• Journal of Radiation Protection and Research
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• v.46 no.3
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• pp.143-150
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• 2021

Since its establishment in 2018, the Young Generation Network (YGN) has been dedicated, with support of the International Radiation Protection Association (IRPA), to a variety of activities to promote communication, collaboration and professional development of students and young professionals in the area of radiation protection and its allied fields. This article reports our recent activities from the middle of 2018 to the beginning of 2021, with highlights on some important events: "Joint JHPS-SRP-KARP Workshop of Young Generation Network" (December 2019 in Japan); contribution to "Nuclear Energy Agency Workshop on Optimization: Rethinking the Art of Reasonable" (January 2020 in Portugal); survey on the impact of coronavirus disease 2019 (COVID-19) on radiation protection among IRPA YGN members (March 2020); and contribution to IRPA15 (15^th International Congress of the IRPA; January-February 2021, online). The discussion and insight obtained from each activity are also summarized. The IRPA YGN will aim to achieve its on-going activities and continue to follow the ways paved in the Strategic Agenda and despite the challenges raised by the COVID-19 pandemic. Namely, running an international survey (for example, on the usage of social media in radiation protection, and on the long-term consequences of the COVID-19 pandemic), engaging national YGNs, extending the network, finding new relationships with networks with an interest in the young generation and participation in (remote) events will be aspired for.

• Journal of the Architectural Institute of Korea Planning & Design
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• v.34 no.12
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• pp.13-20
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• 2018

This research is to analyze the public cases of information facilities in terms of central circulations in multi level volumes such as atrium or court which provide visual intervention between different spaces and physical connections such as bridges. Hunt Library design balances the understood pre-existing needs with the University's emerging needs to create a forward-thinking learning environment. While clearly a contemporary structure within a traditional context of the NCSU campus, the Hunt Library provides a positive platform for influencing its surroundings. Both technical and programmatic innovations are celebrated as part of the learning experience and provide a versatile and stimulating environment for students. Public library as open spaces connecting to an interactive social domain over communities can provide variety of learning environments, or technology based labs. There are many cases of the public information spaces with dynamic networks where participants can play their roles in physical space as well as in the intellectual stimulation. In the research, new public projects provide typologies of information spaces with user oriented media. The research is to address a creative transition between the reading space and the experimental links of the integration of state-of-the-art technology is highly visible in the building's design. The user-friendly browsing system that replaces the traditional browsing with the virtual shelves classified and archived by their form, is to reduce the storage space of the public library and it is to allow more space for collaborative learning. In addition to the intelligent robot of information storages, innovative features is the large-scale visualization space that supports team experiments to carry out collaborative online works and therefore the public library's various programs is to provide visitors with more efficient participatory environment.

• Fashion & Textile Research Journal
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• v.26 no.1
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• pp.88-98
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• 2024

With the growing preference among Chinese consumers for purchasing beauty products through social media networks (SNS), influencer marketing has recently emerged as a crucial strategy for maximizing word-of-mouth effects. This study aims to ascertain the impact of SNS beauty influencers' characteristics on trustworthiness and consumers' intentions to engage in word-of-mouth promotion. Furthermore, the study seeks to explore the moderating role of consumer involvement in the relationship between SNS beauty influencer characteristics and the trust consumers place in them. As part of an empirical analysis, an online survey was administered to 259 Chinese female consumers who had previously purchased beauty products through influencers on SNS. The data gathered were scrutinized by conducting multiple and hierarchical regression analysis to test the proposed hypotheses. The findings indicated that the attributes of "expertise,"' "intimacy," and "homogeneity" in SNS beauty influencers significantly affect influencer trust, whereas "charm" does not have a significant impact. Moreover, consumer involvement was found to moderate the relationship between SNS beauty influencer characteristics (expertise, intimacy, charm, and homogeneity) and influencer trust. Additionally, influencer trust positively influenced the intention to engage in word-of-mouth activities. These findings signify that leveraging influencers possessing qualities such as expertise, intimacy, and homogeneity can help enhance product exposure, popularity, and sales of the beauty industry. This study contributes valuable insights into the strategic utilization of influencer characteristics in the beauty industry and digital marketing, highlighting their pivotalrole in consumer engagement and the success of marketing strategies.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.93-107
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• 2014

