Along with the Sino-U.S. trade dispute , the complex geopolitical situation and other variable factors, supply chain friction has become the norm. The risks associated with the flow of capital, information, and logistics in the supply chain have dramatically increased, making it urgent to improve the chain's resilience in order to mitigate disruption risk and maintain the chain's security and stability. At this stage, the importance of data elements in empowering supply chain governance is becoming more and more prominent, and the scale of data assets owned and the ability to transform them into productivity have become important factors in gaining new competitive advantages. From the perspective of supply chain network characteristics, the penetration and application of data elements in the subject and structural elements of the supply chain have significantly revolutionized the operation mode of the supply chain, and become a key driving factor to enhance risk-taking and ensure the continuity of supply and demand.
This study investigates the enabling mechanisms of data elements on the subject and the structural resilience of the supply chain in order to reveal the supply chain resilience enhancement paths in a more thorough and detailed manner. As a result, given the network characteristics of the supply chain, the subject element and structural element may show different resilience mechanisms against risks. By employing the OLS regression model, the study collects data from Chinese listed companies between 2015 and 2022 and uses text analysis to create indicators that show the application level of enterprise data elements. It then uses supply chain subject resilience and structural resilience to characterize the overall resilience of the supply chain and empirically investigates the impact of data elements on resilience.
The research results show that data elements can effectively enhance the level of enterprise risk-taking and the stability of supply-demand relationship, and the findings remain robust after a series of endogeneity and robustness tests, showing that data elements can effectively enhance both supply chain subject resilience and structural resilience. In addition, the application of data elements by enterprises can effectively enhance the innovation effect and the information effect, thus improving the innovation capability of enterprises and obtaining the information advantage to promote the resilience of the supply chain. At the same time, the enabling effect of data elements on supply chain resilience can be differentiated by the internal and external environments of enterprises, that is, the enabling effect is stronger when the enterprise is large, operates in a highly competitive industry, and is situated in an area with a poor supply chain operating environment and high marketization.
Compared with the results of previous studies, this study expands the research perspective of supply chain resilience, constructs enterprise-level supply chain resilience measurement indexes from the perspective of network characteristics, and enriches the research scope of supply chain in the era of digital economy; and it further empirically examines the two indirect empowerment mechanisms of innovation effect and information effect, which provides a useful supplement to the research on the empowerment mechanism of data elements on supply chain resilience. Through the analysis of the research findings, this study proposes to accelerate the construction of data element marketization, further activate the potential of data elements in enhancing supply chain resilience, and formulate targeted data elements application solutions according to the differences in the internal and external environments of enterprises, which will help the government optimize the policies on data elements and supply chain and support the scientific decision-making of enterprises, and have certain theoretical significance for the implementation of the strategic plan of "focusing on enhancing the resilience and security of industrial chain and supply chain".
Future research may integrate the two dimensions of supply chain entity resilience and relationship resilience into a comprehensive index for research, further combine the characteristics of the supply chain to reveal the mechanism path involving more data elements to empower supply chain resilience improvement, and the subsequent economic impacts of data elements empowering supply chain resilience improvement could be predicted.

At the moment when AI technology is fully penetrated into all fields of social life, the rise of AIGC(Artificial Intelligence Generated Content) represented by ChatGPT, Sora and ERNIE Bot marks the leap of AI technology to a new stage of development, and indicates the approach of the era of "artificial general intelligence". However, while AIGC greatly amplifies human innovation potential, it also presents considerable challenges to the existing ethical governance system. Against this backdrop, this article examines the current state and future trends of AIGC in China, analyzing its ethical risks and generative logic. Drawing from the ethical risk governance trends of AIGC globally, the article proposes a governance framework that is not only attuned to China's national context but also forward-looking. This is aimed at providing robust theoretical guidance and practical paths for the standardized development of AIGC.
This study commences with an analysis of the potential ethical risks associated with AIGC, considering its technical attributes. Initially, it addresses the risk of ethical value imbalance, primarily evident in the intensification of algorithmic discrimination. Following this, it examines the risk of ethical norms being beyond control, which is predominantly showcased in the challenge of accountability. Lastly, it explores the risk of ethical relationship imbalance, which is chiefly characterized by a diminution of human agency. From the perspective of technological risk, the root cause of AIGC ethical risk lies in the complexity and uncertainty of technology. Particularly under the 'black box' phenomenon of algorithms, the behavior of AIGC becomes challenging to anticipate and manage, thereby amplifying ethical risks. At the same time, the tension between technological rationality and value rationality, as well as the limitations of human risk perception and response, further deepen the complexity of ethical risks.
Considering the trends in AIGC governance, this article advocates "trusted governance" as the core strategy to mitigate AIGC ethical risks. It underscores the importance of technology being controllable, accountable, fair, reliable, interpretable, and secure, with the goal of ensuring that technological advancements are transparent, equitable, and contribute positively to society. At the same time, the Artificial Intelligence Law (scholars' proposal draft) also provides important governance basis and guides the legal path of AIGC trusted governance.
Under the principle of 'trusted governance, this article proposes three core strategies. First, it calls for the establishment of a robust data risk governance framework. By integrating cross-border collaborative governance and internal fine governance, it emphasizes adherence to 'reliability' standards. This includes enhancing regulations for cross-border data flows and promoting data ethics and compliance within enterprises to ensure data usage is reliable and secure. Second, it advocates for an optimized liability attribution mechanism to address AIGC infringement risks. This involves assessing subject obligations, product defects, and infringement liability in light of AIGC's technical characteristics to enforce the 'accountability' standard. Third, it recommends integrating a 'people-oriented' approach into the AIGC ethical governance system. This aims to find technological solutions that are tailored to China's context and capable of addressing its unique challenges. In practice, it shall involve two aspects: first, at the organizational level, creating a dedicated AIGC ethical governance body to oversee ethical governance and supervision, thereby enhancing AIGC's controllability; and second, at the normative level, harnessing the 'complementary advantages' of policies, laws, and technology to uphold standards of 'fairness,' 'safety,' and 'interpretability,' guiding AIGC towards positive development.
In the future, it is essential to further integrate market development with China's national conditions, adopting 'trusted governance' as the core and the 'Proposal Draft' as the foundation to construct a comprehensive ethical risk governance system for AIGC. Moreover, there is a need for ongoing exploration in the realm of ethical governance, focusing on the organic integration of technological ethics with legal governance. This approach aims to devise AI ethical governance strategies that are distinctively Chinese, thereby equipping to adapt to and potentially spearhead the high-quality development of the new round of the global digital economy.

International industrial transfer is an important way for emerging countries to participate in globalized mass production and to be embedded in the global value chain division. Since the fourth international industrial transfer, intra-industry transfers have replaced inter-industry transfers, with transnational corporations taking the lead in globalized mass production on the basis of factor endowments. Many countries only take on a part of the production chain; developed countries firmly control the high-end segment of the value chain by virtue of their advanced technologies, and developing countries generally occupy the middle- and low-end production segments. China and the ASEAN countries are typical of the outward-looking economic development model, and under this "low-end embedded" global value chain model, China and ASEAN countries are mainly engaged in low-end processing, assembly and other labor-intensive production processes in the manufacturing industry, and they are still locked in the middle and lower ends of the global value chain by developed countries. At present, how to promote China and ASEAN countries to make leaps forward in the manufacturing global value chain has become an important issue to be resolved.
In order to determine the general rule of the impact of international industrial transfer on the status of global value chains, the article measures the scale of international industrial transfer between China and ASEAN countries from 1995 to 2018 based on the OECD-TiVA (2021) database, using the input-output model and the GVC decomposition method, and further divides the international industrial transfer into two categories, namely, international industrial transfer of intermediate products and international industrial transfer of intermediate products. The theory analyzes the mechanism of the impact of industrial transfer on the status of global value chain, and the positive impact mainly stems from the three aspects of technology spillover, financing constraints and product quality improvement. Negative impacts mainly stem from FDI technology lock-in, "crowding-out effect" and "low-end lock-in" effect. Technological innovation is the endogenous driving force for the upgrading of GVCs in China and ASEAN countries. Firstly, technological innovation can improve the labor productivity of the manufacturing industry, enhance the international competitiveness of products, and achieve product upgrading; secondly, technology can also promote the improvement of the original production process and the research and development of new products, so as to achieve the upgrading of the manufacturing industry's functions; lastly, technological innovation can promote the enterprises to leap from the low-end to the high-end, and ultimately achieve the upgrading of the GVCs.
The research results show that international industrial transfer has an inverted U-shaped relationship with the GVC status of manufacturing industries in China and ASEAN countries, which implies that, in the long run, there is a ceiling effect on the upgrading of manufacturing GVCs in China and ASEAN countries. At present, most of the manufacturing industries have not yet crossed the inflection point, and are in the "moderate range" of international industrial transfer to promote the upgrading of manufacturing GVC status, which is conducive to promoting the upgrading of manufacturing GVC status in China and ASEAN countries, and the inverted U-shape relationship is still robust after a variety of robustness tests. The moderating effect of technological innovation is significant, weakening the inverted U-shaped relationship between the two, and contributing to flatten the inverted U-shaped curve of the impact of international industrial transfer on the GVC status of the manufacturing industry than that in the benchmark regression results. In the short term, technological innovation will increase the innovation investment of enterprises in China and ASEAN countries, which increases the cost and risk of enterprise application; in the long term, as the embedded position in the GVC of China and ASEAN countries continues to improve, technological innovation can reduce the technological dependence on developed countries, achieve technological independence and autonomy and enhance the international competitiveness of enterprises. Extended analysis reveals that forward GVC participation weakens the inverted U-shaped relationship between international industrial transfer and manufacturing GVC position, while backward GVC participation strengthens this "low-end lock-in" effect; the inverted U-shaped curve between international industrial transfer of final products and manufacturing GVC position is steeper than that of international industrial transfer of intermediate products.

Enhancing the resilience and security levels of industrial and supply chains is critical for new developmental strategies. It also represents a crucial strategic choice for a nation's medium-to-long-term economic and social development. Moreover, it serves as an essential cornerstone in building a manufacturing powerhouse. A comprehensive, large-scale, and competitive industrial system has been fundamentally established in China, wherein the overall security and resilience of the industrial chains are being consistently fortified. However, the current global socio-political and economic landscape is fraught with complexities and uncertainties, exposing industrial chains to external risks such as disruptions and decouplings. Internally, these chains suffer from deficiencies like inadequate foundational innovative capabilities and a low position in the global value chain. Therefore, exploring avenues for augmenting industrial chain resilience is paramount for realizing the strategic objectives of high-quality development. Leading firms, acting as the 'architects' in constructing and evolving the entire industrial ecosystem, play a pivotal role in guiding industrial objectives and integrating relationships between upstream and downstream stakeholders. These firms can spearhead concerted efforts in critical cross-cutting technologies within the chain and facilitate the flow and sharing of innovative resources among entities within the chain. Hence, it is instrumental in enhancing industrial chain resilience by fully leveraging the ecological leadership of leading firms.
Existing literature has delved into the roles of leading firms and industrial chain resilience. However, it lacks a nuanced and differentiated analysis of the ecological leadership dimensions exerted by leading firms. Additionally, scant research has been conducted from a micro-enterprise perspective to examine the unique mechanisms through which leading firms, as dominant actors in the industrial ecosystem, contribute to enhancing industrial chain resilience. To bridge this gap, the present study employs grounded theory methodologies and conducts case studies comparing BYD and the Chinese new energy automotive industrial chain to investigate the capability dimensions of ecological leadership by leading firms and the processes by which they enhance industrial chain resilience.
The findings reveal that (1) in their development, leading firms progressively establish ecological leadership capabilities that enhance the resilience of industrial chains. These capabilities are categorized into three dimensions: technological innovation leadership, industrial collaboration and integration, and digital transformation empowerment. Specifically, technological innovation leadership is manifested through capabilities in technological innovation and leading innovation. Industrial collaboration and integration are demonstrated through resource integration and relationship coordination capabilities. Digital transformation empowerment is embodied in digital concept empowerment, digital resource empowerment, and digital structure empowerment. (2) The ecological leadership exerted by leading firms enhances the resilience of industrial chains through three distinct pathways: breakthroughs in key technologies, coordinated industrial linkages, and digital transformation of the industrial chain. Firstly, relying on their capabilities in technological innovation, the leading firms disseminate technology, knowledge, and information to firms within the chain. Then, through autonomous innovation and open, collaborative innovation with intra-chain firms, significant advancements in key technologies are achieved, thereby filling the gaps in weak technological links and considerably bolstering the resilience of critical elements within the industrial chain. Secondly, on the basis of their capabilities in industrial collaboration and integration, leading firms collaborate with large, medium, and small enterprises, as well as upstream and downstream entities, facilitating the development of a stable system for coordinated industrial linkages. This enables prompt adjustments to be made, thereby maintaining stability when disruptions impact the industrial chain. Thirdly, anchored by their capabilities in digital transformation empowerment, leading firms realize comprehensive digital transformation in R&D, production, and product intelligence, markedly enhancing product quality, user experience and competitive advantage. Concurrently, this drives the acceleration of digital transformation among smaller firms within the chain, thereby promoting an ascendancy in the value of the industrial chain and augmenting its efficiency and resilience.
This study, while clarifying the specific dimensions encompassed by the ecological leadership of lead firms, also provides an in-depth and expanded analysis of the essence, manifestation, and functionality of each dimension. From a micro-enterprise perspective, the study unveils specific pathways for augmenting the resilience of industrial chains, enriching the viewpoints in existing resilience studies, and extending the scope of research on the resilience of individual industrial chains to the new energy automotive industrial chain.

Elevating the level of intelligence is a pivotal measure for enterprises to expedite the formation of new quality productive forces. As a new generation of digital technology, artificial intelligence has realized subversive innovation in production methods, production processes and models, accelerated the transformation of production factors to production capacity, and provided new momentum for developing new quality productivity. However, most existing research on artificial intelligence focuses on the macro level. More literature needs to be examining how artificial intelligence technology empowers the development of enterprises' new quality productive forces.
Therefore, this paper delves into artificial intelligence's empowerment path for enterprises' new quality productive forces. This study focuses on A-share listed companies from 2013 to 2022 to explore the impact of artificial intelligence on the new quality of productive forces within enterprises and the mechanisms that facilitate this influence. Drawing on the theoretical insights, it develops a regression model to assess the effects of AI and further examines the mechanisms through three key lenses: technology empowerment, efficiency empowerment, and information empowerment. To ensure the robustness of the findings, the study performs a series of rigorous checks. These include substituting variable indicators, applying the Propensity Score Matching (PSM) technique, conducting Two-Stage Least Squares (2SLS) regression analysis, and employing a quasi-experimental design centered on the smart transformation of pivotal industries. The results consistently demonstrate the robustness and reliability of our conclusions. Lastly, the empowering effect of artificial intelligence on new quality productive forces can significantly differ because of the distinct internal characteristics and external environment of enterprises. Consequently, the study conducts a heterogeneity analysis of the main effects across three core dimensions of enterprise-level,industry-level,and regional differences.
The study finds that artificial intelligence significantly enhances the development of enterprises' new quality productive forces. Mechanism analysis further reveals that this enhancement is achieved by strengthening digital innovation capabilities, improving the efficiency of supply chain, and mitigating the information asymmetry. Heterogeneity analysis demonstrates that there exist notable disparities in the enabling effects of artificial intelligence on new quality productive forces among different types of enterprises. Specifically, state-owned enterprises, labor-intensive enterprises, those situated in high-tech industries, and those located in regions with substantial fiscal support all exhibit more prominent improvements in their quality new productive forces, being empowered by artificial intelligence. This paper enhances the comprehension of the role of artificial intelligence in the production process at the micro-enterprise level, thereby providing valuable insights for promoting the efficient development of enterprises' new quality productive forces.
The innovations of this paper lie in the following three aspects: Firstly, this paper studies the impact of artificial intelligence level on enterprise development in micro-enterprises. Existing research on artificial intelligence primarily focuses on macro-level effects, such as its influence on regions, industries, and labor markets, with relatively scarce studies at the micro-enterprise level. Furthermore, previous works often measured the artificial intelligence level of enterprises using industrial robot penetration rates, whereas this paper employs text analysis and machine learning methods to construct an artificial intelligence index for enterprises. Secondly, this paper enriches the research on new quality productive forces in enterprises. As a newly proposed concept, the new quality productive forces have predominantly been studied qualitatively, exploring its connotations, characteristics, and formation logic. By leveraging data from listed companies, this paper empirically analyzes the influence of artificial intelligence technology on enterprises' new quality productive forces. Thirdly, this paper provides theoretical support for promoting the enhancement and development of enterprises' new quality productive forces. This paper delves into the mechanisms through which artificial intelligence empowers new quality productive forces from the perspectives of technology, efficiency, and information. The heterogeneous effect on the main effect is further discussed in relation to the differences at enterprise ,industry and regional levels. It offers valuable insights for upgrading enterprises' new quality productive forces.

