• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.12
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• pp.688-701
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• 2020

This study analyzes the characteristics and change factors of technological cooperation networks in the automotive industry. Using Social Network Analysis (SNA) of 112,009 autonomous driving-related patents filed from 2000 to 2017 by major automotive firms in the world, we investigate the structure of the technological cooperation network. Network characteristics such as density are analyzed through structural characteristic analysis among the network analysis indicators. The structural characteristics of the technology cooperation network are confirmed through analysis of status characteristic indicators, such as the degree of centrality, betweenness centrality, and closeness centrality. Results show that car makers such as Toyota and Hyundai Motors, as well as parts suppliers such as Bosch and Continental, have high-performance technology developments related to autonomous driving. The structural characteristics of the network show that companies participating in cooperative networks for autonomous driving technology development have increased in number and are diversified, and all of the status characteristics indicators have decreased. This can be interpreted as an increasing number of horizontal and complementary forms of technological cooperation between firms. In addition, it was confirmed that the number of participants in the field of autonomous driving technology has increased, and the networks have become more complex.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.175-196
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• 2022

Information and communication and home appliance industries, which were one of South Korea's main industries, are gradually losing their export share as their export competitiveness is weakening. This study objectively analyzed export competitiveness and suggested export-promising countries in order to help South Korea's information communication and home appliance industries improve exports. In this study, network properties, centrality, and structural hole analysis were performed during network analysis to evaluate export competitiveness. In order to select promising export countries, we proposed a new variable that can take into account the characteristics of an already established International Trade Network (ITN), that is, the Global Value Chain (GVC), in addition to the existing economic factors. The conditional log-odds for individual links derived from the Exponential Random Graph Model (ERGM) in the analysis of the cross-border trade network were assumed as a proxy variable that can indicate the export potential. In consideration of the possibility of ERGM linkage, a parametric approach and a non-parametric approach were used to recommend export-promising countries, respectively. In the parametric method, a regression analysis model was developed to predict the export value of the information and communication and home appliance industries in South Korea by additionally considering the link-specific characteristics of the network derived from the ERGM to the existing economic factors. Also, in the non-parametric approach, an abnormality detection algorithm based on the clustering method was used, and a promising export country was proposed as a method of finding outliers that deviate from two peers. According to the research results, the structural characteristic of the export network of the industry was a network with high transferability. Also, according to the centrality analysis result, South Korea's influence on exports was weak compared to its size, and the structural hole analysis result showed that export efficiency was weak. According to the model for recommending promising exporting countries proposed by this study, in parametric analysis, Iran, Ireland, North Macedonia, Angola, and Pakistan were promising exporting countries, and in nonparametric analysis, Qatar, Luxembourg, Ireland, North Macedonia and Pakistan were analyzed as promising exporting countries. There were differences in some countries in the two models. The results of this study revealed that the export competitiveness of South Korea's information and communication and home appliance industries in GVC was not high compared to the size of exports, and thus showed that exports could be further reduced. In addition, this study is meaningful in that it proposed a method to find promising export countries by considering GVC networks with other countries as a way to increase export competitiveness. This study showed that, from a policy point of view, the international trade network of the information communication and home appliance industries has an important mutual relationship, and although transferability is high, it may not be easily expanded to a three-party relationship. In addition, it was confirmed that South Korea's export competitiveness or status was lower than the export size ranking. This paper suggested that in order to improve the low out-degree centrality, it is necessary to increase exports to Italy or Poland, which had significantly higher in-degrees. In addition, we argued that in order to improve the centrality of out-closeness, it is necessary to increase exports to countries with particularly high in-closeness. In particular, it was analyzed that Morocco, UAE, Argentina, Russia, and Canada should pay attention as export countries. This study also provided practical implications for companies expecting to expand exports. The results of this study argue that companies expecting export expansion need to pay attention to countries with a relatively high potential for export expansion compared to the existing export volume by country. In particular, for companies that export daily necessities, countries that should pay attention to the population are presented, and for companies that export high-end or durable products, countries with high GDP, or purchasing power, relatively low exports are presented. Since the process and results of this study can be easily extended and applied to other industries, it is also expected to develop services that utilize the results of this study in the public sector.

