Technological innovation is the core driving force for cultivating new quality productive forces. Under high economic policy uncertainty, enterprises’ perception of economic policy uncertainty significantly influences their innovation. Investigating the role of economic policy uncertainty perception in corporate innovation strategy choice is important for enhancing enterprises’ innovation capabilities. Based on data from Chinese A-share listed companies from 2007 to 2022, this study employed text mining methods to construct an index of economic policy uncertainty perception and utilized a two-way fixed effects model to empirically examine its impact and mechanisms on corporate innovation strategies. The findings indicated that: (1) an increase in economic policy uncertainty perception prompts enterprises to choose collaborative innovation strategies; (2) among informal institutions, clan culture and board networks negatively moderate this relationship, while financial connection positively moderates it; (3) this impact is more pronounced in state-owned enterprises, mature enterprises, and high-tech enterprises. The research will provide theoretical and practical foundations for enterprises to leverage informal institutions to mitigate economic policy uncertainty and optimize innovation governance.

Clarifying the maturity of industrial technology ecosystems is essential for understanding the current state of industries and providing a scientific basis for formulating industrial strategies. However, existing research predominantly focuses on evaluating single-technology synergies or element similarities, often neglecting a systematic examination of multi-layer network synergies. This paper constructed judgment rules for assessing the stage and level of technology ecosystem maturity from two perspectives: mutual promotion-driven synergy and mapping overlap synergy. It proposed a comprehensive evaluation method that integrates hypernetwork models, transfer entropy, and two-mode network mapping relationships. Using patents from the nanomaterials industry between 2013 and 2022 as a foundation, an empirical analysis was conducted to assess the maturity of this industrial technology ecosystem. The results indicated that through normative and quantitative analyses of multi-layer network synergies, it is possible to effectively overcome the subjectivity and one-dimensional limitations inherent in traditional evaluations, thereby providing an objective basis for determining maturity. From the perspective of driving synergy, R&D cooperation along with patent development within the nanomaterials industry significantly propels market applications; however, there exists insufficient reverse driving effects—suggesting that this sector is currently in its formative stage regarding technological systems. In terms of mapping synergy, there is a notable overlap degree (90%) between core technology patents and market application fields; nevertheless, the level of synergy between R&D entities and their market environment remains relatively low overall—resulting in a medium to high maturity level. Based on these findings, strategies such as enhancing the establishment of market-oriented technology consortia are recommended. These strategies will provide valuable decision support for governments and enterprises aiming to optimize their technological ecosystem layouts while bolstering industrial competitiveness.

With the rise of artificial intelligence-generated content (AIGC), AI has moved from decision-making 1.0 to generative 2.0. Machine intelligence (“machine intelligence”), exemplified by next-generation cognitive computing capabilities, demonstrated significant potential in generating both incremental solutions for iterative optimization and exploratory solutions for value reconstruction. However, whether innovative solutions can be adopted and achieve dual synergy still depends heavily on prudent decision-making by human intelligence (“human intelligence”), which is centered on the cognitive evaluation of the senior management team. In this context, integrating the perspectives of “machine” and “human” intelligence held significant theoretical value in analyzing the synergy mechanisms of enterprise dual innovation. This study developed a process model of “generation of dual innovation solutions - adoption - synergy realization of dual innovation”. Based on survey data from 343 enterprises in the Yangtze River Delta region that are implementing AI innovation, this model was empirically tested using hierarchical regressionand bootstrap methods. The results indicated that: (1) Cognitive computing capability has a significant positive impact on exploratory innovation, exploitative innovation, and dual innovation synergy, and has a stronger driving effect on exploratory innovation. (2) The relationship between cognitive computing capability and dual innovation synergy is only positively moderated by fit cognition, while the moderating effects of risk cognition and complexity cognition are not significant. (3) The three micro-processes of data-driven dynamic capabilities, namely, sense and response, integrated and utilization, and reconstruction and transformation, exhibit a chain-mediated impact on the relationship between cognitive computing capability and dual innovation synergy. The independent mediating effect of digital integration and utilization capability is insignificant. This study revealed the complementary boundaries of human-machine collaboration from the perspective of cognitive division of labor, deepened the theoretical mechanism of AI empowering digital innovation, and will provide practical insights for enterprises to optimize human-machine configuration and coordinate dual innovation with strategic decision-making.

In the context of the current global pursuit of green and sustainable development, industrial intelligence, as a new driving force to promote industrial upgrading and economic growth, has an increasingly prominent impact on regional green innovation and development. Utilizing panel data from 30 provincial-level regions across China, covering the period from 2013 to 2021, this study empirically investigated the effects of industrial intelligence on regional green innovation performance, and combined the panel threshold model to explore the impact mechanism of industrial intelligence on regional green innovation performance under different levels of intellectual property protection. The results showed that industrial intelligence has a significant promoting effect on the improvement of regional green innovation performance, and with the continuous improvement of the level of industrial intelligence, its promoting effect on regional green innovation performance will gradually increase, showing a non-linear characteristics of increasing marginal effect. Intellectual property protection not only plays a positive regulatory role in the process of industrial intelligence improving regional green innovation performance, but also shows a marginal increasing trend as the intensity of intellectual property protection increases. The analysis of regional heterogeneity shows that compared with the eastern and western regions, the impact of industrial intelligence on regional green innovation performance in the central region is more obvious. The research results clarify the mechanism and influence path between industrial intelligence, intellectual property protection and regional green innovation performance at the theoretical level. At the practical level, it will provide theoretical reference and decision-making support for regional innovation entities to leverage the advantages of industrial intelligence and achieve green innovative development under reasonable intellectual property protection levels.

