• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.1
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• pp.105-113
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• 2017

Network externality can be defined as the effect that one user of a good or service has on the value of that product to other people. When a network externality is present, the value of a product or service is dependent on the number of others using it. There exist asymmetries in network externalities between the online and traditional offline marketing channels. Technological capabilities such as interactivity and real-time communications enable the creation of virtual communities. These user communities generate significant direct as well as indirect network externalities by creating added value through user ratings, reviews and feedback, which contributes to eliminate consumers' concern for buying products without the experience of 'touch and feel'. The offline channel offers much less scope for such community building, and consequently, almost no possibility for the creation of network externality. In this study, we analyze the effect of network externality on the competition between online and conventional offline marketing channels using game theory. To do this, we first set up a two-period game model to represent the competition between online and offline marketing channels under network externalities. Numerical analysis of the Nash equilibrium solutions of the game showed that the pricing strategies of online and offline channels heavily depend not only on the strength of network externality but on the relative efficiency of online channel. When the relative efficiency of online channel is high, the online channel can greatly benefit by the network externality. On the other hand, if the relative efficiency of online channel is low, the online channel may not benefit at all by the network externality.

• International Journal of Computer Science & Network Security
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• v.22 no.2
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• pp.145-152
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• 2022

One of the impacts of Covid-19 on education systems has been the shift to online education. This shift has changed the way education is consumed and perceived by students. However, the exact nature of student perception about online education is not known. The aim of this study was to understand the perceptions of Saudi higher education students (e.g., post-school students) about online education during the Covid-19 pandemic. Various aspects of online education including benefits, features and cybersecurity were explored. The data collected were analysed using statistical techniques, especially artificial neural networks, to address the research aims. The key findings were that benefits of online education was perceived by students with positive experience or when ensured of safe use of online platforms without the fear cyber security breaches for which recruitment of a cyber security officer was an important predictor. The issue of whether perception of online education as a necessity only for Covid situation or a lasting option beyond the pandemic is a topic for future research.

• Journal of Information Technology Services
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• v.17 no.2
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• pp.165-177
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• 2018

Recently, online commerces are becoming more common due to factors such as mobile technology development and smart device dissemination, and online review has a big influence on potential buyer's purchase decision. This study presents a set of analytical methodologies for understanding the meaning of customer reviews of products in online transaction. Using techniques currently developed in deep learning are implemented Hierarchical Attention Network for analyze meaning in online reviews. By using these techniques, we could solve time consuming pre-data analysis time problem and multiple topic problems. To this end, this study analyzes customer reviews of laptops sold in domestic online shopping malls. Our result successfully demonstrates over 90% classification accuracy. Therefore, this study classified the unstructured text data in the semantic analysis and confirmed the practical application possibility of the review analysis process.

• Journal of Korean Institute of Industrial Engineers
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• v.42 no.2
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• pp.138-150
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• 2016

This study aims to analyze the structure and characteristics of the domestic online game industry using network analysis. In particular, two-mode network analysis is employed to measure the network structure, centrality, and cluster for two types of online game platforms, online games and mobile games, from 1996 to 2014. We also conduct a dynamic analysis to capture the structural changes in the ecosystem by internal and external environmental changes before and after turning point for each online game platform. It is revealed that the online game econsystem has the higher number of clusters and higher concentration ratio than those of mobile game ecosystem. In dynamic analysis, both platforms exhibit similar trends over time with the increasing number of clusters, enlargement of largest cluster's size, and decreasing concentration ratio. This study is expected to provide fruitful implications for strategic decision making of online game companies and policy making for the online game industry.

• Journal of Internet Computing and Services
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• v.18 no.4
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• pp.7-17
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• 2017

Despite various types of online social networks having different topological and functional characteristics, the kinds of online social networks considered in social recommendations are highly restricted. The pervasiveness of social networks has brought scholarly attention to expanding the scope of social recommendations into more diverse and less explored types of online social networks. As a preliminary attempt, this study examined the information-sharing patterns of a new type of online social network - unilateral (directed) network - and assessed the feasibility of the network as a useful information source. Specifically, this study mainly focused on the presence of shared interests in unilateral networks, because the shared information is the inevitable condition for utilizing the networks as a feasible source of personalized recommendations. As the results, we discovered that user pairs with direct and distant links shared significantly more similar information than the other non-connected pairs. Individual users' social properties were also significantly correlated with the degree of their information similarity with social connections. We also found the substitutability of online social networks for the top cohorts anonymously chosen by the collaborative filtering algorithm.

