• The Journal of Society for e-Business Studies
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• v.17 no.4
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• pp.155-174
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• 2012

As Online Behavioral Advertising is dramatically growing with usefulness of information and user convenience in recent years, there are privacy issues caused by collecting user's behavioral information without their consent. To tackle the problem, the need of Do Not Track regulations is getting much higher. In Korea, however, it has never existed. So, this study is examining the case of the major countries have been enforcing the law and regulations of DNT. After that, it is classified with four domain (law/regulation, corporation, individual, society) to include all stakeholders of OBA. Furthermore, this study may have academic significance by suggesting DNT framework through analysis of them. Providing DNT mechanism consisted of three type (behavioral information, control, DNT system), it can be useful guidelines for companies to support decision making as introduced DNT. As analyzed between DNT and stakeholders based on the study of OBA market, it will be useful basic material of OBA study later.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.6
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• pp.237-244
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• 2022

In this paper, we present VAE-based recommendation using online behavior log and purchase history to overcome data sparsity and cold start. To generate a variable for customers' purchase history, embedding and dimensionality reduction are applied to the customers' purchase history. Also, Variational Autoencoders are applied to online behavior and purchase history. A total number of 12 variables are used, and nDCG is chosen for performance evaluation. Our experimental results showed that the proposed VAE-based recommendation outperforms SVD-based recommendation. Also, the generated purchase history variable improves the recommendation performance.

• Journal of KIISE
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• v.45 no.2
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• pp.165-174
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• 2018

An online community is a virtual group whose members share their interests and hobbies anonymously with nicknames unlike Social Network Services. However, there are malicious user problems such as users who write offensive contents and there may exist data fragmentation problems in which the data of the same user exists in different nicknames. In addition, nicknames are frequently changed in the online community, so it is difficult to identify them. Therefore, in this paper, to remedy these problems we propose a behavior pattern feature vectors for users considering online community characteristics, propose a new implicit behavior pattern called relationship pattern, and identify the nickname of the same user based on Random Forest classifier. Also, Experimental results with the collected real world online community data demonstrate that the proposed behavior pattern and classifier can identify the same users at a meaningful level.

• Journal of Digital Convergence
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• v.18 no.7
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• pp.149-156
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• 2020

This study investigated the key factors affecting online relational intimacy in the context of SNS. Based on the use and gratification theory, self-presentation, relationship formation and information searching were identified as the main needs of SNS usage. These needs were expected to influence online relational intimacy through user satisfaction, subjective well-being, and disclosing information behaviors. The theoretical framework was validated by a longitudinal method. Hypotheses were tested by using the partial least squares to data from 199 Facebook users. Self-presentation and information searching had a significant impact on both user satisfaction and subjective well-being. However, relationship formation did not significantly affect both user satisfaction and subjective well-being. User satisfaction had a significant direct effect only on online relational intimacy. Subjective well-beings played a significant role in enhancing both disclosing information behaviors and online relational intimacy. Finally, it has been found that disclosing information behaviors are a key factor in enhancing online relational intimacy. The results of this study are expected to provide academic and practical implications for the key antecedents of online relational intimacy.

• Management & Information Systems Review
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• v.31 no.3
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• pp.271-305
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• 2012

Due to the growing activity of online communities recently, its influence is gradually growing. Furthermore, it also has a huge effect on corporations in establishing their marketing strategy. One important aspect that occurs is that there is a high possibility that the interest of online community members, which was first organized because of a common interest, will be similar. Thus, there is a growing desire to share information and knowledge that would be mutually useful among them. Therefore, this study aims at revealing the motivation factors on why such knowledge sharing behavior occurs among online community members that are voluntarily organized. The detailed objectives of this survey is to first conduct qualitative research on online community members, and then to examine what are the motivation factors that cause knowledge sharing behavior among online community members. Second, by developing questionnaires according to the analyzed contents of the qualitative research results, the reliability and feasibility of such questions are to be verified. As a result, new motivation factor of knowledge sharing which was not suggested in the existing studies because of characteristics of online community was revealed. If the results of existing related studies and those of this study are compared, the six factors such as desire of showing off, awareness, perceived benefits, pleasure, challenge and sense of belonging except for motivator such as sense of achievement and compensation, trust are newly discovered motivators of knowledge sharing behavior.