Entrepreneurial activities are key factors driving regional economic growth, creating job opportunities, and promoting economic transformation. However, with the intensification of global economic pressures and the entry of the Chinese economy into a "new normal", traditional drivers of economic growth are gradually weakening, posing significant challenges to the entrepreneurial environment. Potential entrepreneurs not only need to contend with increasingly fierce market competition but may also face lower success rates, leading to a decline in entrepreneurial activities across various regions.
As the core driving force of the new wave of technological revolution and industrial transformation, artificial intelligence (AI) is rapidly integrating into various fields in China, spawning new industries, new technologies, new business forms, and new business models, thereby demonstrating its tremendous potential to drive a new wave of innovation and entrepreneurship. This rapid integration has transformed traditional industries, leading to breakthroughs in automation, data analysis, and customer engagement, which in turn accelerates economic growth. However, existing research indicates that the impact of AI on regional entrepreneurial activities has dual characteristics, with both positive creation effects, such as the emergence of new markets and job opportunities, and negative substitution effects, including the displacement of certain jobs and sectors. The complex interplay of these positive and negative effects makes the direction and intensity of AI's impact on regional entrepreneurial activities still inconclusive, leaving uncertainty around the overall outcomes for different regions and industries.
Looking back at past technological revolutions, although technological advancements may temporarily replace certain jobs in the short term, they ultimately stabilize total employment by generating emerging industries and creating new job positions. Therefore, with the rapid development of AI technology and its profound impact on the economic landscape of China, accurately assessing the role of AI in regional entrepreneurial activities is of great significance for improving entrepreneurial theory, guiding entrepreneurial practice, and promoting high-quality regional economic development. To this end, this paper takes the construction of national new-generation AI innovation development pilot zones as a typical case, exploring the intrinsic connections and mechanisms between AI development and regional entrepreneurial activities from the perspective of entrepreneurial choices, utilizing county-level panel data from 2012 to 2022 and a double machine learning model.
The research findings indicate that the construction of national new-generation AI innovation development pilot zones has a significant effect on enhancing regional entrepreneurial activities. Mechanism analysis reveals that AI development indirectly promotes entrepreneurial activities by enhancing entrepreneurial capabilities, alleviating financing constraints, and increasing capital returns, with a synergistic effect between entrepreneurial capabilities and capital returns. Further analysis finds that AI can stimulate entrepreneurial activities in technology-intensive industries and productive service industries, effectively driving regional economic structural transformation and high-quality development. Heterogeneity analysis shows that the positive effect of AI on entrepreneurial activities only becomes apparent when the regional population reaches a certain threshold.
This study contributes to the literature in three aspects: Firstly, by revealing the experiments of AI policies and national new-generation AI innovation development pilot zones, it proposes an innovative research perspective to explain the impact of AI on regional entrepreneurial activities. This not only integrates theoretical explanations from the perspective of entrepreneurial choices but also provides empirical support for institutional theories in the AI era. Secondly, the paper introduces a new chain construction path, namely "entrepreneurial capability—financing constraints—capital returns", providing strong support for further clarifying the complex mechanisms between AI and regional entrepreneurial activities. On the basis of the synergistic mechanism between "entrepreneurial capability—capital returns", it proposes enhancing entrepreneurs' AI literacy and resource integration capabilities, forming a positive feedback loop of "capability enhancement-capital appreciation-reinvestment of capabilities", maximizing the synergistic effect of the mechanism, and further promoting the virtuous interaction between technological innovation and capital flow. Thirdly, methodologically, the paper adopts double machine learning methods to solve the problems of model mismatch and curse of dimensionality in complex analyses, thereby effectively improving the accuracy of the conclusions.

With the comprehensive acceleration of the global economic and social digital transformation and the rise of a new round of technological revolution and industrial transformation, the manufacturing industry is developing in the high-end, intelligent, and green trend. However, many underlying and key technologies in industries such as electronic information and advanced manufacturing in China are still confronted with bottlenecks. The competitive advantage of the ICT manufacturing industry is increasingly constrained by the resilience of the supply chain. The various links of the industrial and supply chain are closely interconnected, involving the complex integration of different production factors such as capital, technology, and talent. As society further develops, the connotation of production factors is gradually expanding and deepening. New quality productive forces, including data, knowledge, technology, and management, are important supports. Therefore, accurately grasping the connotation of new production factors, exploring their impact mechanism on the resilience of the supply chain, and cultivating new quality productive forces to maintain the security and stability of the supply chain are of great significance for the ICT manufacturing industry to accumulate competitive advantages with new factors.
This study synthesizes insights from various scholars, and proposes a comprehensive supply chain resilience framework that addresses three critical dimensions: pre-disruption response capabilities, intra-disruption adaptation capabilities, and post-disruption recovery capabilities. It takes 295 listed companies in the computer, communication, and other electronic equipment manufacturing industry on the A-share market from 2018 to 2022 as samples. Numerical values of new production factor variables are obtained based on the data collected through the CSMAR database, CNRDS database, and corporate annual reports to calculate the supply chain resilience of enterprises over a 5-year period for analysis. By defining variables and constructing a multiple linear regression model, the interaction mechanism among the four factors is further explored.
The results show that data and management factors have a significant positive impact on the resilience of the supply chain in the ICT manufacturing industry, while knowledge and technology factors have a significant negative impact. However, when combined, there is a certain substitution effect between the data and knowledge factors, that is, when the level of data elements is generally improved, the negative impact of risks such as the knowledge paradox on the resilience of the supply chain may be partially suppressed. There are differences in the resilience of the supply chain between upstream and downstream enterprises in the ICT manufacturing industry, with downstream enterprises having better resilience than upstream enterprises, and the new factors have different effects on the resilience of the supply chain between upstream and downstream. There is a significant differentiation in the competitive pattern among industrial regions, and there is regional heterogeneity in the impact of new factors on the resilience of the supply chain.
This paper expands the empirical research on supply chain resilience, and clarifies the connotation of new production factors and quantifies them in the context of the digital economy and new quality productive forces. It conducts an in-depth analysis of the relationship between new production factors and the resilience of the supply chain of ICT manufacturing enterprises from the perspective of factor combinations, providing support for ICT manufacturing enterprises to better cope with uncertainty shocks and maintain supply chain stability. To enhance the resilience and competitiveness of the ICT manufacturing industry's supply chain, it is imperative to foster the synergistic application of new production factors, including the robust safety management of data, knowledge, and technology. Concurrently, there is a need to bolster communication and collaboration with supply chain partners across the upstream and downstream sectors, ensuring the coordinated development of each link within the supply chain. This approach will facilitate the establishment of stable relationships and lead to the collaborative optimization of the supply chain. Furthermore, to capitalize on regional development opportunities, it is essential to seize the momentum of the central region's rise and the Western Development Strategy. This involves actively encouraging and supporting the growth of ICT manufacturing enterprises in the central and western regions.

Promoting the sound and fast development of new quality productive forces is a crucial step for China to respond to the new challenges of the global science and technology revolution and industrial transformation in the new era and achieve high-quality economic development. In the context of the digital era, the infiltration of digital technologies such as big data, artificial intelligence, and blockchain into the real economy has given rise to new models and industries such as smart manufacturing and digital agriculture, and changed the way traditional industries produce and operate. The integration of digital economy and real economy has become an important systemic base and strategic driving force for the development of new quality productive forces. However, infrastructure bottlenecks and uneven regional development limit the role of digital-physical integration in promoting the development of new quality productive forces. How to further promote the integration of digital economy with real economy, with a focus on developing new quality productive forces, is a key question that must be addressed at present.
Existing research has addressed the role of economic activities generated in the process of digital-physical integration in promoting new quality productive forces, but it has not yet clarified how digital-physical integration can better form new quality productive forces. In fact, no single factor can adequately elucidate the intricate dynamics propelling the advancement of new quality productivity; thus, a broader perspective is essential for a more holistic understanding. While a one-size-fits-all approach is insufficient for guiding regions in selecting development drivers that align with their unique circumstances, necessitating further investigation into diverse pathways tailored to specific realities. Additionally, empirical research that systematically examines the evolution of data-physical integration and its qualitative influence on new quality productivity remains scarce.
Considering the complex interactive effects of the elements of digital-physical integration, this paper, based on the view of complex systems, constructs an analysis framework of "digital foundation-digital industry-digital finance-industrial structure-industrial agglomeration-industrial chain modernization" for digital-physical integration. It uses the dynamic qualitative comparative analysis method to analyze and reveal the relationship between the path evolution of digital-physical integration and the emergence of new quality productive forces in the case of 30 provinces, autonomous regions, and municipalities across China from 2013 to 2022.
The research results show that (1) neither the digital economy nor the real economy possesses a single, indispensable condition for fostering high new quality productive forces. (2) In the aggregated configuration analysis, there are mainly three constituting pathways, namely, digital finance empowerment type, full-chain digital upgrade type, and high-tech industry agglomeration type. (3) A comparison of the configuration results indicates that a robust digital foundation and a thriving digital industry are pivotal in catalyzing new quality productive forces during digital-physical integration, whereas digital finance and industrial agglomeration exhibit a substitutable relationship. (4) The inter-group analysis shows that the aggregated configuration has a temporal universality, while the formation of new quality productive forces has undergone an evolutionary process from high-tech industry agglomeration-oriented to digital finance empowerment-oriented and further differentiation. (5) The intra-group analysis indicates that the formation of new quality productive forces requires tailored development, and the agglomeration-oriented configuration of high-tech industries is suitable for areas where the industrial chain is not yet fully developed, while areas with better digital finance development are more poised to cultivate new quality productive forces.
This paper establishes an analytical framework of "digital economy-real economy" linkage configuration for the emergence of new-quality productive forces, revealing the complex causal relationships behind the emergence of new-quality productive forces. It analyzes the development of new-quality productive forces, which has undergone a transformation from the regulation of a materialized economic system to the regulation of a virtualized economic system. The paper has developed the perspective of complex systems regarding the creation of new technologies.Furthermore, it offers tailored recommendations for enhancing new-quality productive forces, tailored to local contexts, such as bolstering the construction of digital infrastructure and the digital industry, encouraging and supporting the development of digital finance, and accelerating the industrial chain's modernization.

Resource depletion, climate change and excessive pollutant emissions have led to the deterioration of the natural environment, seriously constraining high-quality economic development. Heavily polluting enterprises are an important source of environmental pollution, and improving the sustainable performance of heavily polluting enterprises is not only an intrinsic requirement to follow the high-quality development of the economy, but also a responsibility to actively address environmental challenges. Digital transformation has become the core driving force behind the development of enterprises. Therefore, it is necessary to study whether digital transformation can positively affect the sustainable performance of heavily polluting enterprises. However, few studies have explored the impact of digital transformation on sustainable performance from the perspective of heavily polluting enterprises. Then digital transformation empowers businesses to swiftly adapt their resources and competencies to the ever-shifting competitive landscape. It facilitates a dynamic alignment between a company's internal assets and the external environment, optimizing the allocation of existing resources and enhancing the efficiency of resource distribution. However, few existing studies have focused on the mediating role of resource allocation efficiency between digital transformation and enterprise sustainable performance from the integrated perspective of resources and capabilities. Finally, absorptive capacity is an important variable that affects the ability of firms to transform and utilize resources, but few studies have focused on the moderating role of absorptive capacity between digital transformation and sustainable performance of heavily polluting enterprises.
Therefore, this study examines the impact of digital transformation on the sustainable performance of heavily polluting enterprises based on the resource base view and dynamic capability theory, using data from A-share heavily polluting listed companies from 2008 to 2022. The results of the study show that digital transformation can enhance the sustainable performance of heavily polluting enterprises; digital transformation will promote the sustainable performance of heavily polluting enterprises through improving resource allocation efficiency, but resource allocation efficiency does not play a mediating role between digital transformation and the environmental performance of heavily polluting enterprises; absorptive capacity does not play a positive moderating role between digital transformation and the sustainable performance of heavily polluting enterprises; the state-owned nature as well as the positive effect of digital transformation on sustainable performance improvement is more significant for heavy polluters in the growth period.
This study has three theoretical contributions. First, this study further expands the theoretical logic of digital transformation for non-economic value creation. It verifies the key role of digital transformation in achieving sustainable performance of heavily polluting enterprises, and further proves the importance of digital transformation in high-quality economic development. Second, this study further expands the scope of application of dynamic capabilities theory between digital transformation and sustainable performance of heavily polluting enterprises. By identifying the mediating role of resource allocation efficiency in the relationship between digital transformation and sustainable performance of HIPCs, this study further refines dynamic capabilities and opens the "black box" of the process of digital transformation affecting the sustainable performance of HIPCs. Third, it expands the boundary conditions for the impact of digital transformation on the sustainable performance of heavy pollution enterprises. By identifying the weighted role of absorptive capacity in the relationship between digital transformation and sustainable performance of heavily polluting enterprises and regressing it into groups according to the nature of the enterprise and the life cycle of the enterprise, this study enriches the contextualized research from the perspective of resources and capabilities.
Future research could explore if there is a nonlinear relationship between digital transformation and sustainable performance of heavily polluting enterprises. Then, the mechanisms of other factors, such as green innovation and business model innovation are worthy of further analysis, for it will generate valuable insights into the mechanism linking digital transformation and sustainable performance of heavily polluting enterprises under different circumstances. Furthermore, there may be differences in the impact of digital transformation on the sustainable performance of heavily polluting enterprises with the same cultural characteristics, so future research could use data from other countries to determine the applicability of the findings.

In order to maintain the dominant position occupied by the United States in the global science and technology industry chain, the U.S. Department of Commerce has included Chinese firms in the Entity List, which has intensified the uncertainty of market competition and challenged the innovation capacity of Chinese firms. The Entity List is a coercive economic measure by which the United States attempts to decouple China's science and technology, aiming to curb China's ability to sustain innovation in the high-tech field through trade sanctions, export and investment controls, and a ban on cooperation between Chinese and American enterprises and universities, such as high-level talent training exchanges.
The great power game affects the global development pattern, and the capability of scientific and technological innovation has a direct impact on the competitiveness of both sides of the game, and enterprises are the main body of innovation. Therefore, an in-depth study of the impact of the U.S. Entity List on enterprise innovation can help reveal the opportunities and challenges embedded in the event, help enterprises identify bottlenecks in their own innovation and development, adjust their innovation strategies, and seek to develop key technological directions and entry points. At the same time, government departments can more accurately assess and predict the economic and technological risks that may arise from the incident, formulate response programs and countermeasures, and provide support and guidance for the innovative development of enterprises. However, there is inadequate literature that analyzes the impact on firms' innovation capabilities from the perspective of the great power game. Regarding the impact of the Entity List, the existing literature mainly studies the motivation of sanctions, the risk of decoupling and cutting off the industrial and supply chain, the legal logic, and the impact of enterprises' entry into the Entity List on their innovation capability is rarely discussed, and there is no consensus on the impact of the Entity List.
Taking 1216 Chinese A-share listed companies from 2017 to 2022 as samples, this paper adopts a staggered difference-in-difference model to test the impacts on the innovation capability of Chinese enterprises and the possible development paths against the backdrop of the Sino-US game. It uses the number of patent applications to characterize the innovation output, and examine the overall innovation capability and substantive innovation capability of enterprises through the "total number of patent applications" and "number of invention patent applications", respectively. The study then conducts the PSM equilibrium test to prevent the sample self-selection bias in the control group from affecting the robustness of the results.
It is found that (1) under the impact of entering the entity list, the innovation capability of Chinese listed companies appears to be enhanced with a lag and short-term; (2) the channel analysis reveals that firms included in the entity list enhance their innovation capability through the intangible asset reserve, growth capacity improvement, and value discovery channels; (3) the heterogeneity analysis shows that private firms, as well as access to government subsidies, low agency costs, moderate and less social responsibility, and control of management's base compensation, innovation capability is more significantly positively affected by the entity list. Further analysis reveals that access to the entity list enhances innovation efficiency but does not significantly affect innovation persistence, which may explain the short-term nature of the effect, and that organizational resilience is a potential motivator of firms' enhanced innovation capability.
Therefore, enterprises in the Entity List of should enhance continuous R&D investment, optimize the innovation output process with a focus on substantive innovation; optimize the management of intangible assets, and shape a good corporate image; it is also critical to enhance the efficiency and sustainability of innovation, and establish an emergency response mechanism to enhance organizational resilience under crisis events. While the government should promote cooperation among industries, universities and research institutes to accelerate the application and commercialization of innovation outputs, improve the regulation of corporate information disclosure, strengthen the construction of scientific research infrastructures, promote the efficiency and sustainability of innovation, and shorten the R&D cycle of enterprises.

