• 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.

• 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.

• The Korean Journal of Health Service Management
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• v.9 no.1
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• pp.95-107
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• 2015

The purpose of this study is to analyze the characteristics of hospital organization structure, organization culture, organization conflict and the interrelation of influential factor in organization conflict. The data for this study were collected through a self-administered survey with a structured delivered to 755 subjects including several nursing staff members, administration staff members and medical technicians at six hospitals. The structural equation model was used to examine the relation of organization structure, organization culture and organization conflict. The results of the analysis in this study were as follows. First, organization structure had a positive influence on organization conflict, especially in terms of complexity and centralization. But, formalization had a negative influence on organization conflict. Second, organization culture had a positive influence on organization conflict, especially in terms of group culture, developmental culture, and hierarchical culture. But, group culture had a negative influence on organization conflict. Third, organization structure had positive influences, especially on organization culture complexity, centralization, and formalization. But, centralization had negative influences too.

• Journal of Technology Innovation
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• v.15 no.1
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• pp.203-233
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• 2007

This study investigates how characteristics in the value-chain industrial cluster affect the R&D investment and the output of firms in each industries. For this, we try to extract the inter-industrial networking structure from the input-output table in which 17 manufacturing sectors are included. In particular, we will give shape to the "unit structure" which shows the intermediary flows of goods and services between industries in order to get an unit of final demand in a certain industry. Using this "unit structure", we can try the inter-industrial networking analysis and get some indices of centrality and centralization related to the characteristics of each industries in the value-chain industrial cluster. The results show that the centrality in the value-chain industrial cluster does not have any consequence for the R&D investment and the output in each industries. However, there is a correlation between the centralization in the value-chain industrial cluster and the R&D investment and the output of firms in each industries. These results may be very suggestive in bringing up a new frame of industrial cluster policy in a macro level.

• 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.

• Korean Business Review
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• v.3
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• pp.23-46
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• 1990

I. Introduction: In management control, business analysis has to do with a performance evaluation and is accounted much of manager's decision making, Business indicator accounting is the vehicle of decision making and also feedback can be accomplished by it. This study is to build up a logic about what capacity for use the business indicator accounting has in making decision. Therefore it is significant to make clear the adjustment of decision and to study the function of business indicator. II. Adjustment of Decision and Accounting Work: Adjustment of decision is connected with accounting now that business indicator accounting has a function of decision making. And it should be tied up with specialization as a structure of system, organization, communication and stage system. III. Adjustment of Decision and Managerial Accounting: Managerial account makes a great contributin to the management in which each adjustment of decision should be accomplished. Let me make mention of how the adjustment of decision is accomplished concretely, and what contribution che managerial accounting makes. In an adjustment of decision, centralization and decentrialization of enterprise are very import and I think the three problems, such as the extent of sphere (procurement, production and maketing), the face of affairs (planning, implementing and controlling), the final surge (decision making and action) can be accomplished by the business indicator accounting. IV. Structure of System and Disciplinary Approach for Decision: Decision can be classified into syncronized decision and continuous decision, and is closely connected with centralization and decentralization. In the course of systematizing, the sort of decision is classified into a man in charge of decision, and object of decision, conditions of decision, and an adjusting of decision. For it's object, it has an analogical thinking and an analytic subdivision about the target area. And it is premised on getting a scientific understanding. I think a disciplinary approach remains in solving these intricate problems. V. Conclusion: In this study I dealt with a specialization as a structure in management system and a theory that adjustment is a necessary process in decision making. For an adjustment of decision, exchanging informations and communication are necessary, and accounting is in charge of the process. And then the centralization and decentralization of decision should be connected in the way of adjustment of decision. In case of decentralization, the adjustment of decision is accomplished by the exchanging informations through feedback, and in case of centralization, by the all-round planning. And also I found that syncronized decision and decentralized decision are linked together. It is natural that the function of business indicator accounting is called for to render more services for it. Therefore, according to the extent of centralization and decentralization accounting to adjust the decision, can be various. Consequently, in relation to the structure of system. I think it is necessary to make a theoretical and empirical study of the business indicator accounting.

• 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.

• Korea Logistics Conference
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• v.14 no.3
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• pp.117-145
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• 2006

• Journal of the Korean Operations Research and Management Science Society
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• v.1 no.1
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• pp.127-138
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• 1976

Economists have become interested in the centralization and decentralization of planning, in the linking up of models into a homogenous model system, and in multi-level planning. In this study, Input/Output techniques used for explaining the resource allocation mechanisms to be more rational through detailed specifications of a large organization's objectives and explicit linking of centralization and decentralization to such objectives. Also the application of mathematical methods to the higher levels of planning in the optimal allocation resources can't fully describe the actual practice of planning. On the other hand, 1-0 techniques are standard in economic analysis and planning. However, the application procedures to the armed forces hold only when their assumptions are met and when their solutions are convergent. So, it is of limited applicability.

• Journal of Construction Engineering and Project Management
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• v.2 no.4
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• pp.32-41
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• 2012

In recent times, multi-centralization and decentralization as well as large Capital area and suburbanization in the spatial structure of capital area. With rapid growth, urbanization and industrialization are unsystematic, and growth inequality between regions caused negative effects such as discordant centralization and decentralization, fluctuating land value, and gap between living conditions. Accordingly, this study analyzed urban spatial indexes by the self-governed body in the capital area such as Seoul, Incheon, and Gyeonggi province for the analysis of the regional inequality phenomenon. We examined the characteristics of temporal and spatial changes in urban spatial structure in the capital area by utilizing the distribution pattern and density of city indexes such as population, employment, etc, and then drew the commonality of those factors through factor analysis. We evaluated the drawn results through the city standard index by each city, conducted factor score analysis, and identified the interaction between each factor and Housing Purchase Price Composite Indices index, housing rent price index(Housing Jeonse Price Composite Indices), land price fluctuation rate, diffusion ratio of house, and financial independence.