Innovation plays a central role in driving China's high-quality development. However, China remains heavily reliant on international sources for key core technologies and components. In recent years, global economic fluctuations and geopolitical tensions have constrained technological innovation activities, with the risk of innovation disruption persisting and potentially increasing. It is urgent to optimize innovation practices and activate innovation resilience. Technological innovation often has high risks due to its large cost scale and high output uncertainty, and is easily constrained by corporate resources. Patient capital can provide long-term and stable financial support for corporate technological innovation. 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 technological innovation, and is gradually becoming a new quality driving force to enhance corporate innovation resilience.
However, due to the inherent 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 of interests 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 among managers. 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-share listed companies in Shanghai and Shenzhen from 2009 to 2023, this study empirically examines the influence of patient capital on innovation resilience. Meanwhile, from the horizontal strategy dimension and vertical time dimension, this study constructs a framework for the transmission path of patient capital's influence on innovation resilience. Additionally, by analyzing the characteristics of internal innovation decision-makers within enterprises and the characteristics of the external innovation environment, this study explores how patient capital influences innovation resilience under different circumstances.
The results show that the influence of patient capital on corporate innovation resilience presents an inverted U-shaped relationship and the optimal allocation for patient capital is 34.36%. Mechanism tests reveal that patient capital affects corporate innovation resilience through three dimensions: innovation cooperativeness, innovation ambidexterity, and innovation sustainability. Further research based on innovation decision-makers and environmental characteristics shows that the inverted U-shaped impact of patient capital on corporate innovation resilience is further strengthened when managers exhibit higher levels of patience, innovation decision-making power is more concentrated, and intellectual property protection is higher.
Compared to existing research, the possible marginal contribution of this study is three-fold. First, this study reveals the inverted U-shaped relationship between patient capital and corporate innovation resilience from a nonlinear dual perspective, which not only deepens the theoretical understanding of the duality of patient capital, but also provides a scientific basis for corporate capital allocation by identifying the “optimal” threshold. Second,this study explores the role of patient capital in enterprise innovation resilience from three dimensions: innovation cooperativeness, innovation ambidexterity, and innovation continuity. It not only expands the connotation of innovation resilience research at the theoretical level, but also provides new ideas for enterprises to optimize capital allocation and enhance innovation effectiveness in practical application. Third, the study characterizes the innovation decision subjects within the firm in terms of the degree of managerial patience and the allocation of innovation decision-making power, and applies the degree of intellectual property protection to characterize the external innovation environment. From the perspectives of internal decision-making subjects and external environment, it can provide new perspectives and insights 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.

Against the backdrop of the intensifying global public health crisis, geopolitical conflicts and strategic competition among major powers, the risks of "disruption" and "shutdown" of industrial and supply chains are escalating. Enhancing the resilience and security level of industrial chains has become a core challenge for achieving high-quality development. As a universal technology of the fourth industrial revolution, artificial intelligence technology can enhance the dynamic recovery capacity and risk resistance capacity of the industrial chain through means such as automated production, intelligent management and big data analysis, thereby improving the resilience of the industrial chain. However, the current application of AI across different links in the industrial chain remains fragmented and isolated. Enterprises focus more on the application of artificial intelligence technology in their own businesses and ignore its external spillover effects, resulting in the inability to enhance the resilience of the entire industrial chain. To break through the institutional bottleneck of technological empowerment, China established the "National Artificial Intelligence Innovation and Application Pilot Zone" in 2019. This initiative aims to promote the deep integration of AI with traditional industries and key industrial chains. Despite these efforts, research on the impact of such pilot zones on industrial chain resilience remains limited.
Thus, with the establishment of the National Artificial Intelligence Innovation Application Pilot Zones as a quasi-natural experiment, this study selects urban panel data from 2010 to 2023, and uses the dual machine learning (DML) method to systematically evaluate the impact of this policy on the resilience of the industrial chain and its mechanism of action. The marginal contribution of this paper lies in two aspects: (1) In terms of measurement indicators, existing studies mostly adopt data from the robot industry, artificial intelligence patent data, online recruitment data or word frequency data of enterprise annual report texts to measure the application level of artificial intelligence. However, their measurement methods have certain measurement errors, are endogenous to economic development and other problems, and cause-and-effect identification faces certain challenges. This paper adopts the policy shock of the establishment of the National Artificial Intelligence Innovation and Application Pilot Zone as the proxy variable for the development and application of artificial intelligence, thereby more accurately identifying the impact of the development and application of artificial intelligence on the resilience of the industrial chain. (2) In terms of research methods, the dual machine learning (DML) approach is adopted to quantitatively evaluate the effect of pilot zone policies on enhancing the resilience of the industrial chain. By leveraging the advantages of model setting and algorithms, the accuracy and robustness of policy evaluation are enhanced, thereby more accurately identifying the net effect of policies on the resilience of the industrial chain.
Research shows that the policy of establishing the National Artificial Intelligence Innovation and Application Pilot Zones has significantly enhanced the resilience of the industrial chain. This conclusion holds true after the robustness test. The results of the mechanism analysis show that this policy mainly promotes the improvement of the resilience of the industrial chain through internal paths such as the driving effect of data elements, the allocation effect of innovation elements, and the upgrading effect of the industrial structure. The results of heterogeneity analysis show that the enabling effect of this policy is more significant in regions with a higher level of digital infrastructure, a higher degree of market integration, and a lower uncertainty of economic policies.
Drawing on the findings of the research, this paper offers the following policy recommendations. To enhance the effectiveness of the National Artificial Intelligence Innovation Application Pilot Zone, it is essential to strengthen policy implementation through a multi-level support system and deepen the mechanisms for policy experimentation and evaluation. Additionally, the promoting effect of the policies on the resilience of the industrial chain can be further exerted by establishing and improving the data governance system, perfecting the fiscal support structure, deepening the upgrading of human capital, and promoting the upgrading of the industrial structure. Furthermore, to promote the establishment of pilot zones and coordinated regional development, efforts should be made to strengthen digital infrastructure construction, facilitate market integration, and reduce policy uncertainties.

In recent years, the impact of "black swan" events such as the decoupling of Sino-US technology, the conflict between Russia and Ukraine, and the new round of Israeli-Palestinian conflict has intensified the trend of "anti-globalization", accelerated the reconstruction of the global industrial chain, and increased the risk of chain breaking and blocking in China's industrial chain and supply chain. Consequently, these events pose substantial challenges to the innovation-driven growth of businesses, the establishment of a modern industrial system, the cultivation of new quality productive forces, and the pursuit of high-quality development. In particular, the sudden global public health crisis in early 2020 has exacerbated the turbulence of the world's economic and political landscape. The uncertainty of the market environment and the severity of the crisis have caused a huge impact on enterprises. An important emerging issue in management has emerged: In the face of escalating volatility, uncertainty, complexity, and ambiguity (VUCA, "VUCA") in organizational management situations, as well as the risks of "chain breaking and blocking" in the industrial chain and supply chain, and frequent extreme events, how can manufacturing enterprises, as the core entities of the industrial chain and supply chain, quickly adjust to overcome difficulties and achieve resilient growth amid crises and adversities?
This study, grounded in the theory of dynamic capabilities, delves into the effects and underlying mechanisms of digital transformation on the organizational resilience of Chinese manufacturing firms. Drawing on a dataset encompassing 3 446 manufacturing enterprises listed on the Shanghai and Shenzhen A-share markets from 2009 to 2023, the study employs empirical analysis leveraging big data text mining and a bidirectional fixed effects regression model. The findings indicate that digital transformation substantially bolsters organizational resilience. Regarding the mechanism, digital transformation promotes open innovation, thereby improving organizational resilience. Furthermore, an economic model based on multi-period difference-in-differences is employed to test the impact effect, revealing that major unexpected global public health crises have significantly impacted the organizational resilience of Chinese manufacturing enterprises. However, companies with a higher degree of digital transformation demonstrate stronger crisis response capabilities. Various robustness tests support these conclusions. This study provides theoretical and practical insights for enterprises to accelerate digital and intelligent transformation, cultivate dynamic capabilities, and enhance organizational resilience to uncertainty, thereby fostering new productive forces in the context of accelerated technological-economic paradigm shifts.
The research contributions of this paper are mainly reflected in three aspects. While the majority of existing literature on organizational resilience centers on its conceptualization and precursors, there is a dearth of studies examining the mechanisms underlying the development of organizational resilience.This study explores the mechanism of digital transformation on organizational resilience, expands the theoretical mechanism research on organizational resilience, and provides empirical evidence for the promotion effect of digital transformation on corporate organizational resilience. Second, the existing research rarely pays attention to the impact mechanism of digital transformation on corporate organizational resilience in crisis situations. This study addresses this gap by examining, through the lenses of dynamic capability theory and open innovation, how digital transformation fosters open innovation and subsequently enhances organizational resilience; from the perspective of digital innovation, it opens the "black box" of the process of digital transformation affecting organizational resilience. Third, the existing research on major global public health emergencies mainly focuses on the impact of such major public crisis events on the macro-economy and industrial chain security, but few studies focus on how micro-entities of enterprises can effectively respond to the impact of major crises. This study uses the multi-period double difference method to empirically examine the impact of major global public health crises on micro-enterprise entities, and concludes that digital transformation can significantly enhance the ability of enterprises to cope with crises, and provides important theoretical and practical references for accelerating the digital transformation of manufacturing enterprises in the new journey of China's modernization, gaining sustained competitive advantages in the VUCA era, accelerating the promotion of new industrialization, building a modern industrial system, and accelerating the cultivation of new quality productive forces.

