With the in-depth development of globalization and digitalization, companies face increasingly complex and dynamic competitive environments. Knowledge, as the core resource of modern enterprises, has rendered the innovation of its management model crucial to sustainable development. Technological innovations not only greatly enhance the efficiency of knowledge acquisition, transformation, and application but also drive knowledge management toward intelligence, dynamism, and collaboration. As a result, how companies can drive the innovation of knowledge management models has become a pressing issue for both academia and industry. Existing literature mainly studies knowledge management model innovation from a linear perspective and case studies, but it fails to clarify the causal differences of multiple factors in the coordinated and unified process of achieving knowledge management model innovation, nor does it reveal the driving pathways of knowledge management model innovation. Therefore, exploring the antecedent variables and driving pathways of knowledge management model innovation is of practical value. This paper seeks to answer the following questions: How are the antecedent conditions of knowledge management model innovation interlinked? Which condition among the antecedent f actors contributes the most to knowledge management model innovation?This study adopts actor-network theory（ANT） to construct a research framework for knowledge management model innovation. Taking 202 valid questionnaire responses from high-tech enterprises as the research data, it employs fuzzy-set qualitative comparative analysis（fs QCA）, importance-performance map analysis（IPMA）, and artificial neural network（ANN） analysis to examine the impact of factors at different levels on enterprises’ knowledge management model innovation and the configurational effects among these factors.The Fs QCA identifies conditional combinations between variables and reveals different causal paths, making it suitable for exploring multiple causal relationships in complex systems. IPMA helps identify important influencing factors through visual means and quantitatively analyzes the actual performance of these factors. By training on a large amount of data, ANN can reveal non-linear relationships between variables while predicting future trends and outcomes. This combination not only enhances the depth of causal analysis, but also improves predi ction accuracy, making it suitable for multidimensional research on complex systems.The results of the study show that（1） A single condition cannot fully explain the driving mechanisms of knowledge management model innovation;（2） six antecedent conditions from the levels of technological actors, human actors, and coordinating actors have a synergistic impact on knowledge management model innovation,the paths leading to complementary high-level knowledge management model innovations can be categorized into three types: technology-coordination dual-core, human-machine synergistic interaction, and technology-led self-driven;（3） AI + knowledge application and the digital capabilities of organizational members can substitute for each other under certain conditions;（4） the key preconditions for knowledge management model innovation mainly lie at the technological actor level, with AI + knowledge transform ation being the core condition for achieving high-level knowledge management model innovation. The possible contributions of this study are as follows: Firstly, compared to the previous method of linear path analysis, this study identifies multiple implementation paths for enterprises to achieve high-level knowledge management model innovation under different configuration conditions from a configuration perspective, revealing the driving mechanism of multi factor interaction coupling on knowledge management model innovation. Secondly, different from the TOE framework commonly used in configuration research, the introduction of ANT integrates technical actors, human actors, and collaborative actors into the same analysis network, with a network logic proposed for innovative construction of knowledge management models under human-machine collaboration, providing a new exploration path for the localized application of ANT in management contexts. Finally, this study proposes that technology actors, human actors, and collaborative actors jointly constitute the core driving force for knowledge management model innovation, and further analyzes the substitutability of technology actors and human actors in specific configurations, responding to the lack of in-depth explo-ration of human-machine collaboration in existing research.

In the context of unprecedented global changes, enterprises are confronted with escalating risks and uncertainties, making organizational resilience a critical capability for achieving high-quality and sustainable development. Entrepreneurship—characterized by innovation, opportunity recognition, and risk-taking—serves as a vital internal driver for firms to navigate environmental volatility. At the same time, the ongoing wave of digital-intelligent transformation is fundamentally reshaping operational models and organizational structures, offering essential technological support for enhancing resilience. The current studies have mostly linked entrepreneurship to innovation performance or business development, with less attention paid to the unique role in coping with uncertainty shocks. There is still a lack of systematic analysis on how entrepreneurship affects organizational resilience through strategic behavior. Although existing research has shown the role of digital transformation in reshaping organizational resilience, there is limited research that integrates digital transformation into the chain of entrepreneurship and organizational resilience. Environmental uncertainty, as an important moderating factor for corporate strategic behavior, has not received sufficient attention about its boundary conditions in the effectiveness of entrepreneurship, and related research is still relatively limited. Against this backdrop, investigating how entrepreneurship fosters organizational resilience through the pathway of digital-intelligent transformation contributes to a deeper understanding of their interrelationships and provides both theoretical insights and practical guidance for firms seeking to adapt and thrive in complex and dynamic environments.This study uses data from A-share listed companies in China from 2011 to 2022 as the research sample, and sets organizational resilience as the dependent variable, which is measured by performance growth and financial volatility. As the core independent variable, entrepreneurship is characterized by three aspects of an enterprise: innovation capability, strategic decision-making, and self-employment status. This study defines the mediating variable as the degree of digital-intelligent transformation, and identifies environmental uncertainty as the moderating variable, which is categorized into micro-environmental uncertainty and macro-environmental uncertainty. The study employs a two-way fixed effects model to examine the impact of entrepreneurship on organizational resilience empirically. From the perspective of digital-intelligent transformation, it explores the mediating mechanism through which such transformation enhances organizational resilience. In addition, heterogeneity analyses are conducted based on firm characteristics and macro-level environments, and the moderating role of environmental uncertainty in the relationship between entrepreneurship and organizational resilience is also investigated. The study finds that fostering entrepreneurship contributes to enhancing organizational resilience. Entrepreneurship promotes organizational resilience by driving firms’ digital-intelligent transformation. Furthermore, environmental uncertainty positively moderates the relationship between entrepreneurship and organizational resilience. The impact of entrepreneurship on organizational resilience also varies significantly depending on firms’ factor intensity, ownership type, and life cycle stage, as well as the level of digital economy policy support and entrepreneurial activity in the region where the firm is located. Thus,efforts should be made to improve institutional and incentive mechanisms for fostering entrepreneurship;then, measures should focus on accelerating digital-intelligent transformation through technological R＆D and new infrastructure development;next, differentiated policies should be provided in accordance with enterprise types and regional conditions;lastly, enterprises’ environmental adaptability should be enhanced by stabilizing policies and strengthening supervision. The theoretical contributions of this study are threefold. First, it reveals the positive impact of entrepreneurship on organizational resilience, expanding research and guiding practice for sustainability. Second, it highlights the mediating role of digital-intelligent transformation in the relationship between entrepreneurship and organizational resilience. This not only offers new empirical evidence for the theoretical exploration of digital-intelligent transformation,but also provides scientific guidance for enterprises to promote such transformation in practice. Third, it emphasizes the catalytic role of environmental uncertainty in stimulating and reinforcing entrepreneurship, providing new insights into resilience under var-ying conditions.

Accelerating the development of a spatial network of new quality productive forces is essential for optimizing resource allocation and advancing industrial upgrading. It also represents a crucial mechanism for improving locally tailored institutional arrangements and promoting regional coordinated development. Currently, China’s new quality productive forces exhibit pronounced spatial imbalances. A fully efficient and synergistic inter-provincial development pattern has yet to emerge, with persistent challenges including restricted factor flows and inadequate regional cooperation. These obstacles hinder both the overall advancement of new-quality productive forces and the realization of balanced regional development. In this context, this study addresses two core questions: What structural characteristics define China’s spatial network of new quality productive forces, and what network effects does it produce? Answering these questions is of considerable theoretical and practical significance for optimizing the spatial configuration of new quality productive forces and fostering high-quality regional coordination.Using data from 31 provinces in China from 2012 to 2022, this study constructs an evaluation index system for newquality productive forces across five dimensions: new laborers, new labor means, new labor objects, new technologies, and new production organizations. A composite measure is derived through the entropy weight method. A modified gravity model is then applied to establish a provincial-level spatial network of new quality productive forces, and social network analysis is used to examine its overall and node-level structural features as well as block models. To examine the individual characteristics of the spatial correlation network of new-quality productive forces, this study analyzes three dimensions—degree centrality, betweenness centrality, and closeness centrality—to reveal the status and role of each province as a node in the spatial correlation network. QAP regression is then employed to identify the mechanisms shaping the network, and spati al-econometric models are used to test its effects.The key findings are as follows. First, at the overall network level, the density of China’s spatial network of new quality productive forces continues to increase. At the same time, efficiency declines, small-world characteristics strengthen, and a clear “core-periphery” structure emerges. Although interregional collaboration is improving, it remains below the optimal level. Second, at the node level, eastern provinces such as Beijing, Jiangsu, and Shanghai consistently occupy central positions, exerting strong siphoning and intermediary effects, whereas most western and northeastern provinces remain peripheral, marked by significant spillovers and limited cross-regional collaboration. Third, block model analysis reveals strong intra-block but weak inter-block connections: the eastern and central regions form a “bidirectional spillover” block, while most western and northeastern regions function as “net beneficiaries” or “primary beneficiaries”, reflecting limited integration across regions. Fourth, in terms of formation mechanisms, the spatial associations of new labor objects and new technologies are the primary drivers of network formation, while the contributions of new laborers, new labor means, and new production organizations remain limited. Fifth, regarding network effects, overall network density has a significant positive impact on enhancing the development of new quality productive forces and narrowing regional disparities, whereas the influence of node-level structural features is relatively modest. Moreover, educational development and gove rnment intervention further strengthen collaborative improvements in new quality productive forces.To optimize the spatial network of new quality productive forces and enhance regional synergy, this study proposes several policy recommendations. First, policymakers should refine the national spatial configuration of new quality productive forces, promote cross-regional collaborative innovation platforms, and remove administrative barriers. Second, they should reinforce the radiating and driving functions of core provinces while improving the embeddedness of peripheral regions. Third, cross-block linkage mechanisms should be established to facilitate wider resource flows and sharing. Fourth, policymakers should prioritize critical drivers such as new technologies and new labor objects in building regional collaborative innovation networks. Finally, they should improve the governance framework for the spatial network of new quality productive forces to facilitate cross-regional coordination and institutional integration in areas such as education and governmental regulation.

In the context of innovation-driven development and the construction of an intellectual property powerhouse,the cultivation and transformation of high-value patents have emerged as critical drivers of the deep integration between industrial chain and innovation chains.While a limited number of scholars have conducted firm-level research showing that high-value patents can enhance enterprises’ technological innovation capacity,help break the path dependence of low-end lock-in,and promote high-quality development by alleviating information asymmetry,there remains a significant gap at the regional level.Specifically,systematic analysis and mechanism identification are still lacking regarding how high-value patents exert their economic effects by optimizing the a llocation of innovation factors and activating regional synergy mechanisms.This study fills this gap by constructing a comprehensive analytical framework that connects high-value patents to the coupling and coordination of industrial chain and innovation chain via resource reallocation, knowledge spillovers, and institutional mechanisms. The theoretical foundation outlines two primary pathways through which high-value patents facilitate integration. First, by embedding core technologies in upstream production processes, high-value patents help enterprises overcome bottlenecks in key industrial segments, thereby enhancing self-reliance and technological control. Aligning innovation activities with industrial needs strengthens the synergy between R＆D and production, fosters collaborative innovation networks, and facilitates value chain upgrading. Second, drawing on knowledge spillover theory, high-value patents spread through inter-firm cooperation, talent mobility, and innovation networks, thereby promoting the spatial and functional expansion of the industrial chain around innovation hubs. Compared with ordinary patents, high-value patents demonstrate greater scalability and integration potential, fostering systemic innovation synergy and mitigating the “island effect” in technology development. Furthermore, the effectiveness of high-value patents is conditioned by two critical moderating factors: regional absorptive capacity and the strength of intellectual property protection. These institutional and cognitive factors shape the extent to which external knowledge is absorbed, protected, and transformed into integrated inno vation and industrial outcomes.Empirically, the study utilizes panel data from 30 provinces in China’s mainland spanning 2012 to 2023. High-value patents are identified using a machine learning-enhanced income method, while dual-chain integration is assessed through a coupling coordination model. Fixed-effects regression results, supported by robustness checks and instrumental variable methods, confirm the significant positive impact of high-value patents on dual-chain integration. Heterogeneity analysis further reveals that these effects are more prominent in economically advanced and innovation-intensive regions in eastern and central China. Mediation analysis further demonstrates that high-value patents influence dual-chain integration not only directly, but also indirectly by attracting and concentrating innovation resources, which in turn optimizes the spatial and structural alignment between the industrial chain and innovation chain.This study makes three major contributions. Theoretically, it advances the understanding of high-value patents by framing them as systemic enablers of industrial–innovation synergy and highlights, for the first time, the mediating role of innovation factor agglomeration. Methodologically, it introduces a scalable and data-driven method for identifying patent value and evaluating dual-chain integration. To promote the economic effects of high-value patents, targeted measures should be taken from both enterprise and government perspectives across four key areas. First, enterprises should focus on core technologies to enhance the cultivation and market adaptability of high-value patents, while governments need to implement collaborative intellectual property（IP） application mechanisms and advance the integration of such patents with key industries. Second, enterprises ought to strengthen their capabilities in patent management and transformation, and governments should invest in innovation infrastructure and improve the judicial protection system for IP to foster a sound innovation ecosystem. Third, enterprises should develop patent layout strategies based on local industrial strengths, and governments should provide differentiated policies—accelerating the development of high-value patent-intensive industrial clusters in eastern and central regions, and supporting technology-industry collaboration to tap patent potential in northeastern and western regions. Fourth, efforts should be made to improve IP financial support mechanisms, such as developing diversified financial products, and build a talent team for IP services and transformation, thereby constructing a sys-tem of high-value patent transformation clusters.

