New quality productive forces are the qualitative leap of traditional productivity forces and the key for major countries to seize the high ground of development. Based on the profound understanding of the scientific connotation of new quality productive forces and the formation process of new quality productive forces, this paper constructed an evaluation index system of new quality productive forces consisting of 40 indicators from such three dimensions as input quality, production vitality, and output efficiency. The improved CRITIC method was used to measure the level of new quality productive forces of 30 provincial-level regions in China from 2012 to 2022, and the correspondence analysis, kernel density estimation and Dagum Gini coefficient method was also used for the analysis. The results showed that: firstly, the level of new quality productive forces in China and three major regions continue to rise, but its overall input-output efficiency are relatively low; secondly, the advantages and disadvantages of the development of new quality productive forces vary among provincial-level regions, and the quality of talent is a key factor affecting new quality productive forces; and thirdly, the overall differences and regional differences of new quality productive forces continue to expand, and the differences mainly come from different regions. This research can objectively reveal the development status of new quality productive forces in China and correctly identify key issues, thus providing decision-making references for developing new quality productive forces according to local conditions.

    Leveraging digital resources effectively to enhance innovation performance constitutes a critical practical issue in promoting the high-quality development of China′s manufacturing sector. Existing research has predominantly focused on the impact of dynamic capabilities on innovation performance, yet seldom explores the intrinsic connection between dynamic capabilities and innovation capabilities, as well as their synergistic interaction mechanisms within digital-enabled transformation processes. Based on the dynamic capability theory and the "resource-capability-performance" logic framework, this thesis empirically examined the impact and mechanisms of digital transformation on corporate innovation performance using the data from A-share listed manufacturing enterprises during 2013-2022 and employing the fixed-effects models and Bootstrap methods. The findings revealed that: (1) Digital transformation drives the enhancement of innovation performance through technological enablement; (2) Dynamic capability (opportunity perception, learning absorption, resource restructuring capability) and innovation capability exhibit dual transmission mechanisms. Multiple pathways exist in the process where digital transformation drives innovation performance improvement, with dynamic capabilities and innovation capabilities acting as core driving factors; (3) Environmental uncertainty significantly inhibits the enhancing effects of opportunity perception capability, learning absorption capability and innovation capability on innovation performance, while its moderating role in resource reconfiguration capability remains insignificant. Enterprises should therefore strengthen vigilance and real-time monitoring of external environment. The research has further expanded the pathways through which digital transformation drives innovation in Chinese manufacturing enterprises, and provided theoretical basis and practical implications for enterprise digital transformation.

    The new round of revolution in science and technology and industrial transformation is rapidly advancing and sparking widespread discussions within the academic community. This paper conducted a systematic review of research progress in this field, highlighting the fundamental differences between mainstream economics and evolutionary economics regarding innovation and transformation. It further elaborated on the intrinsic connections among the new round of revolution in science and technology and industrial transformation, and innovation-driven development. By examining the technological evolution patterns, societal demands, security imperatives, and economic conditions, this study identified the key drivers of the new round of revolution in science and technology and industrial transformation. It also revealed how these drivers shape unique features in technological domains, innovation models, opportunities and challenges, and the competitive landscape. Against this backdrop, the paper emphasized that proactive transformations in institutional areas such as the science and technology system, innovation frameworks, innovation policies, and industrial policies are crucial for driving these changes. Finally, it will offer insights and future perspectives on advancing research into China′s new round of revolution in science and technology and industrial transformation.

   The high-quality development of the manufacturing industry is the top priority of the high-quality development of China′s economy. As the core driving force of new quality productivity, artificial intelligence has a strong ability to influence the economy and society, and it is of great significance to study whether its introduction can improve the innovation performance of manufacturing enterprises. Based on the data of China′s manufacturing A-share listed enterprises from 2011 to 2019, this paper used the difference-in-differences method to systematically study the impact and mechanism of the application of artificial intelligence technology on the innovation performance of manufacturing enterprises. The results showed that: (1) the application of artificial intelligence technology significantly improves the innovation performance of manufacturing enterprises, and it is still valid after a series of robustness tests; (2) from the perspective of the mechanism, ai technology can improve the innovation performance of enterprises by influencing factors such as government subsidies, the total labor force and labor structure of enterprises; and (3) there is heterogeneity in the application of artificial intelligence technology in the innovation performance of manufacturing enterprises with different property rights and different life cycles. The study held that promoting the integration and application of artificial intelligence technology can not only promote the high-quality development of the manufacturing industry and expand the innovation space, but also become a strong driving force for the realization of China′s manufacturing industry "going from big to strong".

    Currently, the digital transformation of Chinese enterprises is deeply entangled in the misconception of "paying more attention to technology than demand", leading to a significant resource waste and low effectiveness. To address this issue, this paper adopted a scenario-driven perspective to construct a dynamic adaptability model of "three dimensions and one core" through an indepth case study of a Chinese energy sales group. The study explored how enterprises can enhance their dynamic adaptability to internal and external demands through a double-layer feedback loop centered on "demand-driven digitalization of management" and "scenario-driven digitalization of business". The research findings are as follows: (1) The management of a digital feedback closed loop is central to digital transformation, and the iterative processes of demand identification, scenario design, and scenario feedback ensure the adaptability of scenario construction and demand alignment, thereby driving dynamic adaptability through scenario iteration; (2) Scenario capability shapes the trajectory of digital transformation. By upgrading technology, strengthening customer connections, and leveraging data-driven insights, enterprises enhance their internal and external adaptability, fostering the development and innovation of new scenarios; (3) The spiral upgrade of digital scenario construction fuels value creation, enabling enterprises to achieve adaptive dynamic adjustments in response to emerging technologies, evolving demands, and new market opportunities; (4) The scenario-driven value creation path exhibits dual characteristics: stock enhancement and incremental creation. Through demand-responsive management decisions and scenario iteration, a performance feedback loop is established, facilitating dynamic and adaptive value creation. This paper has introduced the concept of "dynamic adaptive value creation through digital scenarios", elucidated the demand-driven and scenario-based dynamic adaptive mechanisms, and it will provide a systematic and adaptive perspective for advancing both the theoretical and practical dimensions of enterprise digital transformation. The findings will offer innovative insights and actionable guidance for enterprises seeking efficient and effective digital transformation.