With the integration of digital economy with technological innovation, organizational innovation and industrial innovation, more and more enterprises have been introducing artificial intelligence technology to replace some low-end repetitive work. The emerging workforce, nurtured during the era of AI's swift advancement, is acutely aware of its influence on their professional competitiveness. This rapid technological evolution has instilled a sense of unease among these young professionals regarding potential job displacement and the necessity to master novel technologies. This phenomenon is commonly referred to as artificial intelligence anxiety.
From the perspective of the cognitive evaluation theory of stress, the study analyzes the impact of AI anxiety on the innovative behavior of the new generation of employees. The paper proposes the following hypothesis that, first, AI anxiety can promote innovative behavior of the new generation of employees, and fully tapping the characteristics and potential of the new generation of employees can better stimulate their innovative vitality under the anxiety of AI. Second, job crafting plays a mediating role between AI anxiety and the innovation behavior of the new generation of employees. AI anxiety can stimulate the new generation of employees to actively think about the changes in work content and work mode. Third, organizational attachment not only moderates the relationship between AI anxiety and job crafting, but also moderates the mediating effect of job crafting between AI anxiety and the innovation behavior of the new generation of employees. It is difficult for employees with high organizational attachment to have innovative behaviors in the context of insecurity. On the contrary, employees with low organizational attachment have an attachment style corresponding to secure organizational attachment. Therefore, employees with low organizational attachment are better able to carry out innovative practices and carry out innovative behaviors at work, and AI anxiety has a stronger effect on stimulating their innovative behaviors through job crafting.
The study conducted a survey in two phases with data from November 2023 to February 2024 through a multi-time point survey methodology. The target population consists of employees across various sectors, including the Internet and Technology (IT) industry, manufacturing, catering services, and education. There are 343 valid responses out of 435 questionnaires, which corresponds to a robust response rate of 78. 85%. The study employs SPSS 23. 0 and Mplus 8. 0 software to assess the reliability and validity of the research model. To evaluate the discriminant validity among the four key variables of artificial intelligence anxiety, organizational attachment, work remodeling, and employee innovative behavior, it conducts the confirmatory factor analysis (CFA) using Mplus 8. 0. Additionally, to ensure that common method bias was not a significant concern in this research, Harman's single-factor test was applied to an exploratory factor analysis of the 40 items pertaining to these four variables. It delves into the mediating effect of job crafting, and the moderating effect of organizational attachment; and then it explores the boundary of the relationship among AI anxiety, job crafting and the innovative behavior of the new generation of employees under different levels of organizational attachment.
The results show that artificial intelligence anxiety positively affects the innovation behavior of the new generation of employees through job crafting, and the degree of organizational attachment of employees plays a moderating role in this process. When employees' organizational attachment is low, the positive mediating effect of AI anxiety on the innovation behavior of new generation employees through job crafting is significant. When employees have a high degree of organizational attachment, the mediating effect above is not significant.
Therefore,it is essential for managers to acknowledge the influence of AI anxiety on the new generation of employees, and harness this anxiety constructively to steer, inspire, and energize employees to proactively redefine their roles and to embrace innovation with confidence. While employees should maintain a secure level of organizational attachment, which is characterized by a healthy balance between commitment and autonomy. To facilitate this, managers are tasked with cultivating an organizational environment that serves as both a "safe haven" and a "security island", providing the new generation of employees with a robust sense of security.

New-quality productive forces are generated by scientific and technological innovation and the realization of key disruptive technological breakthroughs, and it is a powerful engine for high-quality development. New quality productive forces, with strategic emerging industries and future industries as the leading industries, are mainly involved in fields with strong innovation and advanced technologies and are committed to empowering regional high-quality development through scientific and technological innovation. In order to maintain a leading position in the competitive global environment and promote China's high-quality economic growth, it is necessary to stimulate industrial innovation through major disruptive scientific and technological innovations, facilitate transform and upgrade the industrial structure to create a new competitive advantage, and endeavor to cultivate the new quality productive forces.
The high-quality development of cities, as important spatial carriers and geographic activity units for economic and social development, remains an important issue of our time. Can new productive forces, as a key initiative to reshape new dynamics, be an important way to help cities develop in a high-quality manner If so, what is the internal logic of new-quality productive forces influencing the high-quality development of cities？ An overview of the existing literature shows there is a relative lack of empirical research on the relationship between the two. In view of this, this paper tries to put the new quality productive forces and high-quality urban development in the same research framework, and takes entrepreneurial activity as the starting point to explore the mechanism of new quality productive forces on high-quality urban development, so as to further provide a theoretical and practical basis for China's high-quality development.
Employing the panel data of 244 prefecture-level cities in China from 2011 to 2021, this study explores the intrinsic mechanism by which new quality productive forces affect the high-quality development of cities, using new quality productive forces as the entry point. Building on this foundation, the study examines the transmission mechanism through which new quality productive forces influence high-quality urban development by enhancing entrepreneurial activity, with entrepreneurial activity serving as the mediating variable. Subsequently, it delves into the non-linear influence of new quality productive forces on high-quality urban development at varying stages of industrial structure upgrading, talent concentration, and scientific and technological innovation. Then, the study investigates the non-linear impact of new productive forces on high-quality urban development when industrial structure upgrading, talent concentration, science and technology innovation are at different stages, and finally, it analyzes the spatial spillover effect of new productive forces on high-quality urban development according to the spatial levels.
The study finds that (1) new productive forces have a direct role in promoting high-quality development, and from the perspective of the financial environment of entrepreneurship and the level of talent capital, cities with a higher level of financial development and talent capital have an obvious role in promoting high-quality development; at the same time, entrepreneurial activity is an important channel through which the new productive forces promote the high-quality urban development. (2) The impact of new quality productive forces on high-quality urban development is affected by the threshold effect of industrial structure upgrading, talent concentration, and scientific and technological innovation level, and the impact of new quality productive forces on high-quality urban development presents a non-linear effect of increasing marginal utility under the development level of higher threshold variables. (3) The impact of new quality productive forces on high-quality development has a significant positive spatial spillover effect, and the development of local new quality productive forces can drive the high-quality development of areas with close economic ties.
Possible marginal contributions of this article are that,firstly, from the perspective of empirical measurement, the study explores the mechanism of the impact of new quality productive forces on high-quality urban development, and focuses on the impact of new quality productive forces on the high-quality development of cities; moreover, by incorporating industrial structure upgrading, talent concentration and scientific and technological innovation level into the analysis framework, it explores the threshold effect of the new quality productive forces on the high-quality urban development.

An innovation ecosystem (IE) is the evolving set of actors, activities, and artifacts, along with the institutions as well as complementary and dependency relations. Artificial intelligence (AI) is increasingly adopted by organizations for innovation,giving rise to platform-based innovation ecosystems for collaborative innovation and value co-creation. However, most research has focused on the co-evolution of platform architecture and platform governance and the role of boundary resources provided by the platform owner, while heterogeneous artifacts provided by complementors are rarely explored. Specifically, the industrial and managerial literature still lacks information about how heterogeneous artifacts can foster collaborative innovation in the AI innovation ecosystem.
Thus, this paper presents a framework for the collaborative innovation process of technological complementarity by using a comprehensive research model to examine the impact of collaboration in the AI innovation ecosystem. The conceptual framework termed “elements-process-performance” from the perspective of complex system, captures the intrinsic mechanisms of collaborative innovation between platform owners and complementors. The elements of innovation refer to the technological components within the ecosystem, comprising both core components and complementary technologies. The process of innovation encompasses the way complementors integrate these technologies to foster innovation. The innovation performance assesses how the empowerment of core technical components affects collaborative innovation performance.
After reviewing the relevance of studying the innovation system, the study proposes a bipartite network (BN) model to describe the relationships between complementors and technology components based on real-world data from “Baidu Brain” which is an AI open platform in China; then through analyzing the properties of the projected network of the bipartite network, it quantifies the structural features of component interdependency at the node-level, group-level, and whole network-level; the process of innovation is modeled as a bounded, iterative, trial-and-error search over a complex landscape using an NK model, and several simulation experiments are conducted to observe the complex relationship patterns between innovation performance and components' interdependency; finally the study manages to predict the ecosystem innovation performance based on empirically observed patterns from the Baidu Brain ecosystem.
The analysis reveals that complementary component strategies can affect collaborative innovation in ecosystems in four ways: (1) the level of technological interdependence formed by embedding complementary components into the ecosystem through component strategies has a negative impact on the potential for collaborative innovation; (2) the higher the proportion of core components in the system (C-Ratio), the lower the performance of collaborative innovation; (3) it hampers collaborative performance by raising the ratio of core components above a threshold, indicating over-reliance on the platform's core elements without enough complementary components impedes synergy;(4) an inverted U-shaped relationship exists between component interdependency E-I index and innovation performance. These important findings further emphasize that collaboration among different actors in the ecosystem plays a critical role in achieving higher innovation outcomes.
The findings provide insights for management innovation in platform enterprises. Platform enterprises as ecosystem builders and leaders, should effectively manage complementary innovation. As a coordinator, it is necessary to pay attention to the network pattern among collaborative innovation entities and effectively manage and coordinate the actions of internal complementarity through institutional design. In addition, platform enterprises also need to pay attention to the management and coordination of modular components. When the E-I Index of a partition of the complementary dependency network deviates from the optimal level, adjustments can be made by enhancing the standardization and flexibility of core components, encouraging participation from complementary enterprises, increasing the diversity of complementary components, reducing the proportion of core components, and modifying the complementarity of technical components.
This study focuses on how platform enterprises manage and coordinate with complementor enterprises. Given the complexity of technology dependence, integrated application of innovation ecosystems and collaboration innovative theory, it utilizes data-driven and simulation modeling methods to explore the complex relationship logic between technological dependence and collaborative innovation. This paper advances the understanding of the intersection of innovation and AI and provides suggestions for complementary innovation and the ecosystem development.

In the digital era, digital transformation has become an important driving force to promote technological innovation in manufacturing enterprises. It can affect the innovation performance of manufacturing enterprises from the following aspects: first, digital transformation facilitates real-time sharing of information and knowledge, enabling enterprises to identify new development opportunities, which promotes the formulation of novel value propositions and the realization of value creation; second, manufacturing enterprises with advanced digital transformation are more likely to harness digital technology as a robust technological underpinning for their innovation efforts, thereby transforming their innovation development models; third, digital technology can bolster the innovation development model of enterprises, making it more agile and responsive to market demands;fourth, digital transformation can reduce the innovation cost of enterprises: it significantly inhibits cost stickiness by lowering adjustment costs and management expectations, with this inhibitory effect being long-lasting; moreover, digital transformation enables full-process monitoring of the innovation process; finally, digital transformation can improve the quality and quantity of innovation knowledge that manufacturing enterprises can obtain. Therefore, digital transformation exerts a profound influence on the manufacturing industry, and its significance demands the attention of industry stakeholders.
In addition to the close relationship between enterprise digital transformation and its technological innovation, the dynamic capabilities are also crucial in the specific process of building a comprehensive digital and intelligent environment for manufacturing enterprises. According to the study of dynamic capabilities, the implementation of digital transformation can help enterprises respond to changes in the external environment, and acquire and reshape resources to accelerate the adaptation to the external environment, which in turn enhances the dynamic capabilities of the enterprise; at the same time, in the context of digital transformation, the demand for research and development personnel in the manufacturing enterprises has increased dramatically. However, the relationship between the proportion of R&D personnel in these enterprises and their innovation performance remains a subject of debate among scholars. Some researchers posit that the presence of R&D personnel positively influences a company's innovation performance, while others argue that the correlation is not as straightforward and may involve more complex dynamics. Despite this, the literature seldom delves into the role of the proportion of technical R&D personnel in the nexus between a company's digital transformation and its innovation performance. Against this backdrop, in order to explore how digital transformation improves firms' innovation performance and its specific mechanisms, the study selects the Chinese manufacturing companies listed on the A-share market from 2009 to 2022. Following winsorization and other data cleaning procedures, the final sample comprises 1 207 companies, yielding a total of 15 338 observations for analysis. The study applies the Bootstrap method to test the hypotheses, and applies the stepwise method as well as the sequential method to further test the robustness of the results.
The final results show that the digital transformation of manufacturing enterprises positively affects their innovation performance; the absorptive capacity of manufacturing enterprises plays a partial mediating role between their digital transformation and innovation performance; the proportion of R&D personnel in manufacturing enterprises has a moderating effect on the mediating role played by absorptive capacity, i.e., when the proportion of R&D personnel in manufacturing enterprises to the total number of employees is higher, it can effectively enhance the role of absorptive capacity in the digital transformation of enterprises, which will be more effective in enhancing the mediating effect of absorptive capacity between enterprise digital transformation and innovation performance.
In practice,enterprises should tailor their digital transformation strategies to their resources, setting clear 3-5 year digital goals and establishing support systems for talent, standards, and culture. They should leverage professional digital teams for integration and cooperation, creating efficient digital workshops. Since it is crucial to design future digital team structures and platforms, enterprises should focus on absorptive capacity, build open innovation networks, and foster a knowledge-sharing culture to enhance innovation. In addition,in order to strengthen talent development, it is essential to establish performance evaluation systems, increase R&D personnel number to drive innovation and prevent talent loss.

The intensification of anti-globalization and protectionism has led the world into a new period of turbulent transformation. At this stage, new quality productive forces is a key factor for China's high-quality development. New quality productive forces is characterized by high technology, efficiency, and quality, fundamentally transforming productivity through technology, resource allocation, and industrial transformation. Regional disparities exist in China in terms of economy and industry due to varying development conditions, necessitating tailored development strategies as a primary governmental task. It is of significant theoretical and practical importance to analyze the evolution and disparities in new quality productive forces across regions, study influencing factors, identify obstacles, and explore enhancement paths for driving high-quality development so as to promote industrial upgrading and transformation, and lead global economic development.
A new quality productive forces development level index system is constructed based on data from 30 Chinese provinces between 2011 and 2022. The entropy method is used to measure the current state of new quality productive forces development at the national level, in each province, and within the 8 major economic zones. Markov chains and Dagum's Gini coefficient are employed to analyze the dynamic trends and regional development disparities. The DEMATEL-ISM model is utilized to conduct a structural analysis of the influencing factors of new quality productive forces, identifying intrinsic key factors and the logical relationships and pathways between these factors. Main obstacles hindering the development of new quality productive forces are identified through obstacle degree calculations, followed by targeted path analysis for enhancing new quality productive forces in each region, integrating all analytical indicators.
The results show that (1) in terms of the development of new quality productive forces, China has experienced a consistent upward trajectory from 2011 to 2022. Provinces with high development levels include Guangdong, Beijing, Jiangsu, Zhejiang, and Shandong. When divided by region, the overall level of new quality productive forces is highest in the eastern coastal regions. (2) According to Markov chain and Dagum analysis, significant differences exist in the levels of new quality productive forces development across various regions in China. These differences primarily stem from disparities among the 8 major economic zones. The largest regional disparities are between the northwest region and the eastern coastal, southern coastal, and northern coastal regions, with the lowest disparities between the middle reaches of the Yellow River and the middle reaches of the Yangtze River. Within various regions, the southern coastal areas exhibit the greatest disparities, while the eastern coastal areas show lower and more balanced differences. Additionally, there are spatial spillover effects, where regions with high development levels can drive development in regions with lower levels without hindering the development of higher-level regions. (3) Utilizing the DEMATEL-ISM model and obstacle degree analysis, the study reveals that within the 15 primary indicators of new quality productive forces, 8 indicators are identified as root factors, which include aspects related to the human capital structure, while 7 indicators are classified as outcome factors, encompassing elements such as human capital investment. Root factors affecting the structure include levels of technological cooperation flow, development of emerging industries, and future industrial layout. Intermediate influencing factors include human capital investment, telecommunications infrastructure, and human capital structure, among 7 indicators. Surface influencing factors include human capital assurance, transportation infrastructure, and five other indicators. The main obstacles in most regions are inadequate development in technological cooperation flow, future industrial layout, structure of the digital economy, and level of technological innovation. (4)Through path analysis, the study identifies four strategic development paths to bolster the new quality productive forces. The first path is dedicated to surmounting core barriers and igniting the catalyst for their advancement. The second path is geared towards navigating intermediate obstacles and establishing a systematic framework for growth. The third path underscores the importance of regional collaboration to amplify joint development initiatives. The last path concentrates on the pivotal role of core leadership in fostering balanced development of new quality productive forces.