Open source communities have become a vital platform for open innovation in the digital economy era, playing a key role in fostering the development of new quality productivity. Their open and collaborative structure facilitates broadbased knowledge sharing and distributed technological development. Within the governance framework of open source, licenses serve not only as legal authorization instruments but also as core mechanisms of intellectual property（IP） governance, significantly shaping organizational collaboration, rights allocation, and innovation outcomes. However, existing studies have primarily focused on the infectivity of licenses, often overlooking their broader institutional attributes and the behavioral consequences these rules may trigger. From the perspective of IP governance, this paper constructs an analytical framework of “institutional structure–behavioral response–innovation performance” based on the characteristics of governance objects, and systematically examines how three structural features of open source licenses—complexity, infectivity, and compatibility—affect project innovation performance. In addition, the paper introduces license enforcement deviation as a key mediating mechanism to reveal how institutional design influences project outcomes through behavioral pathways.License complexity refers to the difficulty of understanding and enforcing the rules;infectivity reflects the extent to which license terms constrain derivative works;compatibility influences the feasibility of cross-project module integration and knowledge reuse.License enforcement deviation refers to situations in which contributors,after modifying or inheriting code,fail to comply with the original license terms or remove original attribution information without permission.In terms of empirical design, the paper constructs a dataset of 900 open source projects from the Git Hub platform using a multi-dimensional stratified random sampling approach. The sample covers a variety of programming languages, creation years, and levels of development activity to ensure representativeness and diversity. Data collected via Git Hub’s API and repository metadata include license types, code evolution trajectories, and community interaction features. Innovation performance is measured through a composite index based on seven key indicators and which is reduced using principal component analysis（PCA）. The empirical analysis proceeds in three stages. First, multiple linear regression is employed to test the main effects of license complexity, infectivity, and compatibility on innovation performance, clarifying their structural impact mechanisms. Second, a mediation model is constructed by introducing license enforcement deviation as an intermediary variable to assess the indirect effects of institutional rules via behavioral pathways. The significance and robustness of the mediating effects are tested using the Sobel test and Bootstrap resampling methods. Third, robustness checks are conducted through variable refinement（subdividing infectivity levels）, sample adjustment（excluding newly created or inactive projects）, the inclusion of additional control variables（institutional affiliation of the project）, and instrumental variable estimation using two-stage least squares（2SLS）, providing a comprehensive evaluation of the empirical fi ndings.The results show that license structure significantly influences project innovation performance. Specifically, higher levels of complexity and infectivity are associated with more pronounced negative effects, while greater compatibility facilitates collaboration and knowledge integration, thus improving innovation outcomes. The mediating role of behavioral mechanisms is also confirmed, indicating that institutional rules shape project performance indirectly through their influence on contributor behavior. Further analysis reveals heterogeneous effects across different project types: software development projects are more sensitive to institutional structure, whereas AI and data science projects exhibit relatively weaker r esponses to license rules. This paper contributes to the open source governance literature by shifting the analytical focus from static legal classification to the structural logic of institutional rules, and by extending the “institution-behavior-performance” paradigm. It highlights the deep interdependence between rule design and behavioral responses. Theoretically, it reveals how structural governance tools affect innovation in open collaboration environments; practically, it proposes policy implications for optimizing license design, implementing stratified governance strategies and strengthening behavioral regulation. Future research may further expand data sources and analytical dimensions by incorporating multi-platform dynamic data, longitudinal tracking, and variables related to informal governance mechanisms. This would help deepen understanding of the interaction among institutional evolution, behavioral adaptation, and collaborative innovation, and contribute to building a more dynamic and pluralistic framework for open source IP governance.

Digital innovation is currently reshaping the economic system for innovative development at an unprecedented speed and scale. It has emerged as a pivotal engine for accelerating the high-quality development of the digital economy and for driving the evolution of new productive forces. However, constrained by inadequate digital resources and innovation capabilities, traditional enterprises are encumbered by such predicaments as ambiguous digital innovation pathways and ineffective realization of digital innovation value. As a result, they find themselves in a “well-intentioned but underpowered” dilemma when pursuing digital innovation.Studies indicate that integrating into digital platform ecosystems and engaging in dynamic interactions with multiple participants constitutes a critical pathway for enterprises to achieve digital innovation. Nevertheless, the existing literature has yet to uncover the specific digital-innovation pathways available to ecosystem-participating enterprises.Grounded in the complex adaptive systems theory, this study integrates micro-level cognitive factors, meso-level organizational capabilities, and macro-level ecosystem attributes to construct a theoretical analytical framework of "managerial cognition-organizational capabilities-ecosystem characteristics". Employing the fuzzy-set qualitative comparative analysis（fsQCA） method, it analyzes 263 sets of data to explore the configurational effects of managerial cognitive flexibility, digital perception capability, digital resource synergy capability, ecosystem richness, and ecosystem innovativeness on digital innovation among ecosystem-participating enterprises, while elucidating the underlying mechanisms and pathways of such innovation.The findings reveal that（1） no single core condition constitutes a necessary prerequisite for high-level digital innovation; however, managerial cognitive flexibility, as a core condition, exerts a relatively universal influence;（2） the coupling of diverse antecedent conditions forms two configurational pathways, corresponding to two types of configurations for achieving high-level corporate digital innovation: "cognition-opportunity driven" and "cognition-ecosystem interactive". Specifically, in the cognition-opportunity driven pathway, managerial cognitive flexibility, digital perception capability, and ecosystem innovativeness are present as core conditions, ecosystem richness is absent as a core condition, and digital resource synergy capability functions as a peripheral condition. In the cognition-ecosystem interactive pathway, managerial cognitive flexibility, digital resource synergy capability, ecosystem richness, and ecosystem innovativeness are present as core conditions, while digital perception capability serves as a peripheral condition;（3） three configurational pathways are identified, forming two types of configurations that lead to non-high-level digital innovation: "cognition-capability deficient" and "cognition deficient". The research conclusions remain valid after robustness tests.This study makes three significant contributions. First, from the perspective of ecosystem participants, it identifies the key antecedents and operational mechanisms of digital innovation within participating enterprises, addressing the insufficient attention given to their digital innovation in existing research. Second, moving beyond the prevalent focus on linear relationships between isolated influencing factors and corporate digital innovation, this study draws on complex adaptive systems theory to innovatively construct a "managerial cognition-organizational capabilities-ecosystem characteristics" analytical framework. Using the fs QCA method, it explores the driving mechanisms of multi-factor linkage and synergy in promoting digital innovation among participating enterprises, thereby filling a gap in the literature regarding the unclear pathways of such innovation. Finally, the study innovatively incorporates the richness and innovativeness of digital platforms into the research framework, extending the analytical perspective to the ecosystem level and offering a new theoretical lens and framework for examining the relationship between platform ecosystem characteristics and corporate innovation capabilities.

With the improvement of digital infrastructure, digital technologies are reshaping manufacturing enterprises. However, the application of digital technologies in China’s manufacturing industry still lags behind, and enterprises face problems such as high investment costs and organizational risks. CEOs’ cognitive understanding of market opportunities may be the key. Drawing on the theoretical lens of strategic choice theory and competitive dynamics theory, the research focuses particularly on the role of Chief Executive Officers（CEOs） in shaping digital technology implementation through their cognition of market opportunities and the moderating effects of internal and external situational factors. While prior studies have recognized the influence of executives’ cognitive frameworks, there remains a lack of empirical research examining how different types of opportunity cognition—mainstream versus emerging markets—interact with organizational culture and external competitive pressure to influence digital technology implementation.To address this gap,this study proposes an integrated conceptual model in which CEO cognition of mainstream and emerging market opportunities serve as the primary explanatory variables driving the implementation of digital technologies.Simultaneously,developmental culture（as an internal contextual factor） and competitive pressure（as an external contextual factor） are incorporated as joint moderators,forming a triadic interaction mechanism.This framework highlights the dialectical interplay between subjective strategic choice and objective environmental constraints.Data was collected through a two-wave questionnaire survey targeting manufacturing enterprises in South China.After rigorous screening procedures,a total of 306 valid responses were retained,yielding an effective response rate of 61.2%.To mitigate potential common method bias, measures such as time-lagged data collection, targeted respondent verification, and structural design controls were employed. To test for endogeneity issues, this study employs the Copula coupling variable method, which controls for endogeneity by modeling the correlation between independent variables and error terms without the need to introduce instrumental variables. Copula variables were constructed using R language（4.1.0）, and their significance was tested through bootstrap（with 10 000 repeated samplings）. The results show that Copula variables are not significant in all models, indicating that the endogeneity problem in th is study is minimal and the regression results are robust. Empirical results derived from regression analysis reveal several key findings. First, CEO cognition of mainstream market opportunities exerts a significantly stronger positive influence on digital technology implementation than cognition of emerging market opportunities. Second, the joint moderating effect of developmental culture and competitive pressure on the relationship between CEO opportunity cognition and digital technology implementation is significant. Specifically, only under the condition of both high developmental culture and high competitive pressure does the positive impact of CEO cognition—whether focused on mainstream or emerging markets—on digital technology implementation become amplified. This indicates that the internal atmosphere of innovation and learning, when reinforced by external competitive urgency, creates a favorable environment for strategic cognition to be transformed into organizational action. Third, under the other three contextual scenarios of low pressure-low culture, high pressure-low culture, and low pressure-high culture,the effect of CEO cognition of emerging markets on digital technology implementation is non-significant. In contrast, mainstream mark et opportunity cognition remains a consistently significant driver across all conditions. Theoretically, this study contributes to strategic choice theory by emphasizing the necessity of considering joint situational moderators in the cognition–action linkage. It bridges executive cognition and digital transformation literature by distinguishing between types of market opportunity cognition and by contextualizing their effectiveness. Practically, the findings offer guidance to manufacturing enterprises undergoing digital transformation. To effectively activate CEO cognitive capital, these enterprises need to take the initiative in fostering developmental organizational cultures and actively respon d to external market competition. In sum,this study identifies CEO market opportunity cognition as an internal driver and the dual forces of developmental culture and competitive pressure as external contextual enablers.It demonstrates that the successful implementation of digital technologies in manufacturing is not only a matter of strategic cognition but also a function of the alignment between internal cultural readiness and external environmental challenges.This insight enriches the understanding of how subjective managerial perceptions interact with objective contextual factors to shape enterprise-level digital transformation outcomes.

The 2024 Central Economic Work Conference explicitly pointed out the need to comprehensively rectify internal disorderly competition and standardize the behavior of local governments and market entities（enterprises）. At present, there exists a certain degree of industrial homogenization and irrational industrial layout among regions in China—a phenomenon that has led to overcapacity spreading from traditional industries（such as coal, steel, and cement） to emerging strategic industries, including photovoltaics, wind power, new materials, and lithium batteries. Against this backdrop, solving the problem of optimizing inter-regional industrial structure allocation has become a core task for China to transform its economic development mode, break free from low-quality competition, and achieve high-quality economic developm ent under the new normal.With the continuous advancement of scientific and technological revolution and the in-depth development of technological diversification, some traditional industries may gradually decline due to technological obsolescence, while emerging industries will rise driven by technological breakthroughs. However, in the process of investment attraction and industrial planning, if local governments blindly follow similar innovation policy directions（e.g., homogeneous pursuit of high-tech industries） and fail to implement, differentiated development policies based on their own resource endowments and industrial foundations, it will easily trigger the problem of "involutionary" innovation competition—where regions compete for the same market segments with similar technologies, resulting in wasted innovation resources and stagnant industrial upgrading. In contrast, in an environment with high technological diversification, enterprises have more opportunities to access new technological concepts, cross-domain knowledge, and innovative methods（such as integrating digital technology with traditional manufacturing）, which can effectively stimulate innovative inspiration and help avoid the trap of homogeneous competition. Therefore, a critical question arises:Can technological diversification suppress involutionary competition? An explanation is needed in terms of academic theory.To explore the intrinsic relationship between technological diversification and involutionary competition, this study adopts the analytical framework of Marshallian continuum and Schumpeterian break. It focuses on examining how technological diversification suppresses involutionary competition by three pathways: expanding the possibility boundary of innovation, promoting differentiated industrial development, and improving production efficiency and market competitiveness. Furthermore, the study further examines the pathways through Marshallian continuum in the related technical fields and Schumpeterian break in the unrelated technical fields. On this basis, the study uses the patent data of the China National Intellectual Property Administration from 2010 to 2023 to empirically test the above issues. The results show that, firstly, technological diversification significantly suppresses involutionary competition; secondly, the Marshallian continuum effect in the related technical fields and the Schumpeterian break effect in the unrelated technical fields are two key mechanisms; thirdly, inter regional technological complementarity can effectively overcome involutionary competition; fourthly, inhibitory effect is strongest in the western region, followed by the central and eastern regions. In addition, technological diversification has a stronger inhibitory effect in group with higher proportion of non-invention patents; lastly, there is a significant positive moderating effect of the government’s efforts to suppress involutionary competition through technological d iversification.This study has three marginal contributions.First,it elucidates the mechanism by which technological diversification suppresses intra-regional innovation competition and the potential impact pathways.Second,aiming at the limitation of traditional technological proximity indices（which mainly measure the similarity of technical fields and ignore the intensity of competition）,this study constructs a relatively objective index to measure innovation competition.Finally,by considering the potential heterogeneity of regions,differences in patent-mix composition,and the moderating role of government,the study offers important practical value and theoretical significance.Accordingly,the government should expand open access to big-science facilities,funds cloud labs and cross-region consortia,and use patient capital to ferry firms across the “valley of death.” It should calibrate investment to each region’s level :backing emerging niches in lagging areas,widening technology portfolios in middle regions,and channeling abundant resources into basic and breakthrough research in leading cities.Instead of creating subsidy races,the government could systematically support R＆D,enforce competition and anti-“involution” laws and eliminate local market barriers to promote indus-try standards that shift rivalry from price-cutting to value creation.