   The rapid development of artificial intelligence (AI) has injected new vitality into China′s smart manufacturing transformation. How to integrate AI with smart manufacturing to achieve transformation is a critical challenge for Chinese manufacturing enterprises. From the perspective of "cognitive-behavior" integration, this paper explored the process of AI-driven smart manufacturing transformation in manufacturing enterprises through a longitudinal case study of BOE Technology Group. The findings indicated that: (1) The AI-driven transformation process of manufacturing enterprises experiences three stages: automation, integration and autonomy, culminating in a leap from basic intelligence to ecological intelligence; (2) Manufacturing enterprises employ three meaning construction approaches— "technology demand-driven", "strategic awakening-driven", and "cultural reshaping-driven"—which influence the three resource orchestration patterns of "utilization-oriented → exploration-oriented → dynamic-oriented", thus facilitating the integration of AI and smart manufacturing transformation; (3) The role of meaning construction in resource orchestration evolves dynamically in multiple stages, and presents a process of "guidance → reshaping → reinforcement". The results have not only provided a theoretical framework for manufacturing enterprises to achieve smart manufacturing transformation but also enriched the application of resource orchestration theory and managerial cognition theory. The study will offer valuable insights for strategic decision-making in smart manufacturing transformation within manufacturing enterprises.

   The development of new quality productive forces (NQPF) hinges on the scientific and technological innovation, with a focus on industrial upgrading. The current wave of scientific and technological revolutions, driven by artificial intelligence (AI), is providing new momentum for China′s sustained economic growth. Based on the micro-data from Chinese-listed companies from 2009 to 2022, this study constructed an NQPF index system and empirically tested the impact of AI applications on NQPF and its mechanisms via the "innovation" and "industry" pathways by using the two-way fixed effect model. The findings indicated that: (1) AI applications significantly enhance NQPF through multidimensional new quality factors of production; (2) the mechanism test showed that AI applications foster substantial innovation rather than strategic innovation, with substantial innovation positively mediating and moderating the impact of AI on NQPF; (3) the internal specialization and external supply chain efficiency also positively mediate and moderate this impact, while the AI-driven industrial chain integration stimulates NQPF; (4) the heterogeneity analysis revealed that AI′s impact on NQPF is more pronounced in the private, manufacturing, and competitive industry firms. This study has clarified the differences in innovation motivation of AI application effectiveness, elucidated the mechanism of AI-induced industrial fission and chain upgrading, and enriched the research on NQPF influencing factors and the economic consequences of AI applications, which will provide important policy insights for leveraging AI to drive NQPF development.

   Under the policy guidance of enhancing the overall effectiveness of China′s innovation system and fostering an open innovation ecosystem with global competitiveness, the academic community has paid attention to how to improve the effectiveness of innovation ecosystems. This paper revealed the co-evolution mechanisms at both macro- and micro-levels for enhancing the effectiveness of open innovation ecosystems from the perspective of co-evolution. On this basis, it employed the qualitative research method to elucidate the core driving mechanisms for improving the effectiveness of open innovation ecosystems in the Chinese context and analyzed the effective paths for enhancing system effectiveness. The following conclusions were drawn from this research: (1) the core driving mechanism for improving the effectiveness of China′s open innovation ecosystem lies in the emergence and co-evolution of heterogeneous participants at the micro level, as well as the optimization of environmental factors that promote co-evolution at the macro level of the system; (2) the value proposition of "openness-symbiosis" plays a central leading role, enabling sustained improvement in ecosystem effectiveness; (3) fully respecting value co-creation is conducive to the formation of long-term ecological advantages within the ecosystem; and (4) the open and advanced manufacturing service capabilities have become a critical entry point for China′s open innovation ecosystem to maintain a high level of openness and enhance system effectiveness. This study has broadened the theoretical research on innovation ecosystems and it will provide some theoretical references and countermeasures for improving the effectiveness of China′s open innovation ecosystems.

Fiscal policies for scientific research play a pivotal role in advancing the innovation-driven development strategy. Enhancing the quality and efficiency of these policies is essential for improving the overall performance of the national innovation system and achieving Chinese modernization. This study employed a qualitative research approach to systematically examine the practical logic and optimization pathways of China′s fiscal policies for scientific research. The main findings are as follows: (1) The policy framework is grounded in the National Innovation System Theory and the Theory of National Competitive Advantage. The government employs fiscal expenditure and tax incentive policies to orchestrate the allocation of innovation factors and propel the momentum of scientific and technological innovation. (2) Since the 18th National Congress of the Communist Party of China, policy practices have evolved to feature stronger guidance through public spending, more pronounced effects of tax incentives, and a more integrated approach to fiscal coordination. These shifts reflect the adjustment of government functions, the strengthening of enterprises as primary innovation actors, and the development of a modernized industrial system. (3) At present, key challenges include constraints of policy dependency on innovation performance, lack of systematic evaluation of policy synergies, inadequate mechanisms for talent incentives, and underdeveloped mechanisms for research commercialization. Therefore, further efforts are required to realign policy orientation and functional roles to drive enterprise-led innovation, leverage platform development and talent support to overcome barriers in basic research, and enhance institutional guarantees and service systems to foster cross-sectoral collaboration. These insights will contribute to the theoretical and practical discourse on strengthening the foundational systems of all-round innovation.

     Promoting the market-oriented construction of data elements can give full play to the role of data elements and is of great significance for cultivating and developing new quality productive forces. This paper selected the data of A-share listed companies from 2015 to 2022 as research samples, adopted the establishment of data trading platform as a practical exploration to promote the marketization of data elements, and used the progressive differential model to empirically test the impact of data element marketization on the level of new quality productive forces of enterprises. It was found that the marketization of data elements can significantly improve the level of new quality productive forces of enterprises, and this conclusion is still valid after various robustness tests. The mechanism test showed that the marketization of data elements affects the level of new quality productive forces of enterprises by improving the application level of digital technology and the efficiency of resource allocation. The heterogeneity analysis showed that the marketization of data elements has a more significant impact on enterprises in regions with higher digital economy level, labor-intensive enterprises and high-tech enterprises. The research conclusion will have important implications for further promoting the market-oriented construction of data elements and cultivating and developing new quality productive forces from the enterprise level.

