• Journal of Korea Society of Industrial Information Systems
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• v.16 no.5
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• pp.173-184
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• 2011

Today smart devices such as smart phones, smart pads and tablets have become necessities of modern people in both daily life and business as they have widely proliferated. One of the most popular application areas of smart devices is a game-related area. Among these applications social network games, played with other users through social networks, are ranked top in their popularity. Though much research of PC games and online games have been performed, little research of social network games and the factors affecting acceptance of social network games are not vigorous yet. Therefore, we aim to analyze the factors and their structural influence on acceptance of social network games. We add a couple of factors such as Social interaction, Mobility, Subjective Norm, and Flow reg arding the characteristics of social network games and analyze the structural relationships among these factors using Structural Equation Modeling. Analysis results and implications are suggested with concluding remarks.

• Journal of Korean Society for Quality Management
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• v.42 no.3
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• pp.387-406
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• 2014

Purpose: The widespread use of social network services (SNS) has caused users concern about the disclosure of their privacy or personal information. The purpose of this study is to analyze the factors of privacy concern and self presentation that affect the user resistance in the use of social network service. Methods: This study verifies the factors that affecting the user resistance in SNS. The research model suggested in this study is tested via a survey of 260 SNS users. SPSS and Smart PLS had been used to test the suggested hypotheses. Results: This study shows that privacy experience, privacy awareness, self esteem, and social desirability significantly influence perceived risk and that privacy awareness, self esteem, self efficacy, and perceived risk significantly influence perceived trust. It also verifies that perceived risk and perceived trust positively affect user resistance. Conclusion: This paper suggests that high awareness on privacy of SNS user encourages the SNS companies to consider the privacy protection mechanism for eliminating various factors that affecting the risk. This study also shows that the privacy calculus model applies to understanding the mechanism on resistance of SNS user.

• Journal of Korean Society of Rural Planning
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• v.15 no.1
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• pp.47-59
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• 2009

The aim of this study is to analyze the green-tourism centrality considering spatial interaction using Gravity Model and social network method. The degree centrality and prestige centrality were applied as green-tourism centrality index. The rural amenity resources and human resources were counted as attraction factors, and a distance among villages was used as friction factor in gravity model. The weights of rural tourism amenity resources were calculated using the analytic hierarchy process(AHP) method and applied to evaluate green-tourism potentiality. The distance was measured with the shortest path among villages using geographic information system(GIS) network analysis. The spatial interaction from gravity model were employed as link weights between nodal points; a pair villages. Using the spatial interaction, the degree-centrality and prestige-centrality indices were calculated by social network analysis and demonstrated possibility of developing integrated green-tourism region centered on high centrality villages.

• Journal of Information Processing Systems
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• v.17 no.1
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• pp.163-177
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• 2021

Many methods of discovering social networking communities or clustering of features are based on the network structure or the content network. This paper proposes a community discovery method based on topic models using a time factor and an unsupervised clustering method. Online community discovery enables organizations and businesses to thoroughly understand the trend in users' interests in their products and services. In addition, an insight into customer experience on social networks is a tremendous competitive advantage in this era of ecommerce and Internet development. The objective of this work is to find clusters (communities) such that each cluster's nodes contain topics and individuals having similarities in the attribute space. In terms of social media analytics, the method seeks communities whose members have similar features. The method is experimented with and evaluated using a Vietnamese corpus of comments and messages collected on social networks and ecommerce sites in various sectors from 2016 to 2019. The experimental results demonstrate the effectiveness of the proposed method over other methods.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.238-246
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• 2021

The rapid rise of the Internet and social media has resulted in a large number of text-based reviews being placed on sites such as social media. In the age of social media, utilizing machine learning technologies to analyze the emotional context of comments aids in the understanding of QoS for any product or service. The classification and analysis of user reviews aids in the improvement of QoS. (Quality of Services). Machine Learning algorithms have evolved into a powerful tool for analyzing user sentiment. Unlike traditional categorization models, which are based on a set of rules. In sentiment categorization, Bidirectional Long Short-Term Memory (BiLSTM) has shown significant results, and Convolution Neural Network (CNN) has shown promising results. Using convolutions and pooling layers, CNN can successfully extract local information. BiLSTM uses dual LSTM orientations to increase the amount of background knowledge available to deep learning models. The suggested hybrid model combines the benefits of these two deep learning-based algorithms. The data source for analysis and classification was user reviews of Indian Railway Services on Twitter. The suggested hybrid model uses the Keras Embedding technique as an input source. The suggested model takes in data and generates lower-dimensional characteristics that result in a categorization result. The suggested hybrid model's performance was compared using Keras and Word2Vec, and the proposed model showed a significant improvement in response with an accuracy of 95.19 percent.