In this paper, we report what we have observed with regard to user-perspective issue clustering based on multi-layered two-mode network analysis. This work is significant in the context of data collection by companies about customer needs. Most companies have failed to uncover such needs for products or services properly in terms of demographic data such as age, income levels, and purchase history. Because of excessive reliance on limited internal data, most recommendation systems do not provide decision makers with appropriate business information for current business circumstances. However, part of the problem is the increasing regulation of personal data gathering and privacy. This makes demographic or transaction data collection more difficult, and is a significant hurdle for traditional recommendation approaches because these systems demand a great deal of personal data or transaction logs. Our motivation for presenting this paper to academia is our strong belief, and evidence, that most customers' requirements for products can be effectively and efficiently analyzed from unstructured textual data such as Internet news text. In order to derive users' requirements from textual data obtained online, the proposed approach in this paper attempts to construct double two-mode networks, such as a user-news network and news-issue network, and to integrate these into one quasi-network as the input for issue clustering. One of the contributions of this research is the development of a methodology utilizing enormous amounts of unstructured textual data for user-oriented issue clustering by leveraging existing text mining and social network analysis. In order to build multi-layered two-mode networks of news logs, we need some tools such as text mining and topic analysis. We used not only SAS Enterprise Miner 12.1, which provides a text miner module and cluster module for textual data analysis, but also NetMiner 4 for network visualization and analysis. Our approach for user-perspective issue clustering is composed of six main phases: crawling, topic analysis, access pattern analysis, network merging, network conversion, and clustering. In the first phase, we collect visit logs for news sites by crawler. After gathering unstructured news article data, the topic analysis phase extracts issues from each news article in order to build an article-news network. For simplicity, 100 topics are extracted from 13,652 articles. In the third phase, a user-article network is constructed with access patterns derived from web transaction logs. The double two-mode networks are then merged into a quasi-network of user-issue. Finally, in the user-oriented issue-clustering phase, we classify issues through structural equivalence, and compare these with the clustering results from statistical tools and network analysis. An experiment with a large dataset was performed to build a multi-layer two-mode network. After that, we compared the results of issue clustering from SAS with that of network analysis. The experimental dataset was from a web site ranking site, and the biggest portal site in Korea. The sample dataset contains 150 million transaction logs and 13,652 news articles of 5,000 panels over one year. User-article and article-issue networks are constructed and merged into a user-issue quasi-network using Netminer. Our issue-clustering results applied the Partitioning Around Medoids (PAM) algorithm and Multidimensional Scaling (MDS), and are consistent with the results from SAS clustering. In spite of extensive efforts to provide user information with recommendation systems, most projects are successful only when companies have sufficient data about users and transactions. Our proposed methodology, user-perspective issue clustering, can provide practical support to decision-making in companies because it enhances user-related data from unstructured textual data. To overcome the problem of insufficient data from traditional approaches, our methodology infers customers' real interests by utilizing web transaction logs. In addition, we suggest topic analysis and issue clustering as a practical means of issue identification.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.23-46
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• 2021

