Strategic emerging industries are in a critical period, and the formation of dominant design determines their future development path. The strategy of combining openness and exclusivity is playing an increasingly important role in helping enterprises achieve a dominant design. Then, how should enterprises formulate the corresponding “open-exclusive” strategy to promote the formation of dominant design? Based on this issue, an empirical analysis of how open innovation and exclusive mechanisms affect the formation of dominant design in strategic emerging industries was conducted, using patent and standard data from 283 enterprises. The results show that open innovation and exclusive mechanisms have different effects on the dominant design of strategic emerging industries, and open innovation has a significant promotion effect, while exclusive mechanisms will inhibit the formation of dominant design. Ambidextrous innovation has multiple mediating functions between open innovation and dominant design of strategic emerging industries. Exploitative innovation plays a mediating role between exclusive mechanisms and dominant design of strategic emerging industries. It conclusions reveal the internal mechanisms by which openness and exclusivity impact dominant design through ambidextrous innovation, which has significant managerial implications for enterprises seeking to secure a dominant design.

Encouraging rural innovation and entrepreneurship development and fully stimulating the vitality of rural innovation and entrepreneurship will provide sufficient internal driving force for promoting high-quality development of the county economy. Starting from the perspective of new qualitative factor agglomeration, based on Schumpeter’s entrepreneur theory and Romer’s endogenous growth theory, rural innovation and entrepreneurship were regarded as an organic integration of the whole, and a theoretical framework of “rural innovation and entrepreneurship-new qualitative factor agglomeration-high-quality development of the county economy” was constructed, and the balanced panel data of 1569 counties in China from 2014 to 2021 were used for testing. The results show that the benchmark regression results confirm that rural innovation and entrepreneurship have a significant role in promoting high-quality development of the county economy. The mechanism test finds that rural innovation and entrepreneurship mainly promote high-quality development of the county economy by attracting high-quality labor factor agglomeration, digital factor agglomeration, and intelligent factor agglomeration. New infrastructure construction and financial service level can play a positive regulatory role. Heterogeneity discussion finds that this effect is more significant in the central and western regions, national innovative counties (cities) and regions with strong government support. Therefore, it is necessary to continue to increase support for high-quality development of rural innovation and entrepreneurship, adhere to the focus on attracting and cultivating high-quality labor, improving digitalization level, and intelligence level. Accelerate new infrastructure and financial services, promote the integration of “digital and real” and other external environments, and consolidate the foundation for achieving high-quality development.

Digital platforms have both technical and market attributes, but how they influence business model innovation in digital platform companies and the underlying mechanisms of new value creation remain unclear. A longitudinal single-case study is adopted to focus on the development practice process of Kingdee’s digital platform. Based on the business model innovation theory, the value creation mechanism of non-native digital platform enterprises was explored from two dimensions of technology and market. By dividing the development of digital platforms into construction, growth, and expansion stages, the case analysis reveals that the value creation mechanisms in these stages were characterized by market-driven and technology-supported lock-in business model innovation releasing channel value, technology-driven and market-following complementary business model innovation creating complementary value, and dual-driven technology and market complementary and novel business model innovation deepening ecosystem value. Overall, the research conclusions are deemed to enrich and expand the studies on business model innovation and value creation of digital platform enterprises, and provide references for traditional software firms in their transformation into digital platform ones.

As a strategic emerging technology, artificial intelligence (AI) plays a significant role in guiding future societal transformation and has an empowering effect to help enterprises realize disruptive innovation. However, there is still a lack of in-depth analysis of the key elements and mechanisms of AI empowering enterprises to realize disruptive innovation in academia. The impact path of AI-empowered disruptive innovation for enterprises remains unknown. The exploratory multi-case study method based on Grounded Theory was applied to construct a theoretical model of AI empowering enterprises to realize disruptive innovation, and the fuzzy-set qualitative comparative analysis method was employed to explore the complex causal mechanism of AI empowering disruptive innovation in enterprises. The results highlight that five key factors for achieving AI-driven disruptive innovation in enterprises are service ecology, cooperation network, technological transition, context-depth-excavation, and organizational structure innovation. Among the five factors, technological transition is a necessary condition for AI to empower disruptive innovation in enterprises. There are three types and four paths of AI empowering disruptive innovation in enterprises including“technology-service ecology type”, “technology-scene-structural innovation type” and “technology-cooperation network type”. The results provide decision-making references for enterprises on selecting an AI empowerment path to drive disruptive innovation based on their unique circumstances.

The report of the 20th National Congress of the Communist Party of China emphasizes the promotion of high-end, intelligent, and green development in the manufacturing industry. Based on the GML-SBM and SBM-DDF models, the carbon total factor productivity(CTFP) of manufacturing A-share listed companies from 2012 to 2022 was calculated. The impact and channels of intelligent manufacturing on enhancing enterprise CTFP were explored from both input and application perspectives. It is found that intelligent manufacturing significantly improves enterprise CTFP, with technological efficiency change being the main driving force. Intelligent manufacturing primarily enhances enterprise CTFP through four pathways: reducing capital and labor usage costs, promoting capital deepening, and improving capital output efficiency. The impact and channels are stronger for young enterprises and non-state-owned enterprises, enterprises in highly competitive industries and technology-intensive industries, enterprises in regions with high levels of intellectual property protection, and enterprises in industrial bases. The findings provide policy insights for the implementation of intelligent manufacturing strategies and the achievement of “dual carbon” targets, and have significant reference value for the intelligent transformation of manufacturing enterprises.

