• Proceedings of the Korea Society for Simulation Conference
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• 1994.10a
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• pp.12-12
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• 1994

Simulation output knowledge analysis is one of problem-solving and/or knowledge adquistion process by investgating the system behavior under study through simulation . This paper describes an approach to simulation outputknowldege analysis using fuzzy neural network model. A fuzzy neral network model is designed with fuzzy setsand membership functions for variables of simulation model. The relationship between input parameters and output performances of simulation model is captured as system behavior knowlege in a fuzzy neural networkmodel by training examples form simulation exepreiments. Backpropagation learning algorithms is used to encode the knowledge. The knowledge is utilized to solve problem through simulation such as system performance prodiction and goal-directed analysis. For explicit knowledge acquisition, production rules are extracted from the implicit neural network knowledge. These rules may assit in explaining the simulation results and providing knowledge base for an expert system. This approach thus enablesboth symbolic and numeric reasoning to solve problem througth simulation . We applied this approach to the design problem of broadband communication network.

• Proceedings of the Technology Innovation Conference
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• 1997.12a
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• pp.133-158
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• 1997

This paper analyzes the characteristics of technological knowledge flow-structure of Korean manufacturing in dynamic perspective. In doing that, the concept of the knowledge network is introduced which is defined as a set of industries and their interaction(knowledge flow) or linkage. The analysis of the inter-industrial knowledge flows is based on the technological similarity by using R&D researchers'academic background in the year of 1984, 1987, 1990. The analysis is carried out by such methodology as network analysis, indicator analysis and simple statistical analysis. And the final results are drawn both in absolute terms(dimension effect) and in relative terms (proportion effect) respectively. The main findings are as follow. First, the Korean manufacturing knowledge network appears to strengthen existing inter-industrial knowledge linkages rather than to construct new linkages. Second, the network seems to form a dualistic structure in that some high-technology sectors (knowledge production sectors) emerge along with traditional sectors (knowledge absorbing sectors). Third, since the mid-1980s, an inter-industrial fusion is witnessed among technologically intensive sectors, indicating that some sophisticated innovation modes are emerging in Korean manufacturing system.

• Proceedings of the Korea Society of Information Technology Applications Conference
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• 2005.11a
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• pp.139-142
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• 2005

Recently research interest in Knowledge Management (KM) has grown rapidly. Companies regard intellectual capital as important asset and strive to deploy KM in an organization to gain a competitive edge. Many organizations currently engage in knowledge management in order to leverage knowledge both within their organization and externally to their shareholders and customers. Most of the previous research related to KM are dedicated to investigate the role of information technology in extracting, capturing, sharing, coverting organizational knowledge. Knowledge workers, however, are paid less attention though they are the key players in KM activities such as knowledge creation, dissemination, capture and conversion. We regard knowledge workers as a major component of KM and starting point of understanding organizational knowledge activities. Therefore we adopt a method to understand and analyze knowldge workers' social relationships. In this paper we investigate Social Network Analysis (SNA) as a tool for analyzing knowledge network. We introduce the basic concept of SNA and suggest a framework for implementing knowledge network by explaining how SNA can be used for analyzing knowledge network. We also propose a numerical method for identifying knowledge workers using SNA after classifying knowledge workers. The suggested method is expected to help understanding key knowledge players within an organization.

• Knowledge Management Research
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• v.12 no.2
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• pp.69-80
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• 2011

This paper reports on a structural view of a knowledge network comprised of clubs and organisationsexpressly concerned with cultural activities in a regional Australian city. Social network analysis showed an uneven distribution of power, influence, and prominence in the network. The network structure consisted of two modules of vertices clustered around particular categories of creative arts and these modules were linked most frequently by several organisations acting as communication hubs and boundary spanners. The implications of the findings include 'network weaving' for improving the network structure and developing a systemic approach for exploring the structures of social action that form community-based knowledge networks.

• Knowledge Management Research
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• v.19 no.4
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• pp.19-37
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• 2018

Knowledge transfer among employees integrates individual knowledge scattered within a firm, thus increases organizational effectiveness. In particular, the role of knowledge broker, which enables knowledge sharing across multiple teams or subunits, is critical for the success of knowledge management. This study classified the types of knowledge broker that facilitates knowledge flows among team, and examined the influences of various intra- and inter-team social networks. Survey responses from 128 employees of four R&D teams were gathered and analyzed using partial least square structural equation modeling. The results of analysis showed that all types of inter-team networks(i.e., emotional closeness network, frequency of interaction network, and perceived expertise network) had significant influences on related knowledge brokerage behaviors. In case of intra-team networks, only the emotional closeness network showed significant influence. These results proved the necessity of managing various types of intra- and inter-team networks to encourage knowledge brokerage behaviors within a firm.