Collaborative filtering, which is often used in personalization recommendations, is recognized as a very useful technique to find similar customers and recommend products to them based on their purchase history. However, the traditional collaborative filtering technique has raised the question of having difficulty calculating the similarity for new customers or products due to the method of calculating similaritiesbased on direct connections and common features among customers. For this reason, a hybrid technique was designed to use content-based filtering techniques together. On the one hand, efforts have been made to solve these problems by applying the structural characteristics of social networks. This applies a method of indirectly calculating similarities through their similar customers placed between them. This means creating a customer's network based on purchasing data and calculating the similarity between the two based on the features of the network that indirectly connects the two customers within this network. Such similarity can be used as a measure to predict whether the target customer accepts recommendations. The centrality metrics of networks can be utilized for the calculation of these similarities. Different centrality metrics have important implications in that they may have different effects on recommended performance. In this study, furthermore, the effect of these centrality metrics on the performance of recommendation may vary depending on recommender algorithms. In addition, recommendation techniques using network analysis can be expected to contribute to increasing recommendation performance even if they apply not only to new customers or products but also to entire customers or products. By considering a customer's purchase of an item as a link generated between the customer and the item on the network, the prediction of user acceptance of recommendation is solved as a prediction of whether a new link will be created between them. As the classification models fit the purpose of solving the binary problem of whether the link is engaged or not, decision tree, k-nearest neighbors (KNN), logistic regression, artificial neural network, and support vector machine (SVM) are selected in the research. The data for performance evaluation used order data collected from an online shopping mall over four years and two months. Among them, the previous three years and eight months constitute social networks composed of and the experiment was conducted by organizing the data collected into the social network. The next four months' records were used to train and evaluate recommender models. Experiments with the centrality metrics applied to each model show that the recommendation acceptance rates of the centrality metrics are different for each algorithm at a meaningful level. In this work, we analyzed only four commonly used centrality metrics: degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality. Eigenvector centrality records the lowest performance in all models except support vector machines. Closeness centrality and betweenness centrality show similar performance across all models. Degree centrality ranking moderate across overall models while betweenness centrality always ranking higher than degree centrality. Finally, closeness centrality is characterized by distinct differences in performance according to the model. It ranks first in logistic regression, artificial neural network, and decision tree withnumerically high performance. However, it only records very low rankings in support vector machine and K-neighborhood with low-performance levels. As the experiment results reveal, in a classification model, network centrality metrics over a subnetwork that connects the two nodes can effectively predict the connectivity between two nodes in a social network. Furthermore, each metric has a different performance depending on the classification model type. This result implies that choosing appropriate metrics for each algorithm can lead to achieving higher recommendation performance. In general, betweenness centrality can guarantee a high level of performance in any model. It would be possible to consider the introduction of proximity centrality to obtain higher performance for certain models.

• Journal of Intelligence and Information Systems
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• v.19 no.1
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• pp.57-77
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• 2013