The low-carbon city pilot policy not only impacts the urban energy consumption and carbon emissions at the macro level, but also influences the digital transformation, energy conservation, and total factor productivity of industrial, manufacturing, and heavy-pollution enterprises at the micro level. However, how it affects the development of renewables remains unknown. Against this backdrop, a “quasi-natural experiment” was developed from the low-carbon city polit, and the difference-in-difference (DID) model was used to examine its impacts on the performance of renewable energy enterprises. The findings indicate that the low-carbon city polit policy notably promotes the development of renewables by enhancing the performance of renewable energy enterprises in the polit areas, and this promoting effect is strengthened as the policy progresses. The results remain robust after a series of rigorous tests. The policy primarily boosts corporate performance by increasing the net profit of renewable energy enterprises. Additionally, heterogeneities across enterprise regions, ownership types, and business categories are observed: the promotion effects are more significantly occurred in central and western China, state-owned renewable energy enterprises, and renewable power generation enterprises. These results contribute to a deeper understanding of how the development of renewables and energy transition can be achieved through the low-carbon city pilot policy.

Under the tide of marketization, the economic attributes of traditional social relationships have been increasingly enhanced, and the derived social capital has become an important factor affecting income inequality. Based on the panel data from the China Household Finance Survey (CHFS) database during the period from 2013 to 2019, the impact effect of social capital on income inequality and its potential mechanism of action are thoroughly investigated. The empirical results show that social capital plays a positive role in alleviating income inequality, with a 1% increase in social capital leading to a 0.0053 decrease in income inequality. Even after considering endogeneity issues, this conclusion remains robust. Moreover, the reliability of the research results is further confirmed by robustness tests carried out through excluding outliers and replacing the inequality indicator. In terms of mechanisms, social capital can reduce income inequality through three channels including improving labor mobility, reducing risk aversion, and enhancing financing capabilities. Heterogeneity analysis indicates that the economic effect of social capital in reducing income inequality is more pronounced among middle-aged and elderly groups as well as low-income groups.

Co-innovation is considered to be of great significance in addressing the lack of innovation capacity of a single entity. Under the influence of information technology, data assets are found to facilitate resource integration, achievement sharing, and collaborative innovation among enterprises within the supply chain. They are regarded as the core driving force for promoting co-innovation among enterprises. Based on empirical evidence from A-share listed companies in China between 2010 and 2022, the role of data assets in the channels and paths of co-innovation among upstream and downstream collaborators was explored. The results show that data assets exert a significant positive influence on firms’ co-innovation. The enhancement of firms’ total factor productivity, the advancement of human capital structure, and the alleviation of financing constraints are identified as key transmission mechanisms through which data assets contribute to firms’ co-innovation. The promotional effect of data assets on co-innovation is found to be more pronounced in large-scale, high-tech, and manufacturing enterprises. The findings confirm that data assets can enable enterprises to enhance co-innovation and offer both theoretical references and practical foundations for the country to develop new driving forces related to data elements and advance enterprise co-innovation.

Ensuring the stabilization and expansion of foreign direct investment is one of the key priorities in current economic efforts. Exploring the impact of trade friction on foreign divestment and understanding the underlying causes is of great significance for stabilizing foreign investment in the new situation. It took China’s counter-tariff measures against the United States during the Sino-US “trade war” as a starting point to examine the effects of trade friction on foreign divestment in China. It further examined how the increased uncertainty of the import supply chain under trade friction affected foreign divestment in the industrial chain correlation. The findings are as follows. Trade friction exacerbates foreign investment withdrawal in China, especially for export-oriented enterprises, enterprises with a high concentration of imports and those not undergoing digital transformation. Trade friction increases uncertainty of the import supply chain, which is a key factor contributing to foreign divestment. The rising uncertainty of the import supply chain also has spillover effects on foreign divestment in the same industry as well as upstream and downstream industries. The research conclusions can provide a new perspective to explain the phenomenon of foreign divestment in China under trade friction, and also provide a policy basis for achieving “stable foreign investment” from the supply chain aspect.

Accelerating the development of a nationally unified market is essential, creating a technology environment conducive to innovation within this mega-scale market is crucial for implementing the innovation-driven development strategy. As carriers of innovation factors and hubs for knowledge exchange, the ability of regions to acquire heterogeneous knowledge profoundly influences innovation potential. Data on non-local subsidiaries in China from 2003 to 2021 and patent information were utilized to empirically examine the impact of non-local investment on regional innovation.A significant enhancement of regional innovation capability by non-local investment is found. This effect is more pronounced in regions with lower degrees of vertical fiscal imbalance, higher levels of intellectual property protection, and stronger regional accessibility. Mechanism analysis reveals that regional innovation capability is primarily enhanced through two approaches enabled by non-local investment: increased regional technological diversity and elevated levels of non-local collaborative innovation. Upon distinguishing technological diversity, it is discovered that related technology diversity is mainly enhanced by non-local investment, thereby driving incremental innovation. Enhancement of unrelated technology diversity, which potentially facilitates higher-risk, higher-reward disruptive innovation, is not the primary channel utilized.Therefore, precise identification of the differential innovation-promoting effects of non-local investment is critical. To fully unleash its innovation potential, accelerated development of a nationally unified market is required. Unified fundamental market institutions and rules need improvement. Incentive systems for technological innovation must be strengthened. These actions are essential to support higher-quality regional development.