• Journal of Intelligence and Information Systems
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• v.22 no.3
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• pp.143-163
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• 2016

The demographics of Internet users are the most basic and important sources for target marketing or personalized advertisements on the digital marketing channels which include email, mobile, and social media. However, it gradually has become difficult to collect the demographics of Internet users because their activities are anonymous in many cases. Although the marketing department is able to get the demographics using online or offline surveys, these approaches are very expensive, long processes, and likely to include false statements. Clickstream data is the recording an Internet user leaves behind while visiting websites. As the user clicks anywhere in the webpage, the activity is logged in semi-structured website log files. Such data allows us to see what pages users visited, how long they stayed there, how often they visited, when they usually visited, which site they prefer, what keywords they used to find the site, whether they purchased any, and so forth. For such a reason, some researchers tried to guess the demographics of Internet users by using their clickstream data. They derived various independent variables likely to be correlated to the demographics. The variables include search keyword, frequency and intensity for time, day and month, variety of websites visited, text information for web pages visited, etc. The demographic attributes to predict are also diverse according to the paper, and cover gender, age, job, location, income, education, marital status, presence of children. A variety of data mining methods, such as LSA, SVM, decision tree, neural network, logistic regression, and k-nearest neighbors, were used for prediction model building. However, this research has not yet identified which data mining method is appropriate to predict each demographic variable. Moreover, it is required to review independent variables studied so far and combine them as needed, and evaluate them for building the best prediction model. The objective of this study is to choose clickstream attributes mostly likely to be correlated to the demographics from the results of previous research, and then to identify which data mining method is fitting to predict each demographic attribute. Among the demographic attributes, this paper focus on predicting gender, age, marital status, residence, and job. And from the results of previous research, 64 clickstream attributes are applied to predict the demographic attributes. The overall process of predictive model building is compose of 4 steps. In the first step, we create user profiles which include 64 clickstream attributes and 5 demographic attributes. The second step performs the dimension reduction of clickstream variables to solve the curse of dimensionality and overfitting problem. We utilize three approaches which are based on decision tree, PCA, and cluster analysis. We build alternative predictive models for each demographic variable in the third step. SVM, neural network, and logistic regression are used for modeling. The last step evaluates the alternative models in view of model accuracy and selects the best model. For the experiments, we used clickstream data which represents 5 demographics and 16,962,705 online activities for 5,000 Internet users. IBM SPSS Modeler 17.0 was used for our prediction process, and the 5-fold cross validation was conducted to enhance the reliability of our experiments. As the experimental results, we can verify that there are a specific data mining method well-suited for each demographic variable. For example, age prediction is best performed when using the decision tree based dimension reduction and neural network whereas the prediction of gender and marital status is the most accurate by applying SVM without dimension reduction. We conclude that the online behaviors of the Internet users, captured from the clickstream data analysis, could be well used to predict their demographics, thereby being utilized to the digital marketing.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.221-241
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• 2018

As we enter the knowledge society, the importance of information as a new form of capital is being emphasized. The importance of information classification is also increasing for efficient management of digital information produced exponentially. In this study, we tried to automatically classify and provide tailored information that can help companies decide to make technology commercialization. Therefore, we propose a method to classify information based on Korea Standard Industry Classification (KSIC), which indicates the business characteristics of enterprises. The classification of information or documents has been largely based on machine learning, but there is not enough training data categorized on the basis of KSIC. Therefore, this study applied the method of calculating similarity between documents. Specifically, a method and a model for presenting the most appropriate KSIC code are proposed by collecting explanatory texts of each code of KSIC and calculating the similarity with the classification object document using the vector space model. The IPC data were collected and classified by KSIC. And then verified the methodology by comparing it with the KSIC-IPC concordance table provided by the Korean Intellectual Property Office. As a result of the verification, the highest agreement was obtained when the LT method, which is a kind of TF-IDF calculation formula, was applied. At this time, the degree of match of the first rank matching KSIC was 53% and the cumulative match of the fifth ranking was 76%. Through this, it can be confirmed that KSIC classification of technology, industry, and market information that SMEs need more quantitatively and objectively is possible. In addition, it is considered that the methods and results provided in this study can be used as a basic data to help the qualitative judgment of experts in creating a linkage table between heterogeneous classification systems.