In order to narrow the gap between domestic and foreign key technologies, it is critical for China to promote the innovation of key technologies so that it can deeply participate in global economic development. At the same time, in order to mitigate the dilemma of key technologies being controlled by other countries, many scholars at home and abroad have focused on the breakthrough paths of key technologies.
Some related research focuses on exploring the real essence of key technologies or exploring the key technologies′ breakthrough path from internal reasons or the perspective of grand strategy; a systematic research framework has not yet been constructed. In view of this, this article interprets the connotation and characteristics of key technologies from the technological essence and conceptual intersection dimensions and analyzes key technologies′ national dynamic characteristics by overviewing different countries′ invention patents and fundamental research. On this basis, in order to analyze key technologies′ situation in China, this paper further investigates the industries with key technologies and the entrepreneurial differences. Other studies mainly use interdisciplinary theory to explore breakthrough paths with innovative applications of TRIZ theory to solve issues about key technologies. However, existing research fails to systematically consider the practical application of research organizations, TRIZ tools and methods in the key technology system. This paper argues that expanding TRIZ theory to the research of key technological innovation can provide a new systematic framework for the design of key technologies′ breakthrough paths.
On the basis of the above research analysis, this paper finds that key technologies exhibit the nonlinear exponential iteration characteristic and can maintain national security in military, economic, social and other aspects. Meanwhile, the success of its innovation has basic research dependency, breakthrough mechanism systematization, high risks and technological monopoly. From a practical perspective, compared with other major countries′ key technologies, China is leading the world in the quantity but not the quality of its innovation. In terms of China's internal reality, compared with firms from non-manufacturing and low contract-intensive industries, manufacturing and high contract-intensive industries′ have a higher quantity but lower quality in key technological innovations. In addition, compared with state-owned enterprises, mature and declining enterprises, non-state-owned enterprises and growing enterprises have more quantity advantages. Compared with small and medium-sized enterprises, large enterprises have a higher quantity and quality of key technologies.
Hence, the study finds that the TRIZ theory introduces new logical thinking and provides scientific and implementable breakthrough solutions for the key technology system. Through analyzing TRIZ architecture and core principles, it confirms that TRIZ invention theory enlightens the importance of government deployment, enterprise application and system support in the key technologies′ breakthrough path system; while classified breakthroughs, cross-domain knowledge integration, and sustainable basic research are essential elements. Therefore, combined with key points such as generic breakthroughs, basic research, and cross integration,this study makes full use of TRIZ theory, tools, and methods to solve technological conflicts as the principle from the perspectives of enterprises, governments and organizational systems, providing a comprehensive framework for the breakthrough path of key technologies in China. On the overall path, it is proposed that multiple entities should jointly deploy interdisciplinary integration systems. Furthermore, the government should decompose technology conflicts based on the key technology systems′ hierarchical decomposition and then create a hierarchical iterative research pattern. At the same time, by combining methods such as technology innovation levels and invention problems, enterprises should carry out scientific research activities reasonably. Finally, enterprises should provide feedback on the effectiveness of technology market applications, highlight their innovative applications, and optimize the breakthrough path of key technologies.
This paper emphasizes the long-term deployment, maintaining national security, non-linear changes, and other core characteristics of key technologies. Besides, it is particularly important to design differentiated breakthrough plans for key technologies across China because of the reality of imbalanced development. Overall, introducing TRIZ to assist enterprise innovation is a useful solution to effectively make breakthroughs of bottleneck issues in key technologies.

With the new generation of technological revolution and the acceleration of industrial transformation, it is increasingly urgent to promote China's high-quality development through new quality productive forces. The innovation network is a more suitable platform for the repeated exchange of tacit and embedded knowledge than market transactions. It is effective in enhancing enterprise innovation capabilities, achieving breakthroughs in key technologies, and forming new quality productive forces. To promote the qualitative leap in the advanced productive forces of innovation network member enterprises and achieve high-quality development, this paper attempts to answer the following key questions: How does core enterprises' knowledge spillover drive network members' new quality productive forces? What roles do network homogenization and relationship capital play in this process?
On the basis of the knowledge-based view and resource-dependence theory, this paper analyzes the relationship between core enterprise's knowledge spillover and network member's new quality productive forces. The research adopts survey data from 392 enterprises and conducts an empirical analysis using SPSS and Amos. The results show that (1) there is a significant positive relationship between core enterprise's knowledge spillover and network member's new quality productive forces. The influence of core enterprises on network members mainly stems from knowledge spillover, and under its continuous influence, the new quality productive forces of network members can be improved. (2) Network homogenization negatively moderates the impact of core enterprise's knowledge spillover on network member's new quality productive forces. Network homogenization implies a lack of resource, skill, and capability complementarity among members, rendering enterprise resources less unique, valuable, and scarce. Consequently, core enterprise's knowledge spillover cannot provide deeper knowledge and technology interaction for network members, reducing the chance for network members to increase their knowledge diversity and ultimately affecting the development of network member's new quality productive forces. (3) Relational capital positively modulates the relationship between core enterprise's knowledge spillover and network member's new quality productive forces. Strong relationships among innovation network members benefit enterprises in obtaining more social capital, which promotes the exchange and sharing of knowledge and information, and fosters the growth of new talents and the accumulation of new labor materials.
Theoretically speaking, this paper firstly supplements the empirical study of new quality productive forces at the enterprise level. Then it constructs a measurement scale of network member's new quality productive forces, which provides ideas and methods for further empirical research related to new qualitative productive forces at the enterprise level. Secondly, from the perspective of the interaction of innovation network members, this paper explores the impact of core enterprise's knowledge spillover on network member's new quality productive forces, enrich the theoretical connotation of existing research and promote the development of knowledge spillover related theoretical research. Thirdly, by integrating the knowledge-based view and resource dependence theory into the research on the knowledge spillover effects of the core enterprise, the applicability of these theories is enhanced, offering a new perspective to understand the knowledge spillover effects of core enterprises. Lastly, it is a valuable addition to the research on the knowledge-based view and resource-dependence theory to incorporate network homogenization and relational capital into the research framework. This expands the scope of research on the knowledge spillover effects of core enterprises and responds to scholars' recognition of the core enterprise's knowledge spillover effects.
The results hold practical significance as follows: (1) The transformation and enhancement of productive forces quality in innovation networks can not be separated from core enterprise's knowledge spillover. If network members aim to develop new quality productive forces, they must realize the importance of core enterprise's knowledge spillover and expand the channels for enterprises to obtain new knowledge, technical information and other resources. (2)The findings affirm the importance of enterprise knowledge heterogeneity, and suggest that enterprises should select partners based on their own existing knowledge, in innovation networks, members should be clear about who to approach in order to gain more reconfigurable knowledge. (3) Strong relationship capital is the effective support to promote new quality productive forces, and the continuous accumulation of relationship capital can ensure that network members have an advantage in choosing partners for core enterprises. (4)In the process of high-quality development of innovation network, it is necessary to adopt tailored measures for different enterprises.

The development of strategic emerging industries is a key approach to accelerating the formation of new-quality productive forces, which are essential in driving technological innovation, economic transformation, and sustainable growth. However, the current research lacks a comprehensive examination of the assessment frameworks for the developmental progress of strategic emerging industries. Furthermore, there is a scarcity of in-depth, scientifically grounded analyses that leverage the new quality productivity theory. Additionally, research utilizing provincial data as a starting point is relatively sparse. This study aims to measure the development levels of strategic emerging industries in China from the perspective of new quality productive forces and to underscore its critical role in the nation's scientific and technological innovation and analyze the regional disparities in their development.

A comprehensive evaluation index system is constructed, covering three dimensions: technological innovation, industrial market scale, and policy environment support. By utilizing the entropy-weight TOPSIS method and spatial autocorrelation models, the development levels and regional disparities of strategic emerging industries across 30 provinces in China are quantitatively assessed.
The results reveal a clear regional concentration pattern, with eastern coastal areas and the Yangtze River regions exhibiting significantly higher development levels than northwestern and border provinces. This indicates substantial disparities in the development of strategic emerging industries across China. Additionally, the Global Moran's Index confirms positive spatial autocorrelation, meaning that regions with higher development levels are often surrounded by similarly advanced regions. This highlights the persistent issue of regional imbalance, with advanced regions continuing to outpace lagging areas in technological and industrial growth.
Several key conclusions emerge from the analysis. First, regions with higher levels of technological innovation and strong policy support—such as Guangdong, Beijing, and Jiangsu—have made significant progress in fostering strategic emerging industries. These regions benefit from well-established innovation ecosystems, substantial government funding, and robust market demand. On the other hand, western and border provinces, such as Xinjiang and Qinghai, face significant challenges due to weaker innovation infrastructures, lower levels of industrial development, and less favorable policy environments. These disparities underscore the need for targeted policy interventions to bridge the development gap.
To address these challenges, this paper proposes several policy recommendations. First, differentiated regional policies should be developed to support the unique needs of underdeveloped areas, especially in central and western China. These policies could include tax incentives, direct subsidies for innovation, and special funding programs to enhance local technological capabilities and industrial growth. Second, cross-regional collaboration mechanisms should be established to promote resource sharing and technological transfer between more developed and less developed regions. This would facilitate more balanced development across regions and enable the diffusion of innovation to less advantaged areas. Finally, the creation of strategic industry clusters in underdeveloped regions, along with initiatives to attract and retain high-tech talent, would accelerate their integration into the national industrial landscape and foster new quality productive forces.
This study makes several contributions to the literature. First, it extends the theoretical framework of new quality productive forces by applying it to the measurement of strategic emerging industries' development levels. This perspective integrates technological innovation, industrial scalability, and policy support as key factors driving the growth of these industries. Second, it provides a robust empirical analysis of regional disparities in the development of strategic emerging industries across China, offering insights into the spatial patterns of industrial growth and regional innovation ecosystems. Third, the study's findings offer practical implications for policymakers aiming to foster more balanced regional development and to harness the potential of strategic emerging industries in driving national economic transformation.
By fostering innovation, bolstering market capabilities, and intensifying policy backing in regions that are lagging behind, China can achieve more balanced and sustainable growth in these critical industries.

SRDI enterprises are characterized by “small yet specialized” and “focused yet refined” qualities, which endow them with high flexibility and significant innovation potential. However, compared to large enterprises, SRDI enterprises face multiple challenges, including financing difficulties, limited appeal to innovative talent, and weaker risk resilience. These issues introduce significant uncertainty and risk to their innovation resilience. Digital transformation has the potential to ignite innovation, boost dynamism, and enhance resilience through various facilitative effects. However,the way its influence on innovation resilience is characterized by a complex, long-term, and non-linear trajectory. Thus, a thorough research on the mechanisms of digital transformation and its impact on innovation resilience is essential for both theoretical and practical aspects. This is vital for fostering the digital transformation of SRDI enterprises and ensuring their stable and sustainable innovation-driven development.
Drawing on dynamic capability theory and ambidextrous innovation theory, this study analyzes the nonlinear processes and multiple mechanisms through which digital transformation influences the innovation resilience of SRDI enterprises from both dynamic and systematic perspectives. It then empirically examines the nonlinear impact of digital transformation on the innovation resilience of SRDI enterprises employing polynomial regression analysis on panel data from China's A-share-listed SRDI SMEs and "Little Giant" enterprises between 2008 and 2022, and an inflection point analysis is conducted to delineate the distinct stages of the effects of digital transformation. Meanwhile, three mediating variables, namely inter-firm learning ability, R&D investment and financing constraints, are introduced to empirically analyze the impact of digital transformation on the multiple mechanisms of innovation resilience by using the "Jiangting two-step method". Moreover, this study further explores the heterogeneity of these effects across firm classification, firm age, and economic cycle.
This study identifies a significant "decline-rise-decline" inverted-N relationship between digital transformation and the innovation resilience of SRDI enterprises. Mediation analysis reveals that the digital transformation impacts inter-firm learning ability, R&D investment, and financing constraints of SRDI enterprises in a non-linear manner, characterized by increasing, inverted N-shaped, and inverted N-shaped patterns, respectively. Inflection point analysis further identifies two critical thresholds within the digital transformation process of SRDI enterprises, segmenting its impact on innovation resilience into three distinct stages: the "break-in period", the "expansion period", and the "pain period". The "break-in period" is relatively short. The majority of SRDI enterprises are currently in the "expansion period". Additionally, 16% of SRDI enterprises have progressed into the "pain period", a phase characterized by substantial challenges associated with advanced digital transformation efforts. Heterogeneity analysis reveals that digital transformation exerts a more pronounced impact on the innovation resilience of national-level SRDI enterprises and those in the growth stage. Furthermore, during periods of economic downturn, the positive effects of digital transformation on innovation resilience are notably stronger, whereas its influence diminishes during economic upturns.
According to the above findings, this study proposes some management recommendations.First, SRDI enterprises should proactively identify the financing constraints during the "expansion period" and the potential R&D risks in the "pain period", thereby providing appropriate policies and resources to sustain the innovation resilience of SRDI enterprises. Second, SRDI enterprises in the start-up stage should take moderate digital transformation actions, and then shift towards more proactive steps in the growth stage, which can better enhance their dynamic innovation capabilities and innovation resilience. Third, as SRDI enterprises progress into the “pain period”, the government should actively support their digital transformation processes through comprehensive policy measures, ensuring that SRDI enterprises remain pivotal drivers of innovation. With the deepening of digital transformation, future research can focus on whether the impact of digital transformation on the innovation resilience of SRDI enterprises has shifted from a three-stage pattern of "decline-rise-decline" to a more cyclical pattern of four or five stages. It is also of great research value to delve into the formation principle of the inflection points in digital transformation, revealing the complex dynamic mechanisms by which digital transformation shapes innovation resilience.

The high-quality development of China's economy necessitates the unlocking of the potential of data elements, the advancement of productivity reforms and innovation, and the creation of new forms of high quality productive forces. However, current research fails to elucidate the mechanisms by which data elements influence the development of new high-quality productive forces within enterprises. It also falls short in addressing how traditional production factors interact with these data elements. As a result, enterprises struggle to understand how to leverage both data elements and traditional production factors to foster the development of their new high-quality productive forces in their day-to-day operations Therefore, this paper comprehensively constructs a theoretical analysis model of the data elements on the development of enterprises' new quality productive forces from three aspects: direct effect, indirect effect and multiplier effect.
Using the panel data of Chinese listed companies from 2011 to 2022, this study examines the impact of the data elements on the development of new quality productive forces from the perspective of effect decomposition. The results show that (1) data elements can effectively empower the development of new quality productive forces of enterprises, and the conclusion is robust;(2) enterprises' new quality innovation and labor skill structure have a mediating effect in the development of enterprises' new quality productive forces empowered by data elements; (3) data elements has a multiplier effect in the process of new quality innovation and labor skill structure promoting the development of enterprise new quality productive forces; (4) the enabling effect of data elements is only obvious in highly competitive, high-tech and non-heavy pollution industries, and is more effective in regions with complete digital infrastructure, high level of data talent agglomeration and open data.
According to the derived conclusions, data elements exert multiple influences on the development of new high-quality productive forces within enterprises. In terms of direct effects, firstly, data elements can improve the decision-making efficiency and quality of enterprises by driving their management decisions. Secondly, through data sharing and integration, on the one hand, enterprises can improve the communication efficiency among various departments;on the other hand, enterprises can improve the collaborative production efficiency within their respective supply chains. Thirdly, the application of enterprise data elements can promote the transparency of enterprise information to alleviate the information asymmetry between enterprises and investors, thus improving the financing efficiency of enterprises. Finally, the intricacy of a company's production processes can be significantly streamlined through the application of data elements. In terms of indirect effects, first of all, on the one hand, enterprises can realize the efficient connection between innovation supply and demand through the application of data elements, reducing the cost and risk of R&D innovation, and thus improving the efficiency of new quality innovation of enterprises. On the other hand, the application of data elements can optimize the technological process and product design of enterprises to ensure the improvement of the quality of new quality innovation. Secondly, through the application of data elements, enterprises will create jobs of high-skilled labor and have a substitution effect on low-skilled labor, which can improve the skill structure of enterprises' labor and thus promote the formation and development of enterprises' new quality productive forces. In terms of the multiplier effect of data factors, the core logic is that it can help enterprises re-understand the traditional factors of production, expand the allocation space of traditional factors, improve the efficiency of traditional resource allocation, and thus magnify the role of other factors in the development of new quality productive forces. This study specifically focuses on the multiplier effects of data elements on two key traditional factors: technology and labor. It finds that the multiplier effect of data elements on technology is reflected in the expansion of innovation boundary and the acceleration of technology iteration, and the multiplier effect on labor is reflected in the further improvement of the comprehensive quality of labor. To summarize, by constructing the analysis model of “direct effect—indirect effect—multiplier effect”, this paper provides empirical evidence and policy implications for clarifying the enabling effect of data elements on enterprises' new quality productive forces and releasing the potential of data elements' new quality productive forces.

