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.

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.

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.

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.

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".

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.

In the context of the wide application of digital intelligence technology，it is of great significance to clarify the relationship between artificial intelligence and new quality productive forces and its action mechanism for economic transformation and upgrading，as well as high-quality development. Based on the panel data of listed companies in China's new energy vehicle industry （NEV） from 2011 to 2022，this paper used the two-way fixed effect，threshold effect model and tool variable method to discuss the influence and action mechanism of artificial intelligence on the new quality productive forces of the new energy vehicle industry. The results showed that： （1） Artificial intelligence can not only directly promote the improvement of the industrial new quality productive forces，but also indirectly promote the development of industrial new quality productive forces by reducing the stickiness of R&D costs and transaction costs，improving the quality and efficiency of innovation；（2） The promotion effect of artificial intelligence on the industrial new quality productive forces has obvious heterogeneity characteristics and threshold effect. In the non-state-owned enterprises，eastern regions and after Tesla set up factories in China，as well as enterprises with short establishment time，moderate listing time and large number of employees，the promotion effect is more obvious；and （3） Analysis of the transmission effect within the industrial chain reveals that the downstream industry has a stronger transmission effect on the upstream industry. This research has revealed the emergence mechanism of artificial intelligence to promote new quality productive forces，and it will provide important inspiration for the better use of digital intelligence technology to empower new quality productive forces under the new development pattern.

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

The 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.

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.

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.

Realizing the value of data elements constitutes both a significant theoretical issue and an urgent practical demand for enterprise development in the digital economy era. This study systematically reviewed the literature on data value published between 1991 and 2024 in the fields of management and law. Drawing on the grounded theory for data coding, it distilled three fundamental questions of data value: which categories of data are of value? What forms of value can be derived from data? How is the value of data realized? The three issues respectively correspond to the value attributes of data, contents of data value, and the mechanisms by which that value is realized. The research indicated that: (1) The value attributes of data manifest as inherent intangibility, technical reusability, increasing returns to scale in economic terms, and limited exclusivity of rights; (2) The value of data encompasses economic, social, and political dimensions. From the perspectives of data stakeholders, namely data operators, data providers, and data governors, their respective value objectives are to enhance productive efficiency, achieve digital equity, and ensure data security; (3) The mechanism for realizing data value primarily encompasses the stages of value creation, value circulation, and value distribution. Data value creation is fundamentally grounded in data standardization, which specifically includes the assetization, productization, and capitalization of data. The circulation of data value faces pressing challenges in pricing, necessitating the orderly advancement of data trading, openness, and sharing. Through initial allocation and subsequent redistribution of data value, improvements in economic efficiency and the realization of social equity can be promoted. Finally, the paper outlined prospective research directions concerning the theoretical frameworks and methodologies underlying the systemic operation and realization mechanisms of data value.

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.

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.

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.

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 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 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.

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.

Patient capital provides long-term and stable financial support for breakthrough innovation, which is crucial to the development of nations and enterprises. And institutional investors are one of the key targets for cultivating patient capital. Yet there is a scarcity of discussion regarding the impact of institutional investors on breakthrough innovation when they become common owners. We focused on the non-financial common institutional owners with characteristics of patient capital and investigated its impact on corporate breakthrough innovation by the samples of non-financial listed companies in Chinese A-share market from 2007 to 2022. The results of the empirically examination showed that: (1) Non-financial common institutional ownership significantly promotes corporate disruptive innovation; (2) non-financial common institutional owners promote breakthrough innovation by guiding and supervising the adjustment of corporate resource allocation and increasing funds and personnel to research and development (R&D) investment; executive innovation consciousness enhances the promotional effect while strategic aggressiveness suppresses it; and (3) it is more pronounced in growing companies, and non-high-tech companies; non-financial common institutional investors that have held shares for more than one year have a more significant promotional effect on corporate disruptive innovation activities. This paper has enriched the relevant research on the impact of common institutional ownership on innovation and it will provide practical evidence for guiding and regulating patient capital to empower the development of new productive forces.

