Yuanfang Chen, Kok Haur Ng
Digital talent mobility is crucial for enhancing regional competitiveness, yet its influencing factors exhibit notable spatio-temporal heterogeneity. This study examines how interacting macro-, meso-, and micro-level factors shape digital talent mobility across urban agglomerations in China. GeoDetector is first used to identify key interaction effects, which are then incorporated into the Geographically and Temporally Neural Network Weighted Regression model for coefficient estimation under flexible and data-driven spatio-temporal weighting. The results show that the effects of macroeconomic and industrial conditions are highly uneven across space and time. This unevenness is further shaped by healthcare provision and air quality, which influence talent mobility both directly and by modifying the role of labour-market and development conditions. The findings show that digital talent mobility is characterised by interaction-dependent and spatio-temporally dynamic relationships. It provides evidence for more differentiated regional talent policies.