Drivers of artificial intelligence innovation in manufacturing clusters: insights from cellular automata simulations

Juan Yu (), Weihong Xie, Xiuyi Zhao, Zhongshun Li and Liang Guo
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Juan Yu: Guangdong University of Technology
Weihong Xie: Guangdong University of Technology
Xiuyi Zhao: Guangdong University of Technology
Zhongshun Li: Guangdong University of Technology
Liang Guo: Guangdong University of Technology

Palgrave Communications, 2025, vol. 12, issue 1, 1-17

Abstract: Abstract With the rapid development of the global economy and artificial intelligence (AI) technologies, AI-driven innovation has become a key driver of economic growth in manufacturing clusters. This study investigates the main drivers of AI innovation in manufacturing clusters through the lens of evolutionary economic geography theory. Three primary driving factors are identified: cluster resources, cluster networks, and cluster environments. An evolutionary model based on Cellular Automata (CA) is developed to quantitatively analyze their influence, followed by simulation experiments. The results show a positive correlation between these factors and the evolution of AI innovation within industrial clusters. Further case studies of AI-enabled manufacturing clusters, including Zhongguancun, Shenzhen, and Bangalore, substantiate these findings. The study highlights the critical role of resource endowments, AI-driven inter-firm collaboration, and supportive policy frameworks in fostering AI innovation. The findings provide a deeper understanding of clustered innovation ecosystems and the theoretical foundations of collective learning and competitive advantage in the AI era. This research also has broad implications, particularly for interdisciplinary studies in digital humanities, complex network analysis, and the socioeconomic impact of AI-driven technological transformation.

Date: 2025
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DOI: 10.1057/s41599-025-05386-7

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