Amid the digital transformation, virtual influencer, as an emerging digital marketing innovation, is becoming a key medium for brands to connect with Generation Z consumers. This study integrates the Diffusion of Innovations Theory, Cognitive Adaptation Theory, and Information Integration Theory constructs a moderated mediation model with the image of virtual influencers (cartoon-like vs. hyper-realistic) as the independent variable, processing fluency as the mediator, and product type and advertising appeal as the moderating variables. Through two scenario experiments, the study reveals the impact mechanism of their advertising effects. The research findings are as follows: (1) hyper-realistic images significantly enhance advertising effects due to their technological credibility advantage; (2) processing fluency plays a key mediating role between the image and the effect; (3) product type and advertising appeal moderate the mediating path: functional products need to match hyper-realistic images to enhance information processing fluency, while hedonic products are more suited to cartoon-like images to evoke emotional resonance, and under emotional appeals, hyper-realistic images have a greater advantage. This study deconstructs the "technology-cognition" dual-driven path of virtual influencers as a service innovation, providing theoretical basis and strategic insights for brand digital marketing practices.

This paper explored the hierarchical mechanisms through which innovation network embeddedness, network structure, and network content influence corporate technological innovation. By integrating the patent data, alliance network data, and financial data from Chinese biopharmaceutical firms, we constructed the panel data and employed the social network analysis methods and fixed-effects negative binomial regression models to examine the impact of alliance network embeddedness, network structure, and network diversity on corporate innovation performance. The results revealed that: (1) Local embeddedness, overall embeddedness, structural holes, and diversity of alliance types all positively influence corporate innovation performance; (2) Structural holes exhibit varying moderating effects across different levels of embeddedness; and (3) Alliance type diversity negatively moderates the relationship. By introducing the PageRank algorithm, the study confirmed differentiated mechanisms at local and whole network levels during the innovation process. The embeddedness mechanism highlights the importance of strong connections and network cohesion while the structural hole effect underscores the strategic value of network break and weak ties in the network. Moreover, the diversity effect suggests that network complexity hinders innovation benefits from the network. Therefore, managers, when formulating network innovation strategies, should distinguish between ego and whole network strategies, and incorporate information about relationships between alliance partners and types of alliances into the strategic decision-making process.

Corporate environmental responsibility, social responsibility, and corporate governance (ESG) have become important drivers for promoting the high-quality development of enterprises. In the context of digital innovation, it is necessary to conduct in-depth research on how to embed multiple stakeholder responsibilities into enterprises' digital innovation system based on the construction of the ESG system. Based on this, this paper used the data from Chinese listed companies and employed the OLS linear regression model and mediation effect model to examine whether and how ESG performance affect enterprise digital technology innovation. This study found that: (1) In terms of impact effects, ESG performance and its various dimensions have a significant positive promoting effect on enterprise digital technology innovation. (2) In terms of impact mechanism, ESG performance promotes enterprise digital technology innovation through the alleviation of financing constraints and the reduction of enterprise risk levels. (3) In terms of action situation, the promotion effect of ESG performance on enterprise digital technology innovation is more obvious in highly digitized enterprises, highly competitive industries, highly digitized industries, high innovation activity industries, and enterprises in the central and eastern regions. (4) In terms of economic effects, digital technology innovation plays a mediating role in promoting enterprise high-quality development through ESG performance. This paper has examined the mechanism and boundary of ESG performance on enterprise digital technology innovation based on enterprise digital patent data, enriched the relevant research on the economic consequences of ESG performance and the driving factors of digital technology innovation, and it will provide insights for the policy formulation of ESG and digital technology innovation, as well as the promotion of collaborative transformation of digitalization and greening in China.

Whether digital-real integration can correct factor misallocation and thereby comprehensively promote economic and social high-quality development is a vital issue that needs to be addressed in the digital age. This paper used the provincial panel data from China between 2011 and 2022 and employed a two-way fixed effects model to study the impact of digital-real integration on high-quality development and its underlying mechanisms. The study found that: (1) Digital-real integration significantly promotes high-quality development, having positive effects on innovative development, coordinated development, green development, open development, and shared development, while the impact shows certain regional heterogeneity. (2) In regions with a higher level of factors, digital-real integration has a more significant promoting effect on high-quality development. (3) For underlying mechanism, digital-real integration promotes high-quality development by reducing misallocation of capital factors and labour factors, through which reducing factor misallocation and thereby enhancing total factor productivity is a significant path. The conclusions will provide a new theoretical perspective and empirical evidence for deepening the understanding of digital-real integration, improving factor misallocation, optimizing factor input, and thereby effectively enhancing total factor productivity and releasing the potential of digital-real integration. It also will have implications for accelerating the practice of digital-real integration to promote high-quality development.