   With the advent of the digital era, the lack and alienation of digital responsibility have frequently emerged, triggering widespread digital trust crises. Current research on Corporate Digital Responsibility (CDR) remains in its nascent stage, with no unified consensus on its conceptual boundaries. Scholars predominantly focus on fragmented issues such as algorithmic accountability, while in-depth explorations of its research scope, foundational theories, influencing factors, and mechanisms of action remain insufficient. To address this deficiency, this study first synthesized scholars′ definitions and research perspectives to conceptualize CDR, categorizing it into four dimensions: social, environmental, economic, and technological responsibilities. Second, it delineated the research trajectory and core themes of CDR through temporal distribution analysis, co-citation analysis, and keyword co-occurrence network mapping. Furthermore, drawing on existing scholarship, the paper constructed theoretical frameworks grounded in the stakeholder theory and the power-responsibility equilibrium theory from a digital perspective, systematically examining external societal environments and internal organizational factors that influence CDR, along with their operational mechanisms. Finally, by identifying current research limitations and proposing advancements in content exploration, methodological innovation, and contextual embedding, this study has outlined future directions for CDR research. In addition, employing systematic literature review and bibliometric analysis, this paper has conducted a structured synthesis and visualization of extant CDR literature, aiming to provide valuable insights for subsequent research in this field.

   Industrial policy plays a crucial role in leading the development direction and allocating resource elements of China′s NEV industry. However, the dimensions of existing studies on industrial policy change and characterization are relatively single. Therefore, this paper proposed a three-dimensional analytical framework based on the purposefulness, direction and orientation of industrial policies from the perspective of policy evolution. The 1783 NEV industrial policies enacted from 2007 to 2023 are analyzed based on the computational text quantitative analysis method. Unsupervised machine learning LDA topic modeling was used to portray the evolution process of China′s NEV industrial policies, and the supervised learning random forest algorithm was used to predict and classify the policy types and summarize the stage characteristics of policy distribution. The study found that: (1) China′s NEV industrial policy has gone through a process of change from the initial stage to the rapid promotion stage to the high-quality development stage, and the distribution of policies has transitioned from the initial fragmentation and imbalance to a more centralized and balanced state. (2) The policies are mainly selective, regional and market-oriented, but the policy changes show a trend of gradual transfer to functional, industrial and technology-oriented. (3) The process of policy change shows the characteristics of market access system tends to be mature, financial subsidies oriented to be precise, infrastructure construction is increasingly important, and the trend of informationization and intelligence is strengthened. This paper has expanded the theoretical perspective of policy change research, improved the analytical framework of policy tools, enriched the analytical methods of industrial policy change characteristics and laws, and it will provide some practical insights for industrial policy optimization.

     In the era of digital economy, the coordination mechanism formed by "people" and "data" is of great importance, which not only helps to promote the transformation of traditional economic growth mode, but also has an important impact on the high-quality development of enterprises. Based on the 2013-2022 data of A-share listed companies in Shanghai and Shenzhen, a coupled coordination model was constructed to describe the degree of collaboration between human capital and data assets of enterprises, and a fixed effect model was adopted to explore the impact and mechanism of collaboration between human capital and data assets on high-quality development of enterprises from the perspective of value chain. The findings are as follows: (1) "people and data" coordination can significantly promote the high-quality development of enterprises; (2) "people and data" coordination promotes the high-quality development of enterprises by improving their innovation capacity, production capacity and marketing capacity; and (3) for non-high-tech enterprises, those with low degree of industry competition and those with low attention to the digital economy of regional governments, the coordination of "people and data" has a more significant effect on promoting high-quality development of enterprises. This paper has expanded the relevant research of "people and data" coordination, and it will provide a useful reference for enterprises to exert the multiplier effect of data elements in the process of data asset management and cultivate and develop new quality productivity.

    Since the reform and opening-up, a large number of Chinese enterprises have been enlisted in the Fortune Global 500. The emergence of these outstanding firms is closely tied to their effective management practices. However, management theories derived from China′s management practices remain scarce, thus highlighting a significant gap where theoretical development lags behind advanced practices. As a result, the task of building an autonomous Chinese management knowledge system is both pressing and formidable. This paper focused on the development of management theories based on Chinese management practices and reviewed three central debates: (1) whether there exists Chinese management, (2) whether there exists Chinese management theory, and (3) how to develop management theory based on the Chinese practice. The paper summarized the "Four Quadrants and Two Cycles" Communication Path of Chinese and Western Management Theory and Practice and, based on the "Scientific Process" model, explored the logic and methodology of theory building grounded in the Chinese management practice. It also discussed the role of contextual factors in shaping theoretical development. This study has responded to the national call for constructing an autonomous knowledge system and it will provide valuable guidance for Chinese management scholars seeking to build theories rooted in local practices.

   The strategic emerging industries are the core carriers for nurturing new quality productive forces, and the micro-level mechanisms for developing new quality productive forces are yet to be explored. Based on the organizational ecology theory, this study selected China′s A-share listed industrial enterprises in strategic emerging industries from 2010 to 2020 as research samples and investigated the impact of strategic deviation on firms′ new quality productive forces by using the panel data regression method. The results revealed that: (1) strategic deviation significantly enhances firms′ new quality productive forces; (2) the higher the level of industrial turbulence, the weaker the positive effect of strategic deviation on firms′ new quality productive forces, and specifically, under highly turbulent industrial environments, the positive effect of strategic deviation on firms′ new quality productive forces turns into a negative effect; and (3) the faster the industry clock-speed, the stronger the positive effect of strategic deviation on firms′ new quality productive forces, and specifically, rapidly developing industrial environments can amplify the positive role of strategic deviation. This study has expanded the understanding of the economic consequences of strategic deviation and the strategic antecedents of firms′ new quality productive forces at the micro-level, and it will provide a reference for firms to develop new quality productive forces in a context-specific manner. 

