• Korea Trade Review
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• v.47 no.2
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• pp.121-132
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• 2022

This study aims to find out the ESG management keywords in the logistics industry through social network analysis using news article and sustainable management reports. In recent years, global climate change and Covid-19 have spurred companies to step up their new management system called ESG management. ESG is a combination of Environment, Social, and Governance. In the past, companies' financial performance was the most important, but in the current investment market, the movement to reflect ESG management factors in investment decisions is strengthening. This study aims to find out degree centrality, betweenness centrality, and closeness centrality through social network analysis after collecting related keywords to derive ESG management issues of logistics companies. This study collected 2,359 news articles searched under the keywords "ESG", "Logistics". In addition, data on ESG activities were also used for analysis by referring to the sustainable management reports of logistics companies. As a result of the analysis of degree centrality, it was found that ESG management of logistics companies is in progress, focusing on small enterprises and eco-friendly keywords, and is concentrated on social responsibility and eco-friendly activities. In the betweenness centrality analysis, logistics companies such as HMM and CJ Logistics were derived in a high ranking. In the closeness centrality analysis, eco-friendly keywords topped the list, while the number of keywords related to governance was relatively small, suggesting that logistics companies need to improve their governance structure.

• Proceedings of the Korea Technology Innovation Society Conference
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• 2015.05a
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• pp.508-525
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• 2015

The purpose of this study is to derive the demands to develop marine science technology to reduce damage of disasters caused by boating accidents. This study analyzed the press release to identify the factors of damages that can result from boating accidents and derive the demands for technology development to approach from the perspective of marine science technology to avoid the elements of damage. For this purpose, this study analyzed the contents of about 77,000 articles posted for a month after the tragedy of the Sewol (April 16 - May 15) to derive the keywords and used SNA for the network analysis of each keyword. The findings of the analysis showed that there were five networks and each network consisted of different aspects of technology development to prepare for the marine disasters. Based on these findings, this study derived the demands for technology development from the perspective of marine science technology required to prepare for the possible marine disasters caused by vessels in the future.

• Journal of Korean Society for Quality Management
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• v.39 no.2
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• pp.271-280
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• 2011

In this paper, the relationships between code-share networks and performances in airline industry were analyzed by using Social Network Analysis (SNA). We first analyzed the schedule data from OAG (Official Airline Guide) to obtain core-share information of airline industries. SNA was, then, applied to the code-share information. Finally, statistical analysis was conducted to analyze the relationships between code-share social networks and performances. The result shows that the size and out-degree centrality have relatively significant effects on the performance of airline industries, while in-degree and betweenness centrality has less significant effects.

• International Commerce and Information Review
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• v.19 no.1
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• pp.23-42
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• 2017

International Commerce and Information Review has been playing an important role of disseminating the outstanding research results in the fields such as trade information and systems, e-trade, regional studies, e-commerce, service trade, trade laws since 1999. This paper aims to find the research trends and distinguished characteristics in the field of trade information by analyzing research keywords of the research papers published in this journal using a social network analysis method. Research keyword data collected from the homepage of the academic society were cleaned and transformed into the co-occurrence network data, which are suitable for social network analysis. NodeXL Pro was used to analyze and visualize the pre-processed data. Through clustering analysis, the most important subject fields or interests were identified as well as those which worked as intermediaries for interdisciplinary researches.

• Journal of Intelligence and Information Systems
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• v.11 no.2
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• pp.17-29
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• 2005

The purpose of the study was to explore the potential of the Social Network Analysis as an analytical tool for scientific investigation of learner-learner, or learner-tutor interaction within a Computer Supported Corporative Learning (CSCL) environment. Theoretical and methodological implication of the Social Network Analysis had been discussed. Following theoretical analysis, an exploratory empirical study was conducted to test statistical correlation between traditional performance measures such as achievement and team contribution index, and the centrality measure, one of the many quantitative measures the Social Network Analysis provides. Results indicate the centrality measure was correlated with the higher order teaming performance and the peer-evaluated contribution indices. An interpretation of the results and their implication to instructional design theory and practices were provided along with some suggestions for future research.