• Proceedings of the Korea Information Processing Society Conference
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• 2005.11a
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• pp.413-416
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• 2005

기존의 온라인 게임에서 구현되어 있는 수동적인 MOB(Mobile Character)에 '회피' 상태를 추가하고 3 가지 각각의 행동 전이에 따른 행동 패턴을 행동 특성 곡선으로 표현하며 '공격'과 '접근'자극을 스트레스 모형에 적용하여 스트레스에 따른 MOB 에이전트의 행동 패턴 변화를 설명하고 주변의 다른 에이전트들과의 협동을 도모할 수 있는 지능적인 MOB 에이전트를 [1]논문에서 설계하였다. 본 논문에서는 [1]논문에서의 모형을 향상시키기 위하여 행동패턴을 구체화하고 수식을 추가하였으며, 또한 스트레스 카운터를 추가하여 보다 현실적인 모형을 설계하였다.

• 한국IT서비스학회:학술대회논문집
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• 2008.11a
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• pp.453-456
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• 2008

소프트웨어와 음악파일, 영화 등의 불법복제로 인한 사회적인 문제가 대두되어 왔다. 온라인디지털콘텐츠의 산업규모와 중요성이 증대되고 있는 시점에서 동일하게 불법복제로 인한 피해규모와 이용자들의 불법복제 행동이 만연해 있다. 이 연구는 계획된 행동이론(TPB)를 통해 개인의 온라인디지털콘텐츠의 불법복제 행동에 대한 모형을 검증하고 불법복제에 영향을 미치는 요인을 검증하고 인과관계를 확인하고자 한다. TPB의 확대모형을 제안하고자 하는데 단속, 교육 및 홍보, 이전경험, 자기효능감이 불법복제 행동의도와 의도에 영향을 미치는 태도와 주관적 규범에 어떠한 영향을 미치는지 검증해보고 한다. 단속과 교육 및 홍보가 개인의 인식과정을 통해 태도와 내적인 자기 규범에 어떠한 영향을 미칠 것인지는 정책적 전략적 시사점을 얻는데 중요한 요인이 될 것이다. 파일럿 테스트 결과 기존 TPB 모형은 온라인디지털콘텐츠 영역에 있어서도 적합한 모델임이 검증되었고, 단속과 교육 및 홍보가 태도와 주관적 규범에 유의한 영향을 미침을 나타내었다.

• Journal of the Korea Society of Computer and Information
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• v.14 no.4
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• pp.153-160
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• 2009

This study investigated what factors influence the knowledge contribution in online communities. Based on the theoretical framework like self-presentation theory and organizational citizenship behavior theory, we developed the research model and proposed four hypotheses. In order to test our hypotheses with an empirical study, we have conducted a survey which resulted in 192 valid responses in the final sample. The PLS analysis results indicate that knowledge contribution is influenced by self-presentation, innovation, organizational citizenship behavior, and affection similarity of online community users. Practical implications of these findings and future research implications are also discussed.

• Informatization Policy
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• v.18 no.1
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• pp.55-72
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• 2011

This study explores the causes of online helping behavior. This study tests the effects of various explanatory factors on the basis of previous studies on helping behavior. These factors include social demographic, situational, motivational, and social capital factors. According to the survey of 475 university students living in Seoul in 2010, age and religion have significant effects on online helping behavior. In addition, the perceived helping opportunity, time and effort cost to help, and social capital factor have significant effects on online helping behavior. Results reveal that the effect of social capital factor is the largest among other variables. However, it is shown that the effects of benefit from helping, empathy, and personal norm are not statistically significant. It is also revealed that the effect of social capital factor is important across all types of online helping behavior except information helping. The policy implications are discussed.

• Journal of Broadcast Engineering
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• v.27 no.1
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• pp.124-132
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• 2022

Recently, due to COVID-19, online remote classes and non-face-to-face exams have made it difficult to manage class attitudes and exam cheating. Therefore, there is a need for a system that automatically recognizes and detects the behavior of students online. Action recognition, which recognizes human action, is one of the most studied technologies in computer vision. In order to develop such a technology, data including human arm movement information and information about surrounding objects, which can be key information in online classes and exams, are needed. It is difficult to apply the existing dataset to this system because it is classified into various fields or consists of daily life action. In this paper, we propose a dataset that can classify attitudes in real-time online tests and classes. In addition, it shows whether the proposed dataset is correctly constructed through comparison with the existing action recognition dataset.