Technology transfer in China's city system: Process, pattern and influencing factors

Based on the records of patent transfer from the patent retrieval and analysis platform in the State Intellectual Property Office of China, this research built an assessment index and model for technology transfer in China's city system in terms of agglomeration and dispersion, using big data mining technology, geo-coding technology, spatial autocorrelation model and multiple linear regression model. Then we studied the spatial-temporal pattern, agglomeration model and influencing factors of technology transfer in China's city system from 2001 to 2015, and obtained the following results. Firstly, with the increasing capability of city's technology transfer and the growing number of cities involved in transferring technology, the polarization and strong agglomeration of technology transfer in China's city system have been intensified. Secondly, technology transfer in China's city system has experienced a process of constant spatial polarization, the three-pole pattern led by the Beijing-Tianjin-Hebei region, the Yangtze River Delta region and the Pearl River Delta region has been gradually prominent. Thirdly, technology transfer system from global to local scale in China's city system has initially taken shape. Beijing, Shanghai and Shenzhen have become the three global centers of China in technology transfer. Fourthly, technology transfer in China's city system has produced an obvious spatial correlation and agglomeration effect. The four types are mainly in the cluster, and the geographical proximity of technology transfer in China's city system is significant. Last but not least, the influencing factors of technology transfer in China's city system were also verified by multiple linear regression model. We found that the demand and supply capacity respectively represented by the scale of tertiary industry and the number of patent applications has a great influence on the growth of technology transfer capability. In addition, the number of R & D employees is an important factor, but its correlation is low. The findings further confirm that the scale of primary industry has a significant impedance effect on city's technology transfer capability.