   The orderly flow of data elements, as a new type of innovation factor to cultivate new dynamics of economic growth, is the key to stimulating the potential of data elements. From the perspective of data element flow environment, this paper adopted the Laurie gravity model to construct the spatial correlation network of data element flow, examined the circulation of data element flow with the help of social network analysis method, and adopted the stochastic actor model to explore the role mechanism of the driving factors of the evolution of spatial network structure, and measured the relative importance of each driving factor. The results showed that: (1) the flow of data elements is characterized by a network structure, with frequent flow of data elements between provincial-level regions and obvious spatial linkage effects; (2) Under the impetus of policies such as "data in the east and handling capacity in the west"; the sources of plate members are diversified; the roles assumed by regions are dynamic; the flow of data elements is gradually showing polycentric characteristics; and the topology of the spatial correlation network of the flow of data elements is more complex; (3) Endogenous structural factors play a dominant role in the evolution of the network structure, and each provincial-level region promotes the flow of data elements by strengthening the division of labor and cooperation through the industrial chain. Among the exogenous attribute factors, reducing the government′s non-market intervention and enhancing regional innovation capability are conducive to accelerating the flow of data elements. This study will provide a new perspective on the construction of a unified national market.

    Digital industry clusters are a phenomenon of industrial agglomeration in the context of digital economy with digital native enterprises and digital transformation of incumbent enterprises as important components. Due to differences in the ability of different enterprises to access digital resources, digital industry clusters have the problem of poor industry chain integration, the digital divide faced by cluster enterprises has turned from the "access divide" to the "ability divide". From the perspective of "data elementsdigital technology" interaction, the study explored the evolution mechanism of digital industry clusters through an exploratory multicase analysis of Hefei Artificial Intelligence Industry Cluster, Hangzhou Digital Security Industry Cluster, and Wuhan Optoelectronic Information Industry Cluster. The study revealed that: (1) the deepening of interactive degree and promotion of interactive efficiency between data elements and digital technology, drive the clusters to cluster quickly in cyberspace, which has an impact on the cluster development mode and the building of cluster competitiveness; (2) the dualwheel drive of "data elementsdigital technology" promotes the transformation of cluster network relationships from "imitative network embedding" and "progressive network embedding" to "break through network embedding" by changing the digital connection mode of cluster enterprises; (3) in the process of evolution and development, digital industry clusters choose different development modes according to the level of agglomeration they have reached and the problems they have faced, which promotes the extension of the digital economy industry chain. The findings will provide theoretical basis for the efficient and rational allocation of digital resources in China, and promote the sustainable development of digital industry clusters.

 Artificial intelligence (AI) technology has become an essential tool for deepening the theoretical research on corporate innovation, thus increasingly embedding itself into enterprise innovation practices and significantly impacting innovation efficiency. Grounded in a corporate lifecycle perspective, this study leveraged the data from 1,103 Chinese hightech manufacturing enterprises listed between 2016 and 2021, by employing the decision tree analysis and multiple machine learning methods to examine AI′s heterogeneous effects on enterprise innovation across different lifecycle stages. The findings are as follows: (1) Most machine learning models demonstrate superior predictive performance in forecasting firms′ innovation performance (patent authorizations) compared to traditional linear regression models, highlighting machine learning′s capability in capturing nonlinear relationships among variables. (2) Enhancing AI capability and increasing AI patent filings significantly boost innovation capacity in maturestage firms, while the innovation performance of growth and declinestage firms depends less on AI technology. (3) The decision tree analysis further indicated that innovation in growth and maturity stages primarily relies on AI patent activities, whereas innovation in declinestage firms depends more heavily on the size of employees and technical personnel. Overall, while AI technology positively correlates with enterprise innovation performance, this relationship varies significantly across corporate lifecycle stages. This study will extend the lifecycle theory and provide practical guidance for enterprises on strategically deploying AI technology according to their developmental stages.
Keywords:

    Cultivating new quality productive forces serves as an inherent requirement and central focus in driving premium-grade societal progress. Technological innovation in strategic emerging industries plays a significant role in driving the development of new quality productive forces. However, existing literature remains insufficient in exploring the specific driving mechanisms. Based on the TOE (Technology-Organization-Environment) framework, this study employed the NCA (Necessary Condition Analysis) and dynamic QCA (Qualitative Comparative Analysis) methods to analyze the panel data from 30 provincial-level regions in China from 2011 to 2021, investigating the configurational pathways of new quality productive forces formed through the interaction of technological, organizational and environmental factors of technological innovation in strategic emerging industries. The research findings revealed that: First, the single factor of technological innovation is insufficient to constitute necessary conditions for driving the development of new quality productive forces; Second, there are three configurational pathways have been identified for realizing high-level development of new quality productive forces: the technology-intellectual property protection synergy model, the technology-organization dual synergy model, and the technology-organization-environment multiple synergy model, with technological elements consistently serving as core conditions across all pathways; Third, the configurational pathways driving the development of new quality productive forces exhibit certain spatiotemporal variation patterns. Temporally, multiple pathways demonstrate sequential stability with gradually increasing explanatory power; Spatially, the within-group coverage of some pathways shows regional variations. The conclusions of this study will contribute to deepening the understanding of the complex mechanisms underlying the formation pathways of new quality productive forces in China, and provide theoretical foundations and practical guidance for developing new quality productive forces tailored to local conditions.

    The deep integration of digital economy and real economy is not only reflected in the deepening of enterprise digital transformation, but also in its structural optimization. How to promote high-quality economic development with high-quality transformation has been given new significance in digital economy. This paper used the data of Shanghai and Shenzhen A-share listed companies from 2007 to 2021 to explore the impact of enterprise digital transformation structure on innovation strategy. We found that: (1) the enterprise digital structural optimization significantly promotes the innovation strategy to substantive innovation, and independent innovation ability enhancement, human capital re-upgrading, operating cost reduction are important channels; (2) when the degree of innovation competition in the industry, the quality of labor supply in the region and the support from innovation incentive policies is lower, the digital structural optimization has a more obvious promoting effect; and (3) the impact of subdivided digital technology structures on innovation strategy has both commonalities and characteristics, and the digital structural optimization promotes the concentration of innovation fields and the strengthening of innovation spillovers. From the perspective of structure, this paper has enriched the research on enterprise digital transformation, and it will provide solutions for how to alleviate the problem of "valuing quantity over quality" in innovation, thus having implications for enterprises to choose digital transformation paths and governments to design digital economic policies.