Taking the awarding of the 2025 Nobel Prize in economics by Joel Mokyr, Philippe Aghion, and Peter Howitt as an important starting point, this study systematically explores the historical evolution, theoretical core, and contemporary relevance of the "creative destruction" mechanism in Schumpeterian growth theory. Against the backdrop of a new global context characterized by rapid scientific and technological transformation, industrial upgrading, and great-power competition, this study aims to address the central question:how can China achieve high-quality and sustainable economic growth through "destructive innovation" in the new development stage?
At the theoretical level, this study traces the paradigm evolution of growth economics: from Joseph Schumpeter′s concepts of "Innovation-driven Growth" and "Creative Destruction", to Robert Solow′s exogenous growth theory, Paul Romer and Robert Lucas′s endogenous growth theory, and further to the modern Schumpeterian growth model constructed by Aghion and Howitt which incorporates "Creative Destruction" into a general equilibrium framework. Mokyr′s "Institutionalization of Knowledge" supplements the institutional prerequisites for its operation. The core contribution of this study lies in clarifying the hierarchical relationship between "Creative Destruction" at the macro-economic level and "Disruptive Innovation" at the micro market level. As the concretization of the former, the latter explains how latecomer enterprises break the competitive advantages of incumbents through low-end market penetration, new market development, or technological subversion, thereby promoting industrial and economic upgrading.
At the practical level of China′s economy, the evolution of "Creative Destruction" is divided into three stages:(1)The embryonic stage (1978—2001):It provided space for disrupting the old system through institutional deregulation, and relied on the "Disruptive Innovation" of microeconomic agents such as farmers and township and village enterprises to facilitate the materialization of a new equilibrium.(2)The acceleration stage (2002—2012): Driven by the dual engines of "Factors and Innovation", China integrated into the global value chain to absorb and re-innovate technologies, breaking the monopoly of foreign capital in the manufacturing sector. (3) The deepening stage (2013—present):A framework of "proactive government, efficient market, and innovation ecosystem" has been formed. The "Destruction" involves the elimination of backward production capacity and the digital transformation of traditional industries, while the "Creation" focuses on disruptive technological innovation, the cultivation of emerging industries, and the construction of an innovation ecosystem.Empirical cases illustrate the micro-level mechanisms: BYD achieved a high-end breakthrough in manufacturing through "technological iteration", catalyzing the automotive industry′s shift from foreign dominance to indigenous control; Pinduoduo (PDD) unlocked demand in underserved markets via "low-end market penetration",contributing to the balanced development of a unified national market; and TikTok exported digital ecosystems through "overseas market creation," reshaping the global digital services landscape.
This study makes the following contributions: it constructs a three-dimensional collaborative framework of "institutional reconstruction, market evolution, and innovation-driven growth", clarifies the theoretical linkage between micro- and macro-level dynamics, and validates its applicability in the Chinese context through cross-sectoral case studies. Looking ahead, technology-driven development, upgraded globalization, green transformation, and the integration of the digital and real economies will shape the future trajectory of creative destruction. Policy efforts should prioritize core technology R&D, social cost mitigation, talent incentives, intellectual property protection, and international cooperation. China′s economic miracle is fundamentally the outcome of evolving "creative destruction" and invigorated "disruptive innovation", offering practical insights and "Chinese wisdom" to other latecomer nations.

At present, the problem of global climate change is becoming increasingly severe, prompting governments worldwide to issue relevant policies that encourage enterprises to engage in green technology innovation in order to achieve the goal of sustainable development. However, it should be noted that green technology innovation in enterprises differs from general innovation activities. It is characterized not only by the long-term, high-risk nature of typical input sinking, irreversible processes, and uncertain outcomes but also by the dual externalities of knowledge spillover and environmental protection. These characteristics make it difficult for enterprises, under the assumption of economic rationality, to allocate sufficient innovation resources to green technology innovation activities. As a result, they are more susceptible to the “double high” problem of high adjustment costs and high financing costs. Patient capital,as a new financing model, provides new ideas and ways to solve this problem. It has the characteristics of long-term orientation, strong risk tolerance, strategy and relationship, which are highly consistent with the long-term strategic development needs of enterprises. The existing research shows that patient capital can promote the new productivity, ESG performance and innovation efficiency of enterprises, but it lacks the discussion of patient capital on green technology innovation of enterprises. Therefore, drawing on resource-based theory and dynamic capability theory, this study makes an in-depth analysis on this aspect, and points out the mediating role of enterprise ESG performance in it.
The study selects the data of A-share listed companies in Shanghai and Shenzhen from 2009 to 2023 as the research sample, and finally obtains 20 606 samples after relevant screening and elimination. It is found that patient capital has a significant positive impact on the development of green technology innovation in enterprises, which is still valid after adopting a series of robustness tests and overcoming possible endogenous problems. As far as mechanism is concerned, ESG performance is an effective way for patient capital to influence enterprise green technology innovation, that is, the increase of patient capital can promote enterprise green technology innovation by improving ESG performance. Further heterogeneity shows that, as far as the enterprises' own characteristics are concerned, patient capital plays a more significant role in promoting the green technology innovation level of senior executives and mature enterprises. From the external environment of enterprises, patient capital plays a more significant role in promoting the green technology innovation of enterprises in areas with high financial development level and high media attention.
The research contribution of this paper is mainly reflected in the following aspects: Firstly, it broadens the relevant research on the economic consequences of patient capital. Although the concept of patient capital is mature at present, the research on its impact is still lacking. This paper extends its economic consequences to the level of enterprise green technology innovation. Under the guidance of current green development, it is of great practical significance to explore the influence of patient capital on enterprise green technology innovation. Secondly, from the perspective of patient capital, it supplements the relevant research on the influencing factors of enterprise green technology innovation. In recent years, with the policy support and guidance of patient capital in China, patient capital has played an increasingly important supporting role in the long-term value of enterprises, but few documents pay attention to the influence of patient capital on green technology innovation of enterprises. This paper empirically studies the influence of patient capital on enterprise's green technology innovation, and makes up for the gap in the existing literature regarding the influencing factors of enterprise green technology innovation. Thirdly, patient capital-enterprise ESG performance-green technology innovation is brought into a unified analysis framework, which verifies the mediating role of ESG performance in patient capital promoting enterprise green technology innovation. Deepening the knowledge and understanding of the inherent law of the influence of patient capital on green technology innovation of enterprises also provides empirical evidence for enterprises to make better use of external funds to improve ESG performance and realize green technology innovation.

