Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/274311 
Year of Publication: 
2022
Citation: 
[Journal:] Journal of Open Innovation: Technology, Market, and Complexity [ISSN:] 2199-8531 [Volume:] 8 [Issue:] 1 [Article No.:] 6 [Year:] 2022 [Pages:] 1-23
Publisher: 
MDPI, Basel
Abstract: 
We apply a functional data approach for mixture model-based multivariate innovation clustering to identify different regional innovation portfolios in Europe, considering patterns of specialization among innovation types. We combine patent registration data and other innovation and economic data across 225 regions, 13 years, and eight patent classes. The approach allows us to form several regional clusters according to their specific innovation types and captures spatio-temporal dynamics too subtle for most other clustering methods. Consistent with the literature on innovation systems, our analysis supports the value of regionalized clusters that can benefit from flexible policy support to strengthen regions as well as innovation in a systematic context, adding technology specificity as a new criterion to consider. The regional innovation cluster solutions for IPC classes for "fixed constructions" and "mechanical engineering" are highly comparable but relatively less comparable for "chemistry and metallurgy". The clusters for innovations in "physics" and "chemistry and metallurgy" are similar; innovations in "electricity" and "physics" show similar temporal dynamics. For all other innovation types, the regional clustering is different. By taking regional profiles, strengths, and developments into account, options for improved efficiency of location-based regional innovation policy to promote tailored and efficient innovation-promoting programs can be derived.
Subjects: 
cluster algorithm
European innovation
functional data analysis (FDA)
innovation concentration
multivariate cluster analysis
spatio-temporal cluster modeling
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

Files in This Item:
File
Size





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.