   Improving the structural resilience of innovation networks is an important theoretical and practical issue facing the high-quality development of the AI industry. This paper adopted the AI industry in Shanghai as an example, constructed a cooperative innovation network through text mining, and then used the ERGM to reveal the impact of endogenous factors of the innovation network on its structural resilience. The research found that: (1) The evolution of the structural resilience of the innovation network in Shanghai′s AI industry can be divided into three main stages: slow growth, rapid growth, and maintaining stability. The continuous improvement of network heterogeneity is the key driver for the transformation of the innovation network from an assortative core network to a resilient network; (2) The intermediary structure negatively affects the structural resilience of the artificial intelligence industry′s innovation network; (3) The expansive structure negatively affects the structural resilience of the AI industry′s innovation network in general, but the three-star structure positively affects its structural resilience. This study has addressed the gap in research on the resilience of innovation networks in the artificial intelligence industry, and it will help to deepen the theoretical understanding of innovation network resilience, thus having positive implications for enriching the data sources of innovation network research.

   Improving the efficiency of enterprise resource allocation is the key to ensure cost control, enhance productivity and enhance competitiveness. As an important new factor of production in the era of digital economy, it is worth exploring whether enterprises can improve their resource allocation efficiency through marketization of data elements. Based on the data of Shanghai and Shenzhen A-share listed companies from 2011 to 2022, this paper constructed a dual difference model to effectively test the influence effect and action path of the marketization of data elements on the enterprise resource allocation efficiency. The study found that: (1) the marketization of data elements has significantly improved the efficiency of enterprise resource allocation, and the influence on high degree of market segmentation, strong factor flow and high degree of market competition is more obvious in regions and industries; (2) the marketization effect of data element construction on improving the efficiency of enterprise resource allocation is mainly realized through cost saving effect and digital technology innovation; and (3) the marketization of data elements increases the total productivity by affecting the efficiency of enterprise resource allocation, and the overall trend of first decreasing, then decreasing and then increasing. This paper will enrich the relevant research on the economic consequences of the marketization of data elements, reveal the internal mechanism of the marketization of data elements to improve the efficiency of enterprise resource allocation, and provide empirical evidence for further improving the efficiency of enterprise resource allocation and promoting the development of market economy.

    With the deepening development of a new round of scientific and technological revolution and industrial transformation, artificial intelligence (AI) has become an important driving force for the transformation and high-quality economic development of enterprises. This paper selected A-share listed companies from 2004 to 2021 as samples, and adopted the dual machine learning method to evaluate the impact and mechanism of artificial intelligence on enterprise growth. The econometric results showed that: (1) AI significantly promotes firm growth and the conclusions remain valid after a series of robustness tests; (2) The heterogeneity analysis revealed that the promotion effect of AI on enterprise growth is more significant in non-state-owned enterprises, small and medium-sized enterprises, and traditional industries; (3) The mechanism test based on the perspective of AI′s technical-economic characteristics showed that AI mainly exerts its synergy, innovation and substitutability to promote enterprise growth by improving enterprise productivity, reducing management costs, promoting enterprise innovation, and optimizing the structure of labor employment. The paper has revealed the important role and internal mechanism of AI in promoting enterprise growth, and it will provide some important supplements and references for accelerating the development of AI and promoting enterprise transformation and upgrading.

    Innovation, as the primary driving force for development, is a crucial power in propelling China′s economic construction. However, Chinese enterprises still face numerous challenges such as weak innovation capabilities, insufficient continuous innovation, and low coordinated innovation abilities. With the deepening of enterprise informatization processes, digital and intelligent empowerment has become a new driving force for enterprises to enhance their innovative capabilities and gain sustainable competitive advantages. Based on the resource orchestration theory and the innovation ecosystem theory, following the TOE framework and using 275 computer, communication, and electronic equipment manufacturing enterprises as examples, this paper employed the fuzzy-set qualitative comparative analysis (fsQCA) method to investigate various linkage path combinations through which digital and intelligent empowerment can achieve dual innovation in enterprises. The study found that: Firstly, technical level, organizational characteristics, and environmental features cannot individually serve as necessary conditions for achieving high dual innovation; they need to work synergistically. Secondly, exploratory innovation and exploitative innovation have differentiated driving paths. Specifically, there are three driving paths for exploratory innovation: the technology research and development-driven type under the dual logic of organization and environment, the digital and intelligent executive-driven type under the dual logic of technology and environment, and the triple driving type under the logic of technology, organization, and environment; while there is one driving path for exploitative innovation: the dual driving type under the logic of technology and organization. Under digital and intelligent empowerment, the driving paths for exploratory innovation are more diverse, and the development of innovation based on entirely new concepts or technological paradigms relies more heavily on the synergistic linkage between different digital and intelligent conditions. Thirdly, technical conditions are a critical core condition for developing high dual innovation; when technical conditions are sufficiently superior, organizational and environmental conditions exhibit a substitutive relationship and can interact with technical conditions to jointly promote high dual innovation development. This research will help deepen the understanding of the complex paths of enterprise dual innovation and thus provide significant insights for policy formulation.

    Measuring the development level of digital-real integration in Chinese cities and analyzing its spatial-temporal evolution and influencing factors can provide quantitative support for accelerating the Digital China strategy. Taking 283 cities in China as the research objects, this paper explored the spatial-temporal evolution characteristics and influencing factors of digital-real integration in Chinese cities from 2011 to 2021 by adopting methods such as entropy method, coupling coordination degree, kernel density, Dagum Gini coefficient, and spatial econometric model. The results showed that: (1) during the sample period, the level of digital-real integration shows an increasing trend year by year, but the overall level is relatively low, and the trend of widening inter-regional gaps is evident; the eastern region leads, while the central, western, and northeastern regions lag behind, but spatially exhibit the characteristics of blossoming in multiple points, evolving from points to lines and then to planes; (2) the four major regions have their respective evolution patterns in terms of peak shifts, distribution trends, and polarization, with an overall good evolution trend but a slight polarization phenomenon emerging; (3) the relative differences in digital-real integration show a characteristic of first narrowing and then expanding, and the inter-regional differences are the main source of the overall differences; (4) the level of digital-real integration exhibits a pronounced "agglomeration club" trend, with "high-high" and "low-low" agglomerations dominating the spatial agglomeration types, and the agglomeration trend is relatively stable; and (5) the analysis of influencing factors reveals that economic fundamentals, financing constraints, industrial support, government support, and innovation capabilities can significantly improve the level of digital-real integration, but the role of talent security in promoting it is not significant.

