Innovation ecosystems theory revisited: The case of artificial intelligence in China

Beyond the mainstream discussion on the key role of China in the global AI landscape, the knowledge about the real performance and future perspectives of the AI ecosystem in China is still limited. This paper evaluates the status and prospects of China's AI innovation ecosystem by developing a Triple Helix framework particularized for this case. Based on an in-depth qualitative study and on interviews with experts, the analysis section summarizes the way in which the AI innovation ecosystem in China is being built, which are the key features of the three spheres of the Triple Helix -governments, industry and academic/research institutions-as well as the dynamic context of the ecosystem through the identification of main aspects related to the flows of skills, knowledge and funding and the interactions among them. Using this approach, the discussion section illustrates the specificities of the AI innovation ecosystem in China, its strengths and its gaps, and which are its prospects. Overall, this revisited ecosystem approach permits the authors to address the complexity of emerging environments of innovation to draw meaningful conclusions which are not possible with mere observation. The results show how a favourable context, the broad adoption rate and the competition for talent and capital among regional-specialized clusters are boosting the advance of AI in China, mainly in the business to customer arena. Finally, the paper highlights the challenges ahead in the current implementation of the ecosystem that will largely determine the potential global leadership of China in this domain. •The paper aims to both expanding the knowledge about the AI ecosystem in China and contributing to the innovation ecosystems theory.•It also stresses how a qualitative approach is useful to tackle the complexity in emerging environments of innovation .•In addition, it allows the extraction of insights and gaps that are not directly available from a quantitative analysis of the ecosystem.