Revealing the importance of international and domestic cooperation by using artificial neural networks: case of European radical and incremental innovators
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25410%2F23%3A39920818" target="_blank" >RIV/00216275:25410/23:39920818 - isvavai.cz</a>
Nalezeny alternativní kódy
RIV/62690094:18450/23:50018539
Výsledek na webu
<a href="https://www.emerald.com/insight/content/doi/10.1108/EJIM-02-2021-0104/full/html" target="_blank" >https://www.emerald.com/insight/content/doi/10.1108/EJIM-02-2021-0104/full/html</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1108/EJIM-02-2021-0104" target="_blank" >10.1108/EJIM-02-2021-0104</a>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Revealing the importance of international and domestic cooperation by using artificial neural networks: case of European radical and incremental innovators
Popis výsledku v původním jazyce
The purpose of this study is to introduce innovative ideas into the treatment of the radical and incremental innovations and to fill the research gap by using: (1) methods that can perform complicated tasks and solve complex problems leading in creation of radical and incremental innovation and (2) a broad sample of firms across countries. The authors' ambition is to contribute to the scientific knowledge by producing evidence about the novel usage of artificial neural network techniques for measuring European firms' innovation activities appearing in black boxes of innovation processes. Design/methodology/approach In this study, the authors incorporate an international context into Chesbrough's open innovation (OI) theory and, on the one hand, support the hypothesis that European radical innovators benefit more from foreign cooperation than incremental innovators. On the other hand, the results of the analyses show that European incremental innovators rely on domestic cooperation supported by cooperation with foreign public research institutes. Moreover, the use of decision trees (DT) allows the authors to reveal specific patterns of successful innovators emerging within the hidden layers of neural networks. Findings The authors prove that radical European innovators using either internal or external R&D strategies, while the combinations of these strategies do not bring successful innovation outputs. In contrast, European incremental innovators benefit from various internal R&D processes in which engagement in design activities plays a crucial role. Originality/value The authors introduce innovative ideas into the treatment of hidden innovation processes and measuring the innovation performance (affected by domestic or international cooperation) of European firms. The approach places emphasis on the novelty of innovation and the issue of international cooperation in the era of OI by designing the framework using a combination of artificial neural networks and DT.
Název v anglickém jazyce
Revealing the importance of international and domestic cooperation by using artificial neural networks: case of European radical and incremental innovators
Popis výsledku anglicky
The purpose of this study is to introduce innovative ideas into the treatment of the radical and incremental innovations and to fill the research gap by using: (1) methods that can perform complicated tasks and solve complex problems leading in creation of radical and incremental innovation and (2) a broad sample of firms across countries. The authors' ambition is to contribute to the scientific knowledge by producing evidence about the novel usage of artificial neural network techniques for measuring European firms' innovation activities appearing in black boxes of innovation processes. Design/methodology/approach In this study, the authors incorporate an international context into Chesbrough's open innovation (OI) theory and, on the one hand, support the hypothesis that European radical innovators benefit more from foreign cooperation than incremental innovators. On the other hand, the results of the analyses show that European incremental innovators rely on domestic cooperation supported by cooperation with foreign public research institutes. Moreover, the use of decision trees (DT) allows the authors to reveal specific patterns of successful innovators emerging within the hidden layers of neural networks. Findings The authors prove that radical European innovators using either internal or external R&D strategies, while the combinations of these strategies do not bring successful innovation outputs. In contrast, European incremental innovators benefit from various internal R&D processes in which engagement in design activities plays a crucial role. Originality/value The authors introduce innovative ideas into the treatment of hidden innovation processes and measuring the innovation performance (affected by domestic or international cooperation) of European firms. The approach places emphasis on the novelty of innovation and the issue of international cooperation in the era of OI by designing the framework using a combination of artificial neural networks and DT.
Klasifikace
Druh
J<sub>imp</sub> - Článek v periodiku v databázi Web of Science
CEP obor
—
OECD FORD obor
50601 - Political science
Návaznosti výsledku
Projekt
<a href="/cs/project/GA20-03037S" target="_blank" >GA20-03037S: Návrh dynamického znalostního business modelu založeného na principech otevřených inovací</a><br>
Návaznosti
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Ostatní
Rok uplatnění
2023
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název periodika
European Journal of Innovation Management
ISSN
1460-1060
e-ISSN
1758-7115
Svazek periodika
26
Číslo periodika v rámci svazku
2
Stát vydavatele periodika
GB - Spojené království Velké Británie a Severního Irska
Počet stran výsledku
33
Strana od-do
531-563
Kód UT WoS článku
000697396900001
EID výsledku v databázi Scopus
2-s2.0-85115066760