In recent years, the rapid development of artificial intelligence technologies, such as intelligent robots and voice recognition, has become a key force driving the transformation of China′s economy. Artificial intelligence technologies can enhance human intelligence by enabling autonomous learning, communication, interaction, and problem-solving. In order to reduce labor inputs and increase work efficiency, more and more organizations are introducing AI technologies into their work. With the application of AI technology in the workplace, on the one hand, employees recognize that AI usage can help them perform redundant tasks and process complex data, which leads to experience positive emotions . A number of studies have also shown that AI usage can promote employees′ sense of work enthusiasm, job engagement and organizational citizenship behaviors. On the other hand, it can impose new work requirements on employees. The complexity of the technology can induce negative psychological emotions and increase employees′ work stress and job insecurity, which could lead to emotional exhaustion, employees′ burnout, knowledge hiding, and intentions to leave.
Scholars have viewed AI usage as a form of technological stress that leads to anxiety and insecurity, which in turn affects employee performance, proactive behavior, and job crafting. AI usage as a stressor can significantly affect employees′ attitudes and behaviors at work. In addition, organizations that want to stand out in a complex and changing economic environment need to rely on the wisdom of their employees, for example, employee voice. Employee voice in the organization will be affected by work pressure. Therefore, AI usage as a stressor has an impact on employee voice and silence. However, few studies have focused on the double-edged sword effect of its impact on employee voice and silence from the perspective of the cognitive activation theory of stress. This theory posits that employees cognitively appraise the outcomes of stressors as positive or negative, which can spill over into non-work time and trigger adaptive or non-adaptive psychological responses. Regarding AI, such appraisals can lead to problem-solving adaptive responses or emotionally centered non-adaptive ones. In Chinese culture, the concept of "Zi Xing" is reflected in employees′ work as rumination, which is split into problem-solving pondering and affective rumination. AI usage can activate problem-solving pondering and affective rumination among employees, which ultimately influences employees′ voice and silent behaviors.
In Study 1, a scenario experiment was conducted with 240 employees who use AI technology in their daily work, recruited via the Credamo platform in China. A between-subjects experimental design with two factors (AI technology application: high vs. low; job autonomy: high vs. low) was employed. In Study 2 (questionnaire survey) , employees from organizations using AI were recruited via the WJX online data collection platform. A three-stage time-lagged data collection approach was adopted to mitigate the impact of common method bias. Research hypotheses were tested and the empirical results show that through the mechanism of problem-solving pondering, artificial intelligence usage has a positive influence on employee voice and a negative impact on employee silence; via the mechanism of affective rumination, the artificial intelligence negatively affects employee voice and positively influences employee silence; work autonomy strengthens the positive effect of artificial intelligence usage on problem-solving pondering and weakens the positive impact of artificial intelligence usage on affective rumination.
This study makes the following theoretical contributions. First, this study confirms that AI usage can affect employees′ voice and silent behaviors in terms of problem-solving pondering and affective rumination, respectively. This not only broadens the research horizons of the impact of AI usage, but also enriches the antecedent research of employees′ voice and silent behaviors from the perspective of stress cognitive activation theory of stress. Second, this study brings in job rumination in the research framework, which broadened the scope of the impact of AI usage on employees′ attitudes and behaviors, and also provided a novel explanatory framework for the research on the mechanism of the impact of AI usage on employees′ voice and silence. Finally, this study incorporates job autonomy into the impact of AI usage on employees′ voice and silence, further enriching the boundary conditions for the study from the perspective of job resources.

The integration of artificial intelligence across various sectors, including autonomous vehicles, finance, and biomedicine, has catalyzed significant shifts and advancements in both established and burgeoning industries.Building and developing an innovation ecosystem is an important strategy for countries to promote innovation and development. The application of artificial intelligence can drive technological innovation at the source, attract multiple stakeholders to participate in value creation, integrate innovation resources, and jointly build an innovation ecosystem. With the gradual deepening of artificial intelligence empowerment, it has promoted the dynamic evolution of innovation ecosystems and posed new challenges to the theory of innovation ecosystems. The particularity of artificial intelligence technology has led to a lack of in-depth and systematic research on its specific application process and characteristics in the innovation ecosystem, and the internal driving factors and paths for the evolution of the industrial innovation ecosystem empowered by artificial intelligence are not yet fully understood.This paper adopts a longitudinal single case study method with the biopharmaceutical industry as the case study object. The application of artificial intelligence is divided into a technology accumulation period, an integration period, and an industry empowerment period. From a dynamic perspective, the driving factors and paths of the evolution of the biopharmaceutical industry empowered by artificial intelligence are explored, providing theoretical support and practical guidance for promoting the continuous optimization and upgrading of the industrial innovation ecosystem.The results show that (1) policy guidance, technology promotion, market pull, and organizational change jointly drive the evolution of the innovation ecosystem, and the dominant driving factors that play a role vary in different stages of artificial intelligence technology. (2) Artificial intelligence promotes the evolution of innovation ecosystems through pattern innovation, and innovation patterns achieve a comprehensive transformation from point to end and then to automation. (3) The period of technological accumulation is driven by the survival pressure caused by market competition, achieving a single point breakthrough in technology; the technology integration period is driven by policy-guided innovation strategies, achieving an end-to-end research and development model; and the industrial empowerment period is driven by multidimensional synergy led by technology, achieving a model of "AI+automated experiments+expert experience".The novelties of this paper are as follows: Given the lack of industry analysis at the meso level under technological background in the existing research , this paper conducts case studies on the biopharmaceutical industry to analyze the driving factors and paths of the evolution of artificial intelligence enabled industrial innovation ecosystems, expanding the theoretical research on industrial innovation ecosystems. Moreover, it divides the technology empowerment stage into technology accumulation stage, technology integration stage, and industry empowerment stage, and expands the research on innovation ecosystems from a dynamic perspective. Finally, due to the particularity of artificial intelligence technology, there is a lack of systematic research on the driving factors and paths for the evolution of AI-empowered industrial innovation ecosystems domestically and internationally. This study analyzes the evolution process of the innovation ecosystem in the biopharmaceutical industry, and through model innovation, it facilitates the evolution of the innovation ecosystem.The deep empowerment of artificial intelligence technology and the deep integration of artificial intelligence and industrial innovation ecosystem are the fundamental guarantees for promoting the prosperous and orderly development of industries. At the government level, it is necessary to guide the application of artificial intelligence in different industries, considering the focus of different policies such as supply, demand, and environment. At different stages of technological development, efforts should be made to shift from encouraging technological development to emphasizing regulation, and then to achieving balanced development. At the enterprise level, it is necessary to actively layout artificial intelligence, adjust organizational structure in a timely manner according to the policies and market environment, and achieve a dual wheel drive of "technology+mode". At the same time, in the future, multiple case studies or large-scale empirical studies can also be used to test the research conclusions of this article, further tracking the laws and mechanisms of change, and exploring the empowerment of the biopharmaceutical industry by artificial intelligence from different perspectives.

In the new wave of industrial digital transformation, Chinese enterprises are facing multiple challenges including accelerated technological innovation, constantly changing consumer preferences, and the volatility and restructuring of global supply chains. Therefore, enterprises need to re-examine their digital transformation vision and strategy, focusing on how to enhance and reshape their digital capabilities. As the dynamic capability foundation for enterprise digital transformation and upgrading, digital capability is the ability of enterprises to widely integrate digital resources and other organizational resources in the value network using digital technology to promote systematic digital transformation and achieve digital value creation. This enables enterprises to break free from traditional resource dependencies during the digital transformation wave, thereby achieving a sustainable competitive advantage. However, building and enhancing a company's digital capability is a complex system process, and existing research on the factors that affect a company's digital capability is relatively scattered. Previous studies mainly focus on the independent effects of a limited number of factors on digital capability, and the identification of key factors is not comprehensive enough, nor does it consider the possible interrelationships between various influencing factors.
To answer the fundamental question of how enterprises possess digital capabilities, this study adopts the Technology-Organization-Environment (TOE) framework and the ISM-MICMAC system dynamics approach to explore and clarify the hierarchical path of multiple factors affecting the digital capability of enterprises. This study uses literature research method and TOE framework to screen out 19 effective English articles and 23 articles in Chinese, and preliminarily identifies the influencing factors of technology, organization, and environment on the digital capabilities of enterprises. Then, through expert group consultation and opinion summary, it identifies the main 19 influencing factors of enterprise digital capability. Secondly, the ISM method is used to clarify the hierarchical relationship and correlation path between various influencing factors, and converges to enhance the digital capability of enterprises, constructing a multi-level hierarchical explanatory structure model of the influencing factors of digital capability. Finally, this study combines the MICMAC method to identify strong driving force and strong dependent force factors, revealing the importance and role of each influencing factor in the system.
It is concluded that, firstly, in terms of element categories, there are 4 technological factors, 10 organizational factors, and 5 environmental factors that affect the digital capability of enterprises. Secondly, in terms of hierarchical structure, the explanatory structure model of this study consists of eight levels, among which the root factors affecting the digital capabilities of enterprises include six factors such as demand transformation and digital economy level. The intermediate factors include 11 factors such as executive digital cognition and digital human capital, and the surface factors are digital organizational preparation and digital technology application and diffusion. Thirdly, in terms of factor correlation, there are six factors with high driving force and low dependence, among which the deep driving factors are demand transformation, policy orientation, digital economy level, and executive digital background. There are four factors that contribute to high dependence and low driving force. The intermediate factors are digital human capital and technology management, while the superficial factors are digital organizational preparation and digital technology application and diffusion.
The theoretical contributions of this study are threefold. Firstly, by sorting out multiple factors that affect the digital capabilities of enterprises within the theoretical framework of TOE, it advances the systematic understanding of how to enhance the digital capability of enterprises, and makes up for the lack of fragmented discussions on a limited number of antecedents in empirical research. Secondly, it uncovers the hierarchy and interplay of elements in building digital capabilities, offering a new lens on their formation and key components. Thirdly, this study expands the application of system dynamics in the field of enterprise digital research by constructing an explanatory structural model that affects the digital capabilities of enterprises. The exploration of multiple research paradigms to enhance the theoretical framework of enterprise digital capability is beneficial and provides valuable insights for enterprises on building and enhancing their digital capability during digital transformation.

In the present fast-paced business world with high uncertainties, open innovation has become an indispensable strategy for firms seeking more heterogeneous information and staying competitive and thriving. However, there is still a critical need for a robust theoretical framework that can accurately predict and explain the realization of open innovation performance, and enlighten firms on how to cooperate and deal with internal and external knowledge partnerships in an open situation. The theory of HeXie (harmonious) management emerges as a promising avenue to address this need, especially the He principle and the two fields offer a conceptual lens that aligns with the complex demands of open innovation systems and provides insights into how firms can unlock their intrinsic value through open innovation initiatives. The purpose of this study is to delve into the mechanisms that underlie the realization of open innovation performance in high-tech manufacturing firms, utilizing the He principle as a guiding framework.
According to the He principle, the research constructs a comprehensive framework to explore the multifaceted pathways to open innovation performance in firms, and figure out how innovation openness exerts a positive influence on open innovation performance under the dual moderation of inclusive organizational culture and intellectual property protection. Empirical tests are made with the panel data from 662 high-tech manufacturing A-share listed firms spanning the period from 2012 to 2021 in China to validate the theoretical framework. Then, the model of fixed panel effects is built for data analysis: the study first examines the direct effect of innovation openness on financial and market performance, and then respectively analyzes the moderating effects of inclusive organizational culture and intellectual property protection policies; finally, it makes robustness analysis and heterogeneity analysis.
　The findings support the fact that the depth and breadth of innovation openness have a significant positive impact on firms' financial performance. However, it's noteworthy that the breadth of innovation openness does not exert a significant influence on firms' market performance, while the depth of innovation openness has a significant negative effect on market performance. This distinction between financial and market performance underscores the nuanced nature of open innovation outcomes. The study also reveals that the presence of an inclusive organizational culture strengthens the positive relationship between the depth of innovation openness and firms' financial performance. This finding underscores the importance of nurturing a work environment where diverse perspectives are valued and integrated into the innovation process. Lastly, it uncovers that the impact of the breadth of innovation openness on firms' financial performance is significantly enhanced in regions with well-developed intellectual property protection policies. This suggests that the interplay between openness and intellectual property protection can be leveraged to optimize financial performance outcomes. What's more, compared with the breadth of innovation openness, inclusive culture has a more significant moderating effect on the depth of innovation openness on financial performance; compared with the depth of innovation openness, the moderating effect of intellectual property policy on the breadth of innovation openness is more significant on financial performance. Intriguingly, the study further explores heterogeneity in the relationship between innovation openness and firms performance and finds that non-state-owned firms and those led by managers with overseas backgrounds experience a more pronounced impact of innovation openness on their performance. This nuanced insight emphasizes the importance of considering organizational characteristics and leadership profiles in the context of open innovation.
In conclusion, the study significantly contributes to the understanding of the pathways through which high-tech manufacturing firms realize open innovation performance. The theoretical framework of this paper is helpful to guide firms to achieve the dual pursuit of short-term and long-term goals and the balance of uniqueness and legitimacy in the context of open innovation. The conclusions provide valuable guidance for firms aiming to maximize the benefits of open innovation and adapt to the challenges in the dynamic business environment. Furthermore, it informs policymakers and practitioners about the significance of fostering inclusive organizational cultures and tailoring intellectual property protection policies to amplify the positive impacts of innovation openness.

With the vigorous rise of a new round of technological revolution and industrial transformation, digital technology has become a new driving force for global innovation and development. As the most innovative and dynamic industry, the innovation capability of high-tech industries directly affects national technological strength and international competitiveness. In this context, how to use digitization to stimulate the innovation vitality of high-tech industries has become an important issue of widespread concern in the academic community.
Previous studies have revealed that digitalization can positively impact innovation and promote an increase in innovation investment. A few scholars have subdivided innovation investment and studied the impact of digitization on different types of innovation investment. But there is still a lack of research on the internal segmentation of innovation investment and the impact of digital transformation on different types of innovation investment. Due to significant differences in resource requirements, knowledge base, risks, and benefits among different types of innovation activities, the impact of digitalization on different types of innovation investment will also be different. It is necessary to explore the differential impact of digitalization on different types of innovation investment from a heterogeneous perspective. Most existing studies have classified innovation into exploratory innovation and exploitative innovation based on its novelty and knowledge base. Both types of innovation essentially belong to R&D innovation, and the important role of non-R&D innovation has long been ignored. However, the innovation foundation of China's high-tech industry is weak, the innovation endowment has regional heterogeneity, and non-R&D innovation still plays an important role, and the proportion of non-R&D innovation investment in China's high-tech industry has been more than 16% in the past five years. In the context of digitalization, it is necessary to distinguish innovation investment in high-tech industries into R&D innovation investment and non-R&D innovation investment, and further explore the mechanism of digitalization affecting the two kinds of innovation investment. In addition, the government and market are important external driving forces for enterprise innovation, and the government's behavior and market role cannot be ignored. Thus, this study further explores the moderating roles of the intensity of government support and the level of marketization in this process.
With China's high-tech industry as the research object, the study constructs a theoretical framework for the impact of digitalization on heterogeneous R&D investment. Using provincial panel data on high-tech industries from 2012 to 2021, it empirically tests the differential impact of digitalization on different types of innovation investment, and further explores the mechanisms of government and market. The results show that digitalization has a significant promoting effect on both R&D innovation investment and non-R&D innovation investment in high-tech industries, and its marginal impact on R&D innovation investment is much higher than that of non-R&D innovation investment. The mechanism test shows that digitalization has different mechanisms for different types of innovation investment. Digitalization increases R&D innovation investment by increasing R&D personnel, and increases non-R&D innovation investment by promoting industrial agglomeration. Further analysis reveals that the intensity of government support can strengthen the promoting effect of digitalization on R&D innovation investment, but it will weaken the promoting effect of digitalization on non-R&D innovation investment. The degree of marketization significantly suppresses the positive effect of digitalization on R&D innovation investment, but has no significant moderating effect on the relationship between digitalization and non-R&D innovation investment, resulting in market failure.
Compared with existing literature, this study divides innovation investment into R&D innovation investment and non-R&D innovation investment, and includes the two types of innovation investment in the same analytical framework for comparative research, making up for the limitations of existing literature's excessive emphasis on R&D innovation and neglect of non-R&D innovation. Then, from the dual perspectives of the government and the market, it explores the differences in the moderating effects of the two in the process of digitalization affecting high-tech industry innovation investment, provides a theoretical basis for promoting a better combination of a promising government and an effective market, and gives full play to the role of the government and the market in the field of innovation.