Key industrial technologies are the core areas for cultivating technological competitive advantages and seizing opportunities for industrial development, playing a pivotal role in driving breakthroughs in industrial technology. Strategic emerging industries are knowledge- and technology-intensive sectors rooted in major technological breakthroughs and evolving development needs. Their key technologies are the essential and irreplaceable technologies (or links) that play an important role in the industry, reflecting current technological hotspots, difficulties, or future technological breakthroughs. As China’s technological and industrial competitiveness advances rapidly, Western countries have intensified their technological blockades against China. Strategic emerging industries now face the "small courtyard, high fence" technological dilemma, resulting in unbalanced and inadequate development of key technologies. Against this backdrop, it is essential for China's strategic emerging industries to focus on enhancing their independent innovation capabilities and breaking technological barriers. Therefore, in the new round of technological revolution and industrial transformation, identifying and clarifying the development direction of key technologies, and making forward-looking layouts are of great significance for enhancing independent innovation capabilities, achieving high-level technological self-reliance and self-improvement, and accelerating the development of new quality productive forces.
Traditional technology identification methods mostly rely on single-source data or static analysis, making it difficult to reveal the cross-disciplinary relevance and dynamic evolution process of key technologies. Furthermore, they lack systematic consideration in data source construction and dynamic analysis, resulting in insufficient accuracy and agility in technology identification. The methods of multi-source data fusion and knowledge association aggregation provide a new direction for solving the above problems. The former can integrate multi-source data and statistical methods into a unified framework, adapting to the intelligence analysis needs for addressing uncertainty and complexity. The latter functions through the reorganization, association, aggregation and presentation of multi-dimensional and multi-granularity information objects (including knowledge association, knowledge aggregation, etc.).
Drawing on a three-stage intelligence analysis model, this study integrates multi-source data fusion, knowledge association and aggregation methods to construct a key technology identification model for strategic emerging industries.Specifically, first, multi-source data fusion provides a comprehensive data foundation for key technology identification; second, knowledge association is applied to reveal potential correlations between technical topics in multi-source data, which in turn helps identify key technological hotspots; and finally, guided by the theme of knowledge aggregation and integration technology, core themes in the development of generic technologies and industrial-specific technologies are extracted, and on this basis, the development direction of key technologies in strategic emerging industries is further explored.
To verify the scientific validity of the key technology identification method, this study conducts a horizontal analysis by selecting the BERT terminology model and the Gompertz patent model. Three quantitative indicators for knowledge network topology are adopted: Technology Potential Index (TPI), Technology Influence Degree (TID), and Cross-domain Relevance (CDR). The study finds that incorporating multi-source data fusion and knowledge association aggregation into a unified framework to construct an industry key technology identification model can help identify strategic emerging industry key technologies with uncertainty and complexity. In addition, the effectiveness of the proposed method is verified by comparing it with relevant authoritative documents.
The contribution of this paper includes two aspects: Firstly, it enriches the identification methods of emerging technologies. In response to the uncertainty and complexity of key technologies in strategic emerging industries, it integrates multi-source data fusion and knowledge association aggregation into the intelligence analysis model, reduces the difficulty of identifying key technologies in strategic emerging industries, expands the ideas of emerging technology identification, and deepens the identification methods of emerging technologies. Secondly, it reveals key industrial technologies with higher granularity. The study conducts a deep analysis of common and hot technologies in the industry, and compares them with authoritative and public literature to obtain finer grained key technologies. The insights offer theoretical support for government authorities in formulating targeted industrial policies and provide valuable guidance for enterprises to advance their technological innovation strategies.

In recent years, the VUCA characteristics including volatility, uncertainty, complexity, and ambiguity have become increasingly prominent, posing severe survival challenges for enterprises. However, some firms have managed to survive, recover, and even thrive through adverse shocks, largely due to their organizational resilience—a critical capability that enables firms to maintain stability and rebound swiftly from unexpected disruptions. This resilience has become indispensable for corporate survival and sustainable development in today's volatile environment. Meanwhile, the digital wave has swept through, with emerging technologies like artificial intelligence establishing key competitive advantages for enterprises. These technologies are being integrated into daily operations and management models, significantly enhancing stability and recovery capabilities during crises, thereby forming a close connection with organizational resilience.
While existing research has examined the determinants of resilience in manufacturing enterprises from various perspectives, it has yet to fully account for the complex interplay among digital technologies, strategic positioning within industrial chains, and internal resource allocation decisions, all of which are crucial in dynamic environments. It is worth noting that the enabling effect of AI technology on enterprise resilience and its realization mechanisms have not been systematically clarified and empirically tested in the existing literature, which will become a key bottleneck to deepen the understanding of the formation of enterprise resilience in the digital era.
Therefore, to address the gap, this study draws on resource-based theory and utilizes panel data from Chinese listed manufacturing firms (2011-2023). By applying text analysis to measure AI application intensity based on the frequency of AI-related keywords in corporate disclosures, the study quantifies the development level of AI technology adoption and examines its impact on enterprise resilience. Meanwhile, this study explores the specific impact channels of AI technology application on corporate resilience from the perspective of multi-dimensional effects across the upstream, midstream, and downstream of the industrial chain, and analyzes the differentiated impacts of AI technology application under different scenarios.
The research findings indicate that, first, AI technology application significantly enhances enterprise resilience. AI technology positively strengthens corporate resilience through three key mechanisms: empowering digital innovation in the upstream sector, optimizing digital operations management in the midstream sector, and providing digital marketing services in the downstream sector. Heterogeneity analysis reveals that both external factors (industry competition intensity) and internal resource foundations (digital transformation speed, factor intensity, and fixed asset investment levels) significantly amplify AI's positive impact on corporate resilience. However, the analysis of strategic resource allocation patterns shows that improved ESG performance creates "resource crowding-out" effects, while excessive resource reserves lead to "resource surplus," thereby diminishing AI's catalytic role. In summary, the study innovatively constructs an industrial chain collaborative analysis framework, revealing the dynamic law and influence boundary of artificial intelligence technology penetration enhancing enterprise resilience.
Thus, this study proposes the following practical management recommendations for both the Chinese government and enterprises. The government should focus on building an AI technology application support system and strengthening industrial chain coordination policies, with a focus promoting the integrated application of digital technologies represented by AI across the upstream, midstream, and downstream segments of the industrial chain. Enterprises should actively adopt digital technologies represented by AI, optimize industrial chain structures, enhance digital intelligence capabilities, and proactively leverage external environmental pressures to forge their own resilience.
This study contributes to the literature in three innovative ways. First, it introduces an industry-ecosystem perspective to systematically examine how AI application enhances resilience in China's manufacturing sector, bridging a theoretical gap between AI and organizational resilience. Second, it unpacks the mechanisms through which AI affects resilience via digital innovation, operational optimization, and marketing enhancement, while also highlighting how external environments, internal resources, and strategic allocation patterns shape these effects. Third, it offers a comprehensive theoretical lens for understanding the evolutionary path of AI-enabled resilience, providing actionable insights for both scholars and practitioners in the digital transformation era.

Under the guidance of China's "dual carbon" goals, green innovation has emerged as a core driver for the low-carbon transformation of the economy and society, while the traditional pollution control model is shifting toward a more value-creating green development paradigm that emphasizes sustainable growth and innovation. The intensification of global trade frictions has hindered the international flow of green technologies, making the enhancement of independent innovation capabilities a critical pathway to achieving carbon neutrality. However, green innovation is characterized by high investment, long cycles, and significant risks, and these characteristics frequently lead to unsustainable corporate investment decisions, particularly under short-term operational pressures, thereby creating persistent bottlenecks in innovation advancement. The rapid adoption of digital technologies in recent years offers a new breakthrough to address this challenge. Against the backdrop of digital transformation, data, as a core resource, is permeating various industries, becoming the key link between digital industrialization and industrial digitization. Data assets, with their attributes of infinite supply, low-cost reuse, and cross-spatiotemporal sharing, can effectively reduce the trial-and-error costs of green innovation, enhance technological synergies, and drive breakthroughs in energy conservation, emission reduction, and circular economy.
Meanwhile, the transformation of data assets into green innovation outcomes is influenced by multiple factors. High-quality digital talent is essential for unlocking data value, while robust digital infrastructure improves data allocation efficiency. Moderate market competition drives corporate innovation, and strong ESG performance steers data resources toward sustainable development. Existing research has examined the impact of digital finance, the digital economy, and digital policies on green innovation. However, studies on data assets remain largely theoretical, focusing on accounting, valuation, and economic effects, and empirical research in this domain is still in its infancy, particularly concerning the intricate relationship between data assets and corporate green innovation. A deeper understanding of how data assets empower green innovation is crucial for enriching the theoretical framework surrounding data marketization but also for providing actionable insights to policymakers. By better understanding this relationship, policymakers can more effectively align digital economy strategies with sustainability objectives, which is of paramount importance in the context of achieving China's “dual carbon” goals.
Against this backdrop, this study employs the double machine learning model to examine the causal effects and mechanisms through which data assets influence corporate green innovation capabilities. Empirical results demonstrate that data assets significantly enhance green innovation, a finding that remains robust across a series of tests. The analysis reveals that data assets improve green innovation by optimizing human capital structure, particularly in firms with higher ESG performance, where the effect is stronger due to their emphasis on sustainable development and technological advancement. Additionally, digital infrastructure strengthens this relationship by improving the allocation efficiency of data resources, while intensified industry competition further amplifies the effect by increasing firms' sensitivity to technological innovation. Nevertheless, the magnitude of this impact varies across different firm types and regions due to differences in resource endowments and technological readiness: enterprises in megacities, high-tech firms, state-owned enterprises, and large corporations benefit more significantly, likely due to their greater access to resources and technological infrastructure; whereas small and medium-sized city enterprises, non-high-tech firms, non-state-owned enterprises, and smaller businesses exhibit weaker effects due to differences in resource endowments and technological readiness. This study not only uncovers the mechanisms driving green innovation through data assets but also offers policy insights for facilitating corporate green transformation through optimized data resource allocation.
The contributions of this study lie in its integration of data assets as a new production factor and its methodological innovation through the application of double machine learning, addressing limitations in traditional econometric approaches. By clarifying how skilled labor mediates the relationship between data assets and green innovation, it demystifies the role of human capital in fostering technological breakthroughs. Moreover, by examining contextual moderators such as ESG performance, industry competition, and digital infrastructure, the study provides actionable guidance for firms across diverse sectors and regions, informing strategies for leveraging data assets to advance sustainable development objectives.

