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Innovation Networks of Science and Technology Firms in China

Chenxi Liu

Inter-provincial cooperation is common in China

11. August 2023

Chinese technology companies with active websites
collaborative hyperlinks between Chinese corporate websites

Enterprises serve as key actors in shaping urban innovation ecosystems, and their science and innovation activities at the city level are essential focal points for analysing urban innovation networks. Using corporate websites, most enterprises around the world have begun establishing portals to engage with users, showcase product information, and access external innovation resources. Consequently, traditional data such as patents no longer fully capture the dynamics of a firm’s innovation collaboration activities.

Using data from the ISTARI webAI, we constructed the 2022 Chinese technology industry innovation network based on hyperlinks. We used Social Network Analysis (SNA) and the two-dimensional quadrant method (TDQ) to delve into the structural characteristics and spatial patterns of the network and distinguish between different levels of innovation. Moreover, we established a comprehensive system of influencing factors that impact the innovation activities of science and technology enterprises. Through the application of GeoDetector, we analysed the degree of influence exerted by these factors and explored their mechanisms of interaction, uncovering urban heterogeneity within each TDQ perspective. Thus, our methodology (cf. Figure 1) enables a detailed assessment of how Chinese tech companies collaborate at the website level.

Figure 1: Study design.

We found that inter-provincial cooperation is the most common in China, accounting for 77.1%. Intra-provincial cooperation is dominated by same-city cooperation, accounting for 89.2%, while intra-provincial inter-city cooperation only accounts for 10.8%. This indicates that a technology enterprise generally prefers to choose partners who are more geographically distant in online cooperation, while intra-city cooperation is preferred within a particular spatial scope. We also found that the frequency of similar-scale cooperation is nearly twice that of cross-scale cooperation, accounting for 65.4%. For this, cooperation between large enterprises only accounts for 0.6%. Accordingly, enterprises prefer collaborating with partners of similar scale, primarily in second- and third-tier cities. On a spatial level, we found that the distribution of enterprises with operating websites and those with a high frequency of website cooperation is basically the same (cf. Figure 2). The overall trend is high in the southeast and low in the northwest. Geo-detectors reveal the key drivers of cooperation differences in each dimension: science and technology innovation atmosphere, transportation capacity, administrative district level, and digital infrastructure level.

Figure 2: Spatial distribution of website cooperation statistics.

This study provides valuable insights into the spatial differentiation of innovation networks among Chinese science and technology enterprises. It was based on a collaboration between ISTARI.AI, the Center for Geographic Analysis at Harvard University, USA and the School of Urban Design at Wuhan University, China.

For more detailed information, check out the following paper:

Liu, C., Peng, Z., Liu, L., Li, S., 2023. Innovation Networks of Science and Technology Firms: Evidence from China. Land 12, 1283.


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