Treffer: The effects of network characteristics on performance of innovation clusters

Title:
The effects of network characteristics on performance of innovation clusters
Authors:
CHOI, Jinho, SANG-HYUN, Ahn, CHA, Min-Seok
Source:
Expert systems with applications. 40(11):4511-4518
Publisher Information:
Amsterdam: Elsevier, 2013.
Publication Year:
2013
Physical Description:
print, 3/4 p
Original Material:
INIST-CNRS
Subject Terms:
Computer science, Informatique, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Generalites, General aspects, Economie, Economics, Recherche operationnelle. Gestion, Operational research. Management science, Recherche opérationnelle et modèles formalisés de gestion, Operational research and scientific management, Modèles d'entreprises, Firm modelling, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Logiciel, Software, Systèmes informatiques et systèmes répartis. Interface utilisateur, Computer systems and distributed systems. User interface, Organisation des mémoires. Traitement des données, Memory organisation. Data processing, Systèmes d'information. Bases de données, Information systems. Data bases, Amas, Cluster, Montón, Analyse comportementale, Behavioral analysis, Análisis conductual, Apprentissage, Learning, Aprendizaje, Caractéristique fonctionnement, Performance characteristic, Característica funcionamiento, Compétitivité, Competitiveness, Competitividad, Diversité, Diversity, Diversidad, Développement industriel, Industrial development, Desarrollo industrial, Développement régional, Regional development, Desarrollo regional, Développement économique, Economic development, Desarrollo económico, Economie d'échelle, Economy of scale, Gestion entreprise, Firm management, Administración empresa, Indépendance de l'échelle, Scale free, Redes sin escala, Innovation, Innovación, Intention, Intencíon, Invariance échelle, Scale invariance, Invarianza escala, Loi puissance, Power law, Ley poder, Longue durée, Long lasting, Larga duración, Modèle entreprise, Business model, Modelo empresa, Organisation entreprise, Enterprise organization, Organización empresa, Système réparti, Distributed system, Sistema repartido, Ingénierie connaissances, Knowledge engineering, Ingeniería del conocimiento, Modèle organisation, Organizational models, Modelo organizacional, Structure réseau, Network structure, Estructura de redes, Innovation cluster, Learning performance, Openness, Simulation
Document Type:
Fachzeitschrift Article
File Description:
text
Language:
English
Author Affiliations:
School of Business, Sejong University, 98 Gunja-dong, Gwangjin-gu, Seoul 143-747, Korea, Republic of
Department of Physics, College of Natural Science, KAIST, 335 Gwahakro, Yuseong-gu, Daejeon 305-701, Korea, Republic of
Department of Business Administration, College of Economics and Business, Changwon National University, Sarim-dong 9, Uichang-gu, Changwon City, Gyeongsangnam-do, Korea, Republic of
ISSN:
0957-4174
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=27286036
Rights:
Copyright 2014 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems

Economy. Legislation. Training. Society

Operational research. Management
Accession Number:
edscal.27286036
Database:
PASCAL Archive

Weitere Informationen

Industry clusters provide not only economic benefits but also technological innovation through networking within a cluster. In this study, we analyze network-specific structural and behavioral characteristics of innovation clusters with the intention of delving into differences in learning performance in clusters. Based on three representative networks of real world, scale-free, broad-scale, and single-scale networks, the learning performance of entire organizations in a cluster is examined by the simulation method. We find out that the network structure of clusters is important for the learning performance of clusters. Among the three networks, the scale-free network having the most hub organizations shows the best learning performance. In addition, the appropriate level of openness that maintains long-lasting diversity leads to the highest organizational learning performance. This study confirms the roles of innovation clusters and implies how each organization as a member of a cluster should run their organization.