• Journal of the Korean Operations Research and Management Science Society
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• v.34 no.4
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• pp.57-72
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• 2009

As development of information technologies, customer retention has been an important issue in the competing environment. A lot of researches focus on prediction of the churning customers and seeking their characteristics. However, relationships among customers or products have not been considered in existing researches. In this study, product networks are proposed and analyzed to investigate the differences of network characteristics of products purchased by potential churning customers and those of loyal customers. The product networks are constructed from real product purchase data collected from a Korean department store. We investigated the characteristic differences, such as the degree centrality, degree centralization, and density, of two product networks constructed by potential churning customers and the loyal customers. The results indicate that degree centrality, density and degree centralization of the product network of the loyal customers are higher than those of the potential churning customers. And the promotional products of the department store are resulted to be effective in attracting the loyal customers.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.9
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• pp.3611-3634
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• 2015

In this paper, we build a theoretical framework for quantitatively measuring and graphically representing the degrees of closeness centralization among performers assigned to enact a workflow procedure. The degree of closeness centralization of a workflow-performer reflects how near the performer is to the other performers in enacting a corresponding workflow model designed for workflow-supported organizational operations. The proposed framework comprises three procedural phases and four functional transformations, such as discovery, analysis, and quantitation phases, which carry out ICN-to-WsoN, WsoN-to-SocioMatrix, SocioMatrix-to-DistanceMatrix, and DistanceMatrix-to-CCV transformations. We develop a series of algorithmic formalisms for the procedural phases and their transformative functionalities, and verify the proposed framework through an operational example. Finally, we expatiate on the functional expansion of the closeness centralization formulas so as for the theoretical framework to handle a group of workflow procedures (or a workflow package) with organization-wide workflow-performers.

• Journal of Navigation and Port Research
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• v.28 no.2
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• pp.121-127
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• 2004

The shipping industry can be explained by a growing process of concentration and centralization of tonnage. A few largest shipping company control a great deal of shipping tonnage in the total tonnage through the process of concentration and centralization of tonnage in shipping over time. It confirms that a decreasing number of operators take an increasing quantity of tonnage in the total tonnage. Thus, the extremely dualistic structure (extremely unequal distribution of tonnage) are shown not only in the shipping industry in a given country but also in liner shipping in the world In this paper, the two countries, that is, Korea and Egypt, are compared considering the absolute size of tonnage and shipping policy in tenn., of concentration and centralization of tonnage in shipping. In order to measure the degree of concentration of tonnage in shipping, the quantitative measurements; the Lorenz curve and Gini coefficient are introduced. But it should be supplemented with the qualitative measures of tonnage in a given country.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.107-129
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• 2022

It is important to preempt new technology because the technology competition is getting much tougher. Stakeholders conduct exploration activities continuously for new technology preoccupancy at the right time. Gartner's Hype Cycle has significant implications for stakeholders. The Hype Cycle is a expectation graph for new technologies which is combining the technology life cycle (S-curve) with the Hype Level. Stakeholders such as R&D investor, CTO(Chef of Technology Officer) and technical personnel are very interested in Gartner's Hype Cycle for new technologies. Because high expectation for new technologies can bring opportunities to maintain investment by securing the legitimacy of R&D investment. However, contrary to the high interest of the industry, the preceding researches faced with limitations aspect of empirical method and source data(news, academic papers, search traffic, patent etc.). In this study, we focused on two research questions. The first research question was 'Is there a difference in the characteristics of the network structure at each stage of the hype cycle?'. To confirm the first research question, the structural characteristics of each stage were confirmed through the component cohesion size. The second research question is 'Is there a pattern of diffusion at each stage of the hype cycle?'. This research question was to be solved through centralization index and network density. The centralization index is a concept of variance, and a higher centralization index means that a small number of nodes are centered in the network. Concentration of a small number of nodes means a star network structure. In the network structure, the star network structure is a centralized structure and shows better diffusion performance than a decentralized network (circle structure). Because the nodes which are the center of information transfer can judge useful information and deliver it to other nodes the fastest. So we confirmed the out-degree centralization index and in-degree centralization index for each stage. For this purpose, we confirmed the structural features of the community and the expectation diffusion patterns using Social Network Serice(SNS) data in 'Gartner Hype Cycle for Artificial Intelligence, 2021'. Twitter data for 30 technologies (excluding four technologies) listed in 'Gartner Hype Cycle for Artificial Intelligence, 2021' were analyzed. Analysis was performed using R program (4.1.1 ver) and Cyram Netminer. From October 31, 2021 to November 9, 2021, 6,766 tweets were searched through the Twitter API, and converting the relationship user's tweet(Source) and user's retweets (Target). As a result, 4,124 edgelists were analyzed. As a reult of the study, we confirmed the structural features and diffusion patterns through analyze the component cohesion size and degree centralization and density. Through this study, we confirmed that the groups of each stage increased number of components as time passed and the density decreased. Also 'Innovation Trigger' which is a group interested in new technologies as a early adopter in the innovation diffusion theory had high out-degree centralization index and the others had higher in-degree centralization index than out-degree. It can be inferred that 'Innovation Trigger' group has the biggest influence, and the diffusion will gradually slow down from the subsequent groups. In this study, network analysis was conducted using social network service data unlike methods of the precedent researches. This is significant in that it provided an idea to expand the method of analysis when analyzing Gartner's hype cycle in the future. In addition, the fact that the innovation diffusion theory was applied to the Gartner's hype cycle's stage in artificial intelligence can be evaluated positively because the Gartner hype cycle has been repeatedly discussed as a theoretical weakness. Also it is expected that this study will provide a new perspective on decision-making on technology investment to stakeholdes.

