Imagen de Portada

The Formation of Knowledge Flow Networks in the Yangtze River Delta, China: Knowledge Implicitness and Proximity Effect

Guardado en:
Publicado en:Sustainability vol. 17, no. 2 (2025), p. 740
Autor principal: Zhu, Pengcheng
Otros Autores: Chen, Jianglong, Yuan, Feng, Liu, Weichen
Publicado:
MDPI AG
Materias:
China
Yangtze River Delta
Innovations
Patents
Economic development
Explicit knowledge
Collaboration
Sustainable development
Tacit knowledge
Friendly fire
Knowledge management
Acceso en línea:Citation/Abstract
Full Text + Graphics
Full Text - PDF
Etiquetas: Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
Resumen:Knowledge flow as the key to facilitating new technology production and diffusing innovation is crucial for achieving sustainable development. However, previous studies pay less attention to the type of knowledge in knowledge flow network construction, possibly leading to the deviation of conclusions. To fully show the panorama of knowledge flow, this study distinguishes between explicit and tacit knowledge based on the transfer of patent rights data and talent flow data, describes the spatial characteristics of flow networks and uses a multiple regression quadratic assignment procedure model to analyze the proximity mechanism of network formation in the Yangtze River Delta. We find that knowledge flow networks in the Yangtze River Delta cover a wide range but are extremely uneven, mainly concentrated along the Yangtze River and around Hangzhou Bay. In addition, the spatial structures of different types of knowledge flow networks vary. Different dimensions of proximity act in relatively consistent directions for both types of knowledge flows, with geographical and organizational proximity found to exert positive effects on facilitating knowledge flows while cognitive proximity has a negative impact. There is also a substitution effect between geographical proximity and organizational proximity, and a complementary effect with cognitive proximity. These findings provide significant implications for optimizing knowledge flow networks and promoting sustainable development.
ISSN:2071-1050
DOI:10.3390/su17020740
Fuente:Publicly Available Content Database