggest critical success factors(CSFs) and KPIs for measuring performance for IT organizations and empirically validate the casual relationships between those CSFs. For this purpose, we define four perspectives of BSC for IT organizations according to Van Grembergen's study[2000] as follows. The Future Orientation perspective represents the human and technology resources needed by IT to deliver its services. The Operational Excellence perspective represents the IT processes employed to develop and deliver the applications. The User Orientation perspective represents the user evaluation of IT. The Business Contribution perspective captures the business value of the IT investments. Each of these perspectives has to be translated into corresponding metrics and measures that assess the current situations. This study suggests 12 CSFs for IT BSC based on the previous IT BSC's studies and COBIT 4.1. These CSFs consist of 51 KPIs. We defines the cause-and-effect relationships among BSC CSFs for IT Organizations as follows. The Future Orientation perspective will have positive effects on the Operational Excellence perspective. Then the Operational Excellence perspective will have positive effects on the User Orientation perspective. Finally, the User Orientation perspective will have positive effects on the Business Contribution perspective. This research tests the validity of these hypothesized casual effects and the sub-hypothesized causal relationships. For the purpose, we used the Partial Least Squares approach to Structural Equation Modeling(or PLS Path Modeling) for analyzing multiple IT BSC CSFs. The PLS path modeling has special abilities that make it more appropriate than other techniques, such as multiple regression and LISREL, when analyzing small sample sizes. Recently the use of PLS path modeling has been gaining interests and use among IS researchers in recent years because of its ability to model latent constructs under conditions of nonormality and with small to medium sample sizes(Chin et al., 2003). The empirical results of our study using PLS path modeling show that the casual effects in IT BSC significantly exist partially in our hypotheses.

• Journal of Korea Game Society
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• v.4 no.4
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• pp.11-18
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• 2004

The on-line games in the past were played by only two persons exchanging data based on one-to-one connections, whereas recent ones (e.g. MMORPG: Massively Multi-player Online Role-playings Game) enable tens of thousands of people to be connected simultaneously. Specifically, Korea has established an excellent network infrastructure that can't be found anywhere in the world. Almost every household has a high-speed Internet access. What made this possible was, in part, high density of population that has accelerated the formation of good Internet infrastructure. However, this rapid increase in the use of on-line games may lead to surging traffics exceeding the limited Internet communication capacity so that the connection to the games is unstable or the server fails. expanding the servers though this measure is very costly could solve this problem. To deal with this problem, the present study proposes the load distribution technology that connects in the form of local clustering the game servers divided by their contents used in each on-line game reduces the loads of specific servers using the load balancer, and enhances performance of sewer for their efficient operation. In this paper, a cluster system is proposed where each Game server in the system has different contents service and loads are distributed efficiently using the game server resource information such as CPU utilization. Game sewers having different contents are mutually connected and managed with a network file system to maintain information consistency required to support resource information updates, deletions, and additions. Simulation studies show that our method performs better than other traditional methods. In terms of response time, our method shows shorter latency than RR (Round Robin) and LC (Least Connection) by about 12%, 10% respectively.

• Korean Journal of Cognitive Science
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• v.28 no.4
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• pp.315-328
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• 2017

Even though assessment using information and communication technology will most likely lead the future of educational assessment, there is little domestic research on this topic. Computerized assessment will not only cut costs but also measure students' performance in ways not possible before. In this context, this study introduces a tool which can overcome the problems of multiple choice tests, which are most widely used type of assessment in current Korean educational setting. Multiple-choice tests, in which options are presented with the questions, are efficient in that grading can be automated; however, they allow for students who don't know the answer, to find the correct answer from the options. Park(2005) has developed a modified multiple-choice testing system (CMMT) using the interactivity of computers, that presents questions first, and options later for a short time when the student requests for them. The present study was conducted to find out if penalizing wrong answers could lower the possibility of students choosing an answer among the options when they don't know the correct answer. 116 students were tested with the directions that they will be penalized for wrong answers, but not for no response. There were 4 experimental conditions: 2 conditions of high or low percentage of penalizing, each in traditional multiple-choice or CMMT format. The results were analyzed using a two-way ANOVA for the number of no response, the test score and self-report score. Analysis showed that the number of no response was significantly higher for the CMMT format and that test scores were significantly lower when the penalizing percentage was high. The possibility of applying CMMT format tests while penalizing wrong answers in actual testing settings was addressed. In addition, the need for further research in the cognitive sciences to develop computerized assessment tools, was discussed.

