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".

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.

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.

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.

Artificial intelligence is a typical interdisciplinary field that integrates multiple disciplines and domains, exhibiting strong pervasiveness and convergence. Against the backdrop of the deepening implementation of the "AI+" initiative, adopting an integrative perspective to dissect the underlying logic of AI-enabled empowerment and systematically clarifying its essential mechanisms is of paramount theoretical and practical significance. This effort is crucial for identifying key pathways for the deep integration of AI with various sectors, promoting the practical application of AI, and optimizing AI governance. Based on this, this paper proposed that the essence of AI-enabled empowerment lies in the triple integration of "data integration, knowledge integration, and system integration". Specifically: (1) Data integration enables the integration of cross-modality, cross-spatiotemporal, and cross-domain datasets; (2) Knowledge integration drives five core capabilities: association recognition, causal reasoning, contradiction discovery, convergence approximation mutation emergence; (3) System integration achieves the engineering implementation of foundational technologies, functional technologies, and domain-specific technologies. To address challenges in integration—including inadequate data circulation mechanisms, algorithmic bias and decision-making deviations, and increased system security vulnerabilities—the following recommendations are proposed: (1) data circulation frameworks and standardization protocols should be refined to strengthen full-lifecycle data security safeguards; (2) algorithmic bias governance should be enhanced to improve model transparency and interpretability; and (3) the implementation of intelligent system integration should be pioneered to advance engineering resilience and ethical compliance capabilities. This study will provide valuable insights and references for promoting the healthy development of artificial intelligence and driving the intelligent transformation and upgrading of industries.

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.

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.

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 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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

The current complex and ever-evolving landscape has brought to the imperative question of how to facilitate the profound fusion of the digital economy and the tangible business world while concurrently bolstering the innovative capabilities of enterprises. This paper explored the underlying mechanisms through which economic policy uncertainty affects firm innovation, as well as the pivotal role played by digital transformation. Using the data from China's A-share listed companies, this paper revealed a U-shaped relationship between economic policy uncertainty and firm innovation, the elevation of economic policy uncertainty fosters a surge in strategic innovation, while concurrently precipitating a decline in substantive innovation. After incorporating digitization into the analysis framework, it was found that enterprise digital transformation intensifies the negative impact of economic policy uncertainty on innovation. The U-shaped relationship weakens, and the inflection point shifts to the right, approaching an L-shape. Furthermore, the negative effects of digital transformation are more pronounced in companies with high financing constraints and low industry competition. This study will provide important policy implications for China to promote high-quality development in the ever-changing digital economy era.

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.

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.

As a new type of production factor in the digital era, data factorization reconstructs the traditional productivity paradigm, and is an important engine to promote the transition of productivity. On the new journey of Chinese path to modernization, it is of great theoretical and practical significance to study how the value of data elements can improve the digital new quality productive forces of enterprises. Using the pilot cities of big data trading centers as a quasi-natural experiment, this paper adopted the data from 287 prefecture level cities in China from 2012 to 2022, combined with a multi period DID model, to evaluate the effectiveness and mechanism of digital new quality productive forces empowered by monetization of data elements. The research found that the establishment of big data trading centers can significantly enhance digital productivity, and that the promoting effect is mainly reflected in the sample group with a higher degree of government intervention and a larger digital divide. The monetization of data can promote the development of new quality productive forces by optimizing the structure of production factors and promoting the development of the digital economy, but the transmission path of optimizing the traditional factor structure is not yet clear. Further research found that data monetization has significant spatial spillover effects. The establishment of big data trading centers under adjacent spatial weights has a significant negative spillover effect on neighboring regions, while under economic spatial weights, the improvement of local data monetization level will have a positive spillover effect on neighboring regions. The conclusion of this study will not only provide new empirical evidence for relevant departments to lay out the construction of big data trading centers, but also provide useful insights for cultivating digital new quality productive forces.

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.