The breakthrough of core and key technologies by latecomer enterprises is crucial for achieving national technological self-reliance and industrial upgrading. This study adopted a longitudinal single-case research approach, and examined Changan Auto's engine technology to construct an evolutionary process model of breakthrough paths for core and key technologies in latecomer enterprises. The research findings are as follows: (1) the impetus for core and key technology breakthroughs stems from a dual-driven mechanism that combines exogenous and endogenous factors; (2) the breakthrough path follows an "explicit technology traction-implicit capability leap" model, exhibiting a four-stage dynamic evolutionary goal; (3) explicit technological practices evolve progressively through the sequence of "extensive search-knowledge absorption-deep accumulation-knowledge creation-system integration-local leadership", driving the implicit capability system to achieve hierarchical leaps from "technical foundation building-innovation capability cultivation-integration capability construction-global competitiveness ascension"; (4) technological innovation capability develops following the logic of "knowledge absorption-deep accumulation-knowledge creation", establishing latecomer enterprises' dual momentum for outward breakthroughs and inward refinement; and (5) the "explicit technology-implicit capability" breakthrough model triggers bidirectional restructuring between "firm-market" systems, organizationally shifting practices from imitation and adaptation to standard-setting while structurally disrupting conventional market architectures, manifesting a "creative destruction" dynamic. This study will offer latecomers a practical paradigm for breaking the chokehold on core and key technologies, thus forging their own autonomous innovation chains, and facilitating the micro-level upgrading of industrial value chains.

This paper explored the reconstruction and evolution of business models of manufacturing enterprises in the era of artificial intelligence (AI), aiming to provide insights into how enterprises can develop new competitive advantages. By identifying five fundamental issues related to the commercialization of AI and employing a single-case study method, the research drew on the practical experiences of Midea Group in the field of intelligent manufacturing from a value perspective. First, the study conceptualized the business models in the AI era as a dynamic closed-loop analytical framework comprising six interconnected elements: value proposition, value creation, value delivery, value capture, value maintenance, and value transformation. Second, it revealed that the business models of manufacturing enterprises undergo incremental reconstruction driven by technological iterations in AI, with machine intelligence, trustworthy AI, and generative AI technologies serving as the foundations for three distinct stages: AI enabling, AI trust, and AI ubiquitous. Third, the study highlights Midea Group's evolutionary pathway from a "+AI" model to an "AI+" model, offering a practical roadmap for the intelligent transformation of business models. These findings will provide a theoretical foundation and actionable guidance for manufacturing enterprises seeking to leverage AI in creating distinctive value and achieving sustainable competitive advantages.

Under the backdrop of de-globalization，the construction of China's industrial value chain not only pursues efficiency but also takes security into account. Especially for the strategic high-tech industries of latecomer countries，an autonomous and controllable industrial value chain that prioritizes security while considering efficiency profoundly influences their technological catch-up，economic development，and stability and security. In response to the theme of how to build an industrial value chain with the goal of autonomy and controllability in China，this paper conducted a longitudinal case study on China's large unmanned aerial vehicle （UAV） industry from the perspective of system resilience，exploring the typical characteristics，construction paths，and boundary conditions of the "China Chain". The research findings are as follows： Firstly，unlike mass-produced manufactured goods that can begin as "embedded participants" to cultivate competitive advantages through deep engagement in global value chain （GVC） divisions，complex sensitive products like large UAVs must start as "dominant players" to avoid the "low-end lock-in" risks of embedded participation and secure control over core value chain segments. This paper thus extracted five dimensions of the "China Chain" model—applicable products，initial conditions，actors，action pathways，and operational logic—anchored in the "dominant player" starting point. Secondly，a processual theoretical framework for building autonomy and controllable China Chains was proposed： the two ends of the industrial value chain （R&D and service sectors） constitute critical junctures. System resilience operates through anticipatory and process-based mechanisms across these junctures. Technological innovation and organizational synergy serve as dual drivers for achieving autonomy and controllability. Thirdly，opportunity windows and China's aviation industry collaboration model emerge as necessary conditions for constructing autonomy-controllable value chains. This study will contribute to expanding the system resilience theory，enrich the academic discourse on "China Chain"，and unveil the "black box" of their construction. It will also offer practical insights for "chain-leading" enterprises to strengthen their dominant positions and spearhead innovation in global industrial value chains.