• Journal of Technology Innovation
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• v.6 no.1
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• pp.71-98
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• 1998

This paper analyzes the characteristics of technological knowledge flow-structure of Korean manufacturing in dynamic perspective. In doing that, the concept of the knowledge network is introduced which is defined as a set of industries and their interaction(knowledge flow) or linkage. The analysis of the inter-industrial knowledge flows is based on the technological similarity by using R&D researchers' academic background in the year of 1984, 1987, 1990. The analysis is carried out by such methodology as network analysis, indicator analysis and simple statistical analysis. And the final results are drawn both in absolute terms(dimension effect) and in relative terms(proportion effect) respectively. The main findings are as follow. First, the Korean manufacturing knowledge network appears to strengthen existing inter-industrial knowledge linkages rather than to construct new linkages. Second, the network seems to form a dualistic structure in that some high-technology sectors(knowledge production sectors) emerge along with traditional sectors(knowledge absorbing sectors). Third, since the mid-1980s, an inter-industrial fusion is witnessed among technologically intensive sectors, indicating that some sophisticated innovation modes are emerging in Korean manufacturing system. And fourth, by using the relations of the inter-industrial knowledge-flows, we classified manufacturing industries into 3 type ; knowledge-outflow sector, knowledge-inflow sector and knowledge intermediary sector.

• Knowledge Management Research
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• v.16 no.2
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• pp.67-89
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• 2015

Improving productivity of knowledge workers is an important issue in the 21st century referred as knowledge-based society. The core key word is knowledge sharing among constituents of an organization. The purpose of this study is to combine the social network position factors with attitude and behavior factors, and develop an integrated research model for the knowledge sharing among members of an organization. This study adopted the integrated theoretical framework based on social capital, self-efficacy, transactive memory, and knowledge sharing. Surveys were conducted to 42 organizational members from a department in a leading IT outsourcing company to empirically test the proposed research model. In order to validate the proposed research model, social network analysis tool, UCINET, a structural equation modeling tool, SmartPLS, were utilized. The empirical result showed that, first of all, organizational members' familiarity network position had significant influence on knowledge self-efficacy and transactive memory capability. Second, knowledge self-efficacy and transactive memory capability affected knowledge sharing intention. Third, knowledge sharing intention also had an impact on the job performance. However, organizational members' expertise network position had no significant influence on knowledge self-efficacy and transactive memory capability. This finding reveals the importance of the emotional approach rather than the rational approach in knowledge management. The theoretical and practical implications on the research findings were discussed along with limitations.

• Journal of Information Management
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• v.36 no.4
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• pp.199-215
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• 2005

Experts participating in the knowledge expert network externalize their implicit knowledge by providing information or writing reports. Almost all the members of the network share externalized knowledge and the network facilitate the dissemination and diffusion of knowledge. Individuals reproduce another implicit knowledge by internalizing shared knowledge through the network and re-created knowledge is externalized, establishing knowledge circulation. In this paper, we analyze the expert groups of the Global Network of Korean Scientists & Engineers(KOSEN, www.kosen21.org), the Korea's No. 1 science and engineering knowledge expert community, with the application of the theory of policy network proposed by Marsh & Rhodes. According to the principal standards of policy network classification such as the number of participants, interaction among participants, consistency, distribution of resources and dependency, we categorize the KOSEN expert groups as closed policy network and opened issue network, and divide closed policy network into core community and periphery community.

• Asia pacific journal of information systems
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• v.22 no.3
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• pp.75-97
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• 2012