• Journal of the Korean Operations Research and Management Science Society
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• v.34 no.3
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• pp.1-27
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• 2009

Since online knowledge networks usually consist of a larger, loosely knit, and geographically distributed group of "strangers" who may not know each other very well, members may not willingly share their knowledge with others. In order to address this challenge, this study looks Into the factors that are expected to affect knowledge sharing in an online knowledge network. For empirical validation, we choose "the global network of Korean scientists and engineers (KOSEN)" as one of the best practices of online knowledge networks. By using the archival, network, and survey data, we validate two models of knowledge sharing in sequence (i.e., knowledge contribution and knowledge utilization models) and then discuss the results. The findings of this study show that individuals not only contribute but also utilize knowledge in an online knowledge network when they are structurally embedded and perceive a strong reciprocity. In the network. In addition, taking pleasure in helping is found to positively affect knowledge contribution, whereas perceiving usefulness is found to Influence knowledge utilization. Contributions of this study and future research opportunities are also discussed.

• Knowledge Management Research
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• v.11 no.2
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• pp.17-35
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• 2010

As the internet has been used widely, many online communities have been appeared. Initially, many users used online communities for communication and information sharing. Recently, users start using online communities for building, maintaining, and extending social networks which they did in offline environments previously. The importance of online community is considered by many scholars and also companies to use it strategically. Therefore many studies have focused on exploring characteristics of online communities. Most of them have emphasized the importance of online community. Few study focuses on dynamics within online community. By using social network analysis (SNA), this study tries to explore dynamics of online community. Specially, By measuring the centrality of online community and tracing its changes, this study investigates how the relationships among participants in online communities have been changed over the time. Findings of this study indicate that, as participants has joined in an online community over the time, an opinion leader is appeared, and her/his power is changed.

• Journal of Information Processing Systems
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• v.11 no.1
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• pp.1-21
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• 2015

This paper aims to assess the feasibility of a new and less-focused type of online sociability (the watching network) as a useful information source for personalized recommendations. In this paper, we recommend scientific articles of interests by using the shared interests between target users and their watching connections. Our recommendations are based on one typical social bookmarking system, CiteULike. The watching network-based recommendations, which use a much smaller size of user data, produces suggestions that are as good as the conventional Collaborative Filtering technique. The results demonstrate that the watching network is a useful information source and a feasible foundation for information personalization. Furthermore, the watching network is substitutable for anonymous peers of the Collaborative Filtering recommendations. This study shows the expandability of social network-based recommendations to the new type of online social networks.

• International Journal of Computer Science & Network Security
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• v.21 no.6
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• pp.77-88
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• 2021

Heart Failure represents a critical pathological case that is challenging to predict and discover at an early age, with a notable increase in morbidity and mortality. Machine Learning and Neural Network techniques play a crucial role in predicting heart attacks, diseases and more. These techniques give valuable perspectives for clinicians who may then adjust their diagnosis for each individual patient. This paper evaluated neural network models for heart attacks predictions. Several online learning methods were investigated to automatically and accurately predict heart attacks. The UCI dataset was used in this work to train and evaluate First Order and Second Order Online Learning methods; namely Backpropagation, Delta bar Delta, Levenberg Marquardt and QuickProp learning methods. An optimizer technique was also used to minimize the random noise in the database. A regularization concept was employed to further improve the generalization of the model. Results show that a three layers' NN model with a Backpropagation algorithm and Nadam optimizer achieved a promising accuracy for the heart attach prediction tasks.

• Journal of Korea Society of Digital Industry and Information Management
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• v.12 no.4
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• pp.163-169
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• 2016

This study is to analyze factors affecting a growth of online community with perspectives of social network. Particularly this study tries to explore structural phenomenon built by interactions between users in a certain of free board belonging to the online community, in which focuses on one's writing a comments responding on those of others. With using SNA(Social Network Analysis), the social network data calculated from users' interaction shown as their comments were collected to draw out each individual's centrality value representing the structure of the online community and also we estimated duration time and the number of each comments as a proxy variable representing growth of the online community. And then cause-and-effect relationship between individual's centrality value and the duration time and the number of each comment were analyzed. As a result of the analysis, Core-Periphery, Centralization and Reciprocity have significant effects on the duration time and the number of each comment, therefore those significant values representing online structure will give an implication to manage, to promote the online community, to forecast its evolution path and to build critical policies.