Talent Flow in China's Intelligent Connected Vehicle Industry: Evidence From Online Resume Mining

Talent is crucial for accelerating the growth of China's intelligent connected vehicle (ICV) industry. To gain insights into talent flow within the industry, in this article, we propose the technology-profession method to construct a large-scale dataset based on granted patents from China National Intellectual Property Administration and resumes from Zhaopin, an online recruitment platform. By utilizing this method, we accurately identify 175 798 talents and collect 239 479 pieces of talent flow data. In addition, we introduce the stage-level framework to comprehensively map the talent flow. This framework encompasses multiple directed networks spanning two stages of talent careers and three levels ranging from micro to macro. Furthermore, we employ the Global Moran's I and the local indicators of spatial association to examine the spatial autocorrelation of talent flow. The results reveal significant variations in talent flow across different subindustries, stages, and levels within China's ICV industry. Notably, there is a pronounced spatial autocorrelation of talent flow between companies, particularly in South China. Factors such as spatial distance, economic development, and industrial agglomeration influence the patterns of talent flow. These findings offer valuable insights for policy makers, providing guidance on how to effectively manage and intervene in talent flow within the ICV industry.