• Journal of the Korea Society of Computer and Information
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• v.21 no.5
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• pp.141-148
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• 2016

In this paper, we propose a topic network analysis approach which integrates topic modeling and social network analysis. We collected 2,039 scientific papers from five top journals in the field of data mining published from 1996 to 2015, and analyzed them with the proposed approach. To identify topic trends, time-series analysis of topic network is performed based on 4 intervals. Our experimental results show centralization of the topic network has the highest score from 1996 to 2000, and decreases for next 5 years and increases again. For last 5 years, centralization of the degree centrality increases, while centralization of the betweenness centrality and closeness centrality decreases again. Also, clustering is identified as the most interrelated topic among other topics. Topics with the highest degree centrality evolves clustering, web applications, clustering and dimensionality reduction according to time. Our approach extracts the interrelationships of topics, which cannot be detected with conventional topic modeling approaches, and provides topical trends of data mining research fields.

• Journal of Korean Society of Rural Planning
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• v.23 no.2
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• pp.9-17
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• 2017

Rural development policy has changed from hardware based development to community revitalization. The purpose of this study is to analyze social network of collaborative activity among rural organizations as fundamental of community. The material used in this study is a record of collaborative activites in the community newsletter of Hong-Dong area. 161 of collaborative activities (links) and 75 of organizations (nodes) are investigated in network. 6 collaborative activity type ('Education', 'Socializing', 'Meeting', 'Culture', 'Event' and 'Labor') is classified. 'Socializing' is inclusive of approximately half of whole network (50.67%). Closeness centraization, degree centralization and betweenness centralization are measured on top in 'Education', 'Meeting' and 'Event' type. Scatter plot analysis using degree and betweenness centrality index, 'Maeul Revitalization Center', 'Balmak Library', 'Woori-Maeul Medical Co-op', 'Support Center for Female Farmers', 'Hongdong Middle School' and 'Mundang Sustainable Agriculture Education Center' are resulted as the core organization in network. Geographical distribution of collaborative activity is not only concentated in Hong-Dong Myeon but also networked with adjacent administrative district. This study finds its purpose in the detailed analysis of network characteristics of collaborative activity within Hong-Dong area which is representative developed rural community in Korea.

• Perspectives in Nursing Science
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• v.13 no.1
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• pp.29-35
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• 2016

Purpose: This study aimed to explore the underlying structures of students' interaction networks to monitor network changes during the year, to verify the relationship with self-directed learning, and to identify the effect of problem-based learning on interaction and self-directed learning. Methods: A longitudinal study was designed which included 3 parts (A=25, B=27, C=26) with a total of 78 second-year nursing students from 2013 to 2014. Interaction indicators used group network centralization and density, and individual in-degree centrality. Results: Group network centralization showed mean reversion patterns, however, centralization and density showed a slight increase from 2013 to 2014 (Centralization of A part from 52.78 to 36.96, B part from 20.56 to 32.20, C part from 34.40 to 37.24; Density of A part from 0.122 to 0.123, B part from 0.111 to 0.121, C part from 0.109 to 0.121). The individual in-degree centrality is significantly correlated with self-directed learning and the correlation coefficient increased during the year (r=.274 in 2013, r=.356 in 2014, p<.001). Conclusion: Students share information more interactively during the year and the more they share the higher the scores of self-directed learning.

• The Journal of Information Systems
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• v.28 no.1
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• pp.73-96
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• 2019

Purpose The purpose of this study is to examine the effect of communication-structure characteristics on performance in online collaboration using the data from Github, one of representative open source software development platforms. We analyze the impact of in-degree/out-degree centralization and reciprocity of communication network on collaboration performance in each project. In addition, we investigate the moderating effect of owner types, an individual developer or an organization. Design/methodology/approach We collect the data of 838 Github projects, and conduct social network analysis for measuring in-degree/out-degree centralization and reciprocity as independent variables. With these variables, hierarchical regression analysis is employed on the relationship between the characteristics of communication structure and collaborative performance. Findings Our results show that for the project owned by an organization, the centralized structure of communication is not associated with the collaboration performance. In addition, the reciprocity is positively related to the collaboration performance. On the other hand, for the project owned by an individual developer, the centralized structure of communication is positively related to the performance, and the reciprocity does not show the positive relationship on the performance.

• The Journal of Korean Association of Computer Education
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• v.18 no.4
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• pp.41-53
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• 2015

The purpose of this study is to investigate the effects of centralization on shared mental model, satisfaction and achievement in a web-based collaborative learning. Sixty-eight undergraduate students were randomly assigned to 7 groups and they participated in a 4-week web-based collaborative learning. Three highly centralized teams and three less centralized teams were selected, and analyzed for investigating whether there are meaningful differences in shared mental model, satisfaction and achievement according to the degree of centralization. The results showed that there was no significant difference in shared mental model between the highly and less centralized teams. However, the highly centralized teams showed higher level of satisfaction and achievement than the less centralized teams.

• Journal of Intelligence and Information Systems
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• v.15 no.4
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• pp.159-182
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• 2009

We construct product networks from the retail transaction dataset of an off-line department store. In the product networks, nodes are products, and an edge connecting two products represents the existence of co-purchases by a customer. We measure the quantities frequently used for characterizing network structures, such as the degree centrality, the closeness centrality, the betweenness centrality and the centralization. Using the quantities, gender, age, seasonal, and regional differences of the product networks were analyzed and network characteristics of each product category containing each product node were derived. Lastly, we analyze the correlations among the three centrality quantities and draw a marketing strategy for the cross-selling.