Green development is the main melody of sustainable development and high-quality development of enterprises today. Especially in today’s society, where competition is increasingly intense, the green element is not only the key for enterprises to improve their core competitiveness, but also the trade-off index for external stakeholders to participate in enterprise investment and financing. Therefore, if enterprises want to gain a foothold in this highly competitive environment, they need to continuously increase the investment in green innovation and improve the level of green innovation. However, as an entrepreneur, the chairman of the board is at the helm of the formulation and implementation of the enterprise strategy, which has a crucial impact on the development direction of green innovation. Especially in the aspect of innovation spirit, entrepreneurial innovation spirit is a manifestation of the enterprise innovation value system, which is likely to be internalized into the green innovation power of listed companies and promote the development of green innovation of listed companies, but there is a lack of relevant literature to explore.Using sample data from China’s A-share listed companies spanning 2016 to 2023, this study examines the impact of entrepreneurial innovation spirit on green innovation and its underlying mechanism. The results show that entrepreneurial innovation spirit can significantly enhance the green innovation level of listed companies. Mechanism tests reveal that entrepreneurial innovation spirit further boosts the green innovation level of listed companies by improving their commercial credit financing capacity and R＆D investment intensity. Tests on the moderating effect of agency costs indicate that the lower the agency cost, the more pronounced the role of entrepreneurial innovation spirit in promoting green innovation among listed companies. Heterogeneity analysis based on corporate characteristics shows that the promotional effect of entrepreneurial innovation spirit on green innovation is more significant for companies in the growth and maturity stages, as well as for those located in eastern China. The heterogeneity analysis of chairman’s personal characteristics indicates that among the enterprises with higher education level and younger chairman’s age, the promotion effect of entrepreneurial innova tion spirit on green innovation is more significant.This paper mainly has the following innovations and contributions: First, this paper clarifies the definition and measurement of entrepreneurial innovation spirit from the perspective of chairman’s speeches for the first time. In the existing research, some scholars measure R＆D input and output directly, while others measure it by questionnaire. Measured by R＆D investment and innovation,this is more likely the result of the innovation strategy decision-making of the enterprise organization, which is difficult to reflect the role of the entrepreneur as the spiritual subject. The questionnaire survey may focus on entrepreneurs, but it is difficult to reflect the innovation elements. Therefore, this paper creatively defines and measures the entrepreneurial innovation spirit from the perspective of the chairman’s speech, which not only considers the entrepreneur as the main body of the innovation spirit, but also contains the spiritual element of innovation. Second, from the perspective of entrepreneurial innovation spirit, this paper explores its impact on enterprise green innovation for the first time, enriching the literature on the influencing factors of enterprise green innovation. In the existing studies, scholars mainly explore the impact of executive background on enterprise green innovation, while ignoring the internal driving force of entrepreneurial innovation spirit. This study addresses this gap in the existing literature.Third, given the uneven conditions and environments for green innovation across enterprises, there has been uncertainty regarding whether entrepreneurial innovation spirit can truly drive corporate green innovation. This study confirms that entrepreneurial innovation spirit is indeed a key internal driver of green innovation development in listed companies.This finding provides a theoretical basis and management enlightenment for enterprises to promote entrepreneurial innovation spirit and promote the develop-ment of green innovation in listed companies.

As digitalization continues to advance, the emergence of new technologies such as cloud storage and converged storage has eliminated the limitations on personal storage space, making digital hoarding behavior a gradually widespread social phenomenon. As a unique product of the digital age, digital hoarding behavior manifests as excessive accumulation of digital resources, and this behavior is particularly pronounced and typical in the context of work. However, the majority of existing studies have focused on the impact of digital hoarding behavior within the social media, with relatively limited exploration of the underlying triggers in the context of work. Digital hoarding behavior in the workplace not only poses psychological health risks to individuals and reduces employees’ work efficiency, but also triggers a series of severe consequences for businesses, such as decreased productivity and cybersecurity threats. Therefore, exploring the influencing factors of digital hoarding behavior in the workplace is essential for guiding employees in digital resource management and enhanc ing organizational data utilization efficiency.Drawing on attribution theory, this study selects six key conditional factors from two levels, internal and external factors, to construct a causal configuration framework for digital hoarding behavior in the workplace. Specifically, this study takes job insecurity, perceived usefulness, work responsibility, artificial intelligence usage, time pressure, and industry competitive pressure as the core antecedent conditional factors. Then it uses NCA and fuzzy set qualitative comparative analysis（fs QCA） to conduct configuration analysis on 286 samples of enterprise employees, and explores how internal and exter nal factors affect employees’ digital hoarding behavior in the workplace.Through dual testing using the NCA method and the fs QCA method, this study finds that factors including job insecurity, perceived usefulness, work responsibility, artificial intelligence usage, time pressure, and industry competitive pressure cannot individually constitute necessary conditions for high digital hoarding behavior or non-high digital hoarding behavior, nor can they individually constitute sufficient conditions for digital hoarding behavior. This finding indicates that employees’ digital hoarding behavior in the workplace is not driven by a single factor, but rather the result of the synergistic linkage and interaction of multiple antecedent conditions. The study reveals that there are four paths contributing to employees’ high-level digital hoarding behavior, namely technology-internal-driver, pressure-internal-driven, externaldriven, and internal-external synergistic. Additionally, there are three paths leading to employees’ non-high-level digital hoarding behavior, including the motivation-deficiency type, value-absence type and pressure-insufficiency type. This study adopts a configurational perspective to analyze different combinations of antecedent conditions underlying employees’ digital hoarding behavior, thereby deepening the understanding of the joint effects of the antecedent conditions of digital hoar ding behavior.The theoretical contributions of this study are manifested in three aspects. First, this study clarifies the various factors influencing employees’ digital hoarding behavior in the workplace, analyzes the inducements of digital hoarding behavior from the employee perspective, expands and complements the research perspective on digital hoarding behavior, and enriches the understanding of the influence mechanism of digital hoarding behavior. Second, this study constructs a framework for the influence of digital hoarding behavior from the perspective of attribution theory, which expands the application context and scope of attribution theory. Third, this study explores the configurational paths of digital hoarding behavior, further revealing the underlying mechanisms influencing employees’ digital hoarding behavior.This study puts forward practical implications for business managers and employees in the following three aspects. First, it is necessary to manage employees’ personal digital resources well, clarify job responsibilities, and avoid mental health problems caused by excessive sense of responsibility. Second, employees should improve their ability to identify the value of digital information, filter useful information, and enhance their information processing and application capabilities. Third, employees need to plan their work schedules reasonably, and business managers should also provide a relaxed working environment for employees, which will help businesses eliminate employees’ digital hoarding behavior and achieve a win-win situation for both businesses and employees.

Investigating the science-technology coupling is essential for deepening the scientific understanding of technology transfer and synergistic innovation pathways in emerging industries, thereby enhancing the practical impact of research outcomes. Existing studies on strategic emerging industries often focus on single innovation elements from either the scientific or technological domain, rarely exploring the coupling phenomena and the associated feedback effects within the science-technology system from a parallel observation perspective. Drawing upon industrial growth theory, this study attempts to investigate the impact mechanisms and pathways of science-technology coupling on the growth of emerging industries, as well as the moderating role of regional government intervention, by matching and utilizing multi-source heterogeneous data. It designs a method to detect science-technology coupling at the knowledge content level through the coupling of knowledge networks, aiming to explore the impact of this internal techno-scientific synergy on industrial growth characteristics. Concurrently, considering government intervention as a key external driver in China’s industrial innovation system, the study examines its mechanisms, specifically investigating the moderating effects of regional industrial policy intensity and adoption speed on the relationship between science-technology coupling and industrial growth.Then the study proposes two hypotheses. H1: A non-linear, inverted U-shaped relationship exists between sciencetechnology coupling and the growth of emerging industries. H2: Government intervention has a positive moderating effect on the inverted U-shaped relationship between science-technology coupling and industrial growth. First, using scientific literature and technology patents as primary representations of scientific and technological outputs, the study constructs time-series science-technology knowledge networks and calculates the degree of science-technology coupling based on a network portrait projection method. Second, it employs a panel data model to empirically test the mechanism through which science-technology coupling affects industrial agglomeration, activity, and growth. Finally, by quantifying the industrial policy intensity of different regional governments, it analyzes the moderating role of government intervention to identify effective development paths.Using the information and communication technology（ICT） industry as a case study, the study concludes that the coupling of science and technology and the growth characteristics of the ICT industry have progressively strengthened over time, exhibiting significant spatial heterogeneity. A non-linear, inverted U-shaped relationship exists between the degree of science-technology coupling and the growth characteristics of the ICT industry. Notably, government intervention positively and significantly moderates this inverted U-shaped relationship. These findings yield several policy implications. First, it is essential to formulate regionally differentiated strategies for ICT development by dynamically adapting policy objectives to the synergy between science, technology, and policy. This requires tailoring the ICT industrial policy toolkit to meet regional, stage-specific growth demands and constructing a resilience governance framework based on “precise identification of differences, dynamic adaptation of tools, and multi-level risk hedging.” Second, an optimal degree of coupling between the scientific and technological knowledge systems must be maintained. Over-coupling—caused by factors such as cognitive lock-in, path dependency, profit-driven resource concentration, or institutional constraints—should be avoided, as it leads to monolithic resource allocation and innovation pathway homogenization. This negative impact can be mitigated by optimizing resource allocation and benefit distribution mechanisms and strengthening market-oriented, demand-driven innovation. Third, moderate policy intervention can offset technology coupling’s negative effects and boost healthy industrial development. Regional governments should optimize basic research layouts, strengthen industry-academia-research collaboration, and improve technology transfer mechanisms to achieve synergistic science-technology-driven growth, supporting national high-quality economic development and sustainable social progress.The contributions of this study are primarily reflected in three aspects:（1） By integrating and aligning multi-source heterogeneous data, it establishes an “industry-science-technology” connection, and explores the dynamic coupling characteristics of science and technology in emerging industries across diverse regions;（2） This study integrates science-technology relationships into an econometric model to explore technological coupling’s link with industrial growth theoretically, and empirically reveals the synergistic innovation effects and mechanisms of science-technology coupling;（3） It constructs an analysis model for emerging industry growth within the “science-technology-policy” framework, elucidating the dual driving forces of technology and government in achieving emerging industry outcomes.

In the rapidly evolving landscape of the digital economy and under the imperatives of innovation-driven development strategies, breakthrough innovation has emerged as a critical lever for enterprises to transcend technological trajectory lock-in and achieve discontinuous advancements that disrupt existing market paradigms. This study investigates how digital leadership—a paradigm characterized by technological acumen, transformative vision, and data-driven strategic decision-making—catalyzes breakthrough innovation within organizations. Rooted in dynamic capability theory, which emphasizes the continuous cycle of sensing, seizing, and reconfiguring resources to sustain competitive advantage, this study explores the mechanisms through which digital leadership fosters breakthrough innovation, with resource orchestration as a mediator and strategic flexibility as a moderator. The significance of this study lies in its response to a pressing gap in the literature: while prior studies have emphasized resource-based views and absorptive capacity, they have largely overlooked the pivotal role of leadership in navigating digital disruptions and reconfiguring organizational capabilities for non-increment al innovation.The study aims to explore how digital leadership drives breakthrough innovation which is defined as radical technological leaps or business model transformations that break from established trajectories. It proposes that digital leadership, as a higher-order dynamic capability, enhances firms’ ability to overcome path dependencies by reshaping strategic cognition and resource allocation. To this end, the study constructs a theoretical framework proposing that digital leadership exerts a positive impact on breakthrough innovation, with resource orchestration—defined as the systematic process of identifying, integrating, and activating organizational resources—functioning as a pivotal mediating mechanism. Additionally, strategic flexibility, the capacity to swiftly adapt strategic directions and resource configurations, is hypothesized to amplify the effect of digital leadership on resource orchestration, thereby enhancing innovation outcomes in dynamic environments.This study empirically employs a robust two-wave longitudinal survey design, collecting data from 341 enterprises in Eastern China—a region globally recognized for its advanced digital economy and dynamic innovation ecosystems. The sample predominantly comprises manufacturing firms, reflecting the industrial composition of the area, and data were gathered through validated scales adapted from prior literature, ensuring psychometric rigor. Analytical methods, including correlation analysis and hierarchical regression, are employed to test the proposed relationships, with additional robust ness checks conducted across firm subgroups to validate the findings’ consistency.The results reveal several key insights. First, digital leadership significantly and positively impacts breakthrough innovation, underscoring its role in broadening innovation horizons through technological foresight and strategic agility. This finding highlights how digital leadership enables firms to leapfrog traditional innovation constraints. Second, resource orchestration partially mediates the relationship between digital leadership and breakthrough innovation. This mediation elucidates how digital leadership translates into tangible innovation outcomes by dynamically reconfiguring organizational resources, bridging the gap between strategic intent and execution. Third, strategic flexibility positively moderates the linkage between digital leadership and resource orchestration, amplifying the effect in firms with high adaptability. However, robustness tests indicate that this moderating effect weakens in older firms and larger enterprises, where organizational inertia and structural complexity may attenuate the impact of flexibility.In practice, the findings provide actionable guidance for for leaders and policymakers during digital transformation. Firms should develop digital leadership skills in senior management, integrating technology foresight into decision-making. They should also emphasize resource orchestration with modular designs and data-driven valuation to boost resource deployment efficiency. Policymakers can support these efforts by developing digital leadership programs, creating datasharing ecosystems, and establishing adaptable regulations. Enhancing strategic flexibility through agile governance struc-tures further enables firms to capitalize on digital leadership’s transformative potential.