"Specialized, refined, differential and innovative (SRDI)" enterprises are vital for upgrading China's manufacturing sector and securing the industrial and supply chains. In the digital context, effectively coordinating the "invisible hand" of the market, the "visible hand" of the government, and enterprises' dynamic capabilities within the institutional environment is key to enhancing the innovation resilience and core competitiveness of SRDI enterprises. This study, based on the institutional and dynamic capabilities theories, analyzed the data from 360 SRDI enterprises using the fsQCA method, revealing that: (1) while no single factor is necessary for high innovation resilience, digital economy market regulation and sensing capability are core conditions; (2) key paths include "government empowerment-capability synergy," "government coordination-sensing capability," and "government regulation-market adjustment-comprehensive synergy"; and (3) SRDI enterprises must strengthen dynamic capabilities to leverage the digital business environment. These findings will offer theoretical and practical guidance for integrating effective markets, proactive governments, and dynamic capabilities to bolster SRDI enterprises' innovation resilience.

Leading enterprises play a pivotal role in advancing modernization of industrial chains. To better understand the research status and progress in this field, this study employed a scientific knowledge mapping approach to conduct visual analyses—including keyword co-occurrence, author co-occurrence, and keyword burst detection—on relevant literature. The paper systematically reviewed existing research from three dimensions: an overview of leading enterprises' role in industrial chain development, key research themes, and emerging trends. The findings revealed that existing studies primarily focus on three themes: organizational models, leadership mechanisms, and industrial performance. In terms of research perspectives, there is a discernible shift from meso-level to micro-level and back to meso-level analyses. Regarding research contexts, the focus has transitioned from traditional organizational settings to emerging ones. Future research explored the impact of leading enterprises on cross-industry integration, innovation paradigms, and industrial performance under new organizational contexts and diverse industrial environments. This study has clarified the intellectual framework and research boundaries of leading enterprises' role in industrial chain development, and it will provide insights for establishing a governance structure of industrial chains led by core enterprises.

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.

Changes in technological conditions and policy environments worldwide have significantly impacted the international division of labor, leading to a phased contraction of global value chains scales. Utilizing the concept of vertical specialization within the global value chain, this paper studied the mechanisms through which product innovation influences the reconfiguration of manufacturing's global value chain and attempted to use China's experience to seek breakthroughs in the "low-end lock-in" dilemma of the global value chain. The results showed that it has participated deeply in the global value chain but remains at the low stage. Product innovation promotes the upgrading of the global value chain; however, the impact of innovation output and input varies and exhibits significant industry heterogeneity. After incorporating the new development pattern into consideration, the innovation output and input within the domestic circulation have a significant positive impact on the upgrading of the global value chain in manufacturing. In the realm of global trade, the impact of international circulation, along with the innovation outputs and its spillover effects, enhances the global value chain position, while the influence of innovation inputs remains insignificant. The conclusions of this study will provide empirical support for implementing the new development concept, committing to the new development pattern, promoting high-quality development, and constructing a global specialized vertical division of labor system driven by domestic industries and product innovation.

The enactment of the Environmental Protection Tax Law marks a pivotal milestone in formulating the fiscal policies for China's green development. As a prototypical market-based environmental regulation tool, how environmental tax influences corporate green innovation has become a focal issue of concern for both academic and practical sectors. This paper examined the mechanism through which environmental tax affects green innovation among Chinese listed firms, from a perspective of tax policy implementation. Using a sample of A-share listed companies from 2018 to 2022, the study yielded several key findings: First, environmental tax has a significant positive effect on corporate green innovation. This effect is more pronounced among firms located in southern regions, those with higher levels of digitalization, and younger firms. Second, failure tolerance plays a moderating role in the relationship between environmental tax and green innovation—firms with higher failure tolerance exhibit a stronger positive response to environmental tax incentives. Third, the positive impact of environmental tax on green innovation is partly driven by resource reallocation: firms divert financial and technological resources away from other projects, leading to a crowding-out effect. Finally, the study found that as environmental tax expenditure increases, firms are more inclined to invest in substantive rather than symbolic green innovation initiatives. This paper has enriched the literature on how national environmental policies influence corporate green innovation strategies and it will offer a new perspective for assessing the relationship between environmental taxation and green innovation.