Social media has become one of the most popular media in web and mobile application. In 2011, social networks and blogs are still the top destination of online users, according to a study from Nielsen Company. In their studies, nearly 4 in 5active users visit social network and blog. Social Networks and Blogs sites rule Americans' Internet time, accounting to 23 percent of time spent online. Facebook is the main social network that the U.S internet users spend time more than the other social network services such as Yahoo, Google, AOL Media Network, Twitter, Linked In and so on. In recent trend, most of the companies promote their products in the Facebook by creating the "Facebook Page" that refers to specific product. The "Like" option allows user to subscribed and received updates their interested on from the page. The film makers which produce a lot of films around the world also take part to market and promote their films by exploiting the advantages of using the "Facebook Page". In addition, a great number of streaming service providers allows users to subscribe their service to watch and enjoy movies and TV program. They can instantly watch movies and TV program over the internet to PCs, Macs and TVs. Netflix alone as the world's leading subscription service have more than 30 million streaming members in the United States, Latin America, the United Kingdom and the Nordics. As the matter of facts, a million of movies and TV program with different of genres are offered to the subscriber. In contrast, users need spend a lot time to find the right movies which are related to their interest genre. Recent years there are many researchers who have been propose a method to improve prediction the rating or preference that would give the most related items such as books, music or movies to the garget user or the group of users that have the same interest in the particular items. One of the most popular methods to build recommendation system is traditional Collaborative Filtering (CF). The method compute the similarity of the target user and other users, which then are cluster in the same interest on items according which items that users have been rated. The method then predicts other items from the same group of users to recommend to a group of users. Moreover, There are many items that need to study for suggesting to users such as books, music, movies, news, videos and so on. However, in this paper we only focus on movie as item to recommend to users. In addition, there are many challenges for CF task. Firstly, the "sparsity problem"; it occurs when user information preference is not enough. The recommendation accuracies result is lower compared to the neighbor who composed with a large amount of ratings. The second problem is "cold-start problem"; it occurs whenever new users or items are added into the system, which each has norating or a few rating. For instance, no personalized predictions can be made for a new user without any ratings on the record. In this research we propose a clustering method according to the users' genre interest extracted from social network service (SNS) and user's movies rating information system to solve the "cold-start problem." Our proposed method will clusters the target user together with the other users by combining the user genre interest and the rating information. It is important to realize a huge amount of interesting and useful user's information from Facebook Graph, we can extract information from the "Facebook Page" which "Like" by them. Moreover, we use the Internet Movie Database(IMDb) as the main dataset. The IMDbis online databases that consist of a large amount of information related to movies, TV programs and including actors. This dataset not only used to provide movie information in our Movie Rating Systems, but also as resources to provide movie genre information which extracted from the "Facebook Page". Formerly, the user must login with their Facebook account to login to the Movie Rating System, at the same time our system will collect the genre interest from the "Facebook Page". We conduct many experiments with other methods to see how our method performs and we also compare to the other methods. First, we compared our proposed method in the case of the normal recommendation to see how our system improves the recommendation result. Then we experiment method in case of cold-start problem. Our experiment show that our method is outperform than the other methods. In these two cases of our experimentation, we see that our proposed method produces better result in case both cases.

• Journal of the Korean Society of Food Culture
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• v.27 no.6
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• pp.666-676
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• 2012

The purpose of this study was to propose effective marketing communication strategies for overseas Korean restaurants through a multilateral comparison analysis of American, Chinese, and Japanese consumers' attitudes towards communication media and Korean restaurants. The survey was written in English, Chinese, and Japanese, with guideline for surveyors, and conducted using both online and offline methods. Samples were collected from five major cities - Los Angeles, New York, Tokyo, Beijing and Shanghai, which are the foothold for the globalization of Korean food. When it comes to attitudes towards communication media, word-of-mouth showed a high mean value, indicating it as the most useful and reliable media recognized by consumers who visited Korean restaurants. Furthermore, the necessity of recognizing the importance of visual communication in the physical environment of Korean restaurants and specialized websites, featuring restaurants and gourmet food, was observed. Consumers in all three nations chose word-of-mouth as the most useful and reliable media for learning about Korean restaurants. In addition, American consumers highly depended on signage and restaurant exteriors. Chinese consumers highly recognized the usefulness and reliability of offline media, such as newspapers, magazines, and events, while Japanese consumers considered online media, such as gourmet websites, blogs and social networks, as useful and reliable sources. A significantly positive attitude and high value was observed in all who had visited Korean restaurants. American and Japanese consumers had a significantly higher rate of intention to visit Korean restaurants in the future and to tell others about their satisfaction with Korean restaurants. Meanwhile, the average rate of prior preference for Korean restaurants (when choosing restaurants) was the lowest in all three countries. This study is useful for both the Korean government and food enterprises abroad to plan and develop marketing communication strategies properly for overseas Korean restaurants.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.4
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• pp.454-462
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• 2018