   The proposal of the new quality productive force theory provides significant guidance for transforming traditional economic growth models and promoting high-quality development. Accelerating the intelligent transformation of enterprises is not only the main driving force for creation of new quality productive forces, but also the only way to achieve high-quality development. Based on this, this paper studied the empowering path of intelligent transformation from the perspective of developing new quality productive forces, and selected the data of A-share listed manufacturing enterprises from 2012 to 2022 as samples to analyze the impact of intelligent transformation on new quality productive forces and high-quality development. The study revealed the following findings: (1) Intelligent transformation significantly promotes high-quality development, exhibiting a clear linear relationship. (2) The mechanism tests showed that new quality productive forces serve as a mediating mechanism between intelligent transformation and high-quality development. (3) The threshold effect analysis identified a double-threshold effect of R&D investment on intelligent transformation and high-quality development. (4) The regional heterogeneity test demonstrated that intelligent transformation in central cities significantly enhances labor skill improvement, while its impact in non-central cities is limited. (5) The heterogeneity analysis of enterprises found that intelligent transformation in the technology-intensive firms significantly enhances tangible labor resources, whereas its impact is negligible in the asset-intensive and labor-intensive firms. This paper has combined the Marxist productivity theory with the theory of production factors under the category of economics, and it will provide scientific basis for promoting new quality productive forces and achieving high-quality development.

  The digital economy is a key driver of high-level development in county-level economies, but the determining factors and emergent mechanisms of its development remain to be explored. Based on the configurational perspective, this study constructed a framework for analyzing the input and output of the digital ecosystem using configurational analysis. The QCA method was employed to analyze the complex causal relationships behind the emergence of a high-level digital economy in 90 districts, counties and cities in Zhejiang Province. The study found that individual digital ecosystem factors are neither necessary nor sufficient for the emergence of a high-level county digital economy. Industrial foundations are crucial for high-level industrial digitalization, while digital human resources are equally important for achieving digital industrialization. Besides, three emergent mechanisms and four types of digital ecosystems lead to high-level industrial digitalization, market element agglomeration type, market-driven logic government empowerment type (two digital ecosystems), and policy-driven talent core type, while two emergent mechanisms contribute to high-level industrial digitalization, namely, policy-guided market transformation and upgrading type and market resource optimization type. In addition, five types of digital ecosystems result in non-high-level digital economies. The findings will offer some significant theoretical guidance and practical insights for the layout and realization of high-level digital economy development in China. 

    Stimulating the vitality of various market entities is a critical measure to accelerate the establishment of a new development paradigm. However, existing studies primarily focus on the direct effects of the policies for the "Specialized, Refined, Distinctive, and Innovative" (SRDI) enterprises on "Little Giant" enterprises, while overlooking its potential impact on their supply chain partners. Using the data from A-share listed companies in China between 2008 and 2022, this study employed a two-stage staggered difference-in-differences model to investigate how the policies for SRDI enterprises influence the innovation of supply chain partners associated with "Little Giant" enterprises. The findings revealed that the policies inhibit the innovation of supply chain partners through agglomerative effects and population effect, and such inhibitory effect exhibiting a dynamic temporal pattern. On one hand, the policies for SRDI enterprises concentrate the financial capital and intellectual resources on "Little Giant" enterprises, thereby constraining the innovation resources available to their supply chain partners. On the other hand, as the policies for SRDI enterprises progress, their supply chain partners exhibit "free-riding" behavior, reducing their incentives for independent innovation. Further analysis demonstrated that the supply chain partners with higher levels of digitalization and positive and stable expectations about macroeconomic conditions can effectively mitigate this inhibitory effect, stimulate their innovative activity, and enhance both enterprise development quality and supply chain resilience. This study constructs a theoretical framework to evaluate the indirect effects of the SRDI policy based on the supply chain transmission mechanism, providing an important theoretical foundation for a comprehensive understanding of the policy′s effects.

   In the digital economy era, the question arises whether digital intelligence policy, exemplified by the ‘Broadband China’ strategy and national smart city pilots, can synergize to effectively drive digital technology innovation. Based on the panel data from 282 prefecture-level cities in China spanning from 2011 to 2021, this study employed a double machine learning model to assess the multidimensional policy effects of digital intelligence policy on digital technology innovation. The results showed that: (1) Digital intelligence policy exhibit a significant synergistic effect on digital technology innovation, with the ‘double pilot’ policy showing a more pronounced empowerment effect compared to the ‘single pilot’ policy. These findings held true even after conducting a series of robustness checks and addressing endogeneity concerns; (2) Digital intelligence policy not only contribute directly to the dual-drive of digital technology innovation but also support it indirectly through enhancing entrepreneurial activity and agglomeration, strengthening intellectual property legislation and enforcement, and fostering both hard and soft infrastructure of the digital ecosystem; (3) The effectiveness of the synergistic empowerment from digital intelligence policy is closely linked to geographical location and urban characteristics. On the one hand, central cities, southern region and eastern regions of the Hu Huanyong Line can obtain more dividends of digital technology innovation from these synergistic policies. On the other hand, cities with a higher endowment of digital talent, greater government investment in science and technology, and higher levels of digital access exhibit a more pronounced effect of these policies. This research will provide vital insights for local governments to strengthen the coordination of multidimensional policies, explore deeply the synergistic empowerment of policy planning, and accelerate the construction of Digital China.

   Founding team resilience is critical for effectively addressing challenges posed by high uncertainty during the entrepreneurial process, enabling teams to recover, adapt, and grow rapidly while ensuring the survival and development of entrepreneurial ventures. However, existing research on founding team resilience remains in its initial stage. To address this, we clarified the research topic and status of founding team resilience by using a systematic literature review method, finding that existing studies lack consensus on the conceptual definition of founding team resilience, with research themes remaining fragmented, and there is a lack of attention to the unique characteristics and formation processes of founding team resilience in the Chinese context. In view of this, based on the perspective of dynamic process, we identified the concept of founding team resilience, divided it into three dimensions of recovery, stability, and growth, and indicated its connotation characteristics of highly contextualized and dynamically developing. Further, based on the "ternary interaction" model of the Social Cognitive Theory, a theoretical analysis framework was constructed by combining the characteristic basis, action process and contextual factors. The results showed that, founding team resilience is influenced by team structural, team cognitive, and team capabilities characteristics, and its formation is the result of the founding team effectively matching the internal and external context of enterprise to carry out strategic actions and continuous interactive iteration. Finally, we indicated that future research should consider measurement scales, influencing factors and mechanisms, impacts, and research in the Chinese context of founding team resilience. By enriching the perspective of founding team research under uncertain context, and broadening the scope of founding resilience research, we aim to offer some theoretical guidance to help founding teams overcome challenges and promote high-quality development.