As hubs of technological innovation, emerging industries are emerging as critical engines driving the development of new productive forces. Promoting the industrialization of emerging technologies under the guidance of future scenarios constitutes a pivotal pathway for fostering future industries. Knowledge organization serves as the foundation for resource development. Building on theoretical foundations and logical construction, and adhering to the basic analytical paradigm of "initiation-process-outcome," this paper constructs a research framework of "scenario-driven-technological innovation-industrial application" oriented toward future scenarios. In line with this logical framework and the principles of theoretical sampling, heuristic value, and typicality, this study selects Huawei as the case sample, as it is recognized as a leader and advocate in scenario-based innovation.Leveraging Huawei’s open innovation ecosystem in the 5G-advanced domain, this study employs an exploratory single-case research method to systematically explore the process mechanisms through which Huawei responds to future scenario needs via knowledge-organizing behaviors, realizes the industrial application of emerging technologies, and drives the development of new scenarios.This paper summarizes the process mechanism of emerging-technology industrialization with the core logic of "scenario mining and collaborative innovation-scenario embedding and knowledge organization-scenario adaptation and dynamic transition".The research findings are as follows:（1） The industrialization process of emerging technologies can be divided into three key stages :future scenario mining,scenario-based knowledge organization,and scenario-based application adaptation.Scenario mining marks the starting point of technological industrialization innovation.The identification of future scenarios,which integrates strategic orientation,commercial needs,industrial status quo,and technological foundations,provides a "target" for the industrialization of emerging technologies.（2） Scenario-based knowledge organization represents the technical dimension for realizing the industrialization of emerging technologies,and ecological collaboration serves as the "backbone" of high-performance knowledge organization. This paper defines scenario knowledge as a knowledge system with high contextual adaptability, dynamically evolving through the targeted integration of technologies, resources, and domain experience based on the needs and constraints of specific application scenarios. The scenario-based knowledge organization of emerging technologies involves scenario knowledge aggregation and scenario knowledge reorganization.（3） Scenario-based application adaptation acts as the hub for the iterative evolution of the "scenario-technology-industry" triad. Application in original scenarios forms a vertical evolutionary path of "agile trial-and-error-large-scale commercialization", while the development of new scenarios forms a horizontal interaction model of process-based development and parallel development. This not only accelerates the efficiency of transforming technological achievements into industrial value but also reconstructs the industrial chain value network through the continuous expansion of scenario boundaries, forming a future-industry cultivation paradigm of "technological brea kthrough-scenario innovation-industrial evolution".In light of the conclusions, this paper suggests that（1） enterprises should cultivate capabilities in scenario insight, knowledge reorganization, ecological collaboration, and digital governance in the context of the digital economy to break through the traditional linear innovation model and establish a closed-loop management mechanism of "scenario miningknowledge organization-scenario adaptation-scenario iteration".（2） It is necessary to dynamic knowledge management system driven by scenarios and cross-domain, cross-organizational, and interdisciplinary knowledge collaboration mechanisms. Building on existing open-source systems, enterprises should vigorously develop digital platforms that support knowledge generation and simulated knowledge reorganization, and systematically manage scenario knowledge as a core technological competitiveness.（3） Enterprises should promote the three-dimensional collaborative transformation of "technology-industry-market" and synchronously plan transformation paths for technological breakthroughs, industrial restructurin g, and value innovation. The contribution of this paper lies in revealing the theoretical constructs and practical paths for realizing the industrialization of emerging technologies toward future scenarios from the perspective of knowledge organization,and summarizing the industrialization innovation models oriented toward future scenarios.This has important theoretical and practical value for the in-depth application of knowledge organization theory,the expansion of the scenario-driven innovation paradigm,and the acceleration of emerging technology innovation and future industry cultivation.The research conclusions provide a theoretical basis and practical reference for enterprises to build future scenario perception systems and optimize the coupling mechanism between the supply of emer-ging technologies and market demand in the context of de-globalization.

In recent years, AI has undergone continuous and deepening development, with techniques such as machine learning and deep learning being widely applied in emerging fields like biotechnology and new materials. The vertical application and deep integration of AI across diverse domains have catalyzed numerous highly disruptive AI-enabled New Technologies（AIENT）, which hold strategic significance for driving economic growth and facilitating economic transformation and upgrading. AIENT denote novel technologies generated by integrating advanced AI techniques—such as machine learning, deep learning, or reinforcement learning—based on AI algorithms and models. These technologies converge domain-specific data and knowledge, fusing them with human intelligence. AIENT may emerge either through innovative enha ncements to existing technologies by introducing AI introduction, or entirely from the deep integration of AI and HI.However,in the era of big data,the market adoption of AIENT faces formidable challenges.On one hand,the exponential growth of societal knowledge and the substantial incompleteness of disclosure regarding new technology information exacerbate the problem of information asymmetry between technology developers and adopters.On the other hand,AIENT often resides at the interdisciplinary periphery,characterized by the fusion of multi-source knowledge,strong cross-disciplinary nature,high complexity,weak generalizability,and heavy reliance on specialized complementary resources.These attributes lead to a disconnect in technology-market breakthroughs.Furthermore,existing research frequently adopts the perspective of technology developers, focusing on the impact of technological attributes, adopter readiness, and environmental conditions on the market adoption of emerging technologies. This approach neglects the importance of adopter perceptions and fails to concurrently examine the perceptual similarities and differences between developers and adopters regarding the critical factors influencing market adoption. Consequently, varying degrees of perceptual disparity exist between developers and adopters, hinderin g the advancement of synergistic technology-market breakthroughs for AIENT.This study employs a case study methodology, selecting AI+BD material industrial simulation technology as a representative AIENT case. Focusing on exploring the factors influencing AIENT market adoption and the perceptual differences between technology developers and adopters, the study summarizes common factors affecting AIENT market adoption. Pioneeringly, it examines the perceptual similarities and differences between developers and adopters regarding these factors using a logic of reciprocal validation between the two parties. Simultaneously, the study creatively incorporates the organizational attributes of technology developers and their external contexts into the foundational analytical framework. Furthermore, on the basis of an in-depth case analysis, the study constructs an IDT-TAM-TOE framework with practical significance for guiding the vertical application of AIENT in numerous other fields. This framework enables an explicit comparative analysis of the consistencies and differences in perception between the two parties and systematically deconstruc ts the interactions between the supply and demand sides during the AIENT commercialization process.The study reveals significant consensus between developers and adopters on the key factors influencing AIENT market adoption, alongside non-negligible perceptual differences. there are convergence of perceptions. Firstly, technical usefulness is consistently recognized as a critical factor for market adoption. Secondly, technical ease of use, such as adoption cost and complexity, are uniformly acknowledged for their importance in marketization. Thirdly,the R＆D capability, technical expertise, and data processing capabilities of developers are deemed crucial for successful technology implementation. Finally, government policies and support are identified as significant drivers for technology industrialization. Their role in promoting the advancement of computing infrastructure and enhancing algorithmic capabilities is expected to accelerate AIENT application. However, there are significant cognitive differences between technology developers and adopters. For example, developers underemphasize the importance of model trialability, observability, and user market environmental shifts for AIENT market adoption, factors that are focal points for adopters. Additionally, developers place insufficient emphasis on inter-organizational collaboration, whereas adopters express a strong desire for in-depth collaboration with developers on key technologies to enhance competitiveness. Overall, adopters exhibit a broader spectrum of eval-uation concerning the factors influencing AIENT market adoption compared to developers.

The current view that enterprises apply data elements to gain potential benefits has become a consensus in the academic community; however, in practice, many enterprises face the dilemma of continuously increasing investments in data elements without achieving significant innovation outcomes. This necessitates considering the issue of the deep integration between data elements and enterprise innovation. Unlike traditional innovation resources with relatively static and proprietary attributes, data elements have stronger mobility and non-proprietary attributes such as self-growth and noncompetitiveness, and the change of such resource attributes leads to a predicament of insufficient theoretical explanatory power of traditional innovation theories to explain the underlying logic of data elements empowering enterprise innovation capability. How to effectively integrate data elements with innovation processes has emerged as a critical challenge for enterpr ises seeking to survive and thrive in the digital-intelligence era.Drawing on the resource-based view and dynamic capabilities theory, this study explores the core mechanisms through which data elements empower enterprise innovation capability from the perspectives of knowledge accumulation and dynamic capabilities. Following the logical pathway of “data-information-knowledge-innovation”, it first investigates whether data elements promote enterprise knowledge accumulation. Simultaneously, grounded in dynamic capabilities theory, it examines whether data elements enhance enterprise dynamic capabilities. Furthermore, the study seeks to unveil the dualpath mediating effects of knowledge accumulation and dynamic capabilities in the relationship between data elements and enterprise innovation capability. Using listed enterprises as the research subject, the study analyzes data from a balanced panel data-set comprising 1239 A-share listed enterprises spanning 2014 to 2023, which was ultimately obtained by matching corporate annual reports with patent data. Employing machine learning and text analysis word frequency methods, it constructs enterprise data element indicators, and measures enterprises’ knowledge breadth and depth based on patent data. Through constructing a dual-path chain mediation model, this study applies two-way fixed-effects panel regression analysis to empirically investigate the relationships among enterprise data elements, knowledge accumulation, dynamic capabilities, and innovation capability. In addition, this paper investigates the variations in different regions and types of enterpr ises utilize data elements to enhance their innovation capability. The results show that（1） the application of data elements has led to significant changes in corporate knowledge accumulation and dynamic capabilities, but not all of these changes are positive;（2）the changes in corporate knowledge accumulation and dynamic capabilities brought about by data elements have a significant impact on corporate innovation capability;（3） data elements enhance corporate innovation capability by improving knowledge depth; however, the increase in knowledge breadth brought about by data has a negative impact on corporate innovation;（4） data elements enhance organizational agility and flexibility, which are beneficial for corporate innovation;（5） dynamic capability and knowledge accumula tion play a chain mediating role in the process of data elements empowering enterprise innovation.This paper theoretically enriches the understanding of the enterprise knowledge effects generated by data elements, and provides theoretical support for knowledge management in digital environments. The dual-path chain mediation model constructed in this study not only supplements the mechanism of data element innovation effects from the perspectives of knowledge accumulation and dynamic capabilities, but also provides an explanatory basis for the causal relationships of knowledge effects generated by data elements through the lens of dynamic capabilities. By analyzing the heterogeneous impacts of different knowledge dimensions and changes in dynamic capabilities on corporate innovation capabilities, the study explores the micro-level implementation mechanisms that effectively connect data elements with corporate innovation, offering decision-making references for enterprises to formulate efficient digital innovation strategies in the digital-intelligent environment. The managerial implications of this study are as follows: In the context of digital-intelligent transformation, enterprises should not only incorporate data elements into the core of their innovation strategies and build data-driven innovation systems, but also objectively examine the new knowledge management challenges triggered by data elements, prioritize the efficient alignment between knowledge resources and innovation transformation, actively establish agile digital organizational structures, and explore dynamic adaptation mechanisms and efficient conversion mechanisms through which data elements empower enterprise innovation.