Green technology innovation is an important driving force to achieve China's path to modernization，and it is the only way for enterprises to respond to climate change risks with active climate actions for green sustainable development. To explore the correlation and response effects between climate risk，climate action，and corporate green technology innovation，a theoretical analysis framework and a two-way fixed effects panel model were constructed among variables. China's A-share listed companies were selected as the research objects，and empirical analysis was conducted using the panel data from 2008 to 2022. The research results showed that： （1） "Climate risk climate action green technology innovation" forms a sequential response correlation，and the transmission of driving forces forms a "funnel-shaped" screening and filtering. There is no statistically significant correlation between overall climate risk and corporate green technology innovation，and there is great uncertainty about whether climate change can stimulate corporate green technology innovation；（2） Climate risks will encourage businesses to take climate action，but there is a time lag and strategic passivity. Only transition risk significantly affects corporate climate action among climate risks. The speed at which climate action evolves from low-level reactive and defensive to high-level adaptive and proactive depends on the accumulation of internal resources and capabilities，as well as the magnitude of external pressures；（3） There is a significant inverted U-shaped relationship between climate action and corporate green technology innovation，with excessive climate action leading to a crowding out effect on green technology innovation. Only adaptive and proactive climate actions have a significant impact on green technology innovation in climate action. This paper established a systematic analysis framework to avoid the overall fragmentation of a single identification logic. The research conclusion will provide empirical evidence for quickly focusing on the most critical influencing factors of green technology innovation，accurately positioning the development stage，laying theoretical support for correcting climate behavior alienation，scientifically formulating climate action plans，and rationally allocating research and development resources.

Enhancing the independent innovation level of enterprises is of great significance to the sustainable development of industries. This paper selected A-share listed enterprises in China from 2010 to 2022 as a sample and explored the impact mechanism of corporate cooperative R&D on the corporate independent innovation level from the dynamic externality perspective. The results showed that: (1) corporate collaborative R&D helps to improve the level of corporate independent innovation and optimize the innovation structure of enterprises. This conclusion is still valid after endogeneity and robustness tests. (2) The mechanism test showed that corporate cooperative R&D improves the level of independent innovation of enterprises through three externality mechanisms: technology spillover effect, innovation cohort effect, and alleviation of financing constraints. (3) Further analysis showed that cooperative R&D of state-owned enterprises promotes the level of independent innovation more than that of non-state-owned enterprises, and cooperative R&D of high-tech industry, new energy automobile industry, and highly intelligent enterprises promotes the level of independent innovation less than that of other industries; cooperative R&D of enterprises has a medium- and long-term impact on the level of independent innovation. The findings of this paper have validated the promotional effect of cooperative R&D on the independent innovation level of enterprises, enriched the research perspective of cooperative R&D of enterprises, and will provide empirical evidence for the relevant R&D strategies of enterprises.

In the context of “AI+” development, exploring the impact of employee-AI collaboration on innovative behavior has become a crucial research topic in organizational management. Based on activation theory, this study examines the nonlinear relationship between employee-AI collaboration and innovative behavior, along with its underlying mechanisms. Using three-wave data from 301 employees collaborating with AI, we conducted regression analyses to test our hypotheses. The results reveal that:(1) Employee-AI collaboration exerts an inverted U-shaped effect on innovative behavior, suggesting that moderate levels of collaboration optimally foster innovation.(2) Cognitive flexibility serves as a curvilinear mediator, where employee-AI collaboration influences innovative behavior through inducing an inverted U-shaped change in cognitive flexibility.(3) Achievement motivation moderates the inverted U-shaped impact of employee-AI collaboration on cognitive flexibility, both delaying the inflection point of cognitive flexibility decline and mitigating the downward trend. These findings break through traditional linear thinking, deepen our understanding of the nonlinear impact of employee-AI collaboration on innovative behavior, and provide valuable insights for managers to construct effective employee-AI collaborative innovation systems.