ESG information disclosure is regarded as a systematic and comprehensive way of releasing corporate non-financial responsibilities such as ecological environmental responsibilities,social responsibilities,and corporate governance responsibilities to the stakeholders. More and more listed companies have taken ESG information disclosure as an important means of establishing their green corporate image and gaining momentum for achieving high green innovation performance,and how the companies can improve green innovation performance through ESG information disclosure motivation has attracted attention from the research field. Many scholars have found that internal factors such as corporate financial performance,values,risks,and investment evaluation,and external factors like pressure from governmental environment regulation,public environmental concern,and consumers' green consumption intentions,have a great impact on the corporate motivation to disclose ESG information to outsiders. ESG information disclosure is beneficial for corporate reputation management,exerting green spillover effects and value transmission effects to generate altruistic social value. However,there is still controversy about whether ESG information disclosure motivation can positively affect green innovation. Some scholars argue that corporate ESG information disclosure motivation is restricted by macro policy and institutional factors and may have no significant impact on green innovation performance; besides,the impact mechanism of ESG report information disclosure motivation upon corporate green image is still unclear and remains an unsolved “black box” for the present researchers. Therefore,it is of significance to have a comprehensive understanding on the correlation between ESG information disclosure motivation,green image and green innovation and thus know how to help companies promote green development.
Following the motivation theory,the signal transmission theory and the environmental psychological value-belief-norm theory,this study constructs a conceptual model of the relationship among the dual motivations of corporate ESG report information disclosure,green image,green value cognition,and green innovation performance,taking green image as a mediating variable and green value cognition as a moderating variable,to explore the impact mechanism of ESG information disclosure motivation upon green innovation performance. It divides the motivators of corporate ESG information disclosure into egoistic ones and altruistic ones,in which the egoistic motivators mainly refer to those generated by the corporates to meet the needs of self-growth reputation,resource needs,etc. ,while the altruistic motivators mean those generated by the corporates to meet the needs of social responsibility and social value realization. The study adopts the questionnaire survey method of 254 samples from the listed companies that have released ESG reports between 2018 and 2022 and finds that ESG information disclosure motivators,no matter egoistic ones or altruistic ones,both have a significant positive impact upon corporate green innovation performance; green image plays a mediating role in the relationship between ESG information disclosure motivation and green innovation performance; and the top managers' green value cognition positively adjusts ESG information disclosure motivation and green innovation performance. This study also discovers that ESG information disclosure motivation can help companies solve the problems of information asymmetry and agency conflicts,convey environmental,social and governance information to stakeholders,and meet the corporate green development needs. The more attention corporates have paid to ESG information disclosure motivation,the higher the green innovation performance they are likely to achieve.
In summary,by adopting the environmental psychological value-belief-norm theory,this study expands the field of interdisciplinary theoretical research and enriches theoretical research perspectives; it uncovers the impact mechanism “black box” of ESG information disclosure upon green innovation performance,discusses the role and significance of green image construction from multiple dimensions,and provides basic theoretical support for improving the self-utility and social utility of corporate green image,enhancing the top managers' green image awareness,and thus improving the corporate green innovation performance.
In order to gain continuous momentum for improving green innovation performance,the companies should actively promote ESG information disclosure motivation,improve corporate green image and the top managers' green value awareness. Besides,in the current context of China's comprehensive green transformation of economic and social development,the government should strongly enforce the corporate ESG information disclosure laws,insistently strengthen the supervision and technical guidance on ESG information disclosure,and stimulate corporates' enthusiasm for their ESG information disclosure by providing green subsidies and other preferable policies.

In the era of "New Normal" with VUCA characteristics,how to enhance resilience has become the key for enterprises to avoid dangers and shift into safety and achieve sustainable development. As a strategic choice for knowledge expansion, technological diversification is beneficial for enterprises to diversify external risks, improve their perception and adaptability to the environment and then achieve resilient growth. Although existing research pays attention to the effect of technological diversification on enterprise resilience, the "bridge" between the two needs to be further explored. Continuous innovation related to operational efficiency and strategic flexibility is an important ability to achieve continuous development. According to the theoretical logic of "resource-capability-result", continuous innovation drives enterprises to change their mindset, continuously breaking through development bottlenecks and exploring new growth paths through the integration of diversified technologies. Therefore, continuous innovation may play a mediating role between technological diversification and enterprise resilience. In addition, the results of technological diversification depend on the combination of internal technological exploration and external technological relationships, making enterprises embedded in dual network relationships. In particular, the density of internal collaboration network characterizes the close relationship between internal inventors, which directly affects the effective transfer of diversified technological knowledge and information sharing; the heterogeneous resource advantages brought by the high degree of external collaboration network centrality can effectively alleviate the dilemma of knowledge homogenization in high-density internal cooperative networks, and then accelerate the process of diversified technological development and integration. However, existing research has not focused on the boundary effects of dual networks on the relationship between technological diversification and continuous innovation. Hence, this study aims to explore the internal mechanism between technological diversification and enterprise resilience, and investigate the boundary effect of internal collaboration network density and the three-way moderating effect of external collaboration network centrality from the perspective of dual network.
Following the resource-based theory and the theoretical logic of "resource-capability-result", this study conducts the empirical analysis using a sample of Shanghai and Shenzhen A-share listed enterprises in the new energy automobile industry. The resilience and control variables information of these enterprises is collected from CSMAR and patent data is collected from Incopat. Finally, an unbalanced panel dataset of 285 enterprises from 2010 to 2021 is constructed. The hypotheses are tested by the individual and time-fixed effect models using Stata 15.0.
The results show that (1) there is an inverted U-shaped relationship between technological diversification and enterprise resilience; (2) continuous innovation plays the mediating role in the inverted U-shaped relationship between technological diversity and enterprise resilience; (3) internal collaboration network density negatively moderates the relationship between technological diversification and continuous innovation; (4) external collaboration network centrality further mitigates the negative impact of internal collaboration network density on the relationship between overall technological diversification, unrelated technological diversification, and continuous innovation.
The theoretical contributions of the study are as follows: firstly,it clarifies the relationship between technological diversification and enterprise resilience in the context of a long-term and turbulent environment, and expands the theoretical space of technological diversification and enterprise resilience; secondly,the paper provides empirical evidence for the effect of continuous innovation on enterprise resilience and bridges the gap in the discussion of mechanisms underlying technological diversification and enterprise resilience; finally, from the perspective of dual network,it reveals the boundary effects of dual networks in the relationship between technological diversification and continuous innovation, and deepens the contextual mechanism for the transformation of technological diversification into continuous innovation. The practical contributions are proposed. Enterprises should first cultivate internal skills and moderately expand diversified technology fields based on risk assessment. Then it is necessary to seek change proactively. Enterprises should cultivate continuous innovation ability to leverage the positive effects of technological diversification on enterprise resilience. Finally, enterprise should pay attention to collaboration,encourage inventors to "go out", maintain loose partnerships internally and build a wide collaborative R&D network externally.

In order to cope with global climate change more effectively, it is a new mission to achieve low-carbon transformation for governments and businesses worldwide. Global environmental governance needs to make fresh contributions to building green and low-carbon production and lifestyles. Digital technology innovation, including artificial intelligence, big data analysis, and the Internet of Things, offers unprecedented opportunities for building a low-carbon economy. These innovations not only inject new vitality into traditional industries but also help reduce carbon emissions by improving energy efficiency and providing intelligent carbon management tools. Digital technology also gives rise to emerging industries, such as clean energy and intelligent transportation, which inherently have lower carbon footprints. Therefore, digital technology innovation is a key driver in promoting low-carbon transformation, reducing carbon emissions in traditional industries, and fostering new low-carbon economic growth. However, existing research lacks in-depth examination of the impact of technological innovation on carbon emissions, particularly in the field of digital technology innovation. Most studies tend to construct comprehensive indicators for the development of the digital economy to explore the relationship between digital economy development and carbon emissions.In-depth research on how digital technology innovation promotes low-carbon economic transformation is crucial for understanding its impact mechanisms and optimizing policy formulation.
In terms of research methodology, this paper analyzes the direct effects, indirect mechanisms, policy adjustments, and spatial spillover paths of digital technology innovation on low-carbon transformation. In order to examine the role of digital technology innovation in low-carbon transformation from the perspective of urban carbon intensity, the study selects the panel data from 279 Chinese cities from 2006 to 2021 to measure the level of digital technology innovation in Chinese cities with 2.97 million digital technology patents. Empirically, the main model for testing direct effects is the two-way fixed effects model; and then the mediation effect models and moderation effect models are used to analyze the indirect mechanisms and policy adjustments, respectively. The spatial carbon emission reduction effect of digital technology innovation is estimated using spatial Durbin and spatial lag models.
The study concludes that digital technology innovation reduces urban carbon emission intensity by lowering energy consumption intensity and promoting industrial and service sector upgrades through mediation effect channels within cities. External policies such as national smart cities, national intellectual property cities, and national low-carbon city pilots beef up the carbon reduction impact of digital technology innovation. Spatial econometric results show that digital technology innovation produces a synergistic carbon reduction effect through "siphoning" and "demonstration" effects among cities, overall reducing carbon intensity in surrounding economically similar urban agglomerations through spatial spillover. Moreover, the degree of carbon reduction through digital technology innovation varies depending on a city's geographical location and energy endowment, with different types of digital core industry innovations and different types of digital innovation research significantly reducing carbon intensity. Overall, this paper provides policy insights that the government should seize the opportunities of digital technology innovation, fully leverage the low-carbon transformation potential of digital technology, build a network of digital technology innovation spillovers, and appropriately support urban digital technology innovation for carbon peaking and neutrality.
This paper makes marginal contributions in three aspects. First, it explores the factors influencing urban carbon emission reduction from the perspective of digital technology innovation, verifying the direct impact of digital technology innovation on reducing carbon emission intensity and exploring its internal mediation mechanisms and external policy regulation mechanisms. Second, it measures digital technology innovation with patents authorized in the core industries of the digital economy and further uses spatial econometric models to examine the spatial spillover paths of digital technology innovation in reducing carbon emission intensity,which helps to explore the spatial paths of carbon reduction through digital technology innovation. Third, the paper also examines the heterogeneity in reducing carbon intensity through digital technology innovation among cities with different geographical locations and resource differences, as well as among various core industry innovations in the digital economy and different research on digital technology innovation.

In the era of digital technology, data, as a key and unique production factor, provides a possible opportunity for enterprise innovation to break through the constraints of factor endowment. However, many enterprises have difficulties reaching the digital innovation integration stage and have failed to improve their innovation performance through data resources. Therefore, in order to unravel the contradiction between the potential value of digitalization and its practice and grasp the unique points of digital reforms compared with general reforms, it is of great practical significance to systematically study the impact of digitalization on the innovation performance of enterprises from the microperspective. The conclusions of the existing studies linking digitalization and innovation performance are mixed, and the root of the controversy lies in the inconsistency of the measurement methods of digitalization and innovation performance, the contradictory nature of the action mechanism, and the differences in the contexts in which they work. Also,the controversy raises the question of whether there is a non-linear relationship between digitalization and innovation performance.
Digital proximity is a new integrative concept for capturing the digital condition of action subjects from a result-oriented network perspective.This paper aims to fill in the above-mentioned research gap by verifying that the relationship between digital proximity and innovation performance is U-shaped.Drawing on Hanns et al.’s research template,it builds a theoretical framework that deconstructs the U-shape into two forces,namely the blocking force and the driving force. It argues that the blocking force and driving force are respectively manifested in the two states of "dormant period" and "enabling period" of the digital proximity on the innovation performance of enterprises, and the combination of the two potential states makes the interaction between the two present a kind of strategic paradox, which is reflected in the significant U-shaped relationship.That is, at the early stage of product and service digitalization, the degree of digital proximity is low, and the rigidity of resources and practices in the process of digitalization is strong, at this time, digital proximity is negatively correlated with innovation performance; on the contrary, when the enterprise has a high digital proximity, the innovation spillover and promotion effects are gradually released, and the enhancement of the enterprise's digital proximity reaches the threshold of positive stimulation of enterprise innovation, presenting a negative correlation between the early stage of digital proximity and innovation performance and a negative correlation between the later stage and innovation performance. The trend toward a positive correlation between digital proximity and innovation performance in the later stage. In addition, executives' traits (rational factors such as political connection background, R&D background, and irrational factors such as overconfidence) affecting the strategic choices and target performance of enterprises have become one of the important clues in the study of enterprise performance, and may constitute an important boundary condition for the influence of digital proximity on the innovation performance of enterprises, so it is necessary to include executives' traits in the theoretical framework.
On the basis of data from Chinese listed companies, the distance between enterprises and digital industries is used to calculate enterprises' digital proximity, the stochastic frontier model (SFE) is used to measure enterprises' innovation efficiency, the binary variables of executives' political connection background and executives' R&D background are collected and computed, and the composite scores of the executives' personal characteristic indexes are used to assess the executives' overconfidence. Following the organizational inertia theory and the upper echelons theory, the study uses 2 580 observations of 1 044 listed companies from 2010 to 2022 to empirically analyze the influence of enterprises’ digital proximity on innovation performance, and examine the moderating effects of three types of executive traits, namely, executives' political connection background, R&D background, and overconfidence. It is found that, overall, there is a U-shaped relationship between digital proximity and innovation performance, and several robustness tests and endogeneity tests confirm the conclusion. Further, the rational factors in executives' traits (politic connection background, R&D background) positively moderate the U-shaped relationship between digital proximity and innovation performance; while the irrational factor in executives' traits (overconfidence) negatively moderates the U-shaped relationship between digital proximity and innovation performance.

Emerging technologies not only signify the cutting edge of technological innovation but also represent a pivotal aspect of international competition, and they are highly valued by nations, international organizations, and leading corporations worldwide. By swiftly and accurately detecting potential emerging technologies in target domains, they can grasp the opportunities of future technological and industrial development, break through the technological barriers in various fields, and thus enhance the competitive advantage of both nations and enterprises in strategic global competitions. In the context of China's aggressive pursuit of its "carbon neutrality and carbon peak" goals, innovation in power grid technology assumes exceptional importance. Power grid companies are compelled to deeply comprehend and master the evolving trends in emerging grid technologies, undertake crucial technological research, and guide precise research and development investments to secure a dominant position in the international arena.
As vital carriers of scientific information, academic papers and patent literature are predominantly used to evaluate the level of scientific research activities and the innovation in industrial technology, and have become the primary sources for detecting emerging technologies. An in-depth study and analysis of these documents, followed by the extraction and selection of innovative information within them, helps to uncover latent technological knowledge. Although single data source methods are operationally effective, they struggle to accurately reflect the complexity of scientific themes. Conversely, research on detecting emerging technologies through multi-source data remains relatively scarce. Currently, there is a lack of academic consensus on the definition of emerging technologies, leading to different indicators for assessing whether a technology topic is an emerging technology. Identifying these technologies accurately and delineating their quintessential characteristics—impact, growth, coherence, novelty, and uncertainty—is crucial. It is noteworthy that quantified research on these features, particularly uncertainty and ambiguity, is still scarce.
This paper introduces a method for integrating multi-source data, aiming to amalgamate academic and patent data to enhance semantic complementarity between varied data types, thereby boosting efficiency in identifying emerging technologies. The study utilizes the Topical N-Grams (TNG) model to extract technological themes from academic papers and patent documents, followed by manual selection to ascertain key technological themes. According to these themes, it computes five primary feature indicators: impact, growth, coherence, novelty, and uncertainty. These indicators are then amalgamated to calculate an emergence score. Subsequently, the study employs a support vector regression machine model for extrapolating these indicators, identifying emerging technologies with potential for future growth. Focusing on the grid sector, the study collects patent literature from the Derwent Innovation database and academic papers from the Web of Science core collection,limiting document types to "Article" and "Review" and setting the timeframe from 2015 to 2022, with a total of 743 344 academic papers and 1 247 235 patents. The analysis of the annual distribution of academic and patent papers published in the grid field reveals a steady increase in research interest; the emerging technologies in the grid sector for 2022 include research on low-carbon planning for new power systems, electric motor drive and control technology, intelligent inspection technology for transmission lines, smart operation and maintenance technology for digital grids, cooperative control technology in multi-unmanned systems, and internal combustion engine power system technology. Notably, research on low-carbon planning for new power systems has consistently ranked high in emergence in recent times. In the deep development strategy of the current power industry, low-carbon transformation and upgrading are deemed vital. Further indicator extrapolation indicates that research on low-carbon planning for new power systems, intelligent inspection technology for transmission lines, cooperative control technology in multi-unmanned systems, and internal combustion engine power system technology maintain their top-five status, while intelligent wind energy power system integration technology and ion battery and energy storage technology are gradually climbing the ranks, suggesting that these emerging technologies are poised for increased attention and development in the future. In addition to academic papers and patent literature, funding programs and policy texts are also very important sources of information. These resources can provide information about the direction of funding and support for S&T research, as well as the level of government attention and policy orientation towards emerging technologies.

