The effects of network characteristics on performance of innovation clusters

Choi, Jinho; Hyun, Ahn Sang; Cha, Min-Seok

doi:10.1016/j.eswa.2013.01.052

Cited by 37 publications

(32 citation statements)

References 36 publications

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“…Regarding innovation and R&D efficiency, two-stage DEA models have been discussed by Cai and Hanley (2012), Cullmann et al (2011), Lv (2011), Wu, Zhou, and Liang (2010 and Chun, Woo, Seo, and Ko (2015) . Other approaches focus on the dynamic efficiency evaluation of innovation systems ( Kou, Chen, Wang, & Shao, 2016 ) or the characteristics of networkbased innovation systems ( Choi, Sang-Hyun, & Cha, 2013;Garcia & Chavez, 2014 ).…”

Section: Multistage Evaluation Of National Innovation Systemsmentioning

confidence: 99%

A multilevel and multistage efficiency evaluation of innovation systems: A multiobjective DEA approach

Carayannis

Grigoroudis

Goletsis

2016

Expert Systems with Applications

137

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Section: Multistage Evaluation Of National Innovation Systemsmentioning

confidence: 99%

A multilevel and multistage efficiency evaluation of innovation systems: A multiobjective DEA approach

Carayannis

Grigoroudis

Goletsis

2016

Expert Systems with Applications

137

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“…In their analysis, Bellamy et al [3] reveal a positive correlation between the innovative capacity of a network and the level of interconnectedness within the network, which supports our observation. Furthermore Choi et al [7] state that the innovation cluster with the most interconnections to other companies and the highest rate of organizational openness is characterized by the highest learning efficiency. Though there is a distinct difference between innovation cluster and innovation networks, both organizational constructs show conformities in structure.…”

Section: Discussionmentioning

confidence: 99%

Initiatives for Multi Cross Industry Innovation: The case of UNIVERSAL HOME

Kerl

Moehrle

2015

2015 Portland International Conference on Management of Engineering and Technology (PICMET)

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The development of Multi Cross Industry Innovation as a specific form of cross-industry innovation is more and more important in various industries. Many companies have to face the challenges of recent market trends like the continuous blurring of industry boundaries or the increasing complexity of innovations. One option to deal with these challenges is the co-operation across industry boundaries which can lead to Multi Cross Industry Innovation. In order to further investigate this phenomenon, we pose the following question: How do Multi Cross Industry Innovation initiatives emerge and what organizational framework supports the cooperation process in a cross industry network environment?Due to this question, we develop an in-depth case study of UNIVERSAL HOME, a German cross-industry network with one single dominant player. In a first step we present the organization model and specific network characteristics before showing how initiatives were formed and/or emerged in the case. Our findings highlight the support of the participating players' top managements and the connection between the different network activities and the innovation activities of the participating companies.

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“…Strategic management Zimmer et al, 2014Castro, 2015Guzmán et al, 2013Arthurs et al, 2009Davis et al, 2009Ji and Gunasekaran, 2014Yannou et al, 2017Pîrnău et al, 2014Schlossstein and Yun, 2008 Evolutionary economics Esmaeilpoorarabi et al, 2018Nemkova, 2017 Behavioral economics Wang et al, 2017Bayne et al, 2016Alventosa et al, 2016O'Dwyer et al, 2015Chiu and Lee, 2012Calamel et al, 2012Letaifa and Rabeau, 2013Liyanage, 1995Smith, 2007Yoon, 2017 Open Innovation Wyrzykowska, 2015Fang, 2012Choi et al, 2013Schindler et al, 2017Foley et al, 2011 Clusters as Innovation Engines 45 innovation clusters, information flows through formal channels (corporate meetings, forums, fairs, conferences, seminars, specialized magazines, etc.) and informal channels (e.g.…”

Section: The Information Strengthmentioning

confidence: 99%

Clusters as Innovation Engines: The Accelerating Strengths of Proximity

Ferràs-Hernández

Nylund

2018

European Management Review

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As economic competition is becoming global, innovation clusters emerge as a salient level of competitive analysis. We hence study when and how innovation clusters strengthen innovation at firm level. We review insights in this topic from different theoretical perspectives, for example, neoclassical economics, evolutionary economics, behavioral economics, strategic management and open innovation, in search of a comprehensive theoretical framework that explains the relationship between firm innovation and the concentration of innovative activities in the proximity. We then deduce five strengths which drive technological change in innovation clusters, namely attraction, information, interaction, anticipation, and rivalry. The model has implications for researchers, managers, and policymakers and contributes to our understanding of the acceleration of innovation within clusters.

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The effects of network characteristics on performance of innovation clusters

Cited by 37 publications

References 36 publications

A multilevel and multistage efficiency evaluation of innovation systems: A multiobjective DEA approach

A multilevel and multistage efficiency evaluation of innovation systems: A multiobjective DEA approach

Initiatives for Multi Cross Industry Innovation: The case of UNIVERSAL HOME

Clusters as Innovation Engines: The Accelerating Strengths of Proximity

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