Breakthrough innovation(BI) in strategic emerging industry(SEI) represents a crucial pathway for cultivating new quality productive forces. Grounded in the strategic context of achieving high-level self-reliance and self-improvement in science and technology, this study constructs a three-dimensional co-evolutionary analysis framework of “institution-structure-behavior” based on the institutional logic theory and innovation ecosystem theory. By taking China’s semiconductor storage industry as a vertical case, this study revealed the mechanism of achieving BI in SEI. The findings showed that the realization process of BI in SEI includes three strategic stages: technology marketization exploration, core technology route cultivation and ecological breakthrough of independent innovation. Each stage sequentially adopts the linked innovation mode, recombinant innovation mode, and integrated innovation mode to achieve phased strategic objectives. The three-dimensional co-evolution among the institutional logic, innovation ecosystem and innovation strategic behaviors is realized through interactive mechanisms, namely, behavior shaping, structural support, institutional reconstruction and dynamic feedback. This study aims to develop a systematic framework for analyzing the mechanism for achieving BI in SEI, promote the complementary integration of institutional logic theory and innovation ecosystem theory, and will provide insights into innovative development of SEI.

Against the backdrop of the rapid evolution of the global digital economy and the accelerated formation of new quality productive forces, chip technology, as a crucial cornerstone of the modern information industry, provides robust computing power support for artificial intelligence, the Internet of Things, and 5G communications, thereby propelling the high-quality development of the digital industry. This study focused on China’s chip technology sector. Based on jointly authorized patent data from 2009 to 2023, a dynamic innovation cooperation network was constructed, and social network analysis was employed to reveal its structural characteristics and evolutionary patterns. The research findings are as follows:(1) The scale of China’s chip technology innovation cooperation network continuously expanded during the observation period, with network density exhibiting an inverted U-shaped change.(2) Multiple cohesive subgroups emerged within the innovation cooperation network, and the network structure evolved from a single-core to a multi-core form, increasing in complexity over time.(3) The roles of core nodes dynamically changed across different evolutionary stages, exhibiting an alternating leadership pattern of “industry-academia-industry”. This study will provide a reference for policymaking and the optimization of corporate innovation strategies, suggesting measures such as optimizing resource allocation, accelerating multi-core network formation, and deepening diversified collaboration to further facilitate the high-quality development of China’s chip technology industry.

With the intensifying competition in the global market and the rapid advancements in technology, companies face the urgent challenge of how to swiftly and effectively leverage artificial intelligence(AI), a disruptive technology, to enhance their competitiveness. This study focuses on the application of AI in improving corporate competitive position, systematically elucidating the mechanisms through which AI affects competitive position. Using panel data from A-share listed companies in the Shanghai and Shenzhen stock markets from 2013 to 2023, we employ two-way fixed effect models and mediating effect models to systematically examine the impact of AI on corporate competitive position and its multiple mechanisms. The results indicate that the application of AI significantly enhances corporate competitive position, and this conclusion remains robust after a series of robustness and endogeneity tests. Heterogeneity analysis reveals that AI has a more pronounced effect on the competitive position of state-owned enterprises, firms with higher risk-taking capacity, those facing greater environmental uncertainty, enterprises in the digital industry, and enterprises in the secondary and tertiary sectors. Mechanism analysis shows that AI significantly influences competitive position through three main pathways, including improvements in total factor productivity, access to government subsidies, and increased analyst attention. The findings of this study not only provide a new perspective for understanding the role of AI in enhancing corporate competitiveness but also offer a theoretical foundation and decision-making reference for promoting the application and optimization of AI technologies in businesses.

It is important to clarify how to improve the urban status of green technology innovation networks to bridge the development gap of green technology innovation between cities and realize the national “double carbon” goal. Based on the big data of firm-level green technology patents from 2015 to 2021, the study depicts China’s green technology innovation urban network, constructs a theoretical model to enhance the urban status of the green technology innovation network from three aspects: “supply and demand side” business environment, urban administrative level, and economic development level, and uses dynamic QCA method to conduct empirical research. The study found that: first, the green technology innovation urban network has the structure of “interaction inside and outside the circle” with the characteristics of mobility and stability, but the network status of city nodes is unbalanced, and the edge cities are still facing the dilemma of insufficient network embeddedness. Second, the “supply and demand side” business environment, urban administrative level, and economic development level are the multiple concurrent factors that enhance the urban status of the green technology innovation network. The “administrative level” dominance and the “economic level - business environment” synergy are the two equivalent logics to achieve high urban network status. The “supply pull” and “supply-demand linkage” of the “supply and demand side” business environment are their marginal and core conditions, respectively. Third, for the network entities in different positions, the feasible paths to improve the urban status of green technology innovation networks with the business environment are different. The core cities are dominated by the “factor supply side” business environment, while the edge cities rely on the balanced linkage of the “supply and demand side” business environment or the strong driving of the “factor supply side” business environment. On the one hand, under the network-based urban spatial structure, the study provides policy references for the country to build a new development pattern of inter-city green technology innovation cooperation and integration. On the other hand, the findings put forward multiple feasible paths for cities to enhance the green technology innovation network status, and provide empirical evidence for network edge cities to utilize the business environment to mitigate their difficulties in improving urban network status due to their low administrative level and economic level.