As a key strategic resource in the digital economy era, the extent to which firms focus on data elements reflects their strategic emphasis on emerging technological development and plays a guiding role in shaping technology evolution. Using a panel dataset of 39,681 firm-year observations from Chinese listed companies, this study employed the fixed effects models as well as the mediation and moderation analyses to systematically examine the internal pathways through which data element focus promotes artificial intelligence (AI) technological innovation, and explored how external environmental conditions shape this process. The results revealed that: (1) enterprise-level data element focus has a significantly positive impact on AI technological innovation; (2) internally, this effect is primarily driven by the activation of non-precipitated slack resources, while the role of precipitated slack resources is less pronounced; (3) externally, both market competition intensity and the degree of government public data openness enhance the innovation-promoting effect of data focus; and (4) the positive effect of data elements focus is more significant in firms with higher levels of capital, managerial, and technological resources. This study has expanded the boundary of the mechanism of data element values and it will offer practical insights into AI innovation under varying resource structures and environmental conditions.

As finance is a critical component of national core competitiveness, strengthening financial regulation comprehensively and mitigating financial risks effectively are essential directions for achieving the goal of enhancing the efficiency and quality of financial support for the real economy. Using the New Asset Management Regulations (NAMR) as a quasi-natural experiment, this study selected A-share non-financial and non-real estate listed companies from 2014 to 2022 as the research sample and employed a difference-in-differences (DID) model to examine the regulatory effects and mechanisms of the NAMR in curbing corporate investment preferences for financialization over real economic activities. The findings indicated that the implementation of the NAMR significantly increased corporate innovation investment. The mechanism tests revealed that the main channels through which the NAMR enhances corporate R&D investment include reducing the scale of financial assets, refocusing on core business activities, and expanding the employment of high-quality talent. Further analyses showed that the NAMR's effect on boosting innovation investment is more pronounced among non-state-owned enterprises, firms located in regions with higher financialization levels, and industries with lower competition levels. Moreover, additional research found that the NAMR's promotion of corporate R&D investment contributes to improved innovation output and significantly enhances corporate value. The study's conclusions will help clarify the relationship between financial regulation and corporate innovation, thus providing valuable insights for formulation and implementation of financial regulatory policies and promotion of corporate innovation activities.

Intellectual property （IP） barriers have become one of the most significant non-tariff barriers in international trade today. Do they serve as obstacles to or catalysts for independent innovation by Chinese export firms? Existing research has yet to provide a clear answer to this question. Taking the United States' intellectual property infringement investigations against Chinese export firms （commonly known as Section 337 investigations） as an example，this paper empirically examined the impact of IP trade barriers on the innovation activities of Chinese export firms as well as the potential mechanisms at play，using the data from Chinese manufacturing listed companies between 2007 and 2019. The results showed that IP trade barriers significantly promote the innovation of Chinese export firms by encouraging firms to increase R&D investment，reducing reliance on external technology imports，and strengthening government R&D support，thereby significantly improving both the quantity and quality of their patents. Firm growth amplifies the positive effects of IP trade barriers on innovation capabilities，but the innovation-enhancing effects are weaker for non-state-owned enterprises than state-owned enterprises，and firms in highly marketized regions exhibit weaker innovation improvements than those in less marketized regions. Moreover，firms' export dependency，asset specificity，and human capital levels positively moderate the incentivizing effects of IP trade barriers on innovation. The findings of this study will not only deepen the understanding of the dynamics of innovation among Chinese export firms，but also provide policy implications for China to address technological suppression from Western countries and to reshape its new competitive advantages in the global market.

Some outstanding small and medium-sized enterprises (SMEs) at home and abroad have achieved sustainable growth through a micro-innovation strategy, but the question of how micro-innovation strategy promotes the sustainable growth of SMEs has not been deeply answered. In this regard, this study combined with the dynamic capability theory, conducted a longitudinal case analysis of Yiwu Shuangtong Company which is the first brand in the global straw industry and the ‘invisible champion’ of domestic SMEs, in an attempt to analyze the mechanism of micro-innovation strategy of SMEs on the sustainable growth of enterprises. The results showed that: (1) The micro-innovation strategy of SMEs follows the evolutionary path of ‘differentiated’-‘specialized’-‘integrated’-‘platform-based’, which has become the driving force for the sustainable growth of enterprises. (2) Guided by the micro-innovation strategy, SMEs build and continuously improve their dynamic capabilities through the circular path of "environmental insight capability-organizational flexibility capability - change and renewal capability", thereby achieving sustainable growth. (3) Under the influence of different micro-innovation strategies, the mechanisms of dynamic capability improvement of SMEs present four different manifestations including structure optimization, specialized technical knowledge optimization, knowledge coordination optimization, and symbiotic relationship optimization, and the fundamental reason is that enterprises according to their own strategic positioning focus resources on optimizing different key activities in the value chain. This research has not only expanded the theoretical framework of micro-innovation and enterprise growth but also provide theoretical reference and management enlightenment for the sustainable growth of SMEs in China.