In the context of China’s transformation toward an innovation-driven economy, collaborative innovation has become a key strategy to enhance national competitiveness. Innovation consortia—collaborative alliances among enterprises, universities, research institutes, and governments—are emerging as vital platforms to aggregate innovation resources, facilitate knowledge flows, and drive breakthroughs in key technologies. However, the construction levels of such innovation consortia vary significantly across regions and industries in China, and the mechanisms for achieving high-level development remain insufficiently explored. This study aims to identify and analyze the configuration paths that lead to highlevel construction of innovation consortia in China, providing theoretical insight and practical guidance for future policymaking and institutional design.This study builds upon the "strategy tripod perspective" to conceptualize the key factors influencing innovation consortium development. Methodologically, the study adopts dynamic qualitative comparative analysis（QCA）, which is wellsuited for exploring causal complexity in social phenomena. By leveraging dynamic QCA, the study examines how different combinations（configurations） of internal and external conditions lead to high or non-high levels of innovation consortium construction.The empirical analysis is based on data collected from 370 innovation consortia in China. These consortia span a diverse range of industries and regions and have been evaluated based on a multidimensional assessment framework encompassing innovation input, resource agglomeration, collaborative output, governance mechanisms, and institutional support. Variables selected for the configurational analysis include innovation resource integration capability, talent gathering capability, technological achievement transformation capacity, institutional guarantee capability, and industrial development level.The results of the dynamic QCA reveal four distinct configuration paths leading to high construction levels of innovation consortia:（1）Institutional coordination and resource integration-driven type :This configuration is characterized by the absence of mature industrial bases and large-scale participants but relies heavily on strong institutional support and effective innovation resource integration.It is typically seen in emerging sectors requiring strategic coordination and initial resource mobilization.（2）Industry-oriented transformation type :This path features the presence of leading enterprises and a pressing need for industrial upgrading.It emphasizes the role of industrial drivers and organizational leadership in guiding collaborative innovation and transforming traditional sectors.（3）Technology talent-led type :In regions or industries with limited resource endowment and industrial maturity,this configuration leverages high-end scientific and technological talents to compensate for systemic shortcomings. The participation of top researchers and flexible talent mechanisms plays a crucial role in enhancing innovation capacity.（4）Multi-dimensional advantage resource empowerment type: This type thrives in areas with established industrial systems, abundant innovation resources, and comprehensive institutional support. These findings suggest that there is no single optimal path for innovation consortium development. Rather, multiple equifinal paths exist that depend on the interplay of resource availability, industrial context, and institutional arrangements. The study offers several theoretical contributions. First, it extends the application of the strategy tripod perspective to the domain of innovation governance, demonstrating its suitability for explaining the multi-faceted nature of collaborative innovation. Second, it enriches the empirical literature on innovation consortia by identifying configuration types that reflect China’s diverse innovation landscape.Moreover, the study has important practical implications. Policymakers should tailor their support strategies based on the specific configuration types of innovation consortia. For instance, resource integration and institutional innovation are crucial in nascent sectors; industry-led consortia require regulatory incentives and supply chain alignment; talent-driven models benefit from flexible talent policies and achievement transformation mechanisms; and mature regional systems should focus on deepening cross-domain collaboration and market-based innovation resource allocation. In terms of methodological contribution, the adoption of dynamic QCA allows for a nuanced understanding of how multiple conditions interact to produce outcomes. This approach moves beyond linear causality and embraces configurational thinking, which is especially relevant for complex policy systems such as innovation governance.In conclusion, this study provides a novel analytical perspective and robust empirical evidence on the paths to highlevel innovation consortium construction in China. It highlights the importance of context-specific strategies, coordinated governance, and multi-actor collaboration in building a more effective and inclusive national innovation system.

In the context of building a unified national market, deepening market-oriented reforms in capital factors characterized by autonomous and orderly inter-regional capital flow helps improve resource allocation efficiency, unleash market potential, and promote coordinated regional development. However, the "siphon effect" of capital in China’s developed regions remains prevalent. Affected by market segmentation, local protectionism, and other factors, inter-provincial capital flows face obstacles and inefficient allocation. The question of how to promote the smooth and efficient flow and allocation of capital over a broader range has become a hot topic in both policy practice and theoretical research. The rapid development of digital technologies has given rise to the rise and maturation of platforms, with corporate embedding in platform ecological creating broader space and potential for cross-region capital flows. However, existing studies mainly focus on the impact of platform ecological embedding on innovation behavior, digital transformation, supply chain coordination, and value chain upgrading. Few scholars have explored the specific role and mechanism of platform ecological embedding in cross-region capital flows from an investment perspective.In order to explore the impact of platform ecological embedding on cross-regional capital flows and its underlying mechanisms, this study draws on a 36 252 firm-year panel of Shanghai-and Shenzhen-listed companies（2010-2023）,and employs a two-way fixed effects model. It follows a four-stage research protocol. First, platform ecosystem embeddedness was quantified as the ratio of 82 platform-centric keywords extracted from the “Management Discussion ＆ Analysis” section via Python text-mining to total MD＆A words. Second, subsidiaries reported in CSMAR were manually geocoded, the cross-regional capital-flow variable（Cinvest） was constructed as the natural log of the number of non-local subsidiaries plus one, and addresses are validated with Baidu Maps. Third, an OLS two-way fixed-effects model was estimated, incorporating controls for fourteen firm-level financial, governance, and regional-level institutional variables. Finally, the baseline estimate was subjected to an eight-fold robustness sequence: 2SLS with industry-mean and lag-2 instruments, PSM 1:2 nearest-neighbor matching, digital-transformation split-sample, alternative embeddedness and investment metrics, high-dimensional fixed effects, and sample exclusions for COVID-19 and IT industries.The results show that platform ecological embedding significantly promotes cross-regional capital flows. Mechanism tests indicate that platform ecological embedding facilitates inter-regional capital flow by mitigating corporate uncertainty perception and promote high-quality development of enterprise. Heterogeneity analysis reveals that the effect of platform ecological embedding on promoting capital flow across regions is more significant for private enterprises, firms with shortsighted management and higher financing constraints, and the enterprises in areas with backward development of digital economy. Furthermore, platform ecological embedding effectively suppresses the positive impact of inter-regional capital flow on operational cost ratios. Therefore, the government should upgrade digital infrastructure（data centers, industrial internet, 5G）, offer fiscal/talent incentives, and dismantle regional protection via unified market rules and interoperable credit data. For platform firms, managers should treat platform embedding as a dynamic capability by proactively co-evolving with complementors, upgrading data analytics competences, and institutionalizing cross-unit knowledge-sharing routines to transform ecosystem affordances into sustainable cross-regional competitive advantage.With regard to the incumbents, it is essential to strategically embed in third-party or self-built platforms, co-create value with ecosystem partners, and leverage digital tools to synchronize headquarters with geographically dispersed subsidiaries, thereby enhancing responsiveness to heterogeneous market demands and institutional environments.By integrating platform ecological embedding and cross-regional capital flows into a single framework, this study provides evidences for a direct causal link and simultaneously extends the drivers of inter-regional investment and the economic consequences literature on platform ecosystems. It further examines uncertainty-mitigation and developmental-spillover effects, and dissects the micro-mechanism through which platform ecological embedding channels capital across regions, thereby completing the theoretical chain linking embedding to inter-regional capital flows. The findings provide theoretical support and empirical basis for understanding the enabling mechanism of platform ecological embedding to enable cross-regional capital flows in the context of digital transformation, and also provides practical inspiration for optimizing cross-re-gion capital allocation and accelerating the construction of a national unified market.

With the synergistic effect of market instruments and government regulation, green fiscal policy has become a key institutional arrangement to address market failures and activate the driving force of green innovation. From a theoretical perspective, green fiscal policy, relying on a systematic institutional framework and diversified policy tools, can effectively guide innovation factors such as technology and talent to concentrate in cities, helping to build regional sources of green innovation and laying a solid foundation for improving urban green innovation efficiency. In view of this, this paper examines the impact of green fiscal policy on the efficiency of urban green innovation from the dual perspectives of the agglomeration of innovative technologies and talents, with an aim to provide theoretical support and practical guidance for opti mizing the green fiscal policy system and promoting high-quality urban green development.To systematically explore whether green fiscal policy can enhance the efficiency of urban green innovation, this study uses panel data from 285 prefecture-level and above cities in China from 2003 to 2023. It takes the pilot policy of “Comprehensive Demonstration Cities for Fiscal Policies on Energy Conservation and Emission Reduction” as a quasi-natural experiment, and systematically investigates the impact of green fiscal policy on urban green innovation efficiency with a multiperiod difference-in-differences model. The conclusions are as follows: Firstly, the implementation of green fiscal policy can effectively drive the improvement of urban green innovation efficiency. Secondly, green fiscal policy can improve urban green innovation efficiency by promoting the agglomeration of innovative technologies and talents. Thirdly, fair market competition can positively moderate the direct effect of green fiscal policy on urban green innovation efficiency, and also play an active moderating role in the transmission path of “green fiscal policy→agglomeration of innovative technologies（or innovative talents）→green innovation efficiency”. Fourthly, the effect of green fiscal policy on improving urban green innovation efficiency shows significant heterogeneity. The positive impact is more pronounced in the following types of cities: non-old industrial base cities, cities with higher administrative levels, cities with higher levels of digitalization, cities with larger industrial scales, cities with greater fiscal autonomy, and cities with stronger environmental regulatory systems.Drawing on the research findings, this paper presents policy recommendations from four dimensions: First, it suggests establishing a “tiered subsidy–targeted regulation” linkage mechanism to effectively leverage the positive effect of green fiscal policy on improving urban green innovation efficiency. Second, it is necessary to implement a “dual drive of innovative technologies and talents” strategy to fully activate the enabling effect of both technology and talent agglomeration. Third, a “competition guarantee–conduct regulation” collaborative mechanism should be built to give full play to the moderating effect of fair market competition on green fiscal policy’s role in enhancing urban green innovation efficiency. Fourth, a “classified policy–dynamic adjustment” precise policy scheme should be explored to systematically address the p olicy effectiveness differences caused by urban heterogeneity. Compared with existing research, this paper has mainly made further explorations in the following aspects:First, by using the "Comprehensive Demonstration Cities for Fiscal Policies on Energy Conservation and Emission Reduction" as a quasi-natural experiment, this study centers on analyzing the impact of green fiscal policies on urban green innovation efficiency, thereby providing theoretical support to validate the policies’ practical effects in driving green innovation at the city level. Second, by taking the agglomeration of innovative technologies and talents as mediating variables, and fair market competition as a moderating variable, this paper deeply analyzes the transmission mechanism and boundary conditions by which green fiscal policy improves urban green innovation efficiency, with an aim to deepen the understanding of the innovation effects of the pilot policy and offer new perspectives for improving green innovation efficiency. Third, this paper takes into account the heterogeneity of city endowments, economic characteristics, and policy implementation capacities, and identifies how green fiscal policy affects the efficiency of green innovation across different types of cities. The findings aim to serve as a valuable reference for governments in formulating tailored and targeted green fiscal policies that are better suited to local conditions.

In the context of profound adjustments in the global economic landscape and the intertwining of " de-globalization" trends,the resilience of industrial chains has become a crucial issue for coordinating development and security.The convergence of multiple risks,including Sino-U.S.trade frictions,geopolitical conflicts,great power competition,and technological blockades,has significantly heightened the threats of "disruption" and " bottlenecks" in industrial chains,further emphasizing the vulnerabilities of the traditional division of labor system.The report of the 20 th National Congress clearly emphasizes that " efforts should be made to enhance the resilience and security of industrial and supply chains",considering industrial chain resilience as a key lever for building a modern industrial system.Industrial chain resilience not only addresses the practical need to withstand external shocks and ensure stable economic operation,but also acts as a strategic pivot for cultivating new productive forces,facilitating the economic circulation of major countries,and seizing the high ground of the global value chain.The enhancement of industrial chain resilience depends on the collaboration and innovation capabilities of regional industrial systems,while innovative industrial clusters function as the core carriers of regional industrial collaboration and innovation.Through the efficient integration of innovative elements and networked collaboration,they promote resource sharing and risk-sharing among enterprises,becoming a key support for enhancing industrial chain resilience.Existing studies have extensively examined innovation agglomeration and industrial clustering;however,research on innovative industrial clusters—a complex entity closely related to both innovation and industrial agglomeration—remains relatively scarce.Therefore,this paper builds upon the connotation of industrial chain resilience and considers innovative industrial cluster pilot policies as quasi-natural experiments,analyzing the transmission mechanisms through which innovative industrial clusters influence industrial chain resilience.This paper draws on panel data from 261 cities in China from 2010 to 2022 and employs a multi-period difference-indifferences model to evaluate the impact effects and mechanisms of innovative industrial cluster development on urban industrial chain resilience from the perspective of collaborative innovation.The study finds that the pilot policies for innovative industrial clusters significantly enhance the resilience level of urban industrial chains.Mechanism tests indicate that the pilot policies promote the enhancement of industrial chain resilience through four pathways:driving innovation and innovation agglomeration,optimizing industrial structure,increasing efficiency of industrial agglomeration,and upgrading the employment market.Further research finds that the policy effects exhibit significant spatial differences:in large cities with a permanent population of over one million,non-old industrial base regions,and areas with higher cultivation of new productive forces,the promotion effect of innovative industrial clusters on industrial chain resilience is more significant.On the basis of the research findings,this paper offers the following policy recommendations:First,the government should deepen the coordinated development of innovation agglomeration and industrial clustering by promoting close integration among research institutions,universities,and industrial chain enterprises to facilitate technology spillovers and knowledge sharing,strengthen the integration of production,education,research,and application,and accelerate the transformation of innovation achievements.Second,efforts should be made to optimize and upgrade the industrial structure by supporting the development of high-tech and high value-added industries,promoting the transformation of traditional industries,and enhancing the cultivation and introduction of innovative talents to improve labor skills and innovation capabilities.Third,industrial cluster construction should be advanced in accordance with local conditions:the eastern and central regions should focus on improving cluster quality and attracting high-end talents,while the western region should develop characteristic industries aligned with local resource endowments to promote economic diversification.Finally,digital infrastructure construction should be strengthened by accelerating the deployment of 5 G,industrial internet,and big data applications to enhance the digitalization level of industrial chains and improve intelligent collaboration and risk resistance capabilities.