In recent years, a growing number of organizations have rapidly adopted artificial intelligence (AI) technology, aiming to build a sustainable competitive advantage through automation and intelligence. Despite substantial investments in AI, many organizations have yet to realize satisfactory returns. A key reason for this shortfall lies in the disproportionate emphasis on the technical implementation of AI, while neglecting the management of AI use at the employee level. Consequently, AI often fails to be fully integrated into business processes or to realize its full potential. Although AI can effectively reduce costs and improve organizational efficiency, its growing role in tasks previously performed by humans has led to a squeeze on employees' professional value and livelihood. This has given rise to a phenomenon known as “AI disruption awareness”which refers to employees' perception of the threats posed by AI applications. Such awareness may trigger resistance to AI and become a significant barrier to its adoption and effective use. Therefore, as organizations undergo AI-driven transformation, it is essential to understand and address the impact of AI disruption awareness on employees' use of AI.
While academic discussions around AI disruption awareness have grown in recent years, relatively little attention has been paid to its influence on employees' use of AI. Existing research has primarily focused on employees' willingness to use AI, with little attention paid to how AI disruption awareness affects employees' actual AI usage behavior. Moreover, the majority of prior studies have emphasized the negative effects of AI disruption awareness on AI usage, neglecting its potential positive effects. Against this backdrop, this study focuses on employees' AI usage behavior in AI application scenarios, exploring how they adjust their use of AI in response to AI disruption awareness, in order to expand the study of the impact of AI disruption awareness on AI usage. Drawing on the cognitive appraisal theory of stress, the study constructs a model to explore how AI disruption awareness differentially impacts employees' innovative and avoidant use of AI, and the moderating role of strengths-based leadership in this process.
By analyzing two-wave survey data collected from 317 employees, the study yields the following conclusions: AI disruption awareness triggers two distinct strategies of innovative use and avoidant use of AI, and the choice of strategy is influenced by strengths-based leadership. Under the influence of strengths-based leadership, employees tend to make a challenge appraisal of AI disruption awareness, which drives them to adopt innovative usage strategies toward AI. Conversely, in the absence of strengths-based leadership, employees will make a threat appraisal of AI disruption awareness, leading them to adopt avoidant usage strategies toward AI.
The theoretical contributions of this study are as follows: First,it shifts the analytical lens from intention to actual behavior, foregrounding employee agency in AI application. By addressing the key question of “how AI disruption awareness influences employees' AI usage behavior”, this study offers new insights into how employees use AI under the influence of AI disruption awareness. Additionally, by revealing the impact of AI disruption awareness on creative use—a positive AI usage behavior—this study addresses the limitations of previous research, which often adopted a singularly negative perspective. Second, unlike previous studies that mostly explored employees' behavioral performance under AI disruption awareness from a single positive or negative perspective, this study integrates previous research perspectives based on a dialectical perspective, incorporates employees' positive and negative responses into the same framework, and proposes a dual behavioral mechanism of employees' AI disruption awareness, which provides a more comprehensive theoretical explanation for understanding the effects of AI disruption awareness. Third, this study proposes that strengths-based leadership is an important conditioning factor in determining the effect of AI disruption awareness. This not only bridges the gap of past studies' understanding of the boundaries of the differential impact effects of AI impact awareness from a leader's perspective, but also provides effective clues to reconcile the controversy of existing studies on the differential impact effects of AI disruption awareness.

Amid the global digital wave, artificial intelligence (AI) technology, with its powerful data processing and intelligent decision-making capabilities, is increasingly becoming the core driving force for innovation and transformation across various industries. However, the introduction of AI transcends mere tool replacement; it reshapes the working modes of organizations and employees, giving rise to a series of management challenges. A prominent challenge is how to effectively leverage AI to empower employees and stimulate their innovative behavior. A review of the literature reveals that scholars' research on stimulating AI-driven employee innovation behavior mainly focuses on two perspectives: exploration of the double-edged sword effect on AI-driven employee innovation behavior from individuals' perception of AI, and examination of how AI collaborates with employees to promote individual innovation. Nevertheless, these studies overlook the significant role of leadership in stimulating AI-driven employee innovation behavior. Leaders' AI symbolization refers to leaders' explicit expression of support, acceptance, and promotion of AI by taking actions closely related to AI and displaying items that reflect their preference for AI. In light of this, this study investigates the mechanism and applicable boundaries of leaders' AI symbolization on AI-driven employee innovation behavior, aiming to provide guidance for both theory and practice.
Drawing on social cognitive theory, this study reveals the mediating mechanisms of leaders' AI symbolization on AI-driven employee innovation behavior through two aspects: technical cognitive trust (AI trust) and self-cognitive efficacy (AI innovation self-efficacy). Leaders' AI symbolization reflects their recognition, support, and trust in AI. This influences employees to trust AI more, increasing their willingness to accept AI and boosting AI-driven innovation. Additionally, employees' experience with leaders' AI symbolization helps them recognize their own innovative abilities. They gain confidence in solving complex problems and completing tasks innovatively with AI assistance. This enhances their AI innovation self-efficacy and provides psychological support for AI-driven innovation. Furthermore, individuals' cognitive processes towards leadership behavior are not only influenced by leaders' traits and behaviors themselves but also depend on how individuals understand and interpret these traits and behaviors. The inherent complexity of AI has left many leaders with insufficient expertise to fully grasp its implications, often resulting in a tendency to offer only surface-level support without the critical resources, training programs, or strategic direction needed to effectively implement AI solutions and address genuine organizational requirements. This can easily trigger employees' attribution analysis of leaders' AI symbolization motives. Therefore, this study explores the boundary conditions of leaders' AI symbolization influencing AI-driven employee innovation behavior through the dual-mediating cognitive mechanism from the perspective of employees' attribution of leaders' AI symbolization motives.
The analysis of matched data from 488 employees in two stages indicates that leaders' AI symbolization positively affects AI-driven employee innovation behavior through AI trust and AI innovation self-efficacy. Moreover, when employees attribute leaders' AI symbolization motives to performance improvement, the positive impact of leaders' AI symbolization on employees' AI trust is enhanced, thereby boosting AI-driven employee innovation behavior. Conversely, if attributed to impression management, the positive effects of leaders' AI symbolization on employees' AI trust and AI innovation self-efficacy are weakened, thereby reducing AI-driven employee innovation behavior. However, when employees attribute leaders' AI symbolization motives to performance improvement, the moderating effect of leaders' AI symbolization on AI innovation self-efficacy is not significant, and the moderated mediation hypothesis is also not significant.
The theoretical contributions of this study are as follows: First, it enriches the research on leadership factors in the antecedent mechanism of AI-driven employee innovation behavior and expands the influence of leaders' AI symbolization, providing a new perspective for understanding how leaders can effectively stimulate employees' innovative potential through AI symbolization. Second, this study innovatively analyzes how leaders' AI symbolization influences AI-driven employee innovation behavior and its effects through the dual-mediating paths of technical cognitive trust (i.e., AI trust) and self-cognitive efficacy (i.e., AI innovation self-efficacy), offering insights from a social cognitive perspective. Third, drawing on attribution theory, this study explores the boundary conditions of leaders' AI symbolization influencing AI-driven employee innovation behavior from the perspective of employees' attribution of leaders' motives, making an important supplement to the research on leaders' AI symbolization.

