Exploring the effect of city-level collaboration and knowledge networks on innovation: Evidence from energy conservation field

Zhichao Ba, Jin Mao, Yaxue Ma and Zhentao Liang

Journal of Informetrics, 2021, vol. 15, issue 3

Abstract: Collaboration and knowledge networks have been proved to play a crucial role in innovation. From a multilevel network perspective, this study integrates research on the two types of networks and investigates how city-level collaboration and knowledge networks influence innovation in the energy conservation field. To this end, we calculate a city's influence force in its collaboration network based on the weighted PageRank algorithm and propose a novel measurement method of network embedding to gauge the embedding depth and embedding breadth of a city's local knowledge network in the whole knowledge network. Empirical results suggest that a city's aggregation index and influential force in the collaboration network are positively related to its innovation, while geographical distance shows an inverted U-shaped effect. The embedding depth and embedding breadth of a city's local knowledge network have a positive effect, and the structural entropy of its knowledge network generates an inverted U-shaped effect on innovation. Our research contributes to a better understanding of the impact of city-level collaboration and knowledge networks on innovation and points to several general implications for innovation practice and complex network research.

Keywords: Knowledge network; Collaboration network; Innovation; Network embeddedness; Energy conservation (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S1751157721000699
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:infome:v:15:y:2021:i:3:s1751157721000699

DOI: 10.1016/j.joi.2021.101198

Access Statistics for this article

Journal of Informetrics is currently edited by Leo Egghe

More articles in Journal of Informetrics from Elsevier
Bibliographic data for series maintained by Catherine Liu ().