• The Journal of Information Systems
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• v.28 no.2
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• pp.53-72
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• 2019

Purpose The purpose of this study is to examine the impact of tie strength in the network of industrial clustering on the knowledge acquisition, integration and innovation performance of small and medium sized enterprises. We test the positive relationship of weak tie and knowledge acquisition, strong tie and knowledge integration, and the interaction effect of two tie strengths on both processes of knowledge acquisition and integration. By identifying these relationships, we can better understand how to manage the attributes of social networks in terms of tie strength in order to improve the performance of innovation for the small and medium sized enterprises. Design/methodology/approach We collect 200 survey data from 2 industrial cluster respectively: Pankyo and Guroo. In Pankyo, the proportion of IT industry is the highest (35%) while the proportion of manufacturing is highest (35%) in Guroo. Pooling the data from two industrial cluster, we check the reliability and validity of our research model and test the hypotheses. Findings First, we find the positive relationship of weak tie and knowledge acquisition from both industrial clustering. Weak tie is composed of heterogeneous organizations with various background and expertise. The communication and information sharing of organizations in the weak tie network helps the idea generation for organization's innovation, which is the knowledge acquisition process. Second, the relationship of strong tie and knowledge integration is insignificant. Typically the strong tie from long-lasting partnership is expected to be beneficial in the action stage of innovation, which is the knowledge integration process. However it is not identified in our industry cluster. Finally, the interaction effect of weak and strong tie is identified to be effective on both knowledge acquisition and integration processes.

• Journal of the Economic Geographical Society of Korea
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• v.18 no.1
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• pp.115-135
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• 2015

In the glocalization a common phenomenon of several nations reveals knowledge innovation and growth by the important subject of region and state and is studied at theory and practice. the successful cases of regional development in an advanced country have leading innovation through regional innovation system and cluster. therefore we are necessary to analyse how the knowledge spillovers in innovative cluster as the reduced model of regional innovation system guide firm innovation and region growth. this article reviews theories and empirical studies of the knowledge spillovers in the regional innovation system and innovative cluster of innovative geography and proposes a new research issues for further explorations of the knowledge spillovers. Previous studies assist that knowledge spillovers exist in knowledge-based industries of specific local area and local innovation accomplishes through pure knowledge spillover. but limits of these studies include narrow region and technological area, few analytical variable and exclusion of rent knowledge spillover. therefore new research topics related with that exemplifies geographical dimension(concentration and decentralization), technological dimension(knowledge based industry), category of analytic variables(previous indicators, time, and social capital), conceptualization(appropriation means, markets for technology) etc.

• Journal of Families and Better Life
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• v.30 no.6
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• pp.151-166
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• 2012

The major findings are(were) as follows: 1) The Eco-friendly consumption behavior was divided into three(3) clusters, namely "The Middle Group" of eco-friendly consumption behavior(cluster I), "The Inferior group" of eco-friendly consumption behavior(cluster II), and "The Excellent Group" of eco-friendly consumption behavior(cluster III). 2) The differences in the general characteristics among the three clusters were founded on the experience of green consumption information and sources of information. 3) The characteristics of cluster I(The Middle Group) were(are) found to be eco-conscious, and aware of the consequences of behaviors, green market conditions and environmental issues. This cluster was the middle-average group. The characteristics of cluster III(The Excellent Group) were(are) found to have the willingness to pay additional costs, being aware of the social norms of the reference group, having an awareness of eco-institutional conditions, being knowledgeable about environmental policy, and finally, being personally involved in green consumption behavior. This cluster was the high-average group, whereas cluster II(The Inferior Group) was the low-average group.

• The Journal of Economics, Marketing and Management
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• v.10 no.6
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• pp.9-16
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• 2022

Purpose: This research investigates the differences in knowledge exchange activities and performances between startups and large companies in software industry clusters. Research design, data, and methodology: Six independent factors of human resource information, R&D and technology, marketing knowledge, government support information, strategic knowledge, and cooperation information were extracted to test the firm size effect in the relationships with two performance factors such as satisfaction with industry cluster location and satisfaction with financial performances. Data were collected through a survey of entrepreneurs, managers, and employees and tested by statistical analysis methodologies. Results: Three independent factors of human resource information, R&D and technology, and cooperation information were particularly significant in the relationship with both dependent factors. Strategic knowledge significantly affected financial performance. Knowledge exchange activities were more important in startups than in large companies for all eight factors. Conclusion: Policies for software industry clusters need a different approach for startups and large companies.

