Abstract
This article focuses on the innovation and knowledge evolution of industry clusters. We examine the effects of the hub firm and the interaction of network member firms on the upgrading of the cluster. Our study is based on two patterns of knowledge learning and innovation, namely, STI (science, technology, and innovation) and DUI (doing, using, and interacting). This article adopts a knowledge diffusion simulation model to study the exchange of knowledge between cluster network actors in the context of small-world networks. The results indicate that we must pay close attention to the influence of hub enterprises on cluster evolution. Although hub companies may have certain innovation capabilities, if knowledge absorption problems among members are not properly resolved in the cluster network, the innovation performance of the local clusters is likely to be weakened, despite the success of the hub firm.
Highlights
Global economic turmoil brings opportunities and challenges to industry clusters and small and medium-sized enterprises (SMEs) in a local cluster
We focus on innovation networks that are established by SMEs within an industry cluster
The simulation method can reflect the individuals in the system, individual behaviors and interactions between individuals, which is convenient for studying the system evolution based on individual interaction and reproducing the overall dynamic evolution process of the industrial innovation network
Summary
Global economic turmoil brings opportunities and challenges to industry clusters and small and medium-sized enterprises (SMEs) in a local cluster. Local policymakers and researchers have long focused on how to increase competitiveness and enhance the position of a local industrial cluster in the international division of labor. Keeping pace with the trends in industry cluster transformation and upgrading requires deep investigation (Rodriguez et al, 2017; Vrontis et al, 2020). Critical knowledge and innovation resources may cross enterprise boundaries and become shared among interconnected firms in a cluster network (Madhok et al, 2015). Some studies argue that, under certain circumstances, clusters and embedded networks have a negative impact on firms’ innovativeness (Boschma, 2005; Lhuillery & Pfister, 2011; Ozer & Zhang, 2015), leading to decrease in the industry cluster’s performance. We try to start our research from the angle of solving cluster upgrading and knowledge evolution issues
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