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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

50602 - Public administration

Projekt

<a href="/cs/project/GA14-02836S" target="_blank" >GA14-02836S: Modelování efektů přelévání znalostí v kontextu regionálního a místního rozvoje</a><br>

Návaznosti

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

Rok uplatnění

2017

Kód důvěrnosti údajů

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

Název periodika

PLoS One

ISSN

1932-6203

e-ISSN

—

Svazek periodika

12

Číslo periodika v rámci svazku

10

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

21

Strana od-do

—

Kód UT WoS článku

000412029600043

EID výsledku v databázi Scopus

—