• Journal of Korea Society of Digital Industry and Information Management
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• v.10 no.1
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• pp.157-167
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• 2014

Increasing productivity of knowledge workers is a significant issue in the 21st century referred as knowledge-based society. The core key word is behavior of knowledge transfer among members of an organization. The objective of this study is to investigate a model based on Triandis theory and Social Capital theory. This explored the antecedent factors of knowledge Transfer in ITO(Information Technology Outsourcing) Organization. Data were derived from 42 respondents working IT Cooperation in Seoul, Korea. In this paper, we introduce the research model for the knowledge transfer. In order to validate the proposed research model, social network analysis tool, UCINET, a structural equation modeling tool, SmartPLS, was utilized. The empirical result showed that, all antecedent factors (intention of knowledge sharing, anticipated reciprocal relationships, subjective norm, closeness network centrality) of knowledge transfer behavior were significant. In conclusion, findings and implications were discussed and limitations of the study and future research directions were suggested.

• Journal of Information Science Theory and Practice
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• v.9 no.2
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• pp.83-98
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• 2021

Research collaboration is an important strategy to improve research output, and virtual academic communities (VACs) have become an important platform to collaborate on. This paper reveals the influencing factors of researchers' collaboration intention in VACs from two attributes: individual, and inter-members. On the basis of the Social Cognitive Theory, Social Exchange Theory, social network theory, and Five-Factor Model, this paper constructed a model demonstrating the influencing factors of VACs researchers' collaboration intention. A self-administered questionnaire was employed on members of four VACs in China to collect data; subsequently, 558 usable responses were analyzed using structural equation modeling. The result showed that openness, conscientiousness, reciprocity, trust, and the social network characteristic had a significant influence on the collaboration intention of researchers in VACs, while self-efficacy, agreeableness, extroversion, neuroticism, and experience had no significant effects on the collaboration intention of researchers in VACs. This model plays a positive role in promoting the research collaboration intention of Chinese VACs researchers and in guiding the construction of VAC platforms.

• Journal of Digital Convergence
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• v.9 no.1
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• pp.25-36
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• 2011

This paper examines user's loyalty to social network sites (SNS) from switching costs (SC) incurred by both technology and social factors. We propose a research model specifying that the perceived values of resources of the factors affect the SC and the SC determine user's loyalty. Empirical results show that technology variables of ease of use and privacy controllability, and social variables such as network size, usefulness of SNS activities, and awareness of network status have significant effect on SC. In particular, ease of use is negatively associated with SC. Since it is shown that in overall the impact of social factors is stronger than that of technology factors, we can interpret that technological superiority itself does not lead to the success of SNS. Contributions of this paper are: 1) application of SC in SNS research from the resource based perspective, which can be used for developing strategies of sustainable SNS, and 2) provision of different perspective toward the variable of ease of use, which has been considered an important factor of technology acceptance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.5
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• pp.1984-2002
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• 2020

With the appearance of wireless spectrum crisis in traditional cellular network, device-to-device (D2D) communication has been regarded as a promising solution to ease heavy traffic burden by enabling precise content delivery among mobile users. However, due to the channel sharing, the interference between D2D and cellular users can affect the transmission rate and narrow the throughput in the network. In this paper, we firstly present a weighted interference minimization cluster formation model involving both social attribute and physical closeness. The weighted-interference, which is evaluated under the susceptible-infected(SI) model, is utilized to gather user in social and physical proximity. Then, we address the cluster formation problem via spectrum clustering with iterative operation. Finally, we propose the stable matching theory algorithm in order to maximize rate oriented to accomplish the one-to-one resource allocation. Numerical results show that our proposed scheme acquires quite well clustering effect and increases the accumulative transmission rate compared with the other two advanced schemes.

• Child Health Nursing Research
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• v.26 no.2
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• pp.222-237
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• 2020

Purpose: The purpose of this study was to analyze the concept of social support of nursing students using a hybrid model and to derive a definition and attributes of social support through theoretical, fieldwork, and final analysis stages. Methods: Twenty-nine studies were analyzed in the theoretical stage. Seventeen in-depth interviews were conducted with nursing students in the fieldwork stage. In the final analysis stage, the concept of social support was defined and the attributes were derived by integrating the theoretical and fieldwork stages. Results: The attributes of social support of nursing students identified in the final analysis consisted of two dimensions and eight attributes. The two dimensions were structural and functional support. The eight attributes were social network, educational, emotional, informational, economic, positive evaluation, self-esteem support, and support by providing a role model provision. The structural dimension included the social network support attribute. The functional dimension included the remaining seven attributes. Educational support and support by providing of a role model provision were newly derived attributes that reflected specific characteristics of nursing students. Conclusion: Based on the results of this study, we suggest that researchers should attempt to develop a scale to measure the social support of nursing students.