• Journal of Technology Innovation
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• v.17 no.1
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• pp.69-95
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• 2009

This paper examines the motives for location decisions of startups and dynamics of cluster growth. Because the location decision is intrinsically strategic choice by entrepreneurs, it is an interplay of three critical forces; cost-benefit of the choice, R&D ability of new entrants, and R&D capability of incumbents in clusters. The effect of knowledge spillovers influences the cluster growth like a double-edge sword; both a positive effect of technology learning and a negative effect of knowledge de-learning. Using data on 710 bio-tech venture firms in Korea, this paper tests the hypotheses about the factors influencing the growth of the cluster. The empirical analyses suggested that early entrepreneurial activity in the clustered regions were important, however other factors such as the organizational legacy, internal dynamics inside a cluster, and the existence of cooperation norm in the cluster, affected long term viability of the cluster.

• International Journal of Costume and Fashion
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• v.17 no.1
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• pp.41-57
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• 2017

The purpose of this research was to develop a typology of fair-trade apparel consumers and present a clear overview of the influence of product knowledge on consumer behaviors. A two-step cluster analysis was used to classify respondents into sub-groups based on their level of self-perceived knowledge and purchase experience. In addition, ANOVA was used to test the predictive validity of the cluster solution. Income was the only demographic variable that significantly differed across groups. The more familiar and more experienced group had higher income than the other groups. Psychographic data showed that attitudes and moral norms varied across groups. The more familiar and more experienced group had more positive attitudes and higher level of moral norms than the less familiar and less experienced group. In terms of behavioristic data, groups differed significantly in purchase intentions and willingness to pay more for fair-trade apparel. That is, the more familiar and more experienced group was willing to pay more and had higher purchase intentions than the less familiar and less experienced group. Overall, the level of product knowledge and purchase experience were considerably low. This study thus confirmed the need to increase consumers' familiarity related to fair-trade apparel products.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.7 no.6
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• pp.1396-1406
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• 2006

The policy aims of Regional Innovation Cluster(RIC) are not making the RIC itself but promoting the competitiveness of Small and Medium Enterprises(SMEs) in the region. Also, it goes without saying that small and Medium Enterprises (SMEs) do the key roles of regional economic growth. This paper indicates that the innovative capabilities of SMEs are the crucial factors of the success of Regional Innovation Cluster. In order to improve SMEs' capabilities for innovation, knowledges are vital including codified knowledge or tacit knowledge. In cluster, the linkages especially among researchers and factory workers is important to innovative activities, and universities have to of for knowledge, education programs and new researchers fully qualified to SMEs. Also, regional governments and medias positively participate the cluster as helpers. Based upon the research results, some policy implications have been suggested concerning the policy supports to enhance innovative activities of SMEs within the Regional innovation Cluster.

• The Journal of the Korea Contents Association
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• v.7 no.3
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• pp.118-128
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• 2007

The world is interested and actively investing in culture and its cultural contents industry has grown very much. Cultural contents industry is the concentration of human creation and knowledge and the Internet and digital technology have greatly impacted such creation and knowledge. This paper presented a method of digitalization of regional cultural resources, voluntary digitalization strategies, and a method of building an innovative cluster of cultural contents industry. The proposed plan would become the future growth engine of cultural tourism industry.

• Journal of information and communication convergence engineering
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• v.11 no.4
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• pp.241-246
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• 2013

A classic document clustering technique may incorrectly classify documents into different clusters when documents that should belong to the same cluster do not have any shared terms. Recently, to overcome this problem, internal and external knowledge-based approaches have been used for text document clustering. However, the clustering results of these approaches are influenced by the inherent structure and the topical composition of the documents. Further, the organization of knowledge into an ontology is expensive. In this paper, we propose a new enhanced text document clustering method using non-negative matrix factorization (NMF) and WordNet. The semantic terms extracted as cluster labels by NMF can represent the inherent structure of a document cluster well. The proposed method can also improve the quality of document clustering that uses cluster labels and term weights based on term mutual information of WordNet. The experimental results demonstrate that the proposed method achieves better performance than the other text clustering methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.3
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• pp.976-990
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• 2020

The k-means algorithm is widely used in academia and industry due to easy and simple implementation, enabling fast learning for complex datasets. However, k-means struggles to classify datasets without prior knowledge of specific domains. We proposed the repulsive k-means (RK-means) algorithm in a previous study to improve the k-means algorithm, using the repulsive force concept, which allows deleting unnecessary cluster centroids. Accordingly, the RK-means enables to classifying of a dataset without domain knowledge. However, three main problems remain. The RK-means algorithm includes a cluster repulsive force offset, for clusters confined in other clusters, which can cause cluster locking; we were unable to prove RK-means provided optimal convergence in the previous study; and RK-means shown better performance only normalize term and weight. Therefore, this paper proposes the advanced RK-means (ARK-means) algorithm to resolve the RK-means problems. We establish an initialization strategy for deploying cluster centroids and define a metric for the ARK-means algorithm. Finally, we redefine the mass and normalize terms to close to the general dataset. We show ARK-means feasibility experimentally using blob and iris datasets. Experiment results verify the proposed ARK-means algorithm provides better performance than k-means, k'-means, and RK-means.