Artificial intelligence (AI) is rapidly reshaping national governance, social operations, and economic structures, while simultaneously generating multifaceted risks relating to safety, privacy, discrimination, and systemic uncertainty. In an era characterized by rapid technological iteration and the wide proliferation of AI application scenarios, constructing a scientific, systematic, and forward-looking risk-based regulatory framework has become an essential legislative task for China. This article conducts a comparative examination of AI regulatory regimes in the European Union, South Korea, Canada, and the United States, analyzing their respective approaches to risk identification, classification models, and the allocation of regulatory obligations, with the aim of informing China′s future AI legislation.
At the comparative level, the European Union adopts a four-tier framework—unacceptable, high, limited, and minimal risk—and imposes stringent, lifecycle-wide compliance obligations on high-risk systems. Although comprehensive, this framework produces excessively heavy compliance burdens and lacks flexibility in responding to technological dynamics. South Korea employs a horizontal regulatory model centered on high-impact AI, characterized by concise provisions and a streamlined structure, yet its taxonomy does not adequately differentiate among distinct categories of risks. Canada distinguishes between biased-output systems and harm-based systems, but ultimately applies uniform obligations to both categories, resulting in a disconnect between risk classification and regulatory practice. The United States adopts the most flexible structure: a dual-track model distinguishing safety-impacting AI from rights-impacting AI. On the basis of a unified minimum compliance baseline, the U.S. model adds differentiated requirements for rights-impacting systems and incorporates deferral, exemption, and dynamic adjustment mechanisms that enhance regulatory adaptability.
Despite differences in legislative traditions and policy objectives, the four jurisdictions share a common governance logic: risk identification as the regulatory starting point, risk classification as the core organizing principle, and differentiated obligations as the primary regulatory tool. The proportionality principle underlies these systems. The U.S. dual-track model distinguishing safety and rights impacts offers notable advantages in proportionality, precision, and institutional flexibility, thereby providing a valuable template for China in building a multi-level governance structure.
In light of these comparative insights, this article proposes that China optimize its AI risk-governance framework along three dimensions. First, China should establish a layered legislative structure combining common rules + sector-specific rules. A national-level Artificial Intelligence Basic Law should articulate overarching governance principles, risk-classification methods, and baseline regulatory obligations. Sectoral regulatory authorities should then develop technical standards and regulatory rules tailored to specific application scenarios, thereby balancing systemic coherence with operational flexibility and avoiding fragmented governance.Second, China should adopt a dual-track classification framework that distinguishes between safety-impacting AI and rights-impacting AI. Systems involving life safety, critical infrastructure, or public security should be categorized as safety-impacting AI, whereas systems that affect fairness, fundamental rights, or vulnerable groups should be regulated as rights-impacting AI. For systems that present hybrid or overlapping risks, a primary-risk identification mechanism should be introduced to classify them according to their dominant risk attributes. In addition, a combined-obligations mechanism should be implemented to allow both sets of obligations to apply where necessary, thereby enhancing the precision of risk identification and strengthening the applicability of regulatory tools.Third, China should develop a comprehensive system of dynamic adjustment and flexible exemptions. Through presumed-strict classification, application-based exemptions, periodic review, and cross-departmental feedback mechanisms, regulatory measures can be dynamically aligned with technological evolution and sector-specific characteristics. Such mechanisms help prevent regulatory rigidity and excessive compliance burdens, ensuring that governance tools remain adaptive to emerging risks and evolving industrial practices.
In sum, through comparative analysis of foreign regulatory models and the construction of a localized governance pathway, this article argues that the core of China′s AI risk-governance framework lies in risk-based classification as its organizing principle, a layered legislative structure as its institutional foundation, a dual-path classification model as its methodological approach, and dynamic adjustment mechanisms as its regulatory toolset. A governance system guided by unified national principles, supported by differentiated regulatory rules, and coordinated between central and sectoral authorities can achieve a dynamic balance between safeguarding safety and fostering innovation, thereby forming a Chinese model of AI risk governance capable of addressing the complexities of the digital era.

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-industry heterogeneity and alternative measures of capital patience.

Green innovation(GI) has become crucial for the high-quality development of enterprises. With the impact of digital technology on the real economy, digital transformation(DT) has risen to a significant national development strategy. The process of DT implies introducing advanced productivity, which can empower enterprises to transform the development mode to realize GI. However, this process depends to a certain extent on the impact of enterprise agility and ESG information disclosure. Based on the theory of organizational information processing, DT can help enterprises respond to changes in the external environment promptly to promote GI by improving the information processing capability and effectively realizing the agility response of enterprises at the industrial, production, and sales ends. In addition, DT also exists in the interactive communication between enterprises and society. Accroding to the signaling theory, high-quality ESG information disclosure can play a "signaling effect" in conveying a positive enterprise image to society, and it is easy for enterprises to obtain external support and create conditions for GI. Green innovation (GI) is crucial for high-quality enterprise development, while digital transformation (DT) has become a key national strategy, driving advanced productivity. DT enhances enterprise agility and information processing capabilities, facilitating GI through efficient responses across industry, production, and sales. Additionally, DT fosters interactive communication between enterprises and society. High-quality ESG (Environmental, Social, Governance) disclosure acts as a positive signal, attracting external support for GI. Meanwhile, there are multiple interactions between the GI process of enterprises and the government. In recent years, local governments have been paying more and more attention to new quality productive forces. Local governments' focus on new quality productive forces supports industrial upgrading and provides a favorable environment for GI. However, further research is needed to explore the mechanisms of enterprise agility, ESG disclosure, and government attention within the DT-GI framework.
Using the data of Chinese listed enterprises from 2015 to 2023, this paper introduces the mediating variables of agility responsiveness and ESG disclosure and the moderating variable of local government's attention to new quality productive forces and empirically explores how
DT empowers GI of enterprises from the perspective of new quality productive forces. The results show that DT has a positive and significant impact on GI; agility responsiveness and ESG disclosure play a mediating role between DT and GI; local government's attention to new qualitative productivity positively moderates the relationship among DT, ESG disclosure and GI, and at the same time strengthens the mediating role of ESG disclosure between DT and GI. Further analysis reveals that the local government's concern on new quality productive forces can positively moderate the relationship between agility responsiveness and GI, and strengthen the mediating role of agility responsiveness in the relationship between DT and GI only when the degree of enterprise capital redundancy is high.
This paper makes several significant contributions to the literature on digital transformation (DT) and green innovation (GI). Firstly, it shifts the focus from the economic impacts of DT to its role in driving green development and green innovation dividends under the framework of new quality productive forces. This approach bridges gaps in current research and offers new empirical evidence on value creation through green-oriented digital transformation. Secondly, the study reveals the dual mediating roles of agility responsiveness and ESG disclosure quality between DT and enterprise GI. Unlike previous research that examined resource input, information sharing, and governance, this paper explores the theoretical mechanisms underlying these relationships, expanding the understanding of how DT empowers GI. Thirdly, it examines the moderating role of local governments' attention to new quality productive forces in influencing the GI process through DT. This research clarifies the contextual boundaries of DT's impact on GI and aligns with China's emphasis on developing new quality productive forces. It provides new empirical insights into how local government focus can shape enterprises' DT and GI, contributing to both theoretical and practical advancements in the field.