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.

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 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.

The construction and improvement of digital innovation ecosystems provide huge opportunities for artificial intelligence （AI） applications to enhance industry chain resilience. Based on the provincial panel data from 2008 to 2021，this study employed a dual fixed-effects model，a mediation effect model，and a moderation effect model to empirically examine the impact of AI applications on industry chain resilience driven by digital innovation ecosystems. The findings are as follows： （1） AI applications positively influence industry chain resilience，and this conclusion remains robust after a series of robustness tests and addressing endogeneity issues；（2） Driven by digital innovation ecosystems，digital innovation bodies and digital innovation platforms partially mediate the impact of AI applications on industry chain resilience，while the digital ecological environment plays a moderating role in this relationship；and （3） The heterogeneity analysis revealed that the impact of AI applications on industry chain resilience is more pronounced in central and western regions，areas with high-level digital innovation ecosystems，and regions with lower levels of advanced industrial structures. This study has not only unveiled the intrinsic mechanisms through which AI enhances industry chain resilience from a novel research perspective，providing empirical evidence for strengthening chain resilience amidst global transformations，but also advanced quantitative research on digital innovation ecosystems，enriching the existing theoretical content.

The business environment ecosystem serves as a critical antecedent for improving both the quantity and quality of regional SRDI (abbreviation for "Specialized, Refined, Differential and Innovative") enterprises. Leveraging the city-level data from China and employing the fuzzy-set Qualitative Comparative Analysis (fsQCA) approach, this paper investigated the ecological configurations through which the business environment ecosystem, under the dual institutional logics of market-government, influences the quantity expansion and quality improvement of regional SRDI enterprises. It proposed three ecological recipes that facilitate the quantity expansion of SRDI enterprises: "service driven comprehensive empowerment under dual institutional logics", "production-finance nexus driven under market dominance" and "industry-research-government co-promotion under government guidance". In parallel, three ecological recipes are identified to improve the quality of SRDI enterprises: "talent-driven comprehensive empowerment under dual institutional logics", "talent-innovation dual-driven under market dominance", and "talent-finance-government co-promotion under government leadership". Additionally, the cosine similarity analysis method was introduced to compare the similarities between high-performance and low-performance configurations for quantity expansion and quality improvement, aiming to identify paths for optimizing the business environment ecosystem. The results indicated that the "quality-first" optimization paths are generally better than the "quantity-first" paths, while the "stock reallocation" and "incremental leap" optimization paths can promote both the quantity and quality of regional SRDI enterprises. These findings will provide theoretical and practical insights for optimizing urban business environment ecosystems and accelerate the development of high-performing SRDI enterprises across regions.

Under the double pressure of key core technology blockade and market monopoly, integrated civil-military innovation has become the key to consolidate and improve the integrated national strategic system and capability. This paper adopted the traditional special equipment enterprises as the case object, and used the exploratory single case study method to study the paths and mechanisms of integrated civil-military innovation. The research found that: (1) the institutional logic of civil-military integration migrates with the change of enterprise situation, and evolves from a single institutional logic structure dominated by government logic to a multi-interactive structure of government logic, market logic and value logic. (2) The logic of military-civilian integration system guides the evolution of enterprise resource orchestration actions to follow the "resource construction-resource bundling-resource leveraging", and triggers the effect mechanism of "military-civilian resource extraction ability-military-civilian resource extension ability-military-civilian resource remodeling ability" to form the update iteration of resource actions and capabilities. (3) The innovation paths of military-civilian integration is mainly reflected in the two-way breakthrough of key core technologies and market development and the final construction of an independent innovation system, showing a transition from the "integrated innovation path led by government logic" to the "integrated innovation path led by market logic" to the "open innovation path led by value logic". This study has revealed the micro-mechanism of the institutional logic change of special equipment enterprises to drive the realization of civil-military integration innovation, and it will provide some useful management insights for the consolidation and enhancement of the integrated national strategic system and capabilities.