• Journal of Information Science Theory and Practice
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• v.8 no.3
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• pp.64-76
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• 2020

Scientometrics and social network analysis (SNA) measures were used to analyze the international scientific collaboration (ISC) of All India Institute of Medical Sciences (AIIMS) for a period of 10 years (2009-2018). The dataset consists of 19,622 records retrieved from the Scopus database. The mean degree of collaboration 0.95 implied that researchers of AIIMS tend to collaborate domestically (80.29%) and internationally (14.67%). The data exhibits a hyper authorship pattern, and a medium-size research team consists of 4 to 10 authors who contributed a maximum of 62.08% (12,182) publications. 71.97% of research findings are scattered in journal articles. The most preferred journals published 58.55% of medical literature. An undirected collaboration network is constructed in Pajek to study the ISC of AIIMS during the period 2009-2018 which consists of 179 vertices (Vn) and 11,938 edges. The degree centrality (Dc) identified that the United States of America (Dc - 54; CC - 0.99) and United Kingdom (Dc - 41; 0.98) are the most collaborative countries in the whole network as well as the most influential countries. The Louvain community detection method is used to detect influential research groups of AIIMS. The temporal evolution of ISC of AIIMS studied through scientometrics and SNA measures shed light on the structure and properties of ISC networks of AIIMS. It revealed that AIIMS, India has taken keen steps to enrich the quality of research by extending and encouraging the collaboration between institutions and industries at the international level.

• The Journal of Korean Association of Computer Education
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• v.17 no.4
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• pp.69-78
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• 2014

This study aims to investigate better predictors, among learner participation and interaction, for collective self-efficacy and achievement in a web-based collaborative learning environment. Interaction requires communication among two or more learners, while participation does not. In this study, interaction was measured by in-degree centrality and out-degree centrality based on the social network analysis perspective. Multiple regression analysis results from 53 college students who performed team project via online showed that in-degree centrality predicted collective self-efficacy and out-degree centrality predicted achievement, while participation was not a significant predictor.

• The Journal of Society for e-Business Studies
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• v.21 no.3
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• pp.29-50
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• 2016

We explore the impact of individuals'network position toward knowledge sharing intention. In order to identify network positions, we utilize three centrality measures (degree/closeness/betweenness) of individual network participants. The research findings show that the individual network positions significantly affect knowledge sharing intentions. Since an individual with high degree centrality might be the leader or the hub, one makes considerable effort to maintain the network position by actively participating in intra-team and inter-team knowledge sharing, A participant who can quickly interact with many other participants within a team (high closeness centrality) is more interested in intra-team knowledge sharing than inter-team knowledge sharing. Unlike degree centrality and closeness centrality, the betweenness centrality provides a participant with diverse resources located in multiple sub-groups. Although an individual with high betweenness centrality is not at the center of the networks, one plays a crucial role in disseminating and regulating information. Therefore, the individual is likely to have more positive intention toward inter-team knowledge sharing than intra-team knowledge sharing.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.11
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• pp.243-251
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• 2021

The goal of this study is to examine the characteristics of Chinese visitors visiting Thailand, determine the rules, and give a reference for Thai tourism authorities and businesses when developing marketing strategies for the Chinese market. This paper constructs the tourism flow network and takes Bangkok as the major research target. The statistical characteristics of the network are studied using the SNA method, based on the trip notes of Thailand on www.mafengwo.cn, a prominent travel website in China as the data source. The results show that: Shanghai, Beijing, and Tianjin occupy important positions in the network; The flow direction of Chinese tourists to Thailand mainly tends to Bangkok, Chiang Mai, Pattaya, and Phuket Island; Grand Palace have strong tourism flow aggregation, diffusion, and control over other nodes in the whole network structure; Tom Yu Kuang has the greatest degree centrality in all Thai cuisine. The findings of the study can help relevant management departments create tourist policies and modify market strategies by developing the regular characteristics of China's tourism flow to Thailand in the theoretical field.

• Journal of Digital Convergence
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• v.18 no.1
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• pp.111-119
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• 2020

This study takes Chinese independent tourists to South Korea as the research object, mines the data of tourists' digital footprints from online travel notes, and analyzes the characteristics of the tourism flow of Chinese independent tourists to South Korea by using the method of quantitative statistics and social network analysis(SNA). The results show that Seoul, Jeju Island, Busan and Daegu are the important tourist destinations for Chinese independent tourists entering South Korea. In addition, Qingdao, Tianjin, Shenyang, Hong Kong, Foshan and Macao are crucial hubs for Chinese independent tourists to visit South Korea. In future studies, the number of sample data should be increased. The time span of data collection should be extended for studying the annual variation characteristics of tourism flow and the trend of tourism hot spots.