Against the dual imperatives of sustainable development and digital transformation trajectories, the manufacturing sector confronts compound challenges from environmental regulations and resource constraints. The Chinese government has been endeavoring to foster a market-oriented green technology innovation system and enhance the international competitiveness of domestic manufacturing. As the cornerstone of technological revolution, artificial intelligence (AI) is reshaping production paradigms while converging with environmental, social, and governance (ESG) principles to emerge as a pivotal catalyst for green innovation and low-carbon transitions in manufacturing firms.
Existing research has explored the factors influencing green innovation in manufacturing enterprises from multiple levels. However, in the complex green innovation ecosystem, the green innovation of manufacturing enterprises is inevitably affected by the interaction of multiple factors. Notably, the impact of AI adoption on innovation performance and its mechanism of action when manufacturing enterprises use intellectual capital for green innovation remain inconclusive.
Building on this premise, this study integrates technology affordance theory with the perspective of informal environmental regulations to elucidate dynamic mechanisms through which AI technological empowerment interacts with institutional pressures to drive manufacturing firms' green transformation processes. Employing a dual theoretical framework of technological affordance and informal environmental regulations, the study constructs a three-dimensional interactive model, and it analyzes the mechanistic relationships among AI technology adoption, intellectual capital, third-party ESG ratings, and green innovation based on the panel data from China's A-share listed manufacturing firms (2011-2022).
Findings show that, firstly, structural capital, accumulated via organizational learning, enhances resource allocation efficiency and acts as a critical "lubricant" in innovation processes. Simultaneously, relational capital cultivates cross-sector collaborative networks that provide essential resource pipelines and market access, fostering an external ecosystem conducive to value co-creation. Notably, these mechanisms exhibit amplified beneficial effects in complex entities and contextual scenarios with distinctive characteristics. Secondly, deviations from green development pathways often precipitate myopic decision-making traps. In such cases, firms preferentially allocate human capital investments to conventional projects with rapid financial returns, while underinvesting in long-term green innovation initiatives requiring sustained investments. Consequently, increased human capital does not invariably translate to developmental momentum; paradoxically, it may suppress green innovation capabilities. Without strategic planning, AI adoption may exacerbate this inhibitory effect through misdirected human capital allocation. Thirdly, from the perspective of informal environmental regulations, it examines three-way interactive effects involving third-party ESG ratings. Results demonstrate that ESG ratings moderate the synergistic interaction between intellectual capital and AI technology. Specifically, higher ESG ratings incentivize firms to simultaneously accelerate AI adoption and strengthen human/structural capital development. Furthermore, these ratings foster collaborative networks that enhance AI-intellectual capital complementarity. Collectively, these reinforcing mechanisms—spanning technology adoption, capital development, and network cultivation—enhance green innovation efficiency by creating a virtuous cycle where ESG-driven incentives align with optimized technological and intellectual resource allocation.
Thus, three key managerial implications for green innovation of manufacturing firms are presented. First, firms should leverage intellectual capital, especially human capital, by focusing on long-term green development, training employees in green knowledge and skills, and integrating human capital with structural and relational capital. Second, AI technology can significantly enhance green innovation efficiency, so firms need to integrate AI into green innovation processes, optimize organizational structures and management mechanisms with digital technology, and build AI-powered relational networks. Lastly, embracing ESG philosophy is crucial. Firms should incorporate ESG principles into their core philosophy and development strategy, view ESG practices as long-term value investments, and actively disclose ESG information to build good stakeholder relationships and secure resource support for green innovation.
This study makes two primary theoretical contributions. First, through the lens of affordance theory , it unravels the mechanism by which AI adoption influences manufacturing green innovation and its synergy with intellectual capital. This offers a novel analytical framework and research trajectory for industrial green transformation. Second, from the informal environmental governance perspective, the study investigates three-dimensional interactions among ESG ratings, AI adoption, and intellectual capital. The analysis reveals how dual contextual pressures of AI development and ESG performance shape firms' strategic utilization of intellectual resources for green innovation, extending existing literature on corporate environmental performance determinants.

In the era of digital economy, digital innovation is an important way for enterprises to gain sustainable competitive advantage. However, the existing literature still lacks research on the concept connotation, classification, and theoretical construction of digital innovation. In view of the limitations of the research in the field of digital innovation, this study is committed to deepening and clarifying the essence and connotation of digital innovation, expanding the classification system of digital innovation, and further systematically constructing a comprehensive theoretical framework to grasp the whole process of digital innovation from germination to achievement.
Firstly, this study redefines the connotation of digital innovation by literature analysis and puts forward the key role of digital innovation ability in digital innovation. This study holds that digital innovation refers to the process that organizations with digital resources build their own digital innovation capabilities by means of digital technology, and then produce digital innovation results. The connotation of digital innovation under this definition includes four core points, namely, digital resources, digital technology, digital innovation ability and digital innovation output.
Secondly, the study redefines the types of digital innovation. In addition to the traditional digital product innovation, digital service innovation, digital process innovation, digital organization innovation and digital business model innovation, it puts forward a new type of digital innovation-digital technology innovation. This kind of innovation includes not only new digital products and services, but also new digital technologies and solutions, with an emphasis on the innovation iteration of digital technology itself.
Thirdly, on the basis of defining the connotation and types of digital innovation, a brand-new theoretical research framework of digital innovation is constructed. Through a comprehensive literature review and analysis, the framework is developed to encompass three dimensions. The first is the vertical realization mechanism of digital innovation. In accordance with the results of six types of digital innovation output, this study discusses a series of processes for how to realize digital innovation output by relying on digital resources, using digital technology and building digital innovation ability. Next, the horizontal driving mechanism of digital innovation reveals the cause and effect of digital innovation and its boundary conditions: from the motivation and demand of digital innovation, to the implementation and execution of digital innovation, and then to the result and influence of digital innovation. Finally, the influence of digital innovation environment on the process of realizing and driving digital innovation, including market environment, policy environment, and technical environment are explored.
Lastly, grounded in the above theoretical framework, the study puts forward four future research trends and directions of digital innovation. This involves delving into the capabilities for digital innovation, tracing the dynamic evolution and iterative processes of digital technology innovation, examining the underlying mechanisms that drive digital innovation, and enhancing theoretical studies on digital innovation within the unique context of China to offer theoretical insights that can inform and guide the practical implementation of digital innovation.
The research conclusion and theoretical contribution of this paper include three aspects: (1) In definies the connotation of digital innovation and puts forward the key role of digital innovation ability in the process of digital innovation. This not only enriches the understanding and knowledge of the connotation of digital innovation in the existing research, but also highlights the powerful supporting role of digital innovation ability in the process of digital innovation. (2) The types of digital innovation are re-divided, and a new type of digital innovation is put forward. On the basis of existing research, the essence and connotation of digital innovation are further explored. Considering the programmability of digital technology, it can realize its own updating and iterative upgrading in the process of digital innovation, and then a new digital technology is derived. It is proposed that digital technology innovation should also be one of the types of digital innovation, which has certain innovation. (3) The theoretical research framework of digital innovation is constructed. This not only expands and perfects the theoretical research of digital innovation in China, but also has certain guiding value for the practice of digital innovation in enterprises.

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-incremental 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 to function 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 robustness 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 data-sharing ecosystems, and establishing adaptable regulations. Enhancing strategic flexibility through agile governance structures further enables firms to capitalize on digital leadership′s transformative potential.

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 non-competitiveness, 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 enterprises 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 dual-path 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 enterprises 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 accumulation 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.

As a new production factor, data embodies the unique attributes of shareability, replicability, and exponential growth potential. These characteristics are catalyzing profound transformations in both social productivity and the dynamics of production relations. Against this backdrop, the conventional property rights framework, predicated on the concept of ownership, is ill-equipped to address the practical demands of unlocking the intrinsic value of data. There are many theories currently surrounding the structural separation of data property rights, including labor empowerment theory, preemption theory, incentive theory, and so on. In fact, they all revolve around the protection of data property rights and the verification of a certain type of separation right in data property rights separation. For example, labor empowerment theory is actually more in line with the processing and use rights of enterprise data. Although the above theory can provide theoretical support for the structural separation of data property rights, it does not clarify the relationship between various sub rights under the structural separation of property rights and the value derived from the circulation and utilization of data elements. Therefore, this article introduces the theory of structural functionalism in an attempt to clarify the above relationship.
Under structural functionalism, data property rights are an organic whole system, and its internal subsystems such as data resource ownership, data processing and usage rights, data product management rights, and data property registration mechanisms will all have an impact on the overall operation of data property rights and the orderly circulation of data elements. Among them, the rights structure, operational logic, interrelationships, and collaborative mechanisms of each sub right must be developed around the ultimate function of releasing the economic value of data elements. After clarifying the compatibility between structural functionalism and data property rights structural separation, it is currently necessary to analyze the value of property rights structural separation from both structural and functional perspectives, as well as the subsequent rule construction.
At the functional level, the current structural division of data property rights should be elaborated on three aspects: innovating data protection models, unleashing the value of data elements, and balancing the interests of data subjects. Firstly, to address the shortcomings of traditional data rights protection models. Secondly, it meets the demand for value release throughout the entire lifecycle of data element circulation. Thirdly, balance the interests and needs of different data subjects.
At the structural level, the main focus is on the implementation of the structural separation of data property rights, namely the separation of public data ownership, enterprise data ownership, and the scenario based application of personal data. Firstly, in terms of admission, it is necessary to establish a relaxed authorization operation admission mechanism. Secondly, enterprise data should clarify the specific connotation of the separation of three rights. The subject of enterprise data holding rights should be limited to market entities, and the scope of power of data resource holding rights includes holding rights and the right to transfer use. The subject of the right to use data processing can be a legal or natural person who obtains raw data through legal or agreed upon means in the upstream data market. The processing methods include data cleaning, labeling, anonymization, cross matching, storage, etc. The right to profit from the operation of data products is the core power, and its implementation can rely on the establishment of data trading centers in various regions. Pricing, sales models, trading models, platform profit models, etc. are all feasible paths. Thirdly, the scenario based application of personal data. On the one hand, in terms of data classification standards, the principle of equity allocation can be introduced, that is, different equity protection models can be adopted based on the equity classification of different data subjects. On the other hand, in terms of data grading standards, it is necessary to consider flow restriction rules for sensitive data, privacy data, identity data, and biological data. In addition, it is currently possible to establish personal data asset accounts for storing, recording, and managing personal data.

