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.

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.

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.

Breaking public data monopoly，enabling seamless data flow，and releasing data dividends are crucial for constructing new national competitive advantages. This study employed a quasi-natural experiment utilizing the opening of China's municipal government data to construct a staggered difference-in-differences （DID） model，assessing the impact of public data opening （PDO） on enterprise investment efficiency. The findings showed that PDO significantly improves investment efficiency by mitigating both underinvestment and overinvestment inefficiencies，primarily through information empowerment and institutional empowerment mechanisms. The heterogeneity analysis revealed that the impact of PDO on investment efficiency is influenced by enterprise characteristics，public data quality，and policy convergence. Further analysis identified supply chain diffusion，investment scale，and regional spillover effects. Furthermore，PDO has been found to drive "productive" investment through "efficient" investment. In addition，it has been determined that PDO can promote total factor productivity of enterprises. This study will contribute to the theoretical understanding of PDO's role in market decision-making，highlight its positive externalities of PDO，and provide empirical evidence for optimizing resource allocation through data—reflecting the micro-level implementation of the nation's "developing data productivity" strategy. It will also underscore the significance of government data infrastructure in fostering high-quality economic development.

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.

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.

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 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.

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.

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.

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.

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.

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.

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.

Successive shocks are the key exogenous factors triggering construction of supply chain resilience in Chinese advanced manufacturing enterprises. We employed an exploratory longitudinal single-case study method to analyze the process mechanism of Huawei's supply chain resilience cultivation under the US economic and trade sanctions and revealed and refined the intrinsic motivation, action mechanism, and evolutionary process of advanced manufacturing enterprises' dynamic construction of supply chain resilience. Our study found that: first, reducing the uncertainty of external resource acquisition is the direct motivation for Chinese advanced manufacturing enterprises to construct supply chain resilience; second, when the policy shock affects the resource dependency relationship between the case enterprise and its supply chain partners, the enterprise adopts differentiated continuity management measures to reduce uncertainty step by step; and third, in the case of successive shocks, the enterprise achieves the enhancement and optimization of the supply chain resilience capability portfolio through the dual mechanism of capability creation and capability preservation. By integrating resource dependence theory and business continuity management, this paper will provide effective thinking for understanding the motivation of enterprise supply chain resilience construction from a relationship perspective. At the same time, we will open the black box of the relationship between changing dimensions of the resource dependence relationship and selection of constraint absorption strategies, thus cracking the intrinsic mechanism of the supply chain resilience iterative development of advanced manufacturing enterprises.

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.

In the rapid development of digital economy, data has become the pivotal driver of new quality productive forces (NQPF). However, the existing research lacks systematic integration of the correlation mechanism between data and NQPF. This paper reviewed relevant literature following the logic of "Connotation Evolution—Characteristics Analysis—Mechanism Overview—Framework Integration". The results showed that: (1) The evolution of data's connotation in terms of information resource carrying, value form transformation and factor integration is highly consistent with NQPF's essential requirements; (2) The inherent characteristics of data have reshaped the productivity growth logic, providing a new paradigm for enhancing NQPF; (3) Data's role exhibits multifaceted effects across economic dimensions: at the micro-level, it is the coexistence of productivity enhancement and the "digital paradox"; at the meso-level, it drives industrial restructuring while exacerbating market imbalances; and at the macro level, it raises the challenge of imbalance while promoting economic growth; and (4) Synthesizing existing findings, this study innovatively proposed an integrated framework of "factor—technology—organization—pattern", thus revealing data's transition from "factor empowerment" to "systemic emergence" in driving productivity. Through this review, we have advanced theoretical construction from factor deconstruction to framework integration and will outline future research directions to inform theoretical innovation and policy formulation in this field.

The digital-green synergistic development serves as a new engine for the high-quality development of enterprises. However, systematic research is still lacking regarding how these two forces interact and drive the enhancement of supply chain resilience. From the perspective of synergy, this study adopted a dynamic Qualitative Comparative Analysis (QCA) method, by taking 30 Chinese semiconductor enterprises as case studies, to explore the pathways through which such synergy fosters high supply chain resilience. The findings are as follows: (1) None of the six antecedent conditions related to digitalization and greening can independently constitute a necessary condition for driving high supply chain resilience; its achievement relies on the synergistic effect of multiple conditions. (2) Three configurational pathways to high resilience are identified, namely the "organization-dominated and digital technology-driven pathway", the "regulation-led and multi-factor synergistic pathway", and the "green innovation-dominated pathway". Among these, the degree of emphasis on digitalization acts as a foundational condition, while environmental regulation exerts a dual-directional impact. (3) These configurational pathways vary in terms of temporal stability, with the "regulation-led and multi-factor synergistic pathway" demonstrating significant advantages in responding to sudden disruptions. This research has deepened the understanding of the complex mechanisms underlying the formation of supply chain resilience and it will provide some decision-making references for enterprises to build high resilience under the dual development goals of high efficiency and low carbon.