• Journal of the Korean association of regional geographers
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• v.16 no.4
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• pp.369-386
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• 2010

Migration usually derived from the gap of spatial efficiency and the attractiveness difference between the origin and the destination puts forth significant impact on the regional dynamics of population. Both migration and regional planning or policy are structurally interconnected, exerting impact each other within the circular loops. In order to observe migration characters in the regional dimension, this research depends on social network analysis(SNA) methods which easily portray interrelationship and its weight between the origin and the destination. In specific, it focuses on the application of centrality indicators in order to understand the in- and out-migration patterns and visualize them with spring graphs, pinpointing the in- and out-migration administrative units. This research deals with three migration patterns in 2001 and 2008, respectively: i) in- and out-migration between the nation and Chungbuk Province; ii) in- and out-migration within Chungcheong Areas; and iii) in- and out-migration within Chungbuk Province. Judging from Chungbuk examples, the highest in-migration was recorded at Heungduck District in terms of in- and out-migration between the nation and Chungbuk Province. Seoul and Gyeonggi were two major origins towards Chungbuk Province. Within Chungcheong Areas, Daejeon produced the highest in-migration in 2001. However, Chungnam took that position in 2008.

• The Journal of the Korea Contents Association
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• v.19 no.12
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• pp.28-37
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• 2019

It is apparently clear that K-pop has been expanding its influence overseas, with its high growth rate. As a result, attempts have been made to analyze the characteristics of K-Pop in various academic fields. This research quantitatively used the participatory use process of K-Pop contents in voluntary participation and dissemination of the audience in the Trans-Media environment. The author examined the use of participatory K-Pop contents from the view point of reparability through big data content analysis. It has been revealed that K-Pop is spreading globally through social media, fans of various countries like to play K-Pop, and they make up their own content and form a participatory culture. In addition, we looked at when the moments of momentum in which participatory use is soaring were popular content and who was the publisher.

• The Research Journal of the Costume Culture
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• v.28 no.4
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• pp.562-571
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• 2020

Intermedia refers to the convergence of media. The advance of intermedia has not only facilitated the delivery of brand messages in contemporary fashion shows but also facilitated interactive communication. This study investigated the mediating roles played by various media in fashion and fashion shows, focusing on the phenomenon of intermedia in contemporary fashion shows. To investigate the impact of intermedia on contemporary fashion shows, we conducted a social network analysis-a promising approach for research into fashion trends. Analyzing 159 fashion-related articles published in the 2000s, we extracted intermedia-related words (n=253). The relation-ships between keywords made an analysis of between centrality, and cluster variables applied Clauset-Newman-Moore by using KrKwic and NodeXL programs. The results of the between centrality analysis indicated that the most important factors in contemporary fashion shows are "models" and "stages." We found that the impacts of intermedia on contemporary fashion shows can be divided into four categories: "model performance," "symbolic stage management," "new media utilization," and "convergence in arts." Our analysis thus identified considerable synergy between the characteristics of intermedia and contemporary fashion shows. These results have found intermedia-related commonalities in intermedia and fashion show, and this might increase customer interest in fashion, a positive outcome for the fashion industry.

• Journal of Distribution Science
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• v.18 no.3
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• pp.25-36
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• 2020

Purpose: Based on previous researches on social factors of digital item purchase in digital contents distribution platforms such as SNS, we aim to develop the integrated model that accounts for the dynamic and interactive relationship between social structure indicators and digital item purchase. Research design, data and methodology: A PVAR model was used to capture endogenous and dynamic relationships between digital item purchase and network indicators. Results: We find that there exist considerable endogenous and dynamic relationships between digital item purchase and network structure variables. Not only lagged in-degree and out-degree but also in-closeness and out-closeness centrality have significant and positive impacts on digital item purchase. Lagged clustering has a significant and negative effect on digital item purchase. Lagged purchase has a significant and positive impact just on the present in-closeness and out-closeness centrality; but there is no significant effect of lagged purchase on the other two degree variables and clustering coefficient. We also find that both closeness centralities have much higher carryover effect on digital item purchase and that the elasticity of both closeness centralities on the purchase of digital items is even higher than that of other network structure variables. Conclusions: In-closeness and out-closeness are the most influential factors among social structure variables of this study on digital item purchase.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.5
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• pp.390-394
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• 2009

The semantic web-based social network is highly useful in a variety of areas. In this paper we make diverse analyses of the FOAF-based social network, and propose an expert recommendation system. This system presents useful method of ontology-based social network using SparQL, RDFS inference, and visualization tools. Then we apply it to real social network in order to make various analyses of centrality, small world, scale free, etc. Moreover, our system suggests method for analysis of an expert on specific field. We expect such method to be utilized in multifarious areas - marketing, group administration, knowledge management system, and so on.