With the continuous increase in investment in scientific and technological innovation, China's innovation capabilities have been significantly improved in recent years. However, it is worth noting that while R & D investment is rapid and innovation capabilities are accelerating, low innovation efficiency has always been a painsore point that restricts China's economy from achieving innovation-driven development. With the advent of the digital economy era, technological innovation led by the digital economy has become the main direction of cultivating new productivity. Therefore, whether the development of the digital economy can solve the inefficient dilemma of innovation in China is of great practical significance for accelerating the implementation of innovation-driven development strategies and building digital China.
This paper combines the "Solow Paradox" phenomenon of digital technology application and the inefficiency dilemma of regional innovation in China, and aims to use China's provincial panel data to empirically analyze the impact of the digital economy on regional innovation efficiency. Given the spatio-temporal dynamics of variables such as the digital economy and regional innovation efficiency, the study employs the panel data of 30 provinces (cities) in mainland China, excluding Tibet due to the absence of pertinent indicators;and the temporal scope of the sample observations spans from 2013 to 2022.
The potential marginal contributions of this article are threefold. Firstly, in response to the pressing need to expedite the implementation of the innovation-driven development strategy and the digital China initiative, this study tackles the persistent inefficiency in China's regional innovation landscape. Building upon the "Solow Paradox" and the empirical insights from China's regional innovation development, it proposes a nonlinear U-shaped theory that models the relationship between the digital economy and regional innovation efficiency. This theory integrates the technical characteristics of the digital economy with the innovation value chain theory to dissect the driving forces behind the U-shaped pattern of regional innovation efficiency within the context of the digital economy. By this approach, the study aims to uncover the intrinsic mechanisms through which digital economic factors influence and shape the efficiency of regional innovation. Secondly, from the perspective of R & D factor investment bias, the study analyzes the fluctuations in innovation efficiency caused by changes in the structure of R & D labor and R & D capital factor investment, and further considers the superimposed effect of technological progress bias, thereby revealing the deep mechanism of the digital economy affecting changes in regional innovation efficiency. Thirdly, leveraging the theoretical framework of Metcalfe's Law that underscores the value of networks in proportion to the square of the number of connected users, we examine the realistic constraints on improving innovation efficiency in digital economy-enabled regions, thereby providing policy reference for expanding the innovation-driven path of digital technology.
The study reveals a pronounced nonlinear U-shaped relationship between the digital economy and China's regional innovation efficiency, and the development of the digital economy in most regions of China during the study period has not yet reached the critical condition for improving regional innovation efficiency. From a perspective of dimensions, digital industrialization and its non-linear impact on scientific and technological R & D efficiency are the deep driving force for the digital economy to portray the U-shaped shape of regional innovation efficiency, while the lag in both industrial digitalization and achievement transformation efficiency are potential reasons that hinder the improvement of regional innovation efficiency. The development of the digital economy can affect the nonlinear changes in regional innovation efficiency by changing the investment bias of R & D factors. The R & D capital bias of basic research and the R & D labor bias of applied research are the dominant ways in which the digital economy affects the nonlinear changes in regional innovation efficiency. With the increase in the scale of network users, the impact of the digital economy on regional innovation efficiency shows obvious marginal growth characteristics. However, the increase in network value caused by the increase in the scale of network users cannot significantly enhance the positive effect of the digital economy on regional innovation efficiency. Metcalfe's rule in the regional innovation system may face technical constraints of digital transformation.

Against the background of global economic slowdown and geopolitical frictions, enterprises are facing increasing pressure on survival and development. Organizational resilience has become a key capability for firms to cope with adversities and achieve sustainable development, and firms are placing more emphasis on building organizational resilience to ensure survival and long-term growth, yet balancing resilience and cost advantage constitutes a significant dilemma for enterprises. From the perspective of the Resource-Based View (RBV), enterprises choose strategic alliances mainly due to the lack of key internal resources. However, enterprises with strong organizational resilience can independently adjust resource allocation and respond to crises, which may reduce their dependence on strategic alliances. Although strategic alliances offer advantages such as external resource support and synergy (which can help enterprises maintain cost advantages), they also pose problems like high governance costs and collaboration risks. Therefore, enterprises with high resilience may tend to rely on internal resource optimization rather than external alliances when weighing costs and benefits.
Organizational resilience exerts a two-sided impact on cost advantage. On one hand, it can help enterprises avoid high-cost losses and create cost advantages. On the other hand, cultivating and maintaining resilience requires substantial resource investment, which may increase corporate costs and impact the implementation of cost leadership strategies.Scholars hold divergent views on the relationship between organizational resilience and cost leadership strategy. Additionally, the specific impact of the interaction between organizational resilience and strategic alliances on cost advantage also needs further exploration. Controversies exist in relevant fields, calling for more in-depth research.
Therefore,drawing on the resource-based view (RBV), this study examines the effect of organizational resilience on cost leadership strategy. It also explores the underlying mechanisms using panel data from Chinese A-share listed companies between 2010 and 2022. The results show a significant negative effect of organizational resilience on cost leadership strategy. This reveals a possible “dark side” of resilience. Mechanism analysis shows that highly resilient firms rely less on strategic alliances. This limits their access to external cost-optimizing resources and makes it harder to maintain cost leadership. Further analysis divides alliances into equity-based and contractual forms. Resilience significantly reduces participation in equity-based alliances, which help create cost advantages through capital investment, economies of scale, and resource sharing. In contrast, resilience has no significant effect on contractual alliances, which have limited potential for deep resource integration. Instead, resilient firms focus on internal redundancy and adaptive capacity. While this improves risk resistance, it can reduce resource efficiency and increase operating costs.
The study further uses a multi-level contingency framework to explore boundary conditions. At the micro level, managerial myopia strengthens the negative effect of resilience on cost leadership, as short-term oriented managers avoid long-term cost control investments. At the meso level, strategic orientation plays a key moderating role. Growth orientation weakens the negative effect by improving resource acquisition and legitimacy. Profit orientation strengthens it by increasing cost pressures and resource hoarding. At the macro level, market competition weakens the negative effect of resilience on alliance formation. In competitive markets, resilient firms are more likely to form alliances for survival.
This study makes three contributions. First, it challenges the idea that resilience is always beneficial by showing its trade-offs with cost leadership. Second, it extends the RBV by examining both the presence and type of strategic alliances as mediators. This shows that even strong firms may forgo alliance-based cost advantages. Third, it enriches the contingency view by showing how managerial cognition, strategic priorities, and market dynamics together shape the effect of resilience strategies.
The findings have practical implications. Firms should avoid over-investing in resilience without considering cost efficiency. They should adopt alliance strategies suited to their resilience level and match resilience investments to their strategic orientation. In competitive markets, balancing internal flexibility with external cooperation is essential for staying competitive without losing efficiency. This study offers a nuanced view of resilience by revealing its trade-offs, boundary conditions, and governance implications, and lays a foundation for future research on how firms can achieve both resilience and efficiency.