As key production factors and strategic resources with core competitiveness, data elements provide new ways for enterprises to enhance green innovation performance and realize sustainable development. Based on the data of A-share listed companies from 2012 to 2023, this paper empirically explored the effect and mechanism of data elements on corporate green technology innovation and management innovation. The results showed that data elements significantly improve the performance of corporate green technology innovation and green management innovation, and the relationship between data elements and green innovation is strengthened by strategic guidance and green culture. The mechanism analysis showed that data elements promote the green innovation performance of enterprises through "resource agglomeration effect" and "synergistic effect". The heterogeneity analysis found that in technology-intensive fields and enterprises with high intensity of environmental regulation, data elements play a more significant role in promoting green innovation performance. This research has expanded the research in the field of data elements influencing corporate behaviour, and it will provide empirical evidence for exploring the path of corporate green innovation in the digital economy era, thus having implications for enterprises to effectively explore and utilize the potential value of data elements.

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 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.

Strengthening the comprehensive cyber governance system is a critical research focus for advancing China's strategy of becoming a global cyber power. The year 2024 marks both the 10th anniversary of General Secretary of the Communist Party of China Central Committee Xi Jinping's proposal of the cyber power strategy and the 30th anniversary of China's full access to the global cyber. At this critical historical juncture, reviewing systematically the evolutionary path and theoretical logic of comprehensive cyber governance research holds significant theoretical and practical values. Based on a conceptual definition of "comprehensive cyber governance", this study made a systematic review of 507 high-quality journal papers published domestically and internationally. The findings revealed that domestic research emerged earlier and demonstrated more systematic outcomes, evolving through three distinct phases: exploratory rigid control under a legal framework, government-led collaborative regulation, and multi-stakeholder comprehensive governance. Furthermore, leveraging Parsons' AGIL model from structural functionalism, this study constructed a research framework comprising goal attainment, integration, adaptation, and latency, synthesizing four core themes in comprehensive cyber governance research: generative logic and conceptual construction; internal mechanisms of governance actors and tools; governance models and pathway exploration; and efficacy evaluation systems. Finally, in light of governance challenges posed by digital-intelligent technologies (e.g., ChatGPT, DeepSeek), future research directions were proposed, including empirical modeling of collaborative mechanisms, dual-attribute analysis of digital-intelligent technologies, dynamic adaptation of governance pathways, and AI-driven transformation of evaluation systems. The results will contribute to advancing theoretical research on comprehensive cyber governance and provide practical insights for strengthening governance frameworks.

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.

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.

The new generation of science, technology and innovation policy is now becoming a focal point for both policymakers and the academia. Based on the redefinition of concepts such as "innovation", "development", "innovation-driven development" and "policy paradigm", the concept of innovation-driven development policy is proposed as the new generation of science, technology and innovation policy. This paper analyzed the paradigm shift process from science and technology policy to innovation policy and then to innovation-driven development policy, along with its main characteristics, therefore providing a theoretical framework for the formulation of innovation-driven development policy. The main conclusions are as follows: (1) Innovation is a process of diversified value creation and value-added circulation, encompassing the creation of scientific value, technological value, economic value, social value, and cultural value. (2) Innovation-driven development refers to innovation-driven economic, social, environmental and culture development, as well as the development of science and technology. Its essence lies in achieving diversified value creation and value-added circulation under the high coupling of goals and actions of innovation and development. It emphasizes the primary driving force of innovation for development, and the strong coupling relationship between innovation and development as well as the sustainability of development. (3) Innovation-driven development policy is the new generation of science, technology and innovation policy, which refers to the conduct code and action plan adopted by governments and political parties to achieve innovation-driven economic, social, environmental and culture development, as well as science and technology development. (4) Policy paradigm refers to the policy value concepts, policy standards and norms, and policy conceptual system that are commonly recognized by stakeholders in the policy-making process. (5)The science, technology and innovation policy paradigm has undergone a historical evolution from the science and technology policy paradigm to the innovation policy paradigm, and then to the innovation-driven development policy paradigm. The core of this evolution lies in the expansion of understanding of the connotations and extensions of innovation and development, as well as the systematic coupling relationship between innovation and development. This study will provide theoretical and methodological guidance for the formulation of innovation-driven development policy, and lay an important theoretical foundation for the development of innovation-driven development policy science.