• Journal of Korea Technology Innovation Society
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• v.17 no.1
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• pp.242-263
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• 2014

Establishment of efficient cooperative ecosystem of research institutes is important for the efficiency of national innovation system, especially in the era of technology convergence. Performance of institutes inside the ecosystem is dependent on the position of the institutes in the system. This study investigates the relationship between network structure and research performance, and determines significant factors on the research performance. The results of 5 year panel data analysis of SCI journal papers of Korean government research institutes indicate that four network centralities -degree, betweenness, closeness, and eigenvector- and structural holes have significant effect on the research performance of the institutes. Among the four centralities, closeness and eigenvectors are more significant than others. Implications of the results of this study for policy of establishing efficient cooperative system are that increasing the cooperative activities of less active institutes is more effective for research performance than increasing the magnitude of cooperative activities of all institutes. Also, when an institute starts a new cooperative relationship, it is better to have relationship with an active institute first.

• Journal of the Korean Society for information Management
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• v.34 no.1
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• pp.177-195
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• 2017

This study attempts to infer research fronts using factor graph model based on heterogeneous features. The model suggested by this study infers research fronts having documents with the potential to be cited multiple times in the future. To this end, the documents are represented by bibliographic, network, and content features. Bibliographic features contain bibliographic information such as the number of authors, the number of institutions to which the authors belong, proceedings, the number of keywords the authors provide, funds, the number of references, the number of pages, and the journal impact factor. Network features include degree centrality, betweenness, and closeness among the document network. Content features include keywords from the title and abstract using keyphrase extraction techniques. The model learns these features of a publication and infers whether the document would be an RF using sum-product algorithm and junction tree algorithm on a factor graph. We experimentally demonstrate that when predicting RFs, the FG predicted more densely connected documents than those predicted by RFs constructed using a traditional bibliometric approach. Our results also indicate that FG-predicted documents exhibit stronger degrees of centrality and betweenness among RFs.

• Knowledge Management Research
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• v.22 no.1
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• pp.217-242
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• 2021

The purpose of this study is to examine the research trends in the filed of individual knowledge hiding through keyword network analysis. As individuals intentionally hide their knowledge beyond not sharing their knowledge in organizations and the research on knowledge hiding steadily spreads, it is necessary to examine the research trends regarding knowledge hiding behaviors. For keyword network analyses, we collected 346 kinds of 578 keywords from 120 articles associated with knowledge hiding behaviors. We also transformed the keywords to 86 nodes and 667 links by data standardizing criteria and finally analyzed the keyword network among them. Moreover, this study scrutinized knowledge hiding trends by comparing the conceptual model for knowledge hiding based on literature review and the network structure based on keyword network analysis. As results, first, the network centrality degree, knowledge sharing, creativity, and performance was higher than others in Degree, Betweenness, Closeness centrality. Second, this study analyzed ego networks about psychological ownership and individual emotion theoretically associated with knowledge hiding and explored the relationship between variables through comparing with the conceptual model for knowledge hiding. Finally, the study suggested theoretical and practical implications and provided the limitations and suggestions for future research based on study findings.

• Journal of Korea Entertainment Industry Association
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• v.14 no.4
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• pp.169-186
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• 2020

This study selected 127 powerful Korean film producers, directors, and actors whose stable audience drawing power has been proven over the past seven years from 2013 to 2019, and viewed their network through social network analysis(SNA) to explain their power structure. It also explained the changes compared to the results of previous studies conducted on box office hits from 1998 to 2012. The producers who showed the highest audience drawing power over the past seven years were KANG Hae-jung, JANG Won-seok, LEE Eugene, HAN Jae-duk. BONG Joon-ho, KIM Yong-hwa, and RYOO Seung-wan as directors and SONG Kang-ho, HA Jung-woo, and HWANG Jung-min as actors were confirmed to exhibit the most stable audience drawing power. Meanwhile, the network formed by the 127 leading producers, filmmakers, and actors was analyzed based on closeness/ degree/eigenvector/betwenness centrality, and the result discovered a strong network involving JANG Won-seok, HAN Jae-duk, CHO Jin-woong, Don LEE, and HWANG Jung-min. This study is meaningful in that it included producers, the position which has never been discussed in previous local studies to analyze the network influencing star casting, and selected accurate box office hits by checking whether the concerned films actually reached break-even point rather than simply relying on the number of audiences or total revenue they garnered. Nonetheless, it left a hole to be filled in that it did not include the role of the management companies in the network. Therefore, a relevant follow-up discussion would be needed.