There is a close interactive relationship between sci-tech service ecosystem of maker spaces and their multi-dimensional performance, promoting the interdependence, mutual driving, iterative reinforcement, and "spiral rise" of incubation performance, social performance, and innovation performance. However, domestic studies focus on the operational management of maker spaces and the short-term performance of incubated enterprises, while neglecting the systematic role of the full-chain service ecosystem in driving multi-dimensional outcomes.Foreign research has shifted toward sustainability and value co-creation, yet it still fails to clarify how maker spaces integrate human, cultural, and industrial resources and embed themselves in regional innovation systems.Existing literature emphasizes quantifiable explicit outputs but downplays the cultivation of high-growth enterprises like unicorns, lacking in-depth analysis of the collaborative mechanism among incubation, social, and innovation performance. Thus, it is necessary to adopt a multi-stage and wholeprocess perspective to systematically examine the collaborative paths of resource integration, knowledge sharing, and multi-dimensional performance so as to achieve mutual empowerment between maker spaces and the sustainable development of regions.From the perspective of the sci-tech service ecosystem, this study selects 13 provincial-level administrative regions across 5 major urban agglomerations as research cases. It draws on the average values of data concerning the multi-dimensional performance of maker spaces from 2017 to 2023, and employs configuration thinking and the fs QCA method to explore the complex synergistic paths through which the human resource ecosystem, financial capital ecosystem, infrastructure ecosystem, technology service ecosystem, and industrial innovation ecosystem drive the sustained improvement of mak er spaces’ multi-dimensional performance.The research findings reveal that the achievement of maker spaces’ multi-dimensional performance relies on the collaborative coupling of multiple elements within the sci-tech service ecosystem. For maker spaces, the collaborative coupling of various elements follows a consistent path in realizing high incubation performance, high social performance, and high innovation performance—specifically, the driving path of "financial capital ecosystem-human resource ecosystem-infrastructure ecosystem-industrial innovation ecosystem" serves as the core pathway to attain their multi-dimensional performance. No single element constitutes a necessary condition for maker spaces to achieve high incubation, social, or innovation performance; nevertheless, a robust financial capital ecosystem emerges as a key antecedent condition for their high performance.This study frames a "technology-economy-society" coupling path for maker-space performance that is woven through the sci-tech service ecosystem. First, under a regional-innovation integration strategy, maker spaces are embedded in the local innovation ecosystem to address national missions and core technological bottlenecks, thereby completing a full-chain incubation and growth pipeline for technology-based enterprises. Second, an ecological-governance trajectory is followed vertically—resource foundations strengthen incubation performance, breakthrough outputs lift innovation performance, and widened application scenarios amplify social performance—while horizontally expanding social networks, feeding achievements back into incubation capacity, and steering resource flows to cultivate unicorn enterprises to enhance social contributions.. Third, a collaborative path is forged with the sci-tech financial ecology as the pivotal condition, complemented at the margin by talent, large-scale facilities, and industrial-innovation ecologies, to propel multi-dimensional performance efficiently. Patient state capital is cultivated and enlarged within an investment and financing mechanism led by government funds, dominated by corporate finance, with enterprise investment as the main body, and followed by the Science and Technology Innovation Board and private capital.The research conclusions can provide effective support for the deep integration of the innova-tion chain, industrial chain, capital chain, and talent chain through entrepreneurship incubation services, and provide important implications for the accelerated growth of unicorn companies in China. Future research should incorporate government policies like digital transformation into the analytical framework, examine their impacts on the performance of maker spaces, thereby enhancing the explanatory power of research conclusions and facilitating an understanding of the internal logic behind maker spaces transitioning from the "performance improvement" stage to the new "ecological intelligence" stage. Meanwhile, with in-depth digital transformation and upgrading driving the evolution of their models from "physical incubation" to "digital incubation", future research also needs to further focus on and explore the specific paths for high-quality development after this model transformation, so as to fill the gaps in current research in this field.

Strengthening urban innovation systems and optimizing the efficiency of innovation factor allocation are critical for overcoming the "middle-technology trap" and realizing a Chinese-style modernization.Against this backdrop,the digital revolution has positioned data as a crucial production factor.In China,public data accounts for over 80% of the national total data resources,with an estimated value of 10-15 trillion yuan in 2020.The nationwide open datasets have exceeded 2 million items,covering livelihood areas such as transportation,healthcare,education,and environmental protection.Open government data policies can break down information silos,enhance inter-departmental coordinated development,and expand socio-economic benefits.Both domestic and international research consensus holds that enhancing innovation capacity is a key impact of public data openness, though systematic empirical verification remains insufficient. Domestic research mostly focuses on the enterprise perspective, with less research on urban innovation.Additionally, existing literature varies significantly in its research angles, resulting in no unified conclusions regarding how public data openness specifically influences urban innovation. Therefore, it is essential to explore their mechanisms in promoting urban innovation.This study utilizes annual panel data from 271 cities in China from 2009 to 2023 to construct a multi-period differencein-differences model to explore the impact of open public data on urban innovation. The establishment of open government data platforms in cities is treated as a quasi-natural experiment, with the number of patent grants per city as the dependent variable. Mechanism tests are conducted via effective market and proactive government paths. In addition, the paper studies the moderating role of digital inclusive finance and human capital stock, and finally uses a spatial Durbin model to investigate whether the positive effect of open government data on urban innovation has spatial spillovers. The study tests the spatial autocorrelation of the dependent variable—urban innovation capacity. Following the existing literature, it computes the global Moran’s I for each year under three weighting schemes: the contiguity matrix（W1）, the economic-distance matrix（W2）, and the nested economic-geographical matrix（W3）.Empirical results indicate that the implementation of open government data policies significantly promotes urban innovation by stimulating market vitality and enhancing marketization levels to make the market effective, reducing government intervention, optimizing government-business relations, and improving the business environment to promote proactive government action, thus fostering urban innovation through the dual pathways of an effective market and a proactive government. Digital inclusive finance plays a positive moderating role by alleviating financing constraints and providing funding support, while human capital stock amplifies the innovative advantages of open government data by strengthening knowledge spillover effects. Moreover, there is heterogeneity among cities: cities southeast of Hu Huanyong’s line, cities with a higher level of industrial structure upgrading, and large cities exhibit a stronger innovation promotion effect, likely due to more complete digital infrastructure, a more advantageous industrial structure, and stronger human capital, which further confirms the positive moderating effect of human capital stock and digital inclusive finance. In addition, open government data has a significant spatial spillover effect on urban innovation, positively impacting other cities through spatial interactions, and this effect is jointly driven by geographic proximity, economic similarity, and the interaction between the two. Lastly, in the transmission mechanism of public data openness promoting urban innovation capacity, the synergy of an effective market and a proactive government has produced a significant promotion effect.The theoretical contributions are threefold. First, by conceptualizing open government data as a quasi-experiment, we extend existing innovation theory to encompass data governance frameworks. Second, the integration of effective market and proactive government mechanisms addresses a critical gap in understanding state-market synergies within innovation ecosystems. Third, our spatial and moderating analyses provide nuanced insights into cross-city disparities and multiplier effects. Practically speaking, this study offers valuable guidance for policies related to data openness roadmaps, digital finance integration, and intercity innovation networks, providing an actionable blueprint for fostering high-quality urban development.

Currently, the widespread application of digital technology has accelerated a new round of global industrial revolution, with digital transformation as the core driving force. This has compelled traditional firms operating within the industrial system to urgently pursue digital transformation and cultivate management adaptability. However, compared to natively digital firms, traditional firms face dual risks: dismantling the traditional industrial systems and adapting to the new digital systems. Specifically, the management adaptability accumulated in the traditional industrial systems struggles to meet the new requirements of digital ecosystems. Therefore, it is imperative to explore how traditional firms can develop management adaptability during the process of digital transformation, which holds significant implications for traditional fir ms’ sustainable development and the advancement of the national digital economy.Grounded in traditional industrial systems, early research explored organizational revolution from economic evolution theory, biological theory, and chaos theory. Although these theories offer valuable insights into management adaptability development during organizational revolution, their limitations in assumptions, viewpoints, and contexts render them inadequate for providing clear theoretical guidance on adaptability development of firms undergoing digital transformation. Confronted with the inadequacy of traditional theories, current research has developed some models such as cross-system transformation theory, dual-path adaptive revolution theory, and adaptive mechanism theory to explore the development of management adaptability during digital transformation. Yet, existing studies have primarily focused on the development of management adaptability at the single-element level, neglecting to explore the intrinsic interconnections among multiple man agement elements and their interactions with the process of digital transformation.To address these gaps, this study proposes a “Human-Data-Things” perspective to explore how the development of management adaptability in traditional firms evolves around these three core elements during digital transformation. From this perspective, this study employs a longitudinal and multiple-case study to explore the research question. Following principles of typicality, analogy, and differentiation, three traditional firms undergoing digital transformation were selected as case samples for long-term tracking surveys and in-depth interviews. Data from these cases were analyzed through open coding, axial coding, and selective coding. Through data analysis, this study first identifies four stages of the digital transformation processes of traditional firms, including exploratory trials, top-level strategic planning, business interconnection, and ecosystem co-creation. The process is characterized by maximized individual value, blurred organizational boundaries, and comprehensive revolution. Second, this study finds four core elements of management adaptability of traditional firms, including cognitive adaptability, strategic decision-making adaptability, process management adaptability, and performance feedback adaptability. Internally, these elements evolve synergistically and present a “Golden Triangle” model which is composed of human resources, digital resources, and physical resources. Externally, these elements operate cyclically and follow a cognitionfeedback dual-flow logic. The development process of management adaptability exhibits features of synergistic adaptation mech anisms, borderless adaptation objects, transient adaptation processes, and data-converged adaptation evaluation.Based on these results, this study makes three key contributions. First, it innovatively constructs a “Golden Triangle” model and reveals a cognition-feedback dual-flow cycle process of development of management adaptability during digital transformation. This advances the theoretical foundation of management adaptability research in transitioning firms, addressing the theoretical shortcomings in existing literature on organizational revolution. Second, it broadens our understanding of the development process and characteristics of management adaptability development and deepens the insights of co-evolutionary relationship between management adaptability and digital transformation. By moving beyond single-element analyses, this study reveals the internal linkages among four elements of management adaptability and their synchronized evolution with different digital transformation stages, offering a more holistic view of how managerial adaptability develops. Third, drawing on the practices of the case firms, this study provides practical insights for traditional firms seeking to transition from traditional industrial system to the digital system. The study suggests that firms should cultivate targeted management adaptability at different stages of digital transformation, aligning specific adaptive capabilities with evolving strategic and operational needs.

Innovation plays a central rolei n driving China’s high-quality development. However, China remains heavily reliant oni ntern ational sources for key core technologies and components. In recent years, global economic fluctuations and geopoliticaltensio ns have constrained technological innovation activities, with the risk of innovation disruption persisting and potenti allyincreasing. It is urgent to optimize innovation practices and activate innovation resilience. Technological innovationoften has high risks due to its large cost scale and high output uncertainty, and is easily constrained by corporate resources.Patient capital can providel ong-term and stable financial support for corporate technologicali nnovation. Furthermore,through its risk diversification function, patient capital can effectively mitigate the high-risk nature of technological innovation. Therefore, patient capital has the typical characteristics of highly matching with the intrinsic needs of corporate technologicali nnovation, andi s gradually becoming a new quality driving force to enhance corporatei nnovation resilience.However, due to thei nherent profit-seeking and risk-taking nature of capital, the excessive penetration of patient capital may intensify the “goal conflict” caused by investors chasing short-term self-interest. This could lead to an imbalance ofinte rests between investors and innovative subjects and ultimately weaken the sustainability of the innovation ecosystem.Additionally, patient capital may also lead to the development of a comfortable mentality and innovation inertia amongmanagers. These issues raise a series of key questions that deserve deeper inquiry. Can patient capital become the core element to stimulate corporate innovation resilience And how to effectively ensure that patient capital can continue to promote corporate innovation resilience and help enterprises overcome technological blockades and continuously realize technological breakthroughs. Using the data of A-sharel isted companiesi n Shanghai and Shenzhenf rom 2009 to 2023, this study empirically examinesthe influence of patient capital oni nnovation resilience. Meanwhile, from the horizontal strategy dimension and verticaltime dim ension, this study constructs a framework for the transmission path of patient capital’si nfluence oni nnovation resilience.Additionally, by analyzing the characteristics of internal innovation decision-makers within enterprises and the chara cterist ics of the external innovation environment, this study explores how patient capital influences innovation resilienceunde rdifferent circumstances. Theresults show that thei nfluence of patient capital on corporatei nnovation resilience presents ani nverted U-shaped relationshi p and the optimal allocation for patient capital is 34.36%. Mechanism tests reveal that patient capital affects corpora teinnovation resilience through three dimensions:i nnovation cooperativeness,i nnovation ambidexterity, andi nnovati onsustainability. Further research based on innovation decision-makers and environmental characteristics shows that theinve rted U-shaped impact of patient capital on corporate innovation resilience is further strengthened when managers exhib ithigherl evels of patience,i nnovation decision-making poweri s more concentrated, andi ntellectual property protectionis higher. Compared to existing research, the possible marginal contribution of this studyi s three-fold. First, this study reveals theinverte d U-shaped relationship between patient capital and corporate innovation resilience from a nonlinear dual persp ective,which not only deepens the theoretical understanding of the duality of patient capital, but also provides a scientificbasis for corporate capital allocation by identifying the “optimal” threshold. Second,this study explores the role of patient capitali n enterprisei nnovation resilience from three dimensions:i nnovation cooperativeness,i nnovation ambidexterity, andi nnovation continuity. It not only expands the connotation ofi nnovation resilience research at the theoreticall evel, butalso prov ides new ideas for enterprises to optimize capital allocation and enhance innovation effectiveness in practical applicat ion.Third, the study characterizes the innovation decision subjects within the firm in terms of the degree of managerial patience and the allocation ofi nnovation decision-making power, and applies the degree ofi ntellectual property protectionto characterize the externali nnovation environment. From the perspectives ofi nternal decision-making subjects and e xternal environment,i t can provide new perspectives andi nsights for academics to understand the role of the boundary of patient capital, and help promote the in-depth integration of relevant theoretical research and corporate practice.