   Innovation is the first driving force for enterprise development, but with the world entering a century of unprecedented changes, the increase in supply chain uncertainty has brought major challenges to enterprise innovation. This paper constructed a theoretical model to describe the internal mechanism of supply chain uncertainty affecting firm innovation, and provided a measurement method of supply chain uncertainty and makes an empirical test. It was found that the increase of supply chain uncertainty will inhibit enterprise innovation, mainly due to the resource constraint effect caused by supply chain uncertainty resulting in the reduction of commercial credit financing, and to the value wait-and-see effect caused by the contraction of investment in special assets. The internal digital transformation and the increased transparency of the supply chain, the reform of the margin financing system outside the enterprise and the opening of the capital market can help alleviate the negative impact of supply chain uncertainty on enterprise innovation. The uncertainty of different supply chain entities can inhibit the innovation of enterprises, but compared with customer uncertainty, supplier uncertainty has a stronger inhibitory effect on innovation. From the perspective of enterprise innovation output, supply chain uncertainty also has an inhibitory effect on enterprise innovation output, especially the negative impact on joint innovation. This study has revealed the economic consequences of supply chain uncertainty, and it will provide a new perspective and knowledge for maintaining the security and stability of national supply chain and promoting enterprise innovation.

   Accelerating artificial intelligence (AI) technological innovation is crucial for China to gain the strategic advantages in the new round of global competition and to promote the development of new productive forces. Intellectual property (IP) protection is an essential institutional arrangement for stimulating innovative vitality. However, given the typical characteristics of AI innovation, the effectiveness of IP protection in facilitating innovation remains unclear. Based on the identification criteria of AI patents released by WIPO, we constructed a dataset of over 330,000 AI patent applications from 273 Chinese cities from 2010 to 2021. On this basis, the research employed the intellectual property demonstration city policy as a quasi-natural experiment framework and applies a multi-period DID model to examine the impact of IP protection on AI innovation. The results indicated that the IP demonstration city policy significantly promotes urban AI innovation through mechanisms such as the agglomeration of scientific and digital talents, enhanced innovation-oriented public expenditure, and introduction of venture capital. The heterogeneity analysis showed that the incentive effect of IP protection is pronounced in collaborative patents, foundational technology patents, and the output of invention patents. Further research revealed that IP protection not only increases the quantity of urban AI innovation but also contributes to improving innovation quality. This study will provide detailed evidence for the impacts of IP protection on AI innovation and offer valuable implications for fully leveraging the incentive effects of IP policies.

   Under the technology decoupling of China from the US, overcoming the "neck strangling" challenges posed by key core technologies has become a primary problem for Chinese megaprojects. Based on the innovation ecosystem theory, using a longitudinal single case analysis, this paper explored the mechanism of collaborative innovation among numerous participants in mega-projects to analyze the "catch-up" process of China′s high-speed railway technology. The results indicated that: (1) the changes in core platform promote the evolution of the innovation ecosystem; (2) the collaboration models and mechanisms of collaborative innovation among participants evolve at different stages of the innovation ecosystem′s development in mega-projects; and (3) collaboration among institutions, organizations, and knowledge interacts mutually, supporting and promoting the formation, evolution, and technological innovation efficiency of the innovation ecosystem. The study will enrich the theoretical framework of innovation ecosystems based on Chinese practices, expand the scope of research on platform theory and collaborative innovation theory, and provide enlightenment for overcoming the "neck strangling" dilemma of critical core technologies in mega-projects in China.

The digital transformation of manufacturing enterprises has increasingly become a hot spot for both academics and practitioners. Due to the complexity of transformation, the related mode and realization mechanism are still in the exploration stage. Using the method of qualitative meta-analysis and 53 research cases as objects, this paper expanded and deepened the dual perspectives of "process value-added-customer value-added" and explored the internal mechanisms of different digital transformation modes of Chinese manufacturing enterprises. The results showed that: First, the digital transformation consists of two paths, namely process value-added and customer value-added, which are manifested in the positioning of local digitization, global digitization and ecological digitization as well as the choice between goods-dominant logic and service-dominant logic. Second, according to the combination of differentiated paths, the transformation modes can be divided into such five types as leap-over type, reconfiguration type, incremental type, mixed type and disruptive type. Third, the internal mechanism of digital transformation mode can be analyzed by using the logical chain of "transformation context - behavioral mechanism-transformation result". Different digital transformation modes stem from the differentiated resource reorganization mechanism, institutional action mechanism, digital innovation mechanism and transformation results which result from transformation contexts. This study has innovatively extended the micro-combination mechanism of resource orchestration, institutional, and digital innovation theories within diverse digital transformation modes, with its conclusions being instructive and inspiring for both relevant theories and practical endeavors in the digital transformation of manufacturing enterprises.

   The convergence of corporate digitalization and sustainability is a new context and hot topic in today′s academic research. However, there is still a lack of consensus among academics on the concept, dimensions and measurement of the variables of corporate digital sustainability. This study aims to construct a measurement system for corporate digital sustainability. Firstly, on the basis of reviewing existing studies on the connotation of corporate digital sustainability, the concept of corporate digital sustainability was defined in terms of "digital first" and "digitally-enabled", and its characteristics of integration, long-term and scalability were refined; second, a rooted approach was adopted to develop a methodology for measuring enterprise digital sustainability, which is based on the concept of "digital sustainability". Secondly, we adopted a rooted approach to develop a conceptual model of corporate digital sustainability centered on "digital asset sustainability, digital economic value, digital social responsibility and digital environmental management". Finally, we developed an initial scale for measuring corporate digital sustainability, and after a series of analysis and testing, we finally established a second-order, four-factor, fifteen-question optimal measurement model of corporate digital sustainability. In addition, the predictive validity test showed that the scale is a good predictor of corporate competitive advantage. The study will help to make up for the lack of existing research scales on corporate digital sustainability, not only laying a solid foundation for quantitative research on corporate digital sustainability, but also providing a useful reference for the implementation of corporate digital sustainability in the real world in the Chinese context.