The integration of digital economy with real economy has become an important option for addressing the innovation bottleneck of enterprises in key core technology sectors. This paper utilized the text mining method to extract the citation information from invention patents and key core technology information from the annual reports of listed companies. We constructed the indicators for innovation efficiency in enterprises within the fields of digital-real integration and key core technologies. Using a sample of A-share listed companies in China from 2008 to 2022, we employed a two-way fixed effects model to study the impact of digital-real integration on enterprise innovation efficiency (hereafter referred to as enterprise innovation efficiency) and its mechanisms. The research findings indicated that: (1) Digital-real integration significantly enhances enterprise innovation efficiency; (2) It promotes consumption upgrading, optimizes resource allocation, reduces enterprise financing costs, improves supply chain efficiency, and enhances the robustness of strategic decision-making, thereby comprehensively improving enterprise innovation efficiency from the perspectives of demand, supply, technology and strategy; (3) We examined the heterogeneous effects of digital-real integration on enterprise innovation efficiency based on its internal and external characteristics; (4) We analysed the moderating effects of the local government policy environment and the internal operational environment of enterprises on the improvement of innovation efficiency due to digital-real integration. The conclusions of this study will provide theoretical support and policy insights for promoting digital-real integration and enhancing innovation efficiency in key core technology enterprises.

Enhancing total factor productivity (TFP) is a fundamental driver of high-quality and innovation-driven development of enterprises. In the context of China's "carbon peaking and carbon neutrality" strategy, green finance has emerged as a pivotal financial innovation. However, its impact on firms' TFP and the underlying transmission mechanisms remain underexplored. Drawing on the panel data of non-financial A-share listed companies in Shanghai and Shenzhen from 2009 to 2020, this study employed the fixed effects and mediation models to empirically examine the effects of green finance on corporate TFP. The results showed that: (1) green finance significantly improves firm-level TFP; (2) information transparency and green innovation serve as effective mediators in this relationship, whereas the mediating effect of financing constraints is heterogeneous across firms; and (3) the productivity-enhancing effects of green finance are more prominent among state-owned and heavily polluting enterprises, indicating differentiated policy impacts. This study will contribute to the literature on the real economic consequences of green finance and the determinants of productivity, and offer theoretical and policy implications for promoting synergy between green transformation and sustainable economic growth.

Specialized，refined，distinctive and innovative "Little Giant" enterprises （SRDI "Little Giants"） are an important force in the national innovation-driven development strategy，but the existing micro-research on the innovation effect of the recognition policy of SRDI "Little Giants" is not deep enough. For this reason，this paper selected the SMEs listed in 2015-2021 and the national SRDI "Little Giants" recognized during this period as the research samples，and adopted the DID method to examine the impact of the recognition policy on the innovation quality and innovation efficiency of enterprises and the influence mechanism. It was found that the policy of recognizing SRDI "Little Giants" can significantly improve the innovation quality and innovation efficiency of enterprises，and this conclusion still holds after a series of robustness tests. It was further found that the recognition policy improves firms' innovation quality and efficiency mainly through alleviating financing constraints，improving human capital acquisition and facilitating digital transformation. At the same time，the effect of the recognition policy on innovation quality and efficiency of SRDI "Little Giants" is more significant in the second batch of selected enterprises，those applying the financial subsidy support means，and those in the maturity and decline periods. This paper has provided intuitive and powerful evidence for the effectiveness of the policy of recognizing SRDI "Little Giants"，which will have important theoretical and practical value for better assisting the cultivation of SRDI "Little Giants".

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.

As the acceleration of public data openness and sharing becomes a core task in China's digital government construction, understanding how enterprises can leverage public data openness to enhance their technological innovation has become a crucial issue. Based on a sample of A-share listed firms on the Shanghai and Shenzhen Stock Exchanges from 2015 to 2021, this study employed a DID model to empirically examine the impact of public data openness on corporate technological innovation and its underlying mechanisms. The findings revealed that public data openness significantly enhances corporate technological innovation. The mechanism tests indicated that public data openness promotes corporate technological innovation by reducing uncertainty risks, accelerating cross-entity information flow, and lowering transaction costs associated with innovation. The heterogeneity analysis demonstrated that favorable trust environments, higher government governance efficiency, and robust digital infrastructure strengthen the positive impact of public data openness on technological innovation. Further analyses of public data characteristics showed that the enabling effect of public data openness on corporate technological innovation is more pronounced when public data is of lower geographic granularity and higher specialization. The structural analysis of corporate technological innovation revealed that public data openness contributes to both the quality effect and spillover effect of corporate technological innovation. The economic consequence tests indicated that the improvement in corporate technological innovation driven by public data openness ultimately leads to increased corporate value. This study has explored the enabling effects of public data openness from the perspective of micro-level corporate technological innovation, and will provide theoretical insights into how to better achieve the coordinated development of public data openness and innovation-driven strategies.