Over the past forty years of reform and opening up, mechanization and factory-based production have intensified the damage to the natural environment and the excessive consumption of resources. The extensive growth model that comes at the expense of resources and the environment can no longer meet the requirements of sustainable economic development, and there is an urgent need to transform traditional economic growth methods. Innovating in an ecologically symbiotic manner is the driving force for promoting green, low-carbon innovation and sustainable innovation, and it is also a crucial measure for achieving high-quality development in a low-carbon economy. Existing research has mostly used traditional regression methods, focusing on the net effects of individual dimensions of innovation ecosystems and their constituent conditions on low-carbon transformation, with limited exploration of the phenomenon of synergistic integration among innovation ecosystems. The coupling relationships among the various dimensions of innovative ecological symbiosis and the ways in which they drive low-carbon transformation are unclear, and the interactions between multiple innovation dimensions are overlooked, making it difficult to fully explore the nature of complex systems.
In order to make up for the deficiencies of the existing research, specific research questions arise: Is the symbiosis of single dimension of innovation ecosystems and their constituent conditions a necessary condition for low-carbon transformation? What kind of configuration of innovative ecological symbiosis is conducive to promoting low-carbon transformation? What are the complex impact mechanisms among these configurations? This study uses the fuzzy set qualitative comparative analysis method, and employs the total factor productivity index to measure the level of low-carbon transformation, and draws on the data envelopment analysis method used by He Weijun et al. for calculation to comprehensively analyze the complex causal relationships between innovative ecological symbiosis and low-carbon transformation and explore a diversified development path for low-carbon transformation that suits China's national conditions. To ensure the adequacy and availability of sample data, this paper selects panel data from 30 provinces in China (excluding Xizang, Hong Kong, Macao and Taiwan) from 2014 to 2020 as sample cases to analyze the complex mechanism between innovation ecosystem symbiosis and low-carbon transformation. The indicator data is sourced from the China High-tech Industry Statistical Yearbook, China Science and Technology Statistical Yearbook, China Regional Innovation Capability Evaluation Report, and provincial statistical yearbooks.
The research results show that,firstly, the single dimensions of the innovation ecosystem and their constituent conditions do not possess the necessary conditions for low-carbon transformation. Secondly, there are two configurations that can promote low-carbon transformation, namely, institutional logic configuration and platform logic configuration. These two configurations represent multiple pathways for achieving low-carbon transformation under different governance mechanisms in the innovation ecosystem. Furthermore, by observing the consistency among provinces during the panel period, it is found that there are certain group differences between the two configurations, which may be due to differences in infrastructure development, technological foundations, and industrial structures. Finally, among the two configurations that promote low-carbon transformation, there is a complementary relationship between subject coordination, knowledge development and creation, and knowledge flow and diffusion, and these three prerequisites have a universal role in low-carbon transformation. There is a substitutive relationship between institutional logic and platform logic, but this relationship is not singular but depends on specific stages and needs.
The marginal contributions of this paper are reflected in two aspects: on the one hand, it constructs a theoretical model of low-carbon transformation from the perspective of innovative ecosystem symbiosis, enriching and refining the existing research results on innovative ecosystem symbiosis; on the other hand, it explores the necessary and sufficient dual causal relationship between innovative ecological symbiosis at the provincial level in China and the emergence of low-carbon transformation paths. It reveals the diverse development paths that promote and hinder low-carbon transformation of economic development modes through innovative ecological symbiosis, providing a theoretical basis for dynamic research on the impact of innovative ecological symbiosis on low-carbon transformation trajectories.

Enabled by digital technology, innovation ecology can efficiently realize the transfer, interaction, expansion and evolution of knowledge, and form the core competitiveness of the digital age. At present, China has initially explored an innovation ecosystem supported by "new infrastructure" such as 5G, Internet of Things, industrial Internet and artificial intelligence, with industry leading enterprises as the main body and "platform + a large number of small and medium-sized enterprises" as the main structure, which has become a prototype of the integration of China's digital economy and the real economy, and can meet the needs of future scenarios better. The long-term development of the innovation ecosystem led by the industrial Internet platform is the key to the integration of China's digital economy and the real economy.
However, the current academic research on industrial Internet platform dwells on connotation analysis, exploration and application, and business model analysis, and there is a lack of research on industrial Internet clusters and their ecology. In addition, current studies on the value co-creation of innovation ecosystems are mainly conducted from the perspectives of the innovation ecosystem subjects, and the network relationship formed between the subjects and the external innovation environment, but less attention is paid to the co-creation relationship between the platform and manufacturing enterprises, between intelligent devices and humans, and between machines and data to explain the value co-creation process mechanism of the innovation ecosystem led by the industrial Internet platform. The value co-creation mechanism of the innovation ecosystem led by the industrial Internet platform is yet to be revealed.
The innovation ecosystem led by the industrial Internet platform takes digital resources and digital infrastructure as key production factors, reshaping the win-win cooperation logic in the industrial value network, and reflecting unique characteristics different from the general innovation ecosystem. Therefore, drawing on the value co-creation theory of the innovation ecosystem, this study explores three mechanisms, including modularity, scenario, and complementarity from the intrinsic attributes, and analyzes their interactive utility. This paper selects the COSMOPlat Industrial Internet platform as the case study object; it adopts a structured data analysis method to analyze the case, encodes and abstracts the original data, realizes the analysis and induction of the theme, and finally extracts a rigorous theoretical theme, forming a data structure composed of first-order constructs, second-order themes and third-order categories.
It is found that (1) the innovation ecosystem led by the industrial Internet platform is a value creation and value acquisition system composed of multiple participants in different fields. It is an open, supply-and-demand-driven, loosely coupled innovation system based on a multi-platform interactive architecture, and the innovation system can improve the initiative of each subject to participate and the digital ability and innovation diffusion ability through the sharing and symbiosis of technology, resources and scenes, so as to achieve the goal of system innovation. (2) The innovation ecosystem led by the industrial Internet platform achieves a win-win situation of efficiency and collaboration within the ecosystem on the supply side through modularity and complementarity, and integrates the demand side into the ecosystem through the scenario mechanism to form a future-oriented co-creation system. The modularity mechanism effectively integrates information technology infrastructure, big data technology, blockchain technology, and artificial intelligence technology, and builds a multi-platform interactive architecture to promote the integration of data and reality. The scenario-based mechanism accesses the scalable new generation of intelligent technology to construct the user scenario-based experience, and activates multiple subjects to actively embed the ecology through the governance scene to create a win-win situation. The complementary mechanism and modular technology are nested in multiple layers to complete the industry technology complementarity and digital technology complementarity, match scenarios to iterate the solution covering the whole life cycle of users, optimize collaborative innovation among various entities in the ecosystem through the platform enabling process complementarity. (3) The innovation ecosystem led by the industrial Internet platform integrates resources efficiently to achieve value co-creation through the modular mechanism, scenario mechanism, and complementary mechanism, improves the ecological digitization ability, and forms the same side network effect, cross-side network effect and cross-domain network effect of the innovation ecosystem.

Delving into the scenario paradigm and understanding how data elements facilitate the emergence of new quality productive forces is essential for advancing Chinese-style modernization. In the digital economy, data has become a key resource in global competition, with scenario-driven innovation emerging as a distinctive approach. Grasping the scenario paradigm and delving into how data elements facilitate the emergence of new quality productive forces is essential for advancing Chinese-style modernization. The essence of scenario-driven innovation lies in its ability to tightly integrate technology, market demands, and societal needs, thereby creating a dynamic and interactive ecosystem of innovation. Data elements serve as the connective tissue, enabling optimal resource utilization through precise analysis and forecasting, which in turn boosts production efficiency, technological innovation, and industrial upgrading.
Building on a profound understanding of the theoretical essence of scenario paradigms and new quality productive forces, this paper constructs a research framework of "vortex model" with resource allocation as the core and data elements empowering the co-evolution of "scenario-technological innovation-new quality productive forces". Considering the pivotal role of industry in the national economy, "Root Cloud" is selected as an exploratory case study. Initially, extensive secondary data is collected, and open coding is conducted to form first-order codes, which distill the typical operational characteristics and behavioral patterns of the case companies. Subsequently, first-order codes with similar characteristics are aggregated to form abstract and theoretical concepts, resulting in second-order themes. Finally, these second-order themes are synthesized to form an integrated theoretical dimension.
The study examines how Internet platform companies use data elements to foster new quality productive forces under varying industrial scenarios, identifying three key modes: "Product Intelligence," "Smart Manufacturing," and "Industrial Chain Ecology." The findings reveal that (1) optimizing resource allocation, which enhances the efficiency of production factor mobility, is a core indicator for forming new quality productive forces and achieving high-quality development. The integration of data elements with traditional factors, driven by the "digital hand," drives the overall reset of resource sequences and optimizes resource allocation, playing a catalytic role in the formation of new quality productive forces. There are differences in the coupling degree of productivity characteristics under different scenarios and the new quality productive forces.(2) Higher digital intelligence levels in scenarios correlate with more pronounced new quality productivity traits, especially in smart manufacturing and industrial chain ecosystems. (3) The iterative upgrade from "mechanical model - object model - connection model-composite object model" is a "bridge-type" factor for data elements to empower the emergence of new quality productive forces. Continuously innovating and optimizing models, through this series of upgrades, companies like Root Cloud can to accurately integrate data with physical equipment.
In summary, leveraging the advantages of scenario paradigms and harnessing the value of data elements is of significant theoretical and practical importance for accelerating the cultivation of new types of productive forces. This study not only enriches the theoretical connotations of scenario-driven innovation and new types of productive forces but also provides new perspectives and strategies for the realization of Chinese-style modernization. Firstly, the development and utilization of data value are essential. It is a necessary path to achieve new quality productive forces by enhancing the construction capabilities of digital models and optimizing data analysis capabilities, involving data elements in the construction of new quality production factors. Secondly, leveraging scenario advantages to drive technological innovation is crucial. Innovation can be driven by thoroughly exploring data element applications in key fields, identifying specific scenario needs, and aligning data supply with these demands. This approach not only fosters technological advancements but also integrates scientific and technological outcomes into critical development scenarios. It aids in the transformation of traditional industries and nurtures strategic, emerging, and future industries through the creation of digital scenario frameworks. Thirdly, it is important to capitalize on the large scale of data elements and the advantages of the new national system so as to guide the effective supply of market-oriented data elements, promote the industrialization and commercialization of data elements, and open up new tracks to promote the emergence of new quality productive forces.

In recent years, as China transitions from a phase of high-speed economic growth to a stage of high-quality development, enhancing total factor productivity (TFP) has emerged as a critical lever for fostering sustainable economic progress. Amidst this backdrop, corporate innovation stands out as a pivotal engine driving productivity enhancements and long-term economic growth. Solow (1956) underscored the significance of TFP in economic advancement, sparking widespread exploration into the underpinnings of productivity growth. Drawing upon a rich tapestry of research that delves into various facets of corporate heterogeneity, including R&D investments, workforce demographics, and industry characteristics, this study aims to scrutinize the intricate mechanisms by which corporate innovation influences TFP. Specifically, it ventures beyond conventional linear perspectives, adopting a novel lens focused on the dimensions of innovation's breadth and depth, thereby providing a robust theoretical foundation for policy formulation and implementation.
This study harnesses a dataset comprising A-share listed companies in China spanning from 2003 to 2021. By meticulously curating a sample reflective of the broader economic landscape, this investigation offers a comprehensive overview of the nexus between corporate innovation and TFP within the Chinese context. The study employs a multi-dimensional approach to quantify corporate innovation, distinguishing between innovation's breadth and depth. The breadth of innovation, indicative of the diversity and cross-disciplinary nature of a firm's innovative endeavors, reflects the firm's ability to engage in innovation across various technological domains. Conversely, the depth of innovation encapsulates the intensity and commitment of a firm towards innovation, signifying concentrated efforts in core technological advancements and improvements in production processes. Utilizing sophisticated statistical techniques, including regression analysis and robustness checks, the study evaluates the impact of these innovation dimensions on TFP. The analytical framework is further enriched by exploring the moderating effects of ownership structure, technological intensity, marketization level, and intellectual property rights protection on the innovation-TFP relationship. The investigation uncovers an inverted U-shaped relationship between the breadth of innovation and TFP, suggesting that while diversifying innovation across different technological fields initially boosts TFP, beyond a certain threshold, it may lead to diminishing returns. In stark contrast, the depth of innovation demonstrates a consistently positive influence on TFP, underscoring the importance of concentrated innovation efforts. Furthermore, the study reveals nuanced insights into how non-state-owned corporates, compared to their state-owned counterparts, and high-tech firms, relative to lower-tech firms, exhibit distinct patterns in how innovation's breadth and depth affect TFP. These findings highlight the complexity of the innovation-productivity paradigm, influenced by corporate characteristics and the external economic environment. The research also illuminates the critical roles of marketization and intellectual property rights protection. In regions with higher levels of marketization and stronger intellectual property protection, the positive effects of innovation depth on TFP are accentuated, thereby reinforcing the argument for fostering conducive economic and legal conditions for innovation.
This study introduces an innovative lens for assessing corporate innovation, emphasizing the dual dimensions of innovation's breadth and depth. By unraveling the intricate dynamics between different facets of innovation and TFP, it extends the existing literature and offers new pathways for theoretical exploration. Moreover, the findings bear significant implications for policymakers and business leaders, advocating for targeted strategies to cultivate an ecosystem that nurtures innovation and drives productivity growth. In conclusion, this research sheds light on the multifaceted relationship between corporate innovation and TFP, contributing valuable insights into the drivers of high-quality economic development. By advocating for nuanced policies that recognize the complexity of innovation activities, this study underscores the imperative of fostering a supportive environment for innovation, tailored to the diverse needs of corporates across different sectors and developmental stages. In the following research, the sample could be expanded and the specific reasons for the formation of the inverted U-shaped relationship between firms' breadth of innovation and total factor productivity could be further explored, along with further exploration of how to optimize the depth of innovation in order to maximize its positive impact on productivity.

With the continuous development and progress of digital information technologies such as big data and artificial intelligence, the digital economy industry has gradually matured, promoting enterprises to break through existing bottlenecks and overcome the challenges brought by the epidemic. Enterprises from all walks of life have begun to carry out digital transformation, and have made a preliminary attempt to promote the development of emerging digital technologies and the traditional real economy. Although digital transformation has received enough attention in China, the digital transformation of most enterprises is still in the primary stage. In the process of digital technology with the economy constantly empowering enterprises to promote transformation, digital transformation has also brought a series of new topics involving management.
At the organizational level, "people-orientedness" is the underlying logic of digital transformation of enterprises, and in the process of actual digital transformation of enterprises, the reactions and ideas of individual employees are most easily overlooked. In the face of transformation, employees do not always show an attitude of acceptance and recognition. When digital transformation may threaten their original working conditions and resources, employees will have resistance and conflict, which will hinder the process of digital transformation in enterprises. The existing research has paid attention to the phenomenon of employees' resistance to the digital transformation of enterprises and initially discussed the acceptance of employees' digital transformation. However, domestic research mainly focuses on the influence and function of digital transformation on organizational performance and human resource management, and there is still a lack of research on the influencing factors and action paths of employees' resistance to digital transformation.
In this study, semi-structured interviews with multiple-level employees involve enterprises that are currently undergoing or have undergone transformation, including the traditional handicraft industry, the manufacturing industry, and the digital software industry. Forty-five out of fifty-three respondents who have psychological resistance to the digital transformation of enterprises are retained to form the final sample of this study. On the basis of grounded theory, this paper analyzes the original interview data and explores the key influencing factors of employees' psychological resistance to enterprise digital transformation and its mitigation path.
It is found that the five main categories of job requirements, job resources, individual capital, technology acceptance and negative experience have a significant impact on employees' resistance to digital transformation. Among them, job requirements and work resources are external factors; individual capital is internal factor; technology acceptance and negative experience are process factors; and their internal dimensions are independent of each other, which can not only affect employees' psychological resistance to digital transformation of enterprises, but also partially overlap and have a common impact on them. Subsequently, a conceptual model of employees' resistance to digital transformation is constructed based on the resource model and the theory of matching between people and environment. At the same time, it abstracts several slow-release paths for employees to resist digital transformation, and expounds its mechanism in detail.
Compared with the existing research, this paper has made the following contributions. Firstly, it uses multi-source interview data to analyze the root causes and construct a multi-dimensional factor model of employees' psychological resistance to enterprise digital transformation. Secondly, this paper has some enlightenment on the study of employee satisfaction and resistance in organizations and enriches the theoretical research on employee perspectives in organizations in China. Against the backdrop of the digital economy, this study details the psychological resistance mechanism of key elements to employees' digital transformation, and emphasizes the role of external factors such as job requirements and resources, internal factors such as individual capital, and two process factors such as technology acceptance and negative experience, which improves the theoretical framework of employees' level research in enterprises' digital transformation. By analyzing the system, the logical relationship and chain between elements are revealed, and on this basis, the corresponding integration model is constructed, which is helpful for enterprises encountering such special problems to better promote digital transformation.