This study was conducted to examine SNS addiction proneness and interpersonal satisfaction among undergraduate students and the relationships between these two variables, as well as to establish baseline data for appropriate intervention of SNS addiction prevention. The participants of this study were 316 undergraduate students in D and K city, and data were collected between June 30 and July 30, 2017. Data were collected by a self-administered online survey and analyzed by descriptive statistics, t-tests, and Pearson's correlation coefficients using SPSS. The results showed that SNS addiction proneness and interpersonal satisfaction were negatively correlated (r=-0.57, p<0.01), indicating students with higher SNS addiction had lower interpersonal satisfaction. There were no significant differences in SNS addiction proneness and interpersonal satisfaction by gender (t=0.05, p=0.963), number of SNS networks (t=0.66, p=0.513), or number of SNS-only networks (t=-1.24, p=0.216). Students who used SNS for data collection showed significantly higher interpersonal satisfaction (t=3.02, p=0.030); however, there was no significant differences in SNS addiction proneness among purposes for using SNS (t=0.39, p=.759). The results of this study will be useful baseline data for developing an intervention to improve interpersonal satisfaction and prevent SNS addiction among undergraduate students.

• Journal of the Economic Geographical Society of Korea
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• v.6 no.1
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• pp.119-144
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• 2003

This study attempts to figure out the impact of Information and communication technologies (ICTs) on spatial structure and to speculate on spatial strategies in the electronic economy from a geographical perspective. The unprecedented development of ICTs based on the explosive use of the Internet was enough to lead to the expectation that physical distance would not be a significant barrier in business activities. In fact, however, at least at a current stage, the development of ICTs has not automatically removed the inequality in spatial structure. The accessibility to electronic space is different by economic and social status within a country as well as between countries. The importance of place, locality, and place-specific assets has been strengthened in the global economy. Physical proximity is still of great importance because it helps to minimize transaction costs, to exploit place-specific social networks, and to accumulate credibility for successful businesses. Likewise, the development of electronic commerce such as B2B and B2C EC also does not necessarily result in the ignorance of place and locality. Rather, the recognition of the importance of spatial strategies is extremely important for the success in online businesses. As a conclusion, the spatial dimension becomes more important in the digital era for successful businesses and balanced regional developments than ever before. The need for the improvement of ICT infrastructures, the development of human resources, and the establishment of regional innovation systems in peripheral areas cannot be overemphasized even in the digital era.

• The Journal of Industrial Distribution & Business
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• v.10 no.3
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• pp.59-72
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• 2019

Purpose - Blockchain technology, which has the characteristics of credibility, security, integrity and decentralization, has brought innovation to internet platforms that mediate peer to peer transactions, as well as changes to the contents distribution services. Blockchain-based copyright distribution platforms can solve problems which have been articulated on prior internet social networks: increased market dominance of platform business because of centralization with no reward to creators who upload on platforms, and lack of fairness, such as unfair profit distribution between the copyright holder and businesses. With this background, the current research confirmed the factors that affect the intention of usage and behaviors, targeting potential users of blockchain-based copyright distribution platforms. Research design, data, and methodology - Centered around the UTAUT2 Model, the research model was designed with 'Perceived Security' added as Construct, and 'Age' and 'Knowledge Level' added as moderating variables. For data, 607 responses were collected by an online survey, and 601 responses were included in the final analysis. We analyzed the research model and sample by using SPSS 23.0 and AMOS 23.0 on the collected responses. Results - First, results of research on whether Constructs make positive effects on Intention of use is: social influence, facilitating conditions, habit, and perceived security had positive effects on intention of use, and performance expectancy, effort expectancy, hedonic motivation, and economic value did not. Second, results of the research on whether facilitating condition, habit, and intention of use made an impact on using behaviors, it was shown that only habit and intention of use made positive effects. Third, in two groups divided by age above or under 40, group effort expectancy, intention of use, habit, and intention of use had controlling effects, and facilitating condition, intention of use, perceived security, and intention of use had effects in both groups. Conclusions - The research shows that no matter how great a blockchain-based platform is, if advantages of blockchain are not proved in various industries and utilized in real life like the internet, blockchain-based distribution systems will develop slowly. Rather than a short-term inducement emphasizing technology, there is a need for a strategic approach that can foster the environment.