The effective response to the paradoxical and unified contradictions in enterprise digital innovation is not only the premise for comprehensive realization of digital innovation but also an important issue for the development of new quality productive forces driven by digital innovation. However，the academic community currently lacks a systematic review of this knowledge framework. This paper adopted a bibliometric and systematic review method，by screening 108 relevant papers from high-level domestic and international journals to organize the enterprise digital innovation paradox. It constructed a research framework of "concept classification-causes-performance-response mechanisms". The main conclusions are as follows： first，the concept of enterprise digital innovation paradox is defined，forming a three-dimensional classification system of digital technology innovation paradox，digital market innovation paradox，and digital organizational innovation paradox；second，the causes and manifestations of enterprise digital innovation paradox are summarized in the technical，market，and organizational levels；and third，19 mechanisms for coping with digital innovation paradoxes are summarized from seven theoretical perspectives： dynamic capabilities，modularity，optimal differentiation，institutional logic，data ethics，paradoxical leadership，and organizational ambidexterity. Finally，the paper has further proposed potential paradoxical issues that may arise in the future，aiming to provide a detailed theoretical basis and analytical tools for subsequent research.

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.

Enhancing enterprise innovation resilience is the key for enterprises to survive and thrive against adversities in the VUCA era. Based on the integrated framework of internal and external factors, this paper identifies six antecedent conditions of enterprise innovation resilience, including top management team's experiences, digitalization level, innovation resources investment, urban innovation climate, external R&D cooperation, and environmental complexity, and discusses the configuration patterns and dynamic trajectories of high innovation resilience of enterprises by multi-temporal QCA method based on the listed manufacturing enterprises in China's A-share market as the observed samples. The results of the study showed that: (1) Enterprise innovation resilience is the outcome of the interaction of multiple factors, and there is no solitary necessary condition; (2) There are four types of equivalent driving paths that enable enterprises to attain high innovation resilience in the pre-impact and post-impact periods, namely digitalization-R&D driven pattern, top management team-R&D driven pattern, top management team-investment driven pattern, and innovation investment driven pattern. Among them, the digitalization-R&D driven pattern emerges stably in both periods, and there are diverse substitution and complementary mechanisms; and (3) Innovation resources investment, top management team's experiences, external R&D cooperation, and digitalization level are the preponderant factors of the driving paths in both the pre-impact and post-impact periods. The research conclusions are conducive to understanding the complicated formation mechanism of enterprise innovation resilience, and they will provide theoretical and practical insights for enterprises to enhance innovation resilience and achieve high-quality development.

Under the background of green transformation, Chinese enterprises are accelerating the promotion of sustainable development, and digital technology has become a key approach for transformation. This study adopted the green transformation of Fengdeng Environmental Protection (CONBA) as an example and conducts an exploratory single case study to construct a logical system of enterprise green dynamic capability guided by green cognition and practical experience. The study found that the enterprise has formed a "double cognition-investment behaviour-green dynamic capability" capacity building mechanism in the process of green transformation. First, the double cognition undergoes specific evolutionary processes, with green cognition undergoing a process of "aggregation-disintegration-reconstruction", while digital cognition shows a change from "enlightenment-awakening-expansion". Second, green knowledge and green performance guidance is the key to implementing digitalization investment, and the digitalization investment behaviour has shifted from passive acceptance to active adoption, and has developed into an active expansion of external opportunities. Finally, in the green cognition-guided digitalization investment practice, the enterprise gradually builds a green dynamic capability system that covers green value perception, green value reconstruction, and green value transformation. The theoretical framework of this study will provide important insights into how green transformation enterprises can drive green competitiveness through dual cognition.