Achieving the goal of "carbon peaking and carbon neutrality" requires top-level policy design that balances development with low-carbon goals. Policy innovation is key to realizing government-market synergy. Using the panel data from Chinese listed companies （2005-2018），this paper examined the dual pilot of carbon trading policies and the top-10，000 energy-consuming enterprises program as a quasi-natural experiment，exploring the impact of policy linkage on corporate carbon performance via dual machine learning. The key findings include： （1） The combination of government-mandated regulations and market incentives significantly improves carbon performance，with dual-pilot policies more effective in balancing environmental protection and economic growth than single-pilot approaches；（2） The mechanism analysis showed that dual-pilot policies mitigate the inefficiencies of command-based regulations through green innovation，reducing market distortions，and improving financial performance；and （3） The heterogeneity analysis revealed stronger effects in technology-intensive，capital-intensive，high-pollution industries，particularly for large，high-growth firms with strong green innovation foundations. This study will provide new insights into how policy synergies impact corporate green transformation and offer micro-level evidence for environmental policy innovation.

Corporate social responsibility (CSR) represents a transformative evolution in the values of commercial civilization, and the quality of CSR reports serves as the strategic cornerstone for companies to build sustainable competitiveness. By analyzing 4514 CSR reports from China's A-share listed companies between 2021 and 2023, and based on authoritative frameworks including the Sustainability Reporting Guidelines issued by the Global Reporting Initiative (GRI) and the China CSR Reporting Guidelines developed by the Chinese Academy of Social Sciences (CASS), we constructed a CSR quality evaluation system which comprises six first-level indicators (completeness, responsiveness, accessibility, credibility, innovation, and comparability) and 18 second-level indicators. Moreover, by leveraging text mining techniques—specifically, text sentiment analysis and word frequency statistics, we automatically scored CSR report quality across dimensions. Then, the TOPSIS-entropy weight method was employed to determine indicator weights, followed by dimension- and industry-specific analyses. The key findings include: (1) CSR report quality improved significantly from 2021 to 2023, with industry-wide practices demonstrating positive growth; (2) firms excelled in report accessibility and responsiveness, while innovation and comparability remained widespread weaknesses; and (3) inter-industry disparities in CSR report quality were pronounced, with the financial sector leading comprehensively and the entertainment industry requiring urgent improvement. This research has facilitated efficient, objective quantification of CSR report quality, and it will offer valuable references for government policymaking, corporate optimization of report preparation, enhancement of CSR implementation capabilities, and elevation of industry-wide CSR standards.

In the era of digital intelligence，building the ecological dominance of leading-chain enterprises is the key driver in the security and high-quality development of a chain industry. Drawing on the resource concerto theory，this paper conducted a longitudinal case study of Sany Group to explore the evolutionary mechanisms involving digital intelligence empowerment，resource orchestration，and ecological dominance of leading-chain enterprises. The findings revealed that digital intelligence exerts a three-dimensional empowering effect on enhancing the ecological dominance of leading-chain enterprises，manifested through organizational breakdown，cross-border integration，and dynamic adjustment. The leading-chain enterprises cultivate their ecological dominance by adopting a heterogeneous approach in different stages. By framing resource concerto as a proactive capability for resource management，leading-chain enterprises achieve a positive feedback loop in the industrial chain ecology through a combination of "whole machine entry and high flexibility"，"strategic cooperation and vertical integration"，and "perfect ecology and three-dimensional co-evolution". Furthermore，the leading-chain enterprises have undergone an evolutionary process evolving from point-based initiatives to network-centric integration to establishment of ecological dominance. Specifically，the leading-chain enterprises form a driving force of technological innovation，product market guidance，and resource integration in the industrial chain ecosystem through three modes，namely，point breakthrough，chain collaboration，and ecological integration. This research has revealed the resource concerto mechanism of the leading-chain enterprises during the process of the evolution of the ecological dominance empowered by digital intelligence，and it will provide theoretical guidance and practical implications for the digital transformation of industrial chains，dynamic resource management，and cultivation of the ecological dominance of leading-chain enterprises.

