Drivers of artificial intelligence innovation in manufacturing clusters: insights from cellular automata simulations
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...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Springer Nature
2025-07-01
|
| Series: | Humanities & Social Sciences Communications |
| Online Access: | https://doi.org/10.1057/s41599-025-05386-7 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849334871472734208 |
|---|---|
| author | Juan Yu Weihong Xie Xiuyi Zhao Zhongshun Li Liang Guo |
| author_facet | Juan Yu Weihong Xie Xiuyi Zhao Zhongshun Li Liang Guo |
| author_sort | Juan Yu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-bd3ca04d91de44b7b3baa13d5a7176ee |
| institution | Kabale University |
| issn | 2662-9992 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Springer Nature |
| record_format | Article |
| series | Humanities & Social Sciences Communications |
| spelling | doaj-art-bd3ca04d91de44b7b3baa13d5a7176ee2025-08-20T03:45:27ZengSpringer NatureHumanities & Social Sciences Communications2662-99922025-07-0112111710.1057/s41599-025-05386-7Drivers of artificial intelligence innovation in manufacturing clusters: insights from cellular automata simulationsJuan Yu0Weihong Xie1Xiuyi Zhao2Zhongshun Li3Liang Guo4School of Economics, Guangdong University of TechnologySchool of Economics, Guangdong University of TechnologySchool of Economics, Guangdong University of TechnologySchool of Economics, Guangdong University of TechnologySchool of Economics, Guangdong University of TechnologyAbstract 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.https://doi.org/10.1057/s41599-025-05386-7 |
| spellingShingle | Juan Yu Weihong Xie Xiuyi Zhao Zhongshun Li Liang Guo Drivers of artificial intelligence innovation in manufacturing clusters: insights from cellular automata simulations Humanities & Social Sciences Communications |
| title | Drivers of artificial intelligence innovation in manufacturing clusters: insights from cellular automata simulations |
| title_full | Drivers of artificial intelligence innovation in manufacturing clusters: insights from cellular automata simulations |
| title_fullStr | Drivers of artificial intelligence innovation in manufacturing clusters: insights from cellular automata simulations |
| title_full_unstemmed | Drivers of artificial intelligence innovation in manufacturing clusters: insights from cellular automata simulations |
| title_short | Drivers of artificial intelligence innovation in manufacturing clusters: insights from cellular automata simulations |
| title_sort | drivers of artificial intelligence innovation in manufacturing clusters insights from cellular automata simulations |
| url | https://doi.org/10.1057/s41599-025-05386-7 |
| work_keys_str_mv | AT juanyu driversofartificialintelligenceinnovationinmanufacturingclustersinsightsfromcellularautomatasimulations AT weihongxie driversofartificialintelligenceinnovationinmanufacturingclustersinsightsfromcellularautomatasimulations AT xiuyizhao driversofartificialintelligenceinnovationinmanufacturingclustersinsightsfromcellularautomatasimulations AT zhongshunli driversofartificialintelligenceinnovationinmanufacturingclustersinsightsfromcellularautomatasimulations AT liangguo driversofartificialintelligenceinnovationinmanufacturingclustersinsightsfromcellularautomatasimulations |