Integrating artificial intelligence into regional technological domains: the role of intra- and extra-regional AI relatedness

Yijia Chen & Kangmin Wu

Cambridge Journal of Regions, Economy and Society2025https://doi.org/10.1093/cjres/rsae041article
AJG 3ABDC B
Weight
0.58

Abstract

Artificial intelligence (AI) is a key driver of the Fourth Industrial Revolution. Despite growing interest in the geography of AI, our understanding of how AI integrates into regional contexts remains limited. In response, we examine the integration of AI into regional technological domains in China using patent data. Theoretically, we develop a framework by introducing the concepts of intra- and extra-regional AI relatedness. Our findings reveal that the integration of AI into regional technological domains is positively associated with both intra-regional and extra-regional AI relatedness. Additionally, extra-regional AI relatedness can moderate the lack of intra-regional AI relatedness. Finally, we use the USA as a robustness check, which further validates our findings.

12 citations

Open via your library →

Cite this paper

https://doi.org/https://doi.org/10.1093/cjres/rsae041

Or copy a formatted citation

@article{yijia2025,
  title        = {{Integrating artificial intelligence into regional technological domains: the role of intra- and extra-regional AI relatedness}},
  author       = {Yijia Chen & Kangmin Wu},
  journal      = {Cambridge Journal of Regions, Economy and Society},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1093/cjres/rsae041},
}

Paste directly into BibTeX, Zotero, or your reference manager.

Flag this paper

Integrating artificial intelligence into regional technological domains: the role of intra- and extra-regional AI relatedness

Flags are reviewed by the Arbiter methodology team within 5 business days.


Evidence weight

0.58

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.58 × 0.4 = 0.23
M · momentum0.80 × 0.15 = 0.12
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.