Green innovation is an important way for enterprises to achieve high-quality development. In reality, the rhythm and results of green innovation in enterprises show significant differences, but there has been no research on this phenomenon. According to the behavioral theory of firms, the dynamic changes in green innovation willingness and resources of decision-makers caused by environmental performance shortfalls may be an important reason for this phenomenon. Based on the above logic, this study selected Chinese manufacturing listed enterprises from 2012 to 2022 as samples, and used the nonlinear regression method and moderation effect model to focus on the impact of environmental performance shortfalls on the rhythm of enterprise green innovation and situational mechanisms. The study found that: (1) there is a U-shaped relationship between environmental performance shortfalls and the rhythm of enterprise green innovation, and this basic conclusion has passed multiple endogeneity and robustness tests; (2) CEO green experience weakens the U-shaped relationship between environmental performance shortfalls and the rhythm of enterprise green innovation, while slack resource enhances this U-shaped relationship; (3) There is an inverted U-shaped relationship between the rhythm of green innovation and enterprise value, that is, moderate irregularity in the rhythm of enterprise green innovation helps to enhance enterprise value, but excessive irregularity will lead to a decrease in enterprise value. This study has made important contributions to the exploration of the behavioral theory of firms in the field of green innovation, and it will provide a practical basis for enterprises to adjust the rhythm of green innovation and create value.

Green technological innovation is the key to promoting industrial structure upgrading and achieving high-quality development. Based on the practice of the "de-capacity" policy, this paper selected the manufacturing-listed companies from 2009 to 2018 as the research sample, and used the Difference-in-Difference (DID) model to explore the impact, mechanisms and heterogeneity of the policy on green technological innovation. The study found that the "de-capacity" policy significantly promotes firm's green technological innovation through direct and indirect effects. After several robustness tests, it was confirmed that the impact of the "de-capacity" policy on the quality of green technological innovation is more robust. The heterogeneity analysis showed that the "de-capacity" policy positively promotes green technological innovation quality in state-owned firms and firms located in regions with strong policy intervention, but has a slight negative impact on non-state-owned firms. The mechanism analysis revealed that the "de-capacity" policy indirectly promotes firms' green technological innovation by optimizing the allocation of innovation resources and enhancing innovation compensation. This study has provided an in-depth exploration of the effects of suppressive industrial policies on green technological innovation, thus offering valuable insights for the differentiated implementation of the "de-capacity" policy and the practice of green innovation-driven development strategies.

In the context of maritime power and innovation-driven development strategy, inter-organizational cooperative innovation has become a crucial model for overcoming the key technological bottlenecks in emerging marine industries. Under various marine policies, China's strategic emerging marine industries have experienced rapid development. However, sharp issues persist throughout their development process, particularly the fragmentation between academia, industry and research in technological innovation, and lack of effective horizontal and vertical cooperative innovation mechanisms. In response, this paper constructed an undirected weighted network of industrial technological innovation from a cooperative innovation perspective using the joint patent data. The network's topology, spatial distribution, and driving mechanisms are analyzed in depth using network visualization and the ERGM method. The study found that: (1) under the guidance of marine policies, enterprises emerge as the central force in the technological innovation network, with key collaborating entities primarily located in the Bohai Rim area, the Yangtze River Delta, and the Pearl River Delta. (2) Analysis of the internal driving mechanisms of network formation indicated that centrality and transitivity are key internal drivers. The formation and development of the technological innovation cooperation network rely on the clustering effect of core entities and the facilitative role of transitivity mechanisms. (3) Among external driving mechanisms, geographical distance affects collaborative tendencies; collaboration among similar types of organizations enhances efficiency; cross-institutional collaboration offers complementarity; and the impact of cultural differences on the cooperative network is diminishing. The analysis of the current state of industrial technological innovation and driving mechanism factors under existing policies will also provide practical insights for promoting high-quality development of marine strategic emerging industries and the formulation of related policies.