Intelligent prediction of firm innovation activity - the case of Czech smart cities

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25410%2F17%3A39912217" target="_blank" >RIV/00216275:25410/17:39912217 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007/978-981-10-1741-4_9" target="_blank" >https://link.springer.com/chapter/10.1007/978-981-10-1741-4_9</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-981-10-1741-4_9" target="_blank" >10.1007/978-981-10-1741-4_9</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Intelligent prediction of firm innovation activity - the case of Czech smart cities

Popis výsledku v původním jazyce

A knowledge-based environment of smart cities has the potential to increase knowledge spill-over effects within knowledge networks and can help promote innovation activities. Spill-overs occur within knowledge-based networks that also include knowledge entities such as universities and R&amp;D centres. The type of innovation activities, internal R&amp;D and external knowledge acquisition is also a key factor. In addition, there are many studies and reports that show evidence of the intensity of in-house R&amp;D. This form of R&amp;D increases the probability of innovation activity. Some papers deal with the importance of public financial support for innovation activities. They offer evidence that it is especially effective when supporting internationally collaborating firms. Many empirical studies argue and show evidence that both cooperation and knowledge spill-overs support innovation activities. A number of studies are concerned with the analysis of predicting innovation activity, because companies&apos; innovation activity is one of the fundamental determinants for their competitiveness. Most studies use a linear (logistic) regression model for their analysis. However, these studies do not take into account all the recursive terms concerning a company&apos;s innovation activity. Therefore, in the report we demonstrate the use of ensembles of decision trees to model the intrinsic nonlinear characteristics of the innovation process. We apply this method for predicting innovation activity to chemical companies. We show that internal knowledge spill-overs were the most important determinant for the chemical firms&apos; innovation activity during the monitored period. Furthermore, R&amp;D intensity, collaboration on innovation and firm size were also important determinants.

Název v anglickém jazyce

Intelligent prediction of firm innovation activity - the case of Czech smart cities

Popis výsledku anglicky

A knowledge-based environment of smart cities has the potential to increase knowledge spill-over effects within knowledge networks and can help promote innovation activities. Spill-overs occur within knowledge-based networks that also include knowledge entities such as universities and R&amp;D centres. The type of innovation activities, internal R&amp;D and external knowledge acquisition is also a key factor. In addition, there are many studies and reports that show evidence of the intensity of in-house R&amp;D. This form of R&amp;D increases the probability of innovation activity. Some papers deal with the importance of public financial support for innovation activities. They offer evidence that it is especially effective when supporting internationally collaborating firms. Many empirical studies argue and show evidence that both cooperation and knowledge spill-overs support innovation activities. A number of studies are concerned with the analysis of predicting innovation activity, because companies&apos; innovation activity is one of the fundamental determinants for their competitiveness. Most studies use a linear (logistic) regression model for their analysis. However, these studies do not take into account all the recursive terms concerning a company&apos;s innovation activity. Therefore, in the report we demonstrate the use of ensembles of decision trees to model the intrinsic nonlinear characteristics of the innovation process. We apply this method for predicting innovation activity to chemical companies. We show that internal knowledge spill-overs were the most important determinant for the chemical firms&apos; innovation activity during the monitored period. Furthermore, R&amp;D intensity, collaboration on innovation and firm size were also important determinants.

Druh

C - Kapitola v odborné knize

CEP obor

—

OECD FORD obor

50204 - Business and management

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 knihy nebo sborníku

Information innovation technology in smart cities

ISBN

978-981-10-1741-4

Počet stran výsledku

14

Strana od-do

123-136

Počet stran knihy

356

Název nakladatele

Springer

Místo vydání

Singapur

Kód UT WoS kapitoly

—