Data elements, as a core engine driving the development of China's digital economy, play a crucial role in enhancing their application level, which serves as an important pathway for promoting the high-quality development of specialized, refined, distinctive and innovative (SRDI) enterprises. This study used the Chinese SRDI-listed enterprises from 2014 to 2022 as the research sample, leveraged the web crawler technology and machine learning algorithms to innovatively depict the feature word map for the whole lifecycle management of data elements, employed a fixed effects model to empirically test the causal effects of data elements on the high-quality development of SRDI enterprises, and explored how data elements promote high-quality development through "quality enhancement and intelligence improvement". The findings are as follows: (1) Data elements significantly enhance the high-quality development level of SRDI enterprises, with this promoting effect being particularly prominent in non-state-owned enterprises, those in highly competitive markets, as well as labour-intensive and technology-intensive enterprises; (2) Data elements promote the high-quality development of SRDI enterprises through "quality enhancement", with new quality productive forces playing a positive mediating role in this mechanism; (3) Data elements promote the high-quality development of SRDI enterprises through "intelligence improvement", with digital technology innovation playing a positive mediating role in this mechanism. In view of this, the following managerial implications are proposed, including comprehensively unleashing the potential of data elements to consolidate the competitive advantage of SRDI enterprises in niche markets; promoting digital-real economy integration to support the development of new quality productive forces, leading deep structural transformations in SRDI enterprises; and building a self-reliant and self-strengthening digital technology innovation system, empowering SRDI enterprises to occupy strategic high grounds in future development.

The development of "Specialized, Refined, Distinctive and Innovative" (SRDI) small and medium-sized enterprises (SMEs) and tackling key technological challenges in critical areas are of significant importance for China to build a self-reliant and controllable industrial supply chain. Using the sample of A-share listed companies from 2011 to 2021, this study employed the methods such as the difference-in-differences (DID), propensity score matching-DID (PSM-DID), and instrumental variable (IV) regression to examine the impacts of SRDI certification on corporate technological innovation and its mechanisms. The study found that the certification significantly promotes technological innovation, particularly in SMEs, and this result remains robust after controlling endogeneity. Further analysis revealed that high-intensity government subsidies weaken the policy effect, with this inhibitory effect being more pronounced for medium and large-sized enterprises. In contrast, the development of digital finance alleviates the constraints imposed by the imbalance in traditional finance, thereby enhancing the policy's impact on technological innovation. The findings of this study will provide theoretical support for the rationality of China's specialized innovation certification policy system. However, the study suggests that government departments should reconsider the traditional selection mechanism of "self-declaration-expert review-government funding" and promote digital finance as a positive complement to traditional finance to further optimize resource allocation efficiency in the certification process.

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.

Market power, as a critical factor influencing risk management and resource allocation, plays a pivotal role in shaping and executing strategies for digital transformation. Using the data from A-share listed companies between 2017 and 2021, this study applied the fixed effects and moderating effects models, grounded in theoretical analysis, to explore how market power affects enterprise digital transformation, and uncovered several key findings: (1) Market power significantly drives enterprise digital transformation and exhibits characteristics of marginal increasing returns; (2) The business environment plays a moderating role in the impact of market power on digital transformation; (3) The impact of market power on digital transformation exhibits heterogeneity, with a more pronounced promoting effect observed in non-state-owned enterprises, labor-intensive enterprises, and enterprises located in eastern regions; and (4) Market power fosters sustained growth in intangible investments related to digital technologies, indicating a dynamic cumulative effect. The paper will broaden the research horizon on market power and enterprise digital transformation, thus offering practical implications for promoting digital transformation and optimizing the business environment.

As a new form of innovation organization that links multiple innovation entities and promotes the innovation of key core technologies, the innovation consortium has attracted widespread attention. Still, its unique value co-creation mechanism has rarely been discussed. Using the case study method, this paper took China Railway Construction Heavy Industry Corporation Limited. (CRIHI) as an example to find that:(1) The innovation consortium is a tightly complementary value co-creation network led by core enterprises and consisting of innovation entities such as governments (as institutional builders), users (as demanders), technology suppliers of key components, universities, research institutes, etc. (2)The value co-creation mechanism for the innovation consortium to promote the innovation of key core technologies is an "organized value co-creation mechanism driven by both national mission and market demand". Among them, organized value co-creation is a process of "clear and unified innovation target orientation—close interaction between the complementary innovation entities led by core enterprises—accumulation of innovation elements towards core enterprises—innovation of key core technologies", national mission and market demand are sequential cycles driving conditions for organized value co-creation.These findings will contribute to promoting the construction of a theoretical system of innovation consortiums rooted in Chinese practices, expanding the application boundaries of value co-creation theory, enriching the literature on key core technology innovation, and providing essential implications for the establishment of innovation consortiums for key core technology innovation between governments and 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.