Artificial Intelligence in China: The role of Open Innovation, Innovation Ecosystem and Regional Specialization

Since the initiation of opening-up reforms in 1978, China has progressively become a major player in technological innovation, emphasizing a transition from state ownership to market-oriented microeconomic policies. The nation has consistently invested in science, technology, and innovation (STI) with the goal of establishing a robust national innovation system. Recognizing the significance of artificial intelligence (AI) as a General-Purpose Technology (GPT), China has formulated ambitious plans, culminating in a roadmap to position itself as a global AI innovator by 2030. China's innovation approach emphasizes indigenous innovation, seeking to produce innovation to build an innovation system based on collaboration among multiple actors. Open Innovation (OI) has in fact been a key strategy, with the government playing a crucial role in orchestrating national OI activities. The OI approach has met the AI strategy in various innovation policies, including the "National New Generation Artificial Intelligence Open Innovation Platforms" (AIOIPs) and the "Guidelines for the Construction of National New Generation Artificial Intelligence Innovation and Development Pilot Zones" (AI zones), which build the foundation of this thesis. The first chapter of the thesis delves into the evolution of OI in Chinese academia. Despite the popularity of OI among academics and policymakers, a comprehensive literature review on OI's evolution in Chinese academia was lacking. This chapter fills the gap by conducting a systematic review, mapping the OI scholarship in China from external knowledge sourcing to concepts of ecosystems and innovation policy. The second chapter explores the Chinese AI OI ecosystem using the AIOIPs as a framework. Through a patent-based brokerage analysis, the study identifies patterns of OI in the AIOIPs, emphasizing the influence of top-tier Chinese AI firms as producers of knowledge brokers' patents. The chapter provides implications for AI companies and policymakers, highlighting the impact of public policy on knowledge flow dynamics. The third chapter examines the regional dynamics of AI development in China. Using panel fixed effect estimators and a spatial Durbin model, the study reveals that geographical proximity to developed AI regions hinders neighboring areas' AI progress, emphasizing the necessity for collaborative regional strategies. This chapter contributes to understanding the balance between regional strengths and the promotion of cooperation in AI development. Overall, the thesis systematically explores OI in Chinese academia, analyzes the Chinese AI OI ecosystem using AIOIPs, and examines regional AI development dynamics. Each chapter provides valuable insights, contributing to the broader field of innovation management and informing discussions on AI governance and innovation in China.