• Archives of design research
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• v.18 no.1 s.59
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• pp.39-48
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• 2005

In the age of digital culture, TV broadcasting is exercising more influence as a information and communication medium compared to past. With the appearance of satellite broadcasting service in 2002, the broadcasting environment became a diversified field of local TV, cable TV, satellite, internet, etc. and created the time of multi-media and multi-channel. This ongoing change of broadcasting environment made the passive audience of the past, active image makers and new accepters, participants and users of communications, who know how to choose and use media as the active centerpiece, The active acceptor as the centerpiece of channel selections has become the center of the broadcasting, whereby they pick up and enjoy their favorite TV programs and came to remember the list of their favorite channels and zap them finally. In this point of spotting their favorite channels and improving the degree of recognition for the channels, the development of the noticeable brand for a particular program has made a great contribution. The aim of this study, therefore, is to recognize the factors, which are important in the habits of watching TV and to develop professional brands for TV broadcasting programs. The range of the survey for this study was home news programs and broadcasting stations abroad, which were on air from March to May in 2004. The focus of the survey was universal and professional news programs. Through this study, it was ascertained that, in the case of news, developing a brand for an anchor as well as for a professional brand of TV program could be an important element.

• Archives of design research
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• v.19 no.5 s.67
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• pp.119-128
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• 2006

The scale of visual expression has expanded from freeze frame to motion picture as media have developed. Moving pictures such as animation, movies, TV CM and GUI become formative elements whose movement is necessary compared to freeze frame as apparent movement phenomenon and unit structure such as short and scene appear. Therefore, of formative elements such as a shape, color, space, size and movement, movement is importantly distinguished in the moving image. The expression and form of image as a relationship between the signified and signifier explained by Saussure are accepted as a sign by mutual complement even though they limit the content. This makes it possible to infer that the formal feature of movement participates in the message content. To verify this, the result of moving picture visual perception experiment based on the gestalt grouping principle result shows that 70-80 percent of subjects think that 'movement' is the important grouping clue in perception. Movement affects the maintenance of the context of message content in the communication process when the meaning structure of moving picture is analyzed based on the structural feature. The identity can be maintained with if there is a movement with similar directive point even if the color and shape of people, things and background are changed. Second, the clarity of the content is elevated by a distinguished object as a figure by movement. Third, it acts as a knowledge representation which can predict similar movement process of next information processing. Forth, movement gives the content consistency even though more than two scenes have fast switch and complicated editing structure like cross-cutting. Movement becomes a clue which can make grouping information input by visual perception reaction. Also, it gives the order to the visual expression which can be used improperly by formation of structural frame of image message and has the effectiveness which elevates the clarity of signification. Moving picture has discourse with several mixed unit structures because it fundamentally contains time and the common and distinguished expression is needed by media-mix circumstances. Therefore, by the application of gestalt grouping principle to moving picture field, movement becomes the more distinguished than other formative elements and affects the formation of meaning structure. This study propose a viewpoint that develops structural formative beauty and new image expression in the media image field.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.6 no.3
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• pp.179-187
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• 2008

After Canada has struggled with a radioactive waste problem over for 20 years, the Canadian government finally found out that its approach by far has been lack of social acceptance, and needed a program such as public and stakeholder engagement (PSE) which involves the public in decision-making process. Therefore, the government made a special law, called Nuclear Fuel Waste Act (NFWA), to search for an appropriate nuclear waste management approach. NFWA laid out three possible approaches which were already prepared in advance by a nuclear expert group, and required Nuclear Waste Management Organization (NWMO) to be established to report a recommendation as to which of the proposed approaches should be adopted. However, NFWA allowed NWMO to consider additional management approach if the other three were not acceptable enough. Thus, NWMO studied and created a fourth management approach after it had undertaken an comparison of the benefits, risks and costs of each management approach: Adaptive Phased Management. This approach was intended to enable the implementers to accept any technological advancement or changes even in the middle of the implementation of the plan. The Canadian PSE case well shows that technological R&D are deeply connected with social acceptance. Even though the developments and technological advancement are carried out by the scientists and experts, but it is important to collect the public opinion by involving them to the decision-making process in order to achieve objective validity on the R&D programs. Moreover, in an effort to ensure the principles such as fairness, public health and safety, security, and adoptability, NWMO tried to make those abstract ideas more specific and help the public understand the meaning of each concept more in detail. Also, they utilized a variety of communication methods from face-to-face meeting to e-dialogue to encourage people to participate in the program as much as possible. Given the fact that Korea has been also having a hard time in dealing with spent nuclear fuel management, all of these efforts that Canada has made with a PSE program would give good lessons and implications to the Korean case. In conclusion, as a deliberative participation program, PSE could be a possible breakthrough approach for the Korean spent nuclear fuel management.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.305-316
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• 2011