In the context of global competition, business model innovation has become the key for enterprises to maintain competitive advantages. The rise of platform enterprises has driven the transformation of competition among enterprises to competition among ecosystems. Under such circumstances, cross-boundary development has become an important strategic choice for start-up incubation platform enterprises to gain competitive advantages and achieve sustainable operation. A review of existing literature reveals two key observations. On one hand, while prior studies have analyzed the role of internal resources and capabilities in cross-boundary business model innovation from the perspectives of dynamic capabilities and resource orchestration, they have paid insufficient attention to external environmental factors and managers’ attention allocation—both of which drive the configuration of a firm’s resources and capabilities. On the other hand, most research exploring the evolutionary process of platform business models from a dynamic perspective has been based on internet trading platforms or digital platforms built by traditional enterprises, with a primary focus on the value creation logic within a single industry. It is difficult to provide an effective explanation for deconstructing the dynamic process and internal mech anism of cross-boundary innovation of business models of entrepreneurship incubation platform enterprises. Therefore, this study incorporates the cross-boundary innovation of attention allocation and the business model of entrepreneurship incubation platform enterprises into the same research framework, adopts a case study approach, incorporating event analysis and time-interval methods into the process. With Xiaomi Corporation as the case,it explores the evolution process and innovation mechanism of the business model of entrepreneurship incubation platform enterprises.The study adopts triangulation, combining data from semi-structured interviews（primary sources） with secondary materials inclu ding annual reports, books, and news articles. The research results are threefold:（1） The cross-boundary innovation of the business model of entrepreneurship incubation platform enterprises is an attention allocation process of “perceive—match—interpret—action” carried out by managers based on their own needs and internal ＆ external situational factors, which mainly includes four steps: “driver identification—symbolic value activities—strategic actions—substantive value activities”. During this process, managers propose new value propositions based on their perception of industry trends and user demands, carry out strategic actions in accordance with existing resource conditions, and materialize the value propositions into specific value creation, transmission and acquisition behaviors, thereby completing the transformation from symbolic value activities to substantive value activities.（2） The platforms evolve through three distinct stages, each triggered by a different ‘trend（fengkou）’: self-incubation, entrepreneurial incubation and collaborative incubation. In this process, its business model has undergone periodic dynamic changes of user innovation-economic platform business model, entrepreneurial empowerment-economic ＆ ecological platform business model and innovation ecology-ecological ＆ technology platform business model.（3） The business model of entrepreneurial incubation platform enterprises completes cross-boundary innovation through punctuated equilibrium mechanism, which is a process of long-term stability（balance） and short-term rapid change（discontinuity） alternately. In this process, managers’ perception of the new trends（Fengkou） is the starting point for enterprises to start new rounds of cross-boundary innovation. Technological breakthroughs and demand disruptions then render the incumbent mod el obsolete, forcing the enterprises to cross industry boundaries in search of fresh value combinations.This study clarifies the micro-mechanism by which managers’ attention allocation affects the business model innovation of entrepreneurship incubation platform enterprises, deepens the intrinsic theoretical connection between attention allocation and the business models of platform enterprises, and defines the punctuated equilibrium mechanism for crossboundary innovation of business models of entrepreneurship incubation platform enterprises. This study enriches the model of the dynamic evolution process of business models of entrepreneurship incubation platform enterprises from the perspective of attention allocation. It not only addresses the lack of research on the evolution path of business models of entrepreneurship incubation platform enterprises in existing literature, but also provides new ideas for understanding the business model innovation of platform enterprises. The research conclusions of this paper also have guiding significance for the cross-boundary innovation practices of business models of similar entrepreneurship incubation platform enterprises.

With the continuous emergence of technologies such as cloud computing, "Internet Plus", artificial intelligence, big data, the Internet of Things, and blockchain, digital technology transforms traditional information into recognizable digital information. But as companies develop digital products and services, new social responsibility issues have emerged, such as digital security, privacy breaches, and digital ethics, leading to the concept of corporate digital responsibility. Meanwhile, China’s "14th Five-Year National Informatization Plan" calls for a sound digital governance system, an open and secure digital ecosystem, and accelerating Digital China. As a key part of this initiative, boosting breakthrough innovation, accelerating core tech R＆D, and achieving innovation leadership are crucial for high-quality development. Thus, how to make companies take on digital responsibility and promote high-quality development is a key issue for both industry and academia.This study uses A-share listed companies from 2018 to 2023 as samples to explore the impact of corporate digital responsibility on breakthrough innovation. The level of breakthrough innovation is set as the explanatory variable, quantified through the application of natural logarithm transformation to the count of patent grants, serving as a proxy for breakthrough innovation intensity. While corporate digital responsibility（CDR） is designated as the dependent variable. To assess the degree of CDR fulfillment, this study employs text analysis techniques to systematically count the frequency of terms associated with "corporate digital responsibility" extracted from the corporate social responsibility reports of listed companies, thereby providing an empirical measure of firms’ digital responsibility practices. It sets up regression models and conducts regression analysis and robustness testing, the following conclusions are drawn. First, overall, corporate digital responsibility has a good promoting effect on breakthrough innovation. Enhancing the level of corporate digital responsibility helps to improve the value reciprocity effect with stakeholders and reduce the risk level of corporate breakthrough innovation, assisting companies in achieving breakthrough innovation more smoothly. On one hand, when companies fulfill their digital responsibilities, they usually increase the transparency of data management and consumer protection, which helps to build trust with stakeholders（such as customers, suppliers, and investors）, often leading to a better social image and public recognition, thus attracting more partners and consumers, providing more resource support for breakthrough innovation.On the other hand, as companies enhance their digital responsibility, they pay greater attention to data protection and compliance. This helps to reduce legal risks and financial losses stemming from data breaches or violations, thereby augmenting firms’ willingness to pursue breakthrough innovation and creating a safer environment for it.Second, digital transformation serves as a mediator between corporate digital responsibility and breakthrough innovation. This is because corporate digital responsibility encourages firms to achieve more efficient and economical project lifecycles through the secure utilization of digital innovation technologies, thereby enhancing the sustainability and operational efficiency of digital transformation. Digital transformation can optimize companies’ production, operation, and management methods, enhance their resilience in the face of uncertainty, reduce innovation risks, increase firms’ R＆D willingness, and boost the likelihood of breakthrough innovation.Third, corporate performance moderates the effect of corporate digital responsibility on breakthrough innovation. Strong performance creates a positive "halo effect," enhancing stakeholders’ recognition of a company’s digital responsibility efforts. This accelerates resource support from stakeholders, boosting breakthrough innovation. Conversely, poorly performing companies, often viewed negatively by stakeholders, face trust deficits. Stakeholders may doubt their digital responsibility initiatives, reducing the positive impact on breakthrough innovation. Thus, corporate performance significantl y influences how digital responsibility affects innovation levels.Fourth, from the perspective of differences in corporate equity types, for state-owned and non-state-owned enterprises, non-state-owned enterprises can better leverage the impact brought by the enhancement of corporate digital responsibility. While in low marketization areas, corporate digital responsibility has a stronger promoting effect on breakthrough innovation. Because the governance structure of non-state-owned enterprises is flexible, allowing for effective allocation of corporate innovation resources and higher dynamic capabilities, which determines that non-state-owned enterprises have stronger intrinsic motivation in promoting the enhancement of digital responsibility. Moreover, in high marketization areas, government regulatory intensity is lower, while corporate digital responsibility initiatives,such as efforts to ensure data security and research and develop privacy protection technologies,often require government leadership.

While benefiting from global value chains（GVCs）, Chinese manufacturers face constraints like low value-added and high costs, hindering GVC advancement. To escape "low-end lock-in" and integrate into higher-value GVC segments, leveraging digital technology innovation spillovers is crucial. These spillovers, driven by breakthroughs like digital twins and computer vision, utilize digital tech’s connectivity to enable collaboration and integration across sectors. They offer new pathways for manufacturers to increase product value, enhance digital service availability, reduce transaction costs, and ensure stability, thereby facilitating GVC upgrading. Consequently, harnessing these spillovers to reshape and upgrad e manufacturing has become a key focus for both industry and academia.This study takes listed manufacturing enterprises in China from 2011 to 2023 as the research object, and empirically analyzes the impact and mechanism of digital technology innovation spillover on the global value chain upgrading of manufacturing enterprises by using benchmark regression model, mediating effect model, moderating effect model and spatial Durbin model. The research results show that the spillover of digital technology innovation can significantly promote the global value chain upgrading of manufacturing enterprises. After conducting endogeneity tests and robustness tests such as replacing the benchmark model, changing sample intervals, tailing treatment, replacing explanatory variables, and adding other control variables, this conclusion still holds. Heterogeneity analysis indicates that the spillover of digital technology innovation has a stronger promoting effect on the global value chain upgrading of enterprises in the eastern region and technology-intensive manufacturing industries. The mediating effect test reveals that the spillover of digital technology innovation can promote the global value chain upgrading of manufacturing enterprises by promoting the development of new quality productivity. The analysis of the moderating effect indicates that high-standard opening up positively moderates the promoting effect of digital technology innovation spillover on the global value chain upgrading of manufacturing enterprises. The analysis of spatial spillover effects confirms that the impact of digital technology innovation spillover on the global value chain upgrading of manufacturing enterprises has a positive spatial spillover effect.Compared to prior work,this research makes several notable contributions: first, it is the first to link digital-technologyinnovation spillovers to GVC upgrading using a panel of Chinese listed manufacturers（2011-2023）, broadening the research perspective on the global value chain upgrading of manufacturing enterprises. Second, it explores the heterogeneous impacts of digital technology innovation spillover on the global value chain upgrading of manufacturing enterprises across different factor intensities and regional contexts. Such an analysis enriches the empirical landscape in related fields by uncovering context-specific dynamics. Furthermore, the study examines the mediating role of new-quality productive forces and the moderating effect of high-standard opening-up. This investigation refines the causal chain through which digital technology innovation spillovers influence manufacturing firms’ GVC upgrading. Finally, considering the spatial spillover effect of digital technology innovation, the study reveals the law of how digital technology innovation spillover affects the global value chain upgrading of manufacturing enterprises, providing a useful reference for manufacturing enterprises seeking to advance their position in global value chains. In accordance with the analysis, this paper proposes to enhance the level of digital technology innovation and promote the upgrading of the global value chain of manufacturing enterprises, make efforts to optimize the supply of new types of factors and consolidate the foundation for the development of new quality productivity, and build a new highland of institutional opening up and deepen high-standard opening up, so as to provide theoretical references for accelerating the global value chain upgrading of manufacturing enterprises.In the future, the research period can be further extended to obtain more accurate analysis results. Moreover, given that the samples used in this paper are listed manufacturing enterprises, the universality of the conclusions is limited. Future research could delve into the analysis of the effect path of digital technology innovation spillover on the global value chain upgrading of the manufacturing industry from the perspectives of cities and provinces.