This paper explored the reconstruction and evolution of business models of manufacturing enterprises in the era of artificial intelligence (AI), aiming to provide insights into how enterprises can develop new competitive advantages. By identifying five fundamental issues related to the commercialization of AI and employing a single-case study method, the research drew on the practical experiences of Midea Group in the field of intelligent manufacturing from a value perspective. First, the study conceptualized the business models in the AI era as a dynamic closed-loop analytical framework comprising six interconnected elements: value proposition, value creation, value delivery, value capture, value maintenance, and value transformation. Second, it revealed that the business models of manufacturing enterprises undergo incremental reconstruction driven by technological iterations in AI, with machine intelligence, trustworthy AI, and generative AI technologies serving as the foundations for three distinct stages: AI enabling, AI trust, and AI ubiquitous. Third, the study highlights Midea Group's evolutionary pathway from a "+AI" model to an "AI+" model, offering a practical roadmap for the intelligent transformation of business models. These findings will provide a theoretical foundation and actionable guidance for manufacturing enterprises seeking to leverage AI in creating distinctive value and achieving sustainable competitive advantages.

Integrate innovation is a crucial means of driving high-quality economic development and a key focus for achieving Chinese-style innovation. This study conducted a longitudinal case study of "NewMed Medical" to explore the intrinsic mechanisms and evolutionary pathways of integrate innovation from the perspective of the innovation chain. The findings revealed that: (1) Integrate innovation is embedded in the progressive evolution of the innovation chain, following the stages of "discovery-invention-development" and its evolution exhibits multi-dimensional dynamic characteristics, including vertical progression, horizontal expansion, and cross-dimensional interactions; (2) Integrate innovation possesses dual attributes of multi-stage interconnection and multi-actor symbiotic competition and cooperation, manifesting in integrated models such as absorptive, complementary, and collaborative innovation; (3) Integrate innovation drives hierarchical transitions across "elements-resources-ecosystem" characterized by adaptive integration, coordinated collaboration, and coupled integration mechanisms. This study has constructed an evolutionary process model of integrate innovation, and extracted its functional patterns and realization mechanisms, and it will provide theoretical foundations and practical guidance for optimizing and upgrading technological enterprise innovation practices.

The full utilization of data elements by economic entities is not only a crucial pathway for realizing the value of data elements but also a key focal point for digital economic development. Based on government data openness policies, this study employed a difference-in-differences model, utilizing the data from Chinese A-share listed companies, provincial-level regions, and cities from 2009 to 2022. It examined the impact of government data openness on corporate data element utilization levels and its underlying mechanisms. The findings revealed that: (1) Government data openness significantly enhances corporate data element utilization levels; (2) Firms' resource integration capabilities and digital transformation serve as key mediating mechanisms through which government data openness influences corporate data utilization; (3) The application of digital technologies, and levels of intelligent investment, executives' digital backgrounds, and digital industry agglomeration exert positive moderating effects on the relationship between government data openness and corporate data utilization; and (4) The heterogeneity analysis revealed that high-tech industry firms exhibit higher levels of data utilization. This study has explored the corporate data utilization process from the perspective of internal and external resource integration, and it will enrich research on unlocking data value and provide theoretical foundations and management insights for corporate data utilization.

Digital innovation is the core driving force for the high-quality development of digital economy, and how to effectively link the government, market and society to realize the "increase in quantity and quality" of digital innovation is an important issue to be solved. From the perspective of the new structural economic theory and based on the institutional framework of "government-market-society", this study, taking 280 prefecture-level cities in China from 2010 to 2021 as case studies, adopted the dynamic QCA methods to investigate the linkage effect and path selection of digital innovation driven by multiple factors in a spatial and temporal dimension. The study showed that: First, no single institutional factor of government, market and society constitutes a necessary condition to drive the "incremental improvement" of digital innovation. Second, the combination effect of multiple factors to form the "incremental quality" of digital innovation has the characteristics of "different paths to the same destination" and "multiple concurrency". There are three typical combinations of government-led, government-market-led and government-market-society synergistic promotion systems that can realize "incremental" digital innovation. And there are two organizational ways to achieve "quality improvement" in digital innovation: government-society-driven and government-market-society synergistic promotion. Third, the spatial context analysis based on the grouping pattern showed that there are obvious regional differences in urban digital innovation paths, with the government in the eastern region playing a leading role in the "incremental improvement" of digital innovation, while the central and western regions rely mainly on the market to cultivate it. Fourth, in a comparative analysis over multiple time periods, two types of institutional configurations, "government-led" and "government-market-society synergistic", are found to be among the "dominant trajectories" driving digital innovation, which consistently and steadily emerged. The study has both broadened the research horizon of digital innovation and provided new thinking on how to improve the institutional environment for high-level digital innovation.