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AbstractCross-region innovation is widely recognized as an important source of the long-term regional innovation capacity. In the recent past, a growing number of studies has investigated the network structure and mechanisms of cross-region innovation collaboration in various contexts. However, existing research mainly focuses on physical effects, such as geographical distance and high-speed railway connections. These studies ignore the intangible drivers in a changing environment, the more digitalized economy and the increasingly solidified innovation network structure. Thus, the focus of this study is on estimating determinants of innovation networks, especially on intangible drivers, which have been largely neglected so far. Using city-level data of Chinese patents (excluding Hong Kong, Macao, and Taiwan Province of China), we trace innovation networks across Chinese cities over a long period of time. By integrating a measure on Information and Communications Technology (ICT) development gap and network structural effects into the general proximity framework, this paper explores the changing mechanisms of Chinese innovation networks from a new perspective. The results show that the structure of cross-region innovation networks has changed in China. As mechanisms behind this development, the results confirm the increasingly important role of intangible drivers in Chinese inter-city innovation collaboration when controlling for effects of physical proximity, such as geographical distance. Since digitalization and coordinated development are the mainstream trends in China and other developing countries, these countries’ inter-city innovation collaboration patterns will witness dramatic changes under the influence of intangible drivers.
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 6 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
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