• The Journal of the Korea Contents Association
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• v.17 no.1
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• pp.550-566
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• 2017

Recent advances in wearable devices and quantified-self movement increase the number of personal informatics application that may cause an concern to health industry and user. In this light, the goal of this study is to identify more effective ways of design and evaluation of personal informatics application for self-tracking and delivering health information to users. For this goal, this study conducted areal-world study that processes such that user can assess, be aware of, and self-reflect on their data and behavior activity. In doing so, this study aims to determine the psychological effects of forms of health feedback (comparative vs. non-comparative) and presentation modes (text vs. image) on users' tendencies toward health conservation. Results from a between-subjects experiment revealed that health information in a comparative and textual format was more effective in encouraging health conservation in participants than identical information presented in a non-comparative and image format. In addition, participants' level of health consciousness emerged as a significant predictor. Through this analysis of quantitative data and inferences, this study make a number of contributions to the user affordance research and its methodology of health informatics study and designing personal informatics application that support user's behavior change in various contexts.

• The Journal of Korean Academic Society of Nursing Education
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• v.9 no.2
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• pp.264-275
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• 2003

The purpose of this study were to develop a smoking preventive education program for elementary students and evaluate it's effectiveness. This study was a quasi experimental study under the nonequivalent control group with pretest-posttest design. The subjects of this study were 62 who are attending elementary school(31 for each group), 2 different district elementary school. The subjects were matched by grade, similar in anti-smoking educational background of smoking, as well as their residence and income level of their families. The instruments used in this study was 18 criterion referenced test items modeled by Dick & Carey that were developed by researchers for evaluating the subjects' knowledge and attitude about smoking. A pretest was administered a week before treatment The program given to the experimental group is composed of the texts explaining the poisonous substances in tobacco, social and cultural harmfulness of smoking to the body and psychology, indirect smoking, smoking of pregnant women, motives of smoking, refusal skills of smoking; and for the subjects' understanding and the better results of study - pictures, role play, discussion, text through computer based multi-media, puzzle searching for hidden pictures, cross-word puzzle, and finally compensation. The data were collected for 50 days form mid- September to the end of October in the year of 2000, composed of formative evaluation, pre-test and summative evaluation via 2 sessions. Accordingly, the collected data were analysed by t-test, paired t-test, repeated measure ANOVA by the SAS program. This research summarize the findings as follows; 1. There was a significant difference in knowledge between the experimental group(after 1 wks t=10.4680, p=.0001; after 4 wks t= 9.310, p=.0001) and control group(after 1 wks t=0.0420, p= .9669; after 4 wks t= -0.378 p=.7079) in between the results of 1 and 4 week after education in summative evaluation (F=27.45, P=.0001). 2. There was non statistical significant difference in attitude between the experimental group (after 1 wks t=1.2292, p=0.2286 ; after 4 wks t=1.330, p=0.1935) and control group (after 1 wks t=0.1819, p=0.8569 ; after 4 wks t=0.2970, p=0.7685) in between the results of 1 and 4 week after education in summative evaluation(F=0.71, P=0.494). To sum up, the statistics of conclusive analysis evaluative for the children under school age of the 'knowledge acquisition' about smoking harmfulness. On the other hand, as there was already sound attitude about smoking, the evaluation of attitude was non significant difference between control group and experimental group, just there was partially significant difference.

• Journal of Korean Society of Forest Science
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• v.94 no.6
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• pp.362-369
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• 2005

Readability, an indicator measuring the easiness of reading letters, is an important element that determines the communicative effectiveness of self-guided signs. This study examined how the letter design elements of self-guided signs influence on readability to provide basic information for more effective sign designs. Data were collected from August to November of 2003 at a self-guided trail of Naejangsan National Park, Korea. A total of 375 subjects participated in the questionnaire survey, and 94.7% of them were used for data analysis. Among the total of 19 attributes, five attributes such as number of letters, number of type styles, ratio of picture area on the signs, space between letters, type size influenced on readability. These five attributes explained 50.0% of the variation in readability. The number of letters was the most influential attributes on readability, followed by the number of type styles, ratio of picture area on the signs, space between letters, and type size. The effectiveness of signs may be efficiently increased by managing these five major attributes with more concern.

• Knowledge Management Research
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• v.22 no.3
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• pp.31-53
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• 2021

As a communication channel for individuals, social media is affecting various areas such as business, economy, politics, and society. One of the less-studied areas is the law. Therefore, this study collected various information from social media and analyzed its impacts on the legal decisions, especially the Supreme Court decisions in Korea. This study was conducted by compiling information from Internet news articles and public responses. We found that when the negative reactions from the public got higher, the trial duration until the supreme court making the final decisions became shorter. However, we were not able to find the significant relationship between social media reactions and dismissal of appeal nor annulment. Our study would contribute to the information systems and knowledge management research in a sense that the social analytics is applied to the area of legal decisions, instead of using conventional qualitative study methodology. Our study is also meaningful to the practitioners because that big data analytical business can be applied to the field of law by creating a new database for the emerging legal technology. Finally, law makers can think of a better way to standardize the legal decision process to minimize the reverse effects from social media.