   Empowering low-carbon production by digital technology is a key to promote new-quality productive forces of the manufacturing industry. Therefore, in this paper, low-carbon production efficiency was measured by using the super efficiency SBM method, based on the panel data of the manufacturing industry from 2013 to 2022. Green technology innovation and enterprise resilience were also introduced to construct a chain mediation effect model to test the mechanism of how digital empowerment affects low-carbon production efficiency in the manufacturing industry. The results obtained are as follows: (1) Low-carbon production efficiency of China's manufacturing industry exhibits significant spatial and industry heterogeneity, with a decreasing and hierarchical distribution of low-carbon production efficiency values from east to west in space. The efficiency growth rate in the western region is significantly higher than that in the central and eastern regions. The efficiency values of resource processing, automobile manufacturing, and electrical machinery and equipment manufacturing are significantly higher than those of other industries. (2) Digital empowerment improves the low-carbon production efficiency of the manufacturing industry significantly, and the intensity of its impact is increasing year by year. Green technology innovation and enterprise resilience have independent mediating effects in the impact of digital empowerment on low-carbon production efficiency in the manufacturing industry, with the former having a stronger mediating effect than the latter. Digital empowerment also enhances low-carbon production efficiency through a chain intermediary of green technology innovation and enterprise resilience. This paper will provide a theoretical path for the manufacturing industry to unleash the digital empowerment effect fully and to promote low-carbon production, and it will also provide a basis for government departments to optimize and improve industrial policies.

Digital innovation is extremely critical to strengthening corporate competitiveness and driving value chain. Data transactions play an important role in accelerating the high value conversion of data and helping digital innovation breakthroughs. Based on the data of listed companies from 2010 to 2022, this paper used crawler technology, text analysis and manual recognition to identify corporate data transactions, and constructed a dual machine learning model to empirically analyze the impact of data transactions on corporate digital innovation, further expanding the micro-effects and mechanisms of data transactions on digital innovation. The results of the study showed that data transactions have a significant positive impact on corporate digital innovation, and this positive effect is stronger among non-state-owned corporations, large corporations, corporations with high levels of intellectual property protection, and corporations with strong digital infrastructure. The mechanism analysis showed that data transactions have promoted corporate digital innovation through knowledge spillover effect, factor allocation effect, and corporate governance effect. In addition, the effect of the supply-side data transaction, data service transactions, and direct data transactions on promoting corporate digital innovation are more obvious, and data transactions can improve the quality of corporate digital innovation. This study has important implications for encouraging corporates to participate in data transactions, it will expand and strengthen the data factor market, and promote corporate digital innovation through data marketization.

 The new quality productive forces in essence are green, with a significant increase in total factor productivity as its core indicator. Accelerating the formation of new quality productive forces is key to achieving high-quality economic development in China. This paper employed the attention-based view to examine the impact of institutional investor ESG activism on the cultivation of corporate new quality productive forces, and analyzed the mediating role of management attention allocation, as well as the segmented moderating effects of digital media and digital intelligence manufacturing. Based on the hierarchical regression analysis of data from 2,660 Shanghai and Shenzhen A-share listed companies from 2012 to 2022, the study found that: (1) institutional investor ESG activism has a significant positive impact on corporate green total factor productivity; (2) management attention allocation mediates the relationship between institutional investor ESG activism and corporate green total factor productivity; (3) digital media positively moderates the relationship between institutional investor ESG activism and management attention allocation; and (4) digital intelligence manufacturing positively moderates the relationship between management attention allocation and corporate green total factor productivity. The research has clarified the mechanisms through which institutional investor ESG activism influences the cultivation of corporate new quality productive forces, thus enriching the literature on shareholder activism and the cultivation of new quality productive forces. Moreover, by combining the digital context to discuss the green governance role of shareholder activism, it will provide important insights for the coordinated development of digitalization and greening in China.

    To make breakthroughs in the key and core technologies and develop our own aero-engines is an important choice for national rejuvenation. However, there are still many capability dilemmas in technology breakthroughs of aero-engines, and there is an urgent need for capability reconfiguration. The reconfiguration paths for organization, resource and innovation capability were studied with the capability reconfiguration theory and in conjunction with case studies. Some important conclusions were drawn from this study as follows: (1) the technology breakthrough of aero-engines is a dynamic process of multiple capability reconfiguration which integrates organization, resource and innovation capabilities, and this process evolves continuously with technological breakthroughs and includes two stages, i.e. evolutionary reconfiguration and replacement reconfiguration; (2) organization reconfiguration is the foundation of technology breakthrough, and the organization reconfiguration from "plane-engine binding", "plane-engine loosening" to "plane-engine separation" maximizes the innovation autonomy and flexibility; (3) resource reconfiguration is the guarantee of technology breakthrough, and the reconfigurations from closed resource reconfiguration and open resource reconfiguration, to military-civilian resource sharing configuration effectively support technology breakthrough of aero-engines; and (4) innovation capability reconfiguration is the core of technology breakthrough, and the innovation capability reconfiguration from initial imitation to current independent R&D largely enhances the innovation capability of aero-engines.

   An appropriate exploratory innovation pace is crucial for enhancing corporate innovation efficiency and success rates, and it is of great significance for seizing innovation opportunities and realizing innovation-driven development. In the era of digital intelligence, the development and application of artificial intelligence (AI) technology have brought many opportunities for a new round of technological revolution and industrial transformation. However, existing research is insufficient in examining the relationship between the AI application and exploratory innovation pace at the firm level. Based on the organizational information processing theory and the data from Chinese A-share listed companies from 2011 to 2020, this paper conducted an empirical analysis on the relationship between AI application and exploratory innovation pace. The results showed that: (1) AI application has an inverted U-shaped impact on exploratory innovation pace; (2) Top management team regulatory focus moderates the relationship between AI application and exploratory innovation pace; (3) TMT promotion focus and prevention focus have heterogeneous regulatory effects on the relationship between AI application and exploratory innovation pace; and (4) The inverted U-shaped impact of AI application on exploratory innovation pace is stronger in non-state-owned enterprises, mature enterprises, low-tech industries, and enterprises in low-competition market environments. By doing so, this paper has deepened the understanding of the impact of AI application on exploratory innovation pace at the micro-corporate level and it will provide profound insights into the impact mechanism of top management team preference differences on corporate information processing and matching processes.