• Journal of Korea Game Society
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• v.15 no.4
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• pp.69-78
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• 2015

As the online game industry has been growing rapidly, more and more malicious activities to gain economic benefits have been reported as well. Game bot is one of the biggest problems in the online game industry. So we proposed a bot detection method based on the ERG theory of motivation for the first time. Most of the previous studies focused on behavior-based detection by monitoring patterns of the specific actions. In this paper, we applied the motivation theory to analyze user behaviors on a real game dataset. The result shows that normal users in the game followed the ERG theory of motivation in the same way as it works in real world. But in the case of game bots, the theory could not be applied because the game bot has specific reasons, unlike normal game users. We applied the ERG theory to users to distinguish game bot users from normal users. We detected the game bot with high accuracy of 99.78% by applying the theory.

• Korean Journal of Clinical Pharmacy
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• v.24 no.1
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• pp.53-61
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• 2014

Background: The use of health functional food (HFF) is increasing and will continue to rise worldwide. Concerns about HFF-drug interactions are increasing as HFF are becoming more widely used. Therefore, awareness of consumers' perceptions and behaviors associated with HFF use may help health care providers improve their communications with patients. Purpose: The aim of this study was to assess the characteristics, perceptions, and behaviors associated with HFF use in South Korea. Method: The online survey was conducted from September 21th to October 7th, 2013. With the aid of Social Network Service (SNS) and google, the questionnaire was posted online on internet website targeting people aged 15 years or older so that self-reported data covering 4 domains were collected from 257 Koreans. Results: A total of 257 people responded the questionnaire. Among them, 81.3% reported experiences of HFF use. Female were more likely than male to use HFFs. There were no differences in demographic characteristics between HFF users and non-users in relation to age, education, and household income. Higher level of education was associated with high-level perception of HFF function (OR 3.9, 95% CI 1.48, 10.1) and a positive relationship was observed between the maximum number of HFFs used concurrently and age of the respondents. Among the HFF users, 42.6% reported concurrent HFF-medication use. However 73.3% of them did not disclose their use to physician or pharmacist and only 30.2% were informed about potential drug-HFF interactions. Pharmacy was most commonly reported as the source from which the respondents were informed about potential interactions. Conclusion: Many people had used HFF and medications concurrently while not being informed about potential HFF-drug interactions. Pharmacists and physicians should be vigilant for risk of the interactions and actively determine whether the patient is using an HFF before prescribing and administrating medications.

• Journal of the Korean Dietetic Association
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• v.23 no.4
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• pp.408-430
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• 2017

The purpose of this study was performed to investigate dietary and lifestyle habits, dietary behaviors, and food frequency according to the level of smartphone addiction among 408 university students in Kyungnam province. Statistical analyses were performed using the SPSS software package. Based on using the Smartphone Addiction Poneness Scale, 28.4% were potential-risk smartphone users and while 13.2% were high-risk smartphone users. The levels of depression (P<0.05) and stress (P<0.05) and frequency of snacks (P<0.01) were higher in high-risk and potential-risk groups than in the normal group, and meal frequency was highest in the high-risk group (P<0.01). Percentages of using a smartphone at meal time (P<0.01) and snacking while using a smartphone (P<0.01) were higher in potential-risk and high-risk groups than in the normal group. Percentages of skipping meals (P<0.001) and slow eating speed (P<0.01) due to using a smartphone were higher in high-risk and potential-risk groups than in the normal group, and percentages of taste change (P<0.05) were higher in the high-risk group than in the potential-risk and normal groups. Percentages of exercise reduction (P<0.01), body weight increase (P<0.05), sleep disturbance (P<0.001), and increase in stress (P<0.01) due to using a smartphone were higher in the high-risk group than in the normal group. Scores of dietary behaviors avoiding salty food (P<0.01) and excessive drinking (P<0.001) were higher in the high-risk group than in the normal group. Scores for frequency of oil or nuts (P<0.05) and fatty meats (P<0.01) were highest in the high-risk group. Our results suggest that effective nutrition education programs are needed to solve unhealthy dietary and lifestyle habits from high-risk smartphone users in university students.

• Journal of the Korea Convergence Society
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• v.11 no.3
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• pp.181-186
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• 2020

The purpose of this study is to empirically examine the factors affecting the binge-watching behaviors of OTT service users by using a multi-layer perceptron (MLP) artificial neural network. All samples (n=1,000) were collected from 'A survey on user awareness in OTT service' published by a Media Research Center of the Korea Press Foundation in 2018. Our research model includes one dependent variable which is binge-watching behaviors on OTT service and five independent variables such as gender, age, frequency of service usage, users' satisfaction with content recommendation algorithm, and content types mainly consumed. Our findings demonstrate that age, frequency of service usage, users' satisfaction with content recommendation algorithms, and certain types of contents (e.g., Korean dramas, Korean films, and foreign dramas) were found to be highly related to binge-watching behavior on OTT services.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.3
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• pp.147-159
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• 2023

In order to make autonomous vehicles more trustworthy, it is necessary to focus on the users of autonomous vehicles. By evaluating the preferences for driving behaviors of autonomous vehicles, we aim to identify driving behaviors that increase the acceptance of users in autonomous vehicles. We implemented two driving behaviors, aggressive and cautious, in a driving simulator and allowed users to experience them. Biometric data was collected during the ride, and pre- and post-riding surveys were conducted. Subjects were categorized into two groups based on their driving habits and analyzed against the collected biometric data. Both aggressive and cautious driving subjects preferred the cautious driving behavior of autonomous vehicles.