Knowledge residing in the heads of employees has always been regarded as one of the most critical resources within a firm. However, many tries to facilitate knowledge transfer among employees has been unsuccessful because of the motivational and cognitive problems between the knowledge source and the recipient. Social capital, which is defined as "the sum of the actual and potential resources embedded within, available through, derived from the network of relationships possessed by an individual or social unit [Nahapiet and Ghoshal, 1998]," is suggested to resolve these motivational and cognitive problems of knowledge transfer. In Social capital theory, there are two research streams. One insists that social capital strengthens group solidarity and brings up cooperative behaviors among group members, such as voluntary help to colleagues. Therefore, social capital can motivate an expert to transfer his/her knowledge to a colleague in need without any direct reward. The other stream insists that social capital provides an access to various resources that the owner of social capital doesn't possess directly. In knowledge transfer context, an employee with social capital can access and learn much knowledge from his/her colleagues. Therefore, social capital provides benefits to both the knowledge source and the recipient in different ways. However, prior research on knowledge transfer and social capital is mostly limited to either of the research stream of social capital and covered only the knowledge source's or the knowledge recipient's perspective. Social network theory which focuses on the structural dimension of social capital provides clear explanation about the in-depth mechanisms of social capital's two different benefits. 'Strong tie' builds up identification, trust, and emotional attachment between the knowledge source and the recipient; therefore, it motivates the knowledge source to transfer his/her knowledge to the recipient. On the other hand, 'weak tie' easily expands to 'diverse' knowledge sources because it does not take much effort to manage. Therefore, the real value of 'weak tie' comes from the 'diverse network structure,' not the 'weak tie' itself. It implies that the two different perspectives on strength of ties can co-exist. For example, an extroverted employee can manage many 'strong' ties with 'various' colleagues. In this regards, the individual-level structure of one's relationships as well as the dyadic-level relationship should be considered together to provide a holistic view of social capital. In addition, interaction effect between individual-level characteristics and dyadic-level characteristics can be examined, too. Based on these arguments, this study has following research questions. (1) How does the social capital of the knowledge source and the recipient influence knowledge transfer respectively? (2) How does the strength of ties between the knowledge source and the recipient influence knowledge transfer? (3) How does the social capital of the knowledge source and the recipient influence the effect of the strength of ties between the knowledge source and the recipient on knowledge transfer? Based on Social capital theory and Social network theory, a multi-level research model is developed to consider both the individual-level social capital of the knowledge source and the recipient and the dyadic-level strength of relationship between the knowledge source and the recipient. 'Cross-classified random effect model,' one of the multi-level analysis methods, is adopted to analyze the survey responses from 337 R&D employees. The results of analysis provide several findings. First, among three dimensions of the knowledge source's social capital, network centrality (i.e., structural dimension) shows the significant direct effect on knowledge transfer. On the other hand, the knowledge recipient's network centrality is not influential. Instead, it strengthens the influence of the strength of ties between the knowledge source and the recipient on knowledge transfer. It means that the knowledge source's network centrality does not directly increase knowledge transfer. Instead, by providing access to various knowledge sources, the network centrality provides only the context where the strong tie between the knowledge source and the recipient leads to effective knowledge transfer. In short, network centrality has indirect effect on knowledge transfer from the knowledge recipient's perspective, while it has direct effect from the knowledge source's perspective. This is the most important contribution of this research. In addition, contrary to the research hypothesis, company tenure of the knowledge recipient negatively influences knowledge transfer. It means that experienced employees do not look for new knowledge and stick to their own knowledge. This is also an interesting result. One of the possible reasons is the hierarchical culture of Korea, such as a fear of losing face in front of subordinates. In a research methodology perspective, multi-level analysis adopted in this study seems to be very promising in management research area which has a multi-level data structure, such as employee-team-department-company. In addition, social network analysis is also a promising research approach with an exploding availability of online social network data.

• Knowledge Management Research
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• v.14 no.3
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• pp.87-100
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• 2013

As the external knowledge networks of firms have become more and more important to their product and service innovations, many global leading companies such as P & G, IBM, and Samsung Electronics have formulated and implemented their open innovation strategy. This study attempts to empirically analyze the effects of CEOs' supports for external knowledge networks, external knowledge network-oriented cultures and inter-organizational knowledge management systems as the major antecedents to external knowledge networks by using the data based on organizational members' perceptions about them. Based on 847 samples collected from employees in three companies in the medical, the construction and the IT service industries, this study performed a structural equation modeling (SEM) analysis about the effects of the antecedents to the external knowledge networks on product and service innovations through Partial Least Squares (PLS). The empirical findings of this study show that CEOs' supports for external knowledge network positively influence product and service innovations, partially mediated by external knowledge network-oriented cultures and inter-organizational knowledge management systems. And they also show that external knowledge network-oriented cultures and inter-organizational knowledge management systems have a positive effect on product and service innovations, respectively, partially mediated by external knowledge networks. With these new findings, academic and practical implications are discussed.