• The Korean Journal of Coaching Psychology
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• v.6 no.2
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• pp.93-114
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• 2022

The purpose of this study is to investigate whether there was a difference between men and women in conceptualizing a calling in childrearing through social network analysis. The results are as follows. IAs a result of the study, it was found that mothers showed significantly larger networks and higher density than fathers did, indicating that mothers, more than fathers do, experience parenting and child-rearing based more on a concept of calling in childrearing. Parents directly linked responsibility, children's growth, direction, commitment, and parental character to a calling in childrearing. In addition, mothers also linked a sense of identity and parental growth to it. In the case of fathers, only four concepts were directly connected with a calling in childrearing. This shows that mothers, in comparison to fathers, connect essential self-concepts, such as identity as a parent and themes of growth, to a calling in childrearing. In centrality, similar concepts also were evaluated as important. The common parental closeness centrality is composed of responsibility, child growth, direction, and belief starting from the center. In the case of fathers, child growth, responsibility, and belief were evaluated in order, and in the case of mothers, responsibility, parental growth, and identity were evaluated as relatively important concepts.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.6
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• pp.124-140
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• 2023

The rapid growth in shared E-scooters worldwide has led to many studies on the topic. The results of these studies are still in the early stages, and the main factors affecting trips are being identified. In particular, the development of trip-generation models is very important for transportation planning, and a new transportation mode for developing the models for shared E-scooters is lacking both domestically and internationally. This study aims to develop a trip generation model for shared E-scooters using significant variables by thoroughly reviewing previous studies. The trip characteristics of major service areas and other areas may differ owing to the trip characteristics of the mode. The trip generation models were developed based on the service trip density by dividing the areas by service level. The factors affecting shared E-scooter trips in major service areas included the presence of universities, closeness centrality, and cultural areas, while factors affecting the trips in minor service areas included the presence of universities, betweenness centrality, and trip distance. The developed models provide basic information that can be used to establish transport policies for introducing shared E-scooters in cities in the future.

• Journal of Practical Agriculture & Fisheries Research
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• v.22 no.2
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• pp.99-114
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• 2020

In this study we examined the topic analysis and correlation analysis by text mining from the self introduction letter of freshman at Korea National College of Agriculture and Fisheries(KNCAF) in 2020. The analysis items of the 3rd question were and the 4th question were the motivation for applying to college, the academic plan and the career plan. The text mining to the 3rd question showed that the frequency of 'friends' was overwhelmingly high, followed by keywords such as 'thought', 'time', 'opinion', 'activity', and 'club'. In the 4th question, keyword frequency such as 'thought', 'agriculture', 'KNCAF', 'farm', 'father' was high. The result of association rules analysis for each question showed that the relationship with the highest support level, which means the frequency and importance of the rule, was the {friend} <=> {thought}, {thought} <=> {KNCAF}. The confidence level of a correlation between keywords was the highest in the rules of {teacher}=>{friend}, {agriculture, KNCAF}=>{thought}. Also the lift level that indicates the closeness of two words was the highest in the rules of {friend} <=> {teacher}, {knowledge} <=> {professional}. These keywords are found to play a very important roles in analyzing betweenness centrality and analyzing degree centrality between keywords. The results of frequency analysis and association analysis were visualized with word cloud and correlation graphs to make it easier to understand all the results.

• Journal of Internet Computing and Services
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• v.15 no.1
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• pp.125-134
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• 2014

As the social media which was simple communication media is activated on account of twitter and facebook, it's usability and importance are growing recently. Although many companies are making full use of its the capacity of information diffusion for marketing, the adverse effects of this capacity are growing. Because social network is formed and communicates based on friendships and relationships, the spreading speed of the spam and mal-ware is very swift. In this paper, we draw parameters affecting malicious data diffusion in social network environment, and compare and analyze the diffusion capacity of each parameters by propagation experiment with XSS Worm and Koobface Worm. In addition, we discuss the structural characteristics of social network environment and then proposed malicious data propagation model based on parameters affecting information diffusion. n this paper, we made up BA and HK models based on SI model, dynamic model, to conduct the experiments, and as a result of the experiments it was proved that parameters which effect on propagation of XSS Worm and Koobface Worm are clustering coefficient and closeness centrality.