Network Proximity and Communities in Innovation Clusters Across Knowledge, Business, and Geography: Evidence From China

Abstract

The notion of proximity for innovation clusters needs to expand compared to traditional agglomeration literature because it involves multiple dimensions such as geography, business, and knowledge; however, limited research probed into thisit requires multidimensional data and novel methods. This article, therefore, proposes a three-layered framework that uses multisource heterogeneous data and network methods to measure the cluster-proximity in innovation clusters, in order to understand better the combined proximity between organizations within/across network communities. First, we developed a threelayered framework to map the network-based innovation cluster by integrating patent citation, business transaction, and geographic data. Second, in the innovation cluster, we identified the network communities in knowledge, business, and geographic layers, respectively. Third, we measured the cluster proximity within/across communities by using a combined index. We selected A City's machine tool sector in China as a case. This article found that machine-tool firms/organizations have higher cluster proximity within geographic communities that are enriched mostly by business connections. In comparison, they have lower cluster proximity across geographic communities, but the proximity is enhanced both by business connections and knowledge linkages. This may imply that knowledge linkages are more important in across-community proximity, and this needs more policy attention.

Capability upgrading through technological proximity: evidence from a leading Chinese e-bike firm

“…In their study investigating how technological proximity affects collaborative innovation in China, Chen and Xie (2018) argued that technological proximity can promote the development of organizations' innovation ability. In line with these studies, we float the idea that technological proximity plays a role in capability upgrading, as it paves the way for improving learning and knowledge search efficiency (Ardito et al, 2019;Zhou et al, 2021). More precisely, this study maintains that the capability upgrading learning mechanism is implemented through actions related to technological proximity (Li and Wu, 2021), which are likely to be heterogeneous but in a sequence (Cui and Pan, 2015).…”

Unveiling Evolutionary Path of Nanogenerator Technology: A Novel Method Based on Sentence-BERT

“…As a powerful presentation of technological development, the technology evolutionary path can track historical development, explore knowledge diffusion, and predict future technological trends [49][50][51][52]. The technology evolutionary path describes the emergence, transition, and demise of technologies, helping technical managers and related researchers to understand the process and the current state of technological development, in order to identify and locate major technologies and technical priorities quickly [53][54][55][56][57][58][59][60][61][62].…”

Research on innovation features and optimization strategies of industrial clusters from the perspective of TLCN

“…The impact of enterprise innovation performance or industrial policy on the overall economic competitiveness is mostly qualitative analysis, lack of experimental support. Therefore, some researchers have integrated Bayesian network and local interpretability models to explore machine learning methods to identify and predict the key determinants of innovation investment by countries or enterprises (Zhou et al. , 2021).…”