Recommender system is the system which, by using automated information filtering technology, recommends products or services to the customers who are likely to be interested in. Those systems are widely used in many different Web retailers such as Amazon.com, Netfix.com, and CDNow.com. Various recommender systems have been developed. Among them, Collaborative Filtering (CF) has been known as the most successful and commonly used approach. CF identifies customers whose tastes are similar to those of a given customer, and recommends items those customers have liked in the past. Numerous CF algorithms have been developed to increase the performance of recommender systems. However, the relative performances of CF algorithms are known to be domain and data dependent. It is very time-consuming and expensive to implement and launce a CF recommender system, and also the system unsuited for the given domain provides customers with poor quality recommendations that make them easily annoyed. Therefore, predicting in advance whether the performance of CF recommender system is acceptable or not is practically important and needed. In this study, we propose a decision making guideline which helps decide whether CF is adoptable for a given application with certain transaction data characteristics. Several previous studies reported that sparsity, gray sheep, cold-start, coverage, and serendipity could affect the performance of CF, but the theoretical and empirical justification of such factors is lacking. Recently there are many studies paying attention to Social Network Analysis (SNA) as a method to analyze social relationships among people. SNA is a method to measure and visualize the linkage structure and status focusing on interaction among objects within communication group. CF analyzes the similarity among previous ratings or purchases of each customer, finds the relationships among the customers who have similarities, and then uses the relationships for recommendations. Thus CF can be modeled as a social network in which customers are nodes and purchase relationships between customers are links. Under the assumption that SNA could facilitate an exploration of the topological properties of the network structure that are implicit in transaction data for CF recommendations, we focus on density, clustering coefficient, and centralization which are ones of the most commonly used measures to capture topological properties of the social network structure. While network density, expressed as a proportion of the maximum possible number of links, captures the density of the whole network, the clustering coefficient captures the degree to which the overall network contains localized pockets of dense connectivity. Centralization reflects the extent to which connections are concentrated in a small number of nodes rather than distributed equally among all nodes. We explore how these SNA measures affect the performance of CF performance and how they interact to each other. Our experiments used sales transaction data from H department store, one of the well?known department stores in Korea. Total 396 data set were sampled to construct various types of social networks. The dependant variable measuring process consists of three steps; analysis of customer similarities, construction of a social network, and analysis of social network patterns. We used UCINET 6.0 for SNA. The experiments conducted the 3-way ANOVA which employs three SNA measures as dependant variables, and the recommendation accuracy measured by F1-measure as an independent variable. The experiments report that 1) each of three SNA measures affects the recommendation accuracy, 2) the density's effect to the performance overrides those of clustering coefficient and centralization (i.e., CF adoption is not a good decision if the density is low), and 3) however though the density is low, the performance of CF is comparatively good when the clustering coefficient is low. We expect that these experiment results help firms decide whether CF recommender system is adoptable for their business domain with certain transaction data characteristics.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.53-74
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• 2018

Since South Korea has an economic structure that has a characteristic which market-dependent on overseas, the tourism industry is considered as a very important industry for the national economy, such as improving the country's balance of payments or providing income and employment increases. Accordingly, the necessity of more accurate forecasting on the demand in the tourism industry has been raised to promote its industry. In the related research, economic variables such as exchange rate and income have been used as variables influencing tourism demand. As information technology has been widely used, some researchers have also analyzed the effect of media on tourism demand. It has shown that the media has a considerable influence on traveler's decision making, such as choosing an outbound destination. Furthermore, with the recent availability of online information searches to obtain the latest information and two-way communication in social media, it is possible to obtain up-to-date information on travel more quickly than before. The information in online media such as blogs can naturally create the Word-of-Mouth effect by sharing useful information, which is called eWOM. Like all other service industries, the tourism industry is characterized by difficulty in evaluating its values before it is experienced directly. And furthermore, most of the travelers tend to search for more information in advance from various sources to reduce the perceived risk to the destination, so they can also be influenced by online media such as online news. In this study, we suggested that the number of online media posting, which causes the effects of Word-of-Mouth, may have an effect on the number of outbound travelers. We divided online media into public media and private media according to their characteristics and selected online news as public media and blog as private media, one of the most popular social media in tourist information. Based on the previous studies about the eWOM effects on online news and blog, we analyzed a relationship between the volume of eWOM and the outbound tourism demand through the panel model. To this end, we collected data on the number of national outbound travelers from 2007 to 2015 provided by the Korea Tourism Organization. According to statistics, the highest number of outbound tourism demand in Korea are China, Japan, Thailand, Hong Kong and the Philippines, which are selected as a dependent variable in this study. In order to measure the volume of eWOM, we collected online news and blog postings for the same period as the number of outbound travelers in Naver, which is the largest portal site in South Korea. In this study, a panel model was established to analyze the effect of online media on the demand of Korean outbound travelers and to identify that there was a significant difference in the influence of online media by each time and countries. The results of this study can be summarized as follows. First, the impact of the online news and blog eWOM on the number of outbound travelers was significant. We found that the number of online news and blog posting have an influence on the number of outbound travelers, especially the experimental result suggests that both the month that includes the departure date and the three months before the departure were found to have an effect. It is shown that online news and blog are online media that have a significant influence on outbound tourism demand. Next, we found that the increased volume of eWOM in online news has a negative effect on departure, while the increase in a blog has a positive effect. The result with the country-specific models would be the same. This paper shows that online media can be used as a new variable in tourism demand by examining the influence of the eWOM effect of the online media. Also, we found that both social media and news media have an important role in predicting and managing the Korean tourism demand and that the influence of those two media appears different depending on the country.

• 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.