• Journal of Korea Port Economic Association
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• v.32 no.1
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• pp.17-28
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• 2016

This study examines the cruise course network structure in the Asian regions and the centrality of ports using social network analysis (SNA). For network analysis of Asian cruise courses, a data network of cruise courses was constructed using data on courses of cruise ships operating in Asian ports collected from the reports of the Cruise Lines International Associations.There are 249 nodes or ports of ship companies that provide cruise courses to Asia between from October 2015 to June 2016, and these nodes connect 545 ports. Density analysis based on ports where cruise ship companies operated cruise ships showed that, from October 2015 to June 2016, the density was 0.009, which was lower than the average of global port network density (2006 to 2011) and railroad network density. In addition, was calculated to be, which means that connection with all ports was possible through 2,180 steps. In the analysis of the Asian cruise course network centrality, Singapore ranked first in both out-degree and in-degree in connection centrality, followed by Hong Kong, Shanghai, Ho Chi Minh, and Keelung. Singapore also ranked first in the result betweenness centrality analysis, followed by Penang, Dubai, and Hong Kong. From October 2015 to June 2016, the port with the highest Eigenvector centrality was Hong Kong, followed by Ho Chi Minh, Singapore, Shanghai, and Danang. In the case of the domestic ports Incheon, Busan, and Jeju, connection centrality, betweenness centrality, and Eigenvector centrality all ranked lower than their competitor Chinese ports.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.137-148
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• 2014

Recommender system has become one of the most important technologies in e-commerce in these days. The ultimate reason to shop online, for many consumers, is to reduce the efforts for information search and purchase. Recommender system is a key technology to serve these needs. Many of the past studies about recommender systems have been devoted to developing and improving recommendation algorithms and collaborative filtering (CF) is known to be the most successful one. Despite its success, however, CF has several shortcomings such as cold-start, sparsity, gray sheep problems. In order to be able to generate recommendations, ordinary CF algorithms require evaluations or preference information directly from users. For new users who do not have any evaluations or preference information, therefore, CF cannot come up with recommendations (Cold-star problem). As the numbers of products and customers increase, the scale of the data increases exponentially and most of the data cells are empty. This sparse dataset makes computation for recommendation extremely hard (Sparsity problem). Since CF is based on the assumption that there are groups of users sharing common preferences or tastes, CF becomes inaccurate if there are many users with rare and unique tastes (Gray sheep problem). This study proposes a new algorithm that utilizes Social Network Analysis (SNA) techniques to resolve the gray sheep problem. We utilize 'degree centrality' in SNA to identify users with unique preferences (gray sheep). Degree centrality in SNA refers to the number of direct links to and from a node. In a network of users who are connected through common preferences or tastes, those with unique tastes have fewer links to other users (nodes) and they are isolated from other users. Therefore, gray sheep can be identified by calculating degree centrality of each node. We divide the dataset into two, gray sheep and others, based on the degree centrality of the users. Then, different similarity measures and recommendation methods are applied to these two datasets. More detail algorithm is as follows: Step 1: Convert the initial data which is a two-mode network (user to item) into an one-mode network (user to user). Step 2: Calculate degree centrality of each node and separate those nodes having degree centrality values lower than the pre-set threshold. The threshold value is determined by simulations such that the accuracy of CF for the remaining dataset is maximized. Step 3: Ordinary CF algorithm is applied to the remaining dataset. Step 4: Since the separated dataset consist of users with unique tastes, an ordinary CF algorithm cannot generate recommendations for them. A 'popular item' method is used to generate recommendations for these users. The F measures of the two datasets are weighted by the numbers of nodes and summed to be used as the final performance metric. In order to test performance improvement by this new algorithm, an empirical study was conducted using a publically available dataset - the MovieLens data by GroupLens research team. We used 100,000 evaluations by 943 users on 1,682 movies. The proposed algorithm was compared with an ordinary CF algorithm utilizing 'Best-N-neighbors' and 'Cosine' similarity method. The empirical results show that F measure was improved about 11% on average when the proposed algorithm was used