• Asia pacific journal of information systems
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• v.26 no.1
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• pp.66-79
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• 2016

The availability of detailed data on customers' online behaviors and advances in big data analysis techniques enable us to predict consumer behaviors. In the past, researchers have built purchase prediction models by analyzing clickstream data; however, these clickstream-based prediction models have had several limitations. In this study, we propose a new method for purchase prediction that combines information theory with machine learning techniques. Clickstreams from 5,000 panel members and data on their purchases of electronics, fashion, and cosmetics products were analyzed. Clickstreams were summarized using the 'entropy' concept from information theory, while 'random forests' method was applied to build prediction models. The results show that prediction accuracy of this new method ranges from 0.56 to 0.83, which is a significant improvement over values for clickstream-based prediction models presented in the past. The results indicate further that consumers' information search behaviors differ significantly across product categories.

• Journal of the Korea Society of Computer and Information
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• v.24 no.8
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• pp.95-103
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• 2019

According to the Statistics Korea in 2017, the 10 leading causes of death contain a cardiac disorder disease, self-injury. In terms of these diseases, urgent assistance is highly required when people do not move for certain period of time. We propose an unusual event detection algorithm to identify abnormal user behaviors using dust, vision and activity sensors in their houses. Vision sensors can detect personalized activity behaviors within the CCTV range in the house in their lives. The pattern algorithm using the dust sensors classifies user movements or dust-generated daily behaviors in indoor areas. The accelerometer sensor in the smartphone is suitable to identify activity behaviors of the mobile users. We evaluated the proposed pattern algorithms and the fusion method in the scenarios.

• Journal of Multimedia Information System
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• v.9 no.3
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• pp.201-208
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• 2022

Customized service is a vital and mandatory element for apps in improving their technical performance and app customer analysis. While apps require users' consent for their data extraction and usage, many of the terms and agreement forms are written intricately, making it harder for users to fully understand the whole concept of users' data collection for customized services. Ever since the Facebook-Cambridge Analytica scandal, personal data privacy has been re-examined, forcing many app companies to reinforce a reliable solution to data privacy issues. However, there has not been a secured solution, which worries many people about the future advanced issues when metaverse platforms are actively used in daily apps. The research aims to collect the reactions and behaviors of everyday app users who utilize apps with customized services to understand the nature of privacy data issues and the users' opinions about the future implementation of metaverse platforms. The method of the research was an online questionnaire that targeted university students. The study revealed many fearful and anxious reactions about personal data and further metaverse issues where most app users were uneducated about how current apps collect and utilize users' private data.

• Archives of design research
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• v.19 no.1 s.63
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• pp.317-328
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• 2006

So far, the behaviors of Web users have been predicted or analyzed mostly by their demographic characteristics or by considering in which context they gain access to that. But now there is a question about whether those characteristics are the only factors to trigger their use of Web. If the answer is not affirmative, what types of additional factors could cause such an action and how they characterize it should be discussed. User profile information has been considered one of the crucial elements to define user characteristics in user-centered UI design sector, and in order to apply it to UI design, it's needed to meditate on the above-mentioned questions. In this study, it's first attempted to have a good understanding of the users of different media and to review existing user classification methods. Next, user classification variables and relevant scales were prepared to sort out users according to their type of using Web, and case study was conducted to identify the behavioral characteristics of users and classify them according to their behavioral features. Finally, the user profile features of individual user groups were figured out based on data that were gathered by making an experiment, and data mapping was fulfilled between the behavioral characteristics and user profile characteristics to find out what types of behaviors were caused by the characteristics of user profile. As a result, it's found that user characteristics could have an impact on not only their general information and relevant contexts but their attitude of using different media and personality type. There were some problems with the experimental design, but more accurate information on the relationship of user behaviors to user profile characteristics will be obtained if those problems are eliminated. As user behaviors could be predicted only by user profile characteristics, user classification is expected to make a contribution to enhancing the efficiency of UI design.

• Journal of Korean Library and Information Science Society
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• v.32 no.1
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• pp.261-283
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• 2001

This study investigates the antecedents of the complaining behaviors and complaint responses of the library and information center users based on the theoretical backgrounds and suggests eight propositions and conceptual model for the library and information center. It examines as the antecedents, satisfaction/dissatisfaction, attitude toward complaining, likelihood of success, materials/facilities/service importance, attribution, loyalty, and justices; and as complaint responses. exit, voice(redress seeking), negative word-of-mouth, and third party complaints.