Modelling innovation performance of European regions using multi-output neural networks

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25410%2F17%3A39911517" target="_blank" >RIV/00216275:25410/17:39911517 - isvavai.cz</a>

Result on the web

<a href="http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0185755" target="_blank" >http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0185755</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1371/journal.pone.0185755" target="_blank" >10.1371/journal.pone.0185755</a>

Result language

angličtina

Original language name

Modelling innovation performance of European regions using multi-output neural networks

Original language description

Regional innovation performance is an important indicator for decision-making regarding the implementation of policies intended to support innovation. However, patterns in regional innovation structures are becoming increasingly diverse, complex and nonlinear. To address these issues, this study aims to develop a model based on a multi-output neural network. Both intra-and inter-regional determinants of innovation performance are empirically investigated using data from the 4th and 5th Community Innovation Surveys of NUTS 2 (Nomenclature of Territorial Units for Statistics) regions. The results suggest that specific innovation strategies must be developed based on the current state of input attributes in the region. Thus, it is possible to develop appropriate strategies and targeted interventions to improve regional innovation performance. We demonstrate that support of entrepreneurship is an effective instrument of innovation policy. We also provide empirical support that both business and government R&amp;D activity have a sigmoidal effect, implying that the most effective R&amp;D support should be directed to regions with below-average and average R&amp;D activity. We further show that the multi-output neural network outperforms traditional statistical and machine learning regression models. In general, therefore, it seems that the proposed model can effectively reflect both the multiple-output nature of innovation performance and the interdependency of the output attributes.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

50602 - Public administration

Project

<a href="/en/project/GA14-02836S" target="_blank" >GA14-02836S: Modelling of knowledge spill-over effects in the context of regional and local development</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2017

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

PLoS One

ISSN

1932-6203

e-ISSN

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Volume of the periodical

12

Issue of the periodical within the volume

10

Country of publishing house

US - UNITED STATES

Number of pages

21

Pages from-to

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UT code for WoS article

000412029600043

EID of the result in the Scopus database

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