TY - JOUR
T1 - Urban innovation and intercity patent collaboration
T2 - A network analysis of China's national innovation system
AU - Yao, Li
AU - Li, Jun
AU - Li, Jian
N1 - Publisher Copyright:
© 2020 Elsevier Inc.
PY - 2020/11/1
Y1 - 2020/11/1
N2 - This research investigates the impact of extralocal interactions in intercity coinvention networks on innovation in cities. Adopting a social network lens, we argue that the innovation performance of a city hinges on its centrality in intercity coinvention networks, its ability to fill structural holes in these networks, and its node cohesiveness and transitivity within ego networks. Using a unique longitudinal data set of patents granted from 2001 to 2016 in China, we construct two types of networks—those involving collaborations among universities as well as research institutes (URI) and those involving industry actors only (II)—and identify six major stylized facts in regards to the formation of a complex intercity innovation network within China's national innovation system. A random-effects negative binomial regression model reveals positive effects of the degree centrality and structural holes variables on urban innovation in both URI and II networks, while a fixed-effects model suggests that the effects are only significant for II networks. Our study confirms that city innovation not only is determined by local innovative activities but also is enhanced when cities are deeply embedded in intercity innovative networks.
AB - This research investigates the impact of extralocal interactions in intercity coinvention networks on innovation in cities. Adopting a social network lens, we argue that the innovation performance of a city hinges on its centrality in intercity coinvention networks, its ability to fill structural holes in these networks, and its node cohesiveness and transitivity within ego networks. Using a unique longitudinal data set of patents granted from 2001 to 2016 in China, we construct two types of networks—those involving collaborations among universities as well as research institutes (URI) and those involving industry actors only (II)—and identify six major stylized facts in regards to the formation of a complex intercity innovation network within China's national innovation system. A random-effects negative binomial regression model reveals positive effects of the degree centrality and structural holes variables on urban innovation in both URI and II networks, while a fixed-effects model suggests that the effects are only significant for II networks. Our study confirms that city innovation not only is determined by local innovative activities but also is enhanced when cities are deeply embedded in intercity innovative networks.
KW - Coinvention
KW - Extralocal interactions
KW - Intercity innovation network
KW - National innovation system
KW - Network embeddedness
KW - Urban innovation
UR - http://www.scopus.com/inward/record.url?scp=85088826996&partnerID=8YFLogxK
U2 - 10.1016/j.techfore.2020.120185
DO - 10.1016/j.techfore.2020.120185
M3 - Article
AN - SCOPUS:85088826996
VL - 160
JO - Technological Forecasting and Social Change
JF - Technological Forecasting and Social Change
SN - 0040-1625
M1 - 120185
ER -