Global climate change and energy crises present urgent and complex challenges to human society, driving the need forinnovative solutions that can enhance sustainability and energy security. In recent years, thei ntegration of digital technol ogies to facilitate the transition toward smart energy systems has become a focal point of research and policyi nitiatives. This exploration aims tol everage the capabilities of digital technologies to optimize energy systems,i mprove operational efficiencies, and support sustainable development. Technological convergence is central to this transformation, define das the process ofi ntegrating elements from different technological domains to create new functionalities and value. In thedi gital age, the convergence of digital technology with energy technologyi s particularly significant asi t promotes novel solutionscritical for smart energy transitions.Collaboration plays a crucial rolei n enabling technological convergence. The rapid pace of technological evolution and the increasing complexity of interdisciplinary challenges make partnerships with external organizations an essential strategy. Establishing collaborative networks helps overcome disciplinary boundaries, promotes the exchange of knowledge, and accelerates the convergence process. However, despite thei mportance of collaborationi n driving convergence, the existing literatur eon digital and wind power technology integration, especially within the context of China, remains sparse. Althou ghpolicies supporting thisi ntegration have beeni mplemented, comprehensive studies from a patent-based perspective that detail the convergence trends and collaborative dynamics arel imited. Furthermore, research has often focused prim arilyo n technological elements, overlooking the role of collaborative actors and the evolution of their relationships. Thisoversight may hinder a comprehensive understanding of the mechanisms that drive convergence.To address these research gaps, this study aims to utilize patent co-classification data to explore the convergencel andscape of digital and wind power technologies in China and to uncover the dynamic evolution of the collaboration networks underpinning this process. Byf ocusing on collaborative patent dataf rom 2005 to 2022, sourcedf rom the IncoP at database, the st udy a pplies social network analysis to construct and analyze technological convergence networks, organizational collaborationnetworks, and two-layer networks that combine both dimensions. The findings reveal several keyi nsights:（1） The technological convergence process exhibits distinct evolutionary stages,marked by an accelerating pace over time. Thisi s evidenced by thei ncreasingly prominent small-world characteristics within the convergence network, suggesting enhanced connectivity and efficiency in knowledge flow across technological domains. The core technological areas of convergence have shifted over the study period, evolving from traditional wind turbinetech nologies to power supply, distribution, and energy storage technologies, and more recently, to digital data processi ngtechnologies. This progression highlights the growing role of digital solutionsi n the wind power sector, underscoringtheir importance for future innovation.（2） The organizational collaboration network has shown increasingly evidentscale-fre e properties,i ndicating that a few dominant organizations account for the majority of collaborative activities. State-ownedenterprises, such as the State Grid Corporation of China, have emerged as pivotal actors within this network, formin g ext ensive internal collaboration networks. However, these networks demonstrate limited external openness, whichmay restrict broader knowledge transfer and the incorporation of diverse perspectives.（3） The expansion of the two-lay ernetworkhas been accompanied by an increase in the technological breadth managed by organizations. In recent years,technology clusters centered on G06（computing,calculating or counting） have gained prominence as hotspots for inter-orga nizational technological cooperation. Thisresearch makes several contributions to the existing literature. By systematically revealing the trends of digital andw ind power technology convergence through patent network analysis, it provides empirical insights that fill a significa ntresearch gap. The study also enhances the understanding of how collaborative relationships evolve over time, offering acomprehen sive view of thei nteraction between technological and organizational factors. Thesei nsights are useful for policymakers,industry leaders and researchers, as they provide practical guidance for strategic planning and management pract ices aimed at fostering deeper integration of digital and renewable energy technologies. Ultimately, the findings supp or tthedevelopment of targeted strategies to strengthen collaboration, encourage innovation, and facilitate the transition tomore sustainable and efficient energy systems in China.

In the context of global economic restructuring and rapid technological advancement,innovation has become the core driver of high-quality development. However,China’s innovation system exhibits a structural imbalance in ambidextrous innovation: exploitative innovation,which focuses on incremental improvements,dominates,while exploratory innovation,aimed at breakthrough technologies,lags behind. This imbalance stems from micro-level corporate dilemmas,including short-termism in resource allocation and path dependency,which are exacerbated by traditional financial systems favoring short-term returns. Against this backdrop,the concept of "patient capital" has emerged as a strategic solution to foster long-term innovation. Although prior studies confirm that patient capital fosters corporate innovation,they leave two critical gaps: the differential impact on exploratory versus exploitative innovation has not been disentangled,and the cognitive pathway through which patient capital shapes managers’ resource-allocation decisions—turning long-term funds into ambidextrous R＆D choices—remains theoretically underdeveloped.This study examines how patient capital influences corporate ambidextrous innovation,with a focus on the mediating role of managerial myopia. Using the data of A-share companies listed in China’s Shanghai and Shenzhen Stock Exchanges from 2008 to 2023,the study employs fixed-effects models and instrumental variable methods to address potential endogeneity issues. In this study,patient capital is set as the independent variable,which is jointly measured by the proportion of relational debt and the proportion of stable equity. The dependent variable is corporate ambidextrous innovation,where exploitative patents refer to those with the first four digits of IPC（International Patent Classification） that have appeared in the past five years,and exploratory patents refer to those with the first four digits of IPC that have never appeared before. Managerial myopia is measured by the proportion of short-term perspective words in the MD＆A（Management’s Discussion and Analysis） section of annual reports,with a linear transformation of multiplying by 100 twice,and is used to test the mediating effect.The findings reveal three key insights: First,patient capital has an asymmetric effect on ambidextrous innovation—it linearly promotes exploratory innovation but exhibits a U-shaped relationship with exploitative innovation. Initially,patient capital suppresses exploitative innovation due to resource reallocation toward exploratory projects,but beyond a critical threshold of 167.77,it synergistically enhances both types of innovation. Second,managerial myopia acts as a mediator: patient capital mitigates short-termism,which in turn positively affects exploratory innovation and exhibits an inverted U-shaped relationship with exploitative innovation. Moderate myopia may temporarily boost exploitative innovation,but excessive myopia harms both innovation types. Thirdly,the mediating path of patient capital’s impact on ambidextrous innovation is heterogeneous. For exploratory innovation,patient capital significantly enhances it by alleviating managerial myopia. For exploitative innovation,however,there exists a dual mechanism: when the scale of patient capital is below the critical threshold,alleviating myopia weakens exploitative innovation;once it exceeds the threshold,alleviating myopia simultaneously drives the synergistic development of both types of innovation.This study contributes to the literature in three ways:（1） It uncovers the non-linear and asymmetric effects of patient capital on ambidextrous innovation,enriching the understanding of how capital heterogeneity influences innovation strategies.（2） It introduces managerial myopia as a cognitive mediator,bridging the gap between financial resources and innovation outcomes.（3） It provides empirical evidence for policymakers and firms to optimize capital allocation and governance mechanisms,emphasizing the need to cultivate long-term-oriented investment ecosystems.The implications are twofold. For policymakers,expanding patient capital,particularly in strategic industries,is crucial to cross the tipping point for synergistic innovation. For firms,aligning managerial incentives with long-term innovation metrics and dynamically adjusting resource allocation can mitigate short-termism and unlock dual innovation potential. Limitations include the exclusion of non-listed firms and reliance on textual measures for managerial myopia. Future research could explore cross-in-dustry heterogeneity and alternative measures of capital patience.

Under the dual pressures of environmental crises and market competition,enterprises are increasingly compelled to pursue green transformation as an inevitable choice. As a core pathway for achieving this strategic objective,green sustainable innovation is regarded as the key to overcoming the impasse. However,due to practical bottlenecks such as high upfront investment risks and long payback periods,the problem of insufficient internal driving force among enterprises has become increasingly prominent. How to build a long-term incentive mechanism to activate sustained innovation momentum has become a core issue that urgently needs to be addressed in the current process of industrial transformation. Meanwhile,artificial intelligence（AI） technologies,represented by industrial robots,are profoundly reshaping the industrial landscape. Their great potential in improving production efficiency and optimizing resource allocation offers new possibilities for resolving the dilemma of green innovation. Therefore,how does the application of robots affect the level of green sustainable innovation in enterprises? What are the underlying mechanisms? These are the central issues this paper seeks to explore. Although existing studies have examined the relationship between AI and green innovation,in-depth analysis of its impact on sustainable innovation and the specific pathways involved remains insufficient.This study employs a panel dataset of A-share listed firms in China spanning the period from 2013 to 2023. Firm-level green patent data is matched with industrial robot statistics from the International Federation of Robotics（IFR） to construct measures of robotic application intensity and green sustainable innovation. A two-way fixed effects model is utilized to empirically test the core hypotheses. To further examine the underlying mechanisms,a mediation analysis framework is applied. To address potential endogeneity concerns arising from reverse causality and omitted variable bias,several robustness strategies are implemented,including time trend controls,system GMM estimation,and double machine learning regression.This study offers several theoretical contributions. First,it broadens the analytical perspective on the green impact of AI by shifting the focus from general green innovation to green sustainable innovation,which holds greater long-term strategic value. Second,a novel dual-pathway analytical framework is proposed to explain how robotic application fosters green persistence,specifically through the upgrading of human capital and the attraction of green investors via signaling effects. Third,the analysis investigates the heterogeneity of this relationship across different contexts,including firm ownership structures,industry characteristics,and the intensity of environmental regulation,thereby enhancing the understanding of the conditional mechanisms involved.The empirical results indicate that robotic applications significantly enhance firms’ levels of green sustainable innovation. Mechanism analyses reveal that this effect operates primarily through two channels:（1） enhancing the level of human capital,and（2） attracting the attention of green investors via signaling effects. Moreover,the positive impact is more pronounced among non-state-owned enterprises,certified green factories,firms in less polluting industries,and those subject to stronger environmental regulatory constraints. Thus,the government should raise the penetration of robots across manufacturing,create dedicated funds and demonstration projects,using tax breaks,subsidies and low-interest loans to incentivize firms to adopt robots for green upgrades. Robots’ green-innovation boost is uneven. It is necesasry to spotlight green-factory pioneers to set replicable models. For heavy polluters,SOEs firms in weak-regulation regions,where the impact of robot adoption tends to lag,governments should craft targeted programs that pair strict environmental rules with incentives to embed robots in green production,driving full-scale sustainable transformation.Overall,this study contributes to the growing literature on the green development implications of AI,sheds light on the “black box” through which robotic applications drive long-term green innovation,and offers theoretical grounding and practical insights for firms seeking to leverage intelligent technologies for sustainable development,as well as for policymakers aiming to design effective AI-driven green innovation incentives. Future research could examine whether the synergybetween robotics,digital transformation,and AI platforms can amplify green sustainable innovation via energy optimization and process improvements;and researchers could introduce moderating factors such as green finance and leverage quasi-natural experiments or stronger instruments to mitigate endogeneity.

In the context of the digital economy and innovation-driven development strategy,digital transformation has emerged as a critical pathway for enterprises to enhance competitiveness and achieve high-quality development. However,Chinese enterprises confront challenges such as insufficient funding,talent shortages,and institutional barriers in their digital transformation endeavors,making policy intervention imperative. At present,within the academic community,there is a paucity of empirical research that targets innovation system reform as a means to investigate digital transformation. Consequently,elucidating the impact of innovation system reforms on digital transformation against the backdrop of China’s digital economy and innovation-driven development strategy is of paramount importance. This clarification provides enterprises with insights into addressing transformation challenges and accelerating their transition in the new context. To explore the impact of innovation system reform on digital transformation of enterprises,this paper uses a quasi-natural experiment approach based on listed company data from 2011 to 2023. Digital transformation-related data come from the companies' annual reports,while the rest are from the GTA database. The samples of companies in abnormal status like ST and *ST have been removed. All the continuous variables are winsorized at the 1st and 99th percentiles. From both theoretical and empirical perspectives,it investigates whether innovation system reform impacts corporate digital transformation and,if so,how it does so by influencing technological,organizational,and environmental factors. Rigorous empirical tests were conducted,including the benchmark regression,robustness tests ,a placebo test,and heterogeneity and mechanism tests. The analysis yields several key findings. First,the innovation system reform significantly promotes digital transformation in enterprises. This conclusion remains robust after a series of tests,including the parallel trend test. Moreover,the impact of the innovation system reform on the digital transformation of high-tech enterprises is even more pronounced. Second,the innovation system reform not only drives technological innovation and attracts clusters of innovative talent but also strengthens intellectual property protection,further advancing enterprise digital transformation. Third,the impact of innovation system reform on digital transformation exhibits heterogeneity. Its effects are most evident in enterprises within pilot zones focused on regional coordination,open cooperation,and industrial transfer,as well as in non-state-owned enterprises and high-tech firms. From a deeper perspective on transformation,the innovation system reform not only accelerates incremental technological innovation in enterprises but also profoundly guides the fundamental restructuring of technological architecture and application directions,injecting new vitality and momentum into enterprise development. This paper makes novel contributions to the study of enterprise digital transformation. First,it provides a systematic examination of the causal effects of the innovation system reform on digital transformation from a micro perspective. Prior studies predominantly analyze the effects of innovation system reforms at the macro level or through micro lenses such as total factor productivity,with limited focus on institutional innovations in driving digital transformation. Although some research explores the impact of national big data pilot zones on digitalization,the innovation system reforms,as a systematic innovation reform policy,demands more urgent attention. By centering on the innovation system reforms and employing a difference-in-differences model with micro-level firm data,this study reveals the relationship between innovation system reform and enterprise digital transformation,offering direct evidence of the policy's micro transmission mechanisms and filling a critical research gap. Second,using the Technology-Organization-Environment (TOE) theoretical framework,this study analyzes the mechanisms through which the innovation system reform operates. It demonstrates that the policy promotes digital transformation by incentivizing technological innovation,attracting innovative talent,and strengthening intellectual property protection. Thereby it provides a new theoretical perspective and empirical support for understanding how policies influence firm behavior. Third,this study examines the heterogeneous impacts of the innovation system reform across different pilot zones,ownership types,and technological levels. The results offer valuable references for governments to formulate targeted policies,guide enterprise transformation directions,and support firms during transitions,underscoring the practical policy implications of this research.