Digital transformation is a key driver of the current global economy, acting as a catalyst for industrial upgrading and enhancing corporate competitiveness to foster sustainable economic growth, which the Chinese government has prioritized as a core national strategy. By 2022, China’s digital economy reached an impressive scale of 50.2 trillion yuan, accounting for 41.5% of the GDP, underscoring the critical role of digital integration in economic advancement. The interplay between digital transformation and the real economy is profound, with substantial implications for corporate digitalization. This transformation enhances information flow, optimizes resource allocation, and stimulates innovation and productivity, thereby creating a favorable environment for high-quality development. However, the relationship between digital transformation and productivity is complex and multifaceted, warranting further investigation.
This paper centers on new quality productive forces, a concept emphasizing the urgency to enhance total factor productivity through technological innovation and the effective integration of resources. New quality productive forces represent a paradigm shift that not only focuses on quantitative growth but also emphasize qualitative improvements in productivity, reflecting the evolving demands of a modern economy. While existing literature recognizes the positive impacts of digital transformation—such as increased management efficiency, optimized specialization, and enhanced innovation capabilities—significant discrepancies remain regarding its overall effectiveness in different contexts. Some scholars have raised concerns about the multifaceted nature of the transformation process, noting that it is often fraught with complexities, including cultural resistance, inadequate infrastructure, and the need for substantial investment in skills and technologies. These factors can result in delayed effects, preventing the anticipated benefits of digital initiatives from being immediately realized.Consequently, it is essential to delve deeper into how digital transformation influences new quality productivity, as this understanding holds implications for both theoretical exploration and practical application. By analyzing the mechanisms through which digital initiatives contribute to productivity improvements, we can uncover valuable insights that guide policymakers and business leaders in crafting strategies that foster sustainable growth.
To explore this relationship, this paper adopts the Technology-Organization-Environment (TOE) framework, which posits that the success of technological innovation is influenced by three dimensions: technology, organization, and environment. From a technological perspective, the size of a firm and its type of technology dictate its capacity for transformation. Larger enterprises, with their abundant resources, typically exhibit a greater ability to adapt to new technologies, thereby enhancing new quality productivity. In contrast, high-tech firms leverage their technological advantages to achieve breakthroughs through digital transformation. Organizationally, the decision-making capabilities of management are crucial for the success of transformation efforts, as they significantly impact resource integration and efficiency. Additionally, the competitive environment plays a vital role; market pressures compel firms to rapidly adopt new technologies, enhancing resource allocation efficiency and driving productivity growth. This paper constructs a theoretical framework encompassing firm size, technology type, market competition, managerial capabilities, and resource allocation efficiency. This paper proposes hypotheses that will be empirically tested using data derived from A-share listed companies. Utilizing the entropy method, this paper measures new quality productivity through dimensions like innovation leadership, enterprise upgrading, technology-driven growth, and sustainability.
The results show that digital transformation positively impacts new quality productivity in a U-shaped manner. As the level of transformation increases, its beneficial effects amplify. Furthermore, the analysis suggests that larger firms, those in high-tech industries, and enterprises in competitive markets experience more significant advantages from digital transformation. Additionally, this paper identifies that managerial capabilities exert a nonlinear moderating effect, while resource allocation efficiency serves as a significant mediating factor, enhancing capital and labor allocation.
The contributions of this paper are threefold. First, it provides a nuanced examination of new quality productive forces, focusing on innovation leadership, enterprise upgrading, and sustainable development.Second, it enriches the existing body of literature by elucidating the nonlinear relationship between digital transformation and productivity. Lastly, the study explores the nonlinear moderating effect of managerial capability improvement and the mediating effect of resource allocation efficiency, revealing their key roles in digital transformation. It also addresses literature gaps and provides practical insights for businesses to leverage digitalization for enhanced productivity and sustainable growth.

In the contemporary scientific and technological context, brain-computer interface (BCI) technology has become a cutting-edge area attracting extensive attention. By enabling real-time sharing and deep integration of neural information among individuals, BCI offers more efficient and precise tools for group decision-making, knowledge innovation, and social practice; however, it also intensifies group differentiation and integration, challenging the conventional operation and power structure of social organizations and presenting new issues in social equity and ethics. Thus, it is crucial to probe the social relationship changes caused by BCI .
This study focuses on exploring the profound influence of BCI technology on social relations, with the aim of comprehensively understanding its implications and providing guidance for the harmonious development of technology and society. The research method mainly involves a comprehensive review of literature and in-depth cross-disciplinary analysis. By collecting and analyzing a large number of research materials from fields such as neuroscience, information engineering, sociology, and ethics, this paper systematically expounds on the relationship between BCI technology and social relations. These materials include academic papers, research reports, and relevant case studies, which form the basis for in-depth exploration.
The research concludes that BCI technology brings about far-reaching changes in social relations. At the individual level, it remolds self-cognition. For example, through BCI-controlled devices like brain-controlled prosthetics, individuals' perception of their body's functions and boundaries is transformed, affecting their self-identity and mental health. Interpersonal interaction patterns also change. BCI-based emotion recognition and subconscious information transmission enable more direct and in-depth communication, but they also raise issues such as privacy infringement. In terms of social behavior norms, the combination of BCI and VR technology creates new virtual social experiences, changing social interaction habits and norms, and blurring the line between virtual and real social interactions.
At the group level, BCI technology innovates collaborative models. In complex projects, it allows for the real-time sharing of members' neural-cognitive patterns, improving collaboration efficiency and knowledge synergy. It also reconstructs social network structures. The connection basis in social networks shifts from traditional relationships to neural-activity-based similarities and complementarities, which has a significant impact on information dissemination and social power structures. Moreover, it reshapes group identity and class relations. The different levels of BCI technology application among groups lead to identity differentiation and class-related changes, exacerbating social inequality.
The innovation of this paper lies in its multi-perspective and in-depth analysis. This paper provides a comprehensive analysis of BCI technology, covering not only the technical aspects but also delving into its social, ethical, and philosophical implications. Such a holistic approach allows for a more thorough understanding of the technology's potential impacts on various aspects of society and human life. In addition to the in-depth analysis, this paper also proposes a series of regulatory and guarantee mechanisms to ensure the responsible development and application of BCI technology. For instance, it suggests the establishment of robust ethical review mechanisms. These mechanisms would play a crucial role in ensuring that BCI research and applications adhere to ethical standards, thereby preventing potential ethical dilemmas and misuse of the technology. Moreover, the paper emphasizes the importance of constructing a comprehensive legal regulatory system. This system would aim to protect personal privacy and rights in the context of BCI technology. Given the intimate connection between BCI and personal neural data, ensuring the security and privacy of this highly sensitive information is of paramount importance. The proposed legal framework would help safeguard individuals from potential privacy violations and other related risks. Furthermore, the paper proposes the formulation of social fairness guarantee systems. The benefits of BCI technology should be distributed in an equitable manner across society. This requires the implementation of policies and measures that can promote equal access to and benefits from BCI technology, particularly for disadvantaged groups.
In summary, through its multi-faceted analysis and the proposal of practical regulatory and guarantee mechanisms, this paper makes a significant contribution to the responsible and sustainable development of BCI technology. It provides valuable insights and suggestions for policymakers, researchers, and other stakeholders in the field, helping to ensure that BCI technology develops in a direction that is beneficial to all of society.

Sci-tech self-reliance and self-strengthening(SRSS) at a higher level is critical for national development at the current stage, with the strategic goal of becoming a world scientific and technological power. The high-level research universities are part of the national innovation system and strategic scientific and technological force, playing a unique role in serving the strategic goal of sci-tech self-reliance and self-strengthening at higher levels.
In order to reveal the common characteristics and inherent laws of research universities in serving high-level SRSI, this study selects 20 "Double First-Class" universities for empirical research and ground theory analysis, and 6 main categories, 21 sub-categories and 52 influencing factors are then extracted. By exploring the potential correlation between the main categories, the study establishes the evidence chain of key factors based on the typical model of the typical model of the main category and builds the key factor model of high-level SRSS to present a comprehensive picture of the various factors that need to be considered and addressed to achieve the strategic goal.
It is concluded that the key factors for research universities to contribute to high-level SRSS include six aspects, but they are lagging behind the demand of the target of high-level SRSS. First of all, the innovative talents cultivation is far from the need of SRSS.Secondly, interdisciplinary integration still faces many difficulties. Due to disciplinary barriers and the uneven distribution of resources, interdisciplinary research is often difficult to carry out smoothly. Thirdly, the level and depth of the deep integration of industry, universities and research still need to be strengthened. Fourthly, most of the cooperation between universities and industry and research institutions remains at the surface level, with the need for deeper interaction and partnership to be established. Fifthly,international cooperation also needs to be continuously improved. Although the university has carried out some international cooperation and exchange activities, cooperation with world-class international universities and research institutions is still limited. Finally, a culture of innovation for excellence and embracing failure has not been established.This culture will encourage researchers to explore unknown areas, challenge traditional ideas, and not to fear failures and setbacks.
A series of recommendations for research universities are proposed. Firstly, in the innovation of the scientific research organization model, universities should increase the proportion of organized scientific research, and strengthen the top-level design, so as to build up an interdisciplinary and multi-team scientific research force, which is better to improve the efficiency of scientific research resource allocation, and carry out original leading scientific and technological breakthroughs which can enhance the source supply of innovation-driven research. Secondly, in terms of talent training, universities should constantly improve the training mode of innovative talents to provide the talents independently that meet the needs of SRSS, especially to cultivate a large number of top innovative talents. Thirdly, the discipline construction should conform to the profound reform of the scientific research paradigm; it requires the reform of innovation mechanism, environment supporting and encouraging scientific cooperation. Besides, disciplinary construction should conform to scientific laws and meet the needs of scientific research. Fourthly, the focus should shift from the technology transfer from university to industry to the deep cooperation of industry-university-research-application. Fifthly, research universities should be supported in international cooperation to lead big science or big instrument projects, participate more actively in the global scientific network, and enhance the universities′ original innovation capability and international influences. Finally, the innovation culture of pursuing excellence acts as a fundamental element that sustains high-level SRSS and ensures its continuous advancement. Behind the international competition in science and technology lies not just a contest of technology, equipment, and capital, but also a deeper rivalry between systems and cultures, where cultural factors exert a more profound and foundational influence. Given the high social standing and influence of research universities, it is imperative to reinforce their role in shaping innovative culture. This includes promoting the scientific spirit, fostering an environment conducive to innovation, and advocating for the enhancement of moral education, risk awareness, and ethical standards in science and technology in the culture of innovation.

Innovation can help SMEs reduce costs, improve efficiency, and enhance the quality of their products and services, bringing long-term growth. However, due to their own financial and technological constraints, SMEs generally have certain barriers to innovation. How to help SMEs ease financing constraints and enhance technological innovation capability has become an urgent issue to be addressed. Supply chain finance plays an important role in stimulating the innovation activities of SMEs. On the one hand, the adoption of supply chain finance can make it easier for SMEs to obtain lower-cost funds, thus alleviating their financing constraints. On the other hand, close partnerships based on supply chain finance ecological networks can promote business dealing and core technology sharing in the chain, thus accelerating the formation of collaborative innovation networks, which is conducive to improving the innovation capability of SMEs. Existing studies have verified that supply chain finance can alleviate financing constraints, while financing constraints can affect firms' R&D investment efficiency. However, the logical link between supply chain finance, financing constraints, and R&D investment efficiency has not yet been established.
Given the above research deficiencies, this paper aims to explore the effect of supply chain finance on the R&D investment efficiency of SMEs. It focuses on the external influence mechanism as well as the internal conduction path and verifies the role that financing constraints play between supply chain finance and R&D investment efficiency. The data of 109 GEM-listed SMEs from 2016 to 2022 is selected for empirical analysis. Firstly, the original data are subjected to descriptive analysis, a correlation test, and a variance inflation factor test to eliminate interfering factors. Subsequently, the data is analyzed based on three-stage DEA analysis, binary logistic regression, multiple linear model regression, and panel Tobit model regression to verify the suppressing effect and moderating effect among the variables. Finally, key variables were replaced for robustness testing to ensure the accuracy of the findings. The results show that supply chain finance can significantly enhance the R&D investment efficiency of SMEs, and financing constraints play a suppressing effect between the two (i. e. , by controlling for the factor of financing constraints, the effect of supply chain finance on the R&D investment efficiency will be more enhanced). Further, in firms with higher supplier concentration and customer concentration, the enhancement effect of supply chain finance on R&D investment efficiency is more significant.
This study makes a contribution in two ways. First, it introduces the concept of "efficiency" in measuring firms' innovation performance. Through the three-stage DEA method, the whole process of "injection of innovation resources", "incubation of innovation results", "realization and commercialization" is tracked, which is more comprehensive than other innovation performance measurement methods. In addition, three-phase DEA overcomes the disadvantages of traditional DEA, which ignores the environmental impact and cannot be adjusted. It is able to measure more realistic R&D investment efficiency, which provides a basis and guidance for the comprehensive assessment of SMEs' innovation performance and the long-term development of innovation activities. Secondly, it confirms the direct effect between supply chain finance and R&D investment efficiency, and verifies the suppressing effect of financing constraints and the moderating effect of supply chain concentration. This lays the foundation for further exploring the internal mediating variables and external moderating variables of the relationship between supply chain finance and R&D investment efficiency. The financing constraints and supply chain concentration variables selected in this paper can highly correspond to two important directions for the development of supply chain finance business: one is to continue to strengthen the role of support for SMEs' financing, and the other is to build a supply chain finance ecosystem that promotes SMEs to be embedded in the synergistic system of value co-creating and risk-sharing with supplier firms and customer firms. The research on suppressing and moderating effects in this study responds to the call of reality, and the conclusions are conducive to giving full play to the advantages of supply chain financing and collaborative innovation, so as to provide dual support for SMEs' innovation activities in terms of both finance and technology.

Since the outbreak of the China-American trade friction in 2018, China has faced increasingly severe challenges in key core technology fields. Despite a surge in the number of international patent applications, China's dependence on foreign intellectual property has not diminished, indicating an underlying weakness in innovation quality. Against this backdrop, the “Specialization, Sophistication, Differentiation, and Innovation” (SSDI) policy for small and medium-sized enterprises (SMEs), initiated by China's Ministry of Industry and Information Technology in 2011 and formally implemented in 2018, aims to cultivate “Little Giant” companies that excel in innovation and dominate niche markets. These firms are vital for driving technological innovation, enhancing industrial chain levels, and strengthening international competitiveness. However, while existing research suggests that the SSDI policy has encouraged increased R&D investment and innovation output, there is a paucity of in-depth analysis on whether it effectively improves the quality of corporate innovation. This research aims to contribute to a more comprehensive understanding of the factors that influence innovation policy outcomes. This is crucial for policymakers seeking to refine the SSDI policy and for firms looking to optimize their innovation strategies in the face of evolving market conditions and policy environments.〖HJ*2/5〗
This paper first proposes hypotheses on the impact and mechanisms of the SSDI policy on the technological innovation of “Little giant” enterprises, as well as the differential effects of the policy under varying levels of market transparency through a theoretical model. Further, this paper utilizes data from listed companies on the Shanghai and Shenzhen A-shares market from 2018 to 2023 which is sourced from the CSMAR and CNRDS databases, and employs a multi-period difference-in-differences (DID) method to test the above hypotheses. The research findings reveal that the SSDI policy significantly enhances both the innovation quality and quantity of “Little Giant” enterprises. Moreover, the policy has a gradient promotion effect, meaning that “Little Giant” enterprises outperform provincial and municipal SSDI enterprises in terms of technological innovation levels, and the latter, in turn, outperform other enterprises. By alleviating financing constraints, providing risk compensation, and promoting the accumulation of human capital, the SSDI policy substantially improves the innovation performance of “Little Giant” enterprises. In terms of human capital, it is the high level of human capital that is the key factor in improving the innovation quality of “Little giant” enterprises. Further research finds that the enterprises adopt different innovation strategies under varying levels of corporate information transparency. Specifically, when corporate information transparency is low, “Little Giant” enterprises receiving SSDI policy support tend to opt for strategies that increase the quantity of innovation rather than its quality, thereby engaging in “innovation pandering” behavior. In contrast, when corporate information transparency is high, the SSDI policy effectively promotes the innovation quality of “Little Giant” enterprises. These findings are pivotal as they not only substantiate the SSDI policy efficacy but also highlight the conditional nature of this effectiveness, contingent upon the level of corporate information transparency. By revealing the differential impact of the policy in various market conditions, this study offers valuable insights for policymakers aiming to refine the SSDI policy and for “Little giant” enterprises seeking to optimize their innovation strategies.
The possible marginal contributions of this paper are threefold. Firstly, current research on the effect of the SSDI policy mostly focuses on the quantity of innovation, this paper examines the impact of the policy on both the quantity and quality of enterprise innovation at the same time, which helps to comprehensively assess the effect of the policy. Secondly, most of the existing innovation policies only support technological innovation of enterprises from the aspect of financial support, and lack the design of human capital incentives. This paper not only analyzes the role of financial support of the policy, but also pays special attention to the role of different human capitals in the process of innovation, which provides an important idea for the incentive design of the innovation policy. Thirdly, the impact of innovation policy on the quality of enterprise innovation is controversial, and why the effect of the SSDI policy remains to be further analyzed. Following the information asymmetry theory, this paper analyzes the differentiated innovation strategies that enterprises may adopt under different market transparency conditions, and emphasizes the importance of constructing a transparent and efficient market information mechanism to stimulate the innovation vitality of enterprises.