With the rapid development of information network technology, enterprises face growing uncertainty in the digital economic environment. To address this challenge, enterprises' innovation networks are expanding outward, creating an urgent need to collaborate with other actors in these networks to establish value co-creation mechanisms. Enterprises, as the micro-foundation of economic operation, are the key subjects of digital innovation. However, for traditional enterprises, there may be many barriers to digital innovation. Therefore, many enterprises form digital innovation networks by cooperating with other enterprises, particularly their digital service providers. They integrate digital innovation resources by embedding in digital innovation networks and accelerate the construction of new value creation models. This phenomenon is defined as digital innovation network embeddedness. Digital innovation networks represent a collaborative ecosystem where different participants, including different network participating enterprises, come together to generate shared value using digital innovation resources. Traditional value co-creation theories emphasize dyadic relationships between enterprises and consumers. Yet, the rapid advancement of digital technology has expanded this concept of value co-creation from dyadic interactions to complex networks involving multiple participants. The scope of value co-creation has thus broadened from firm-consumer interactions to inter-firm collaborations. As a result, digital service providers and client enterprises actively exchange and integrate resources, enabling value co-creation among enterprises embedded in digital innovation networks. Simultaneously, the widespread application of digital technologies has raised concerns about corporate digital responsibility (CDR), such as data privacy and algorithmic ethics. CDR emphasizes the fair and ethical use of digital technologies by organizations when engaging with stakeholders in the digital ecosystem. However, research on CDR remains in its nascent stage, with existing literature primarily comprising conceptual qualitative studies and lacking quantitative investigations into how CDR influences corporate development.
Following social network theory, this study divides digital innovation network embeddedness into two dimensions: digital innovation network structural embeddedness and digital innovation network relationship embeddedness. A theoretical framework is constructed to explore how digital innovation network embeddedness facilitates enterprise value co-creation, with CDR introduced as mediating variables to reveal the new mechanisms for multi-stakeholder value co-creation. Through hierarchical regression and Bootstrap test on 317 sets of corporate questionnaire data, the research findings are as follows. First, digital innovation network structural embeddedness and digital innovation network relationship embeddedness not only directly promote value co-creation but also indirectly enhance it by elevating CDR. Digital innovation network structural embeddedness expands firms' access to heterogeneous digital resources and data elements, strengthening their centrality within the network and thereby fostering resource exchange and risk-sharing. Digital innovation network relational embeddedness improves the transparency of tacit knowledge sharing and reduces collaboration risks through trust and reciprocity mechanisms. Second, CDR partially mediates the relationship between digital innovation network embeddedness and value co-creation. By fulfilling corporate digital responsibility and establishing ethical norms and trust mechanisms, enterprises attract more stakeholders to participate in resource integration and thus achieve value co-creation.
　This research contributes to the literature in the following ways. First, this study extends research on the impact mechanisms of digital innovation network embeddedness, and extends the focus from individual-firm performance to value co-creation among multiple stakeholders. Second, this study deepens the micro-foundations of value co-creation under service-dominant logic and elucidates the mechanisms through which actors embed themselves in digital innovation networks. Third, this study enriches empirical research on CDR and verifies its critical mediating role in how digital innovation network embeddedness empowers value co-creation.At the practical level, this study offers the following insights for enterprises. Enterprises should actively embed themselves in digital innovation networks, occupying structural holes and strengthening partnerships to access non-redundant resources and emotional support. Furthermore, they should prioritize fulfilling CDR and enhance reputation and trust through transparent digital practices.

In recent years, global climate change has become increasingly severe, with China actively assuming major-country responsibilities as one of the principal contributors to worldwide carbon reduction. Under the "Dual Carbon Goals" framework, the government vigorously promotes green transformation in the manufacturing sector, advances new industrialization, and cultivates innovative green development models. To achieve win-win outcomes in both economic and environmental performance, manufacturing enterprises have begun experimenting with business model innovations (BMI), such as servitization transformation and circular economy practices. Given its strategic and systemic nature, BMI not only provides manufacturing enterprises with more adaptable production methods and operational patterns but also serves as a potential breakthrough in driving green development and reducing carbon emissions through reconstructing value creation and delivery processes.
Existing studies predominantly focus on the economic impacts of BMI through questionnaires or case studies, while rarely investigating its role in curbing corporate carbon emissions from an enterprise perspective. However, BMI can optimize resource allocation and reduce resource consumption during value creation and delivery processes, making it crucial to explore whether BMI effectively inhibits corporate carbon emission intensity. This study addresses three core questions: (1) Through what pathways does BMI influence carbon emission intensity? (2) How does external environmental regulation affect the relationship between BMI and carbon emission intensity? To answer these questions, the study conducts empirical research using data from Chinese listed manufacturing enterprises between 2012 and 2022.
Grounded in Resource-Based View (RBV) and Dynamic Capability Theory (DCT), this study develops a composite BMI measurement system incorporating subjective and objective indicators using the entropy weight method. It further examines BMI's impact on carbon emission intensity, while investigating the mediating roles of green innovation and digital transformation, along with the moderating effect of local environmental regulation intensity. The findings demonstrate the following: (1) BMI significantly reduces corporate carbon emission intensity, and this conclusion remains valid after a series of robustness tests; (2) The emission-reduction effect is more pronounced in state-owned enterprises, heavily polluting industries, and large-scale firms; (3) Green innovation and digital transformation partially mediate the BMI-emission intensity relationship. Moreover, the mediation role of green innovation is primarily evident in high-quality innovation, namely the output of green invention patents; (4) The moderating effect of local environmental regulation intensity on the relationship between BMI and firm carbon emission intensity is context-dependent. This negative moderating effect is contingent upon firms’ resource endowments: while statistically insignificant in the full sample, it manifests significantly among small-sized firms or firms with low R&D investment.
In light of these findings, the study derives managerial and policy implications as follows. For businesses, it is essential to prioritize the role of business-model innovation on the road to sustainable development by integrating it into strategic dimensions such as carbon-emission governance. By building an innovation matrix of ecological value propositions—intelligent value creation— value delivery circulation, enterprises can achieve the co-evolution of economic and environmental benefits. For governments, the task is to leverage macro-regulatory capacity, balancing the stringency of rules with room for innovation, so as to accelerate corporate green transition and digital upgrading. Policies should be fine-tuned for specific targets through differentiated supervision, guiding enterprises to convert compliance pressure into long-term drivers of low-carbon transformation.
This study contributes theoretically in three dimensions: First, by employing the entropy weight method to measure corporate BMI, it enriches interdisciplinary research on BMI and carbon emission drivers at the micro level. Second, this study constructs the transmission chain of "business model innovation→green innovation/digital transformation→carbon emission intensity", and validates the critical mediating role of substantive green innovation. Third, regarding the moderating effect of local environmental regulation intensity, this study reveals its pronounced context-dependency. It highlights the central role of enterprises’ resource endowments within the regulatory mechanism triggered by external environmental pressures, thereby broadening the theoretical perspective for environmental institutional theory in the context of business model innovation.

Entity List sanctions serve as a pivotal tool for the United States in its crackdown on China. A notable instance was in April 2018 when the U.S. government imposed a technology embargo on ZTE, marking the beginning of a broader technology blockade against China. Under these sanctions,targeted enterprises face restrictions on purchasing U.S.-controlled technologies, products, and services, and even with license applications, they often face strict scrutiny and rejections, leading to operational instability and higher uncertainties. As economic globalization and specialization deepen, enterprises form close supply chain networks. However, the sanctions on midstream enterprises can trigger a chain reaction, transmitting risks to upstream and downstream enterprises, increasing supply chain risks, and weakening overall competitiveness. This situation raises the question of what strategies upstream and downstream enterprises should adopt to deal with the supply chain risks caused by the technology blockade.
From the perspective of supply chain, this paper examines the two-way spillover effect of midstream enterprises on the collaborative innovation of upstream and downstream enterprises after the U.S. technology blockade. It draws on the CNRDS database to compute patent and supply chain matching data to gauge enterprise collaborative innovation. After rigorous sample processing, 2 679 valid samples emerge, comprising 1 466 "year-midstream-upstream enterprise" matches and 1213 "year- midstream-downstream enterprise" matches. Subsequently, a benchmark model is constructed, followed by in-depth empirical analysis. In the mechanism analysis, the study respectively measures the operational risk of upstream and downstream enterprises based on the annual stock daily return volatility of these enterprises, and the higher the value, the greater the operational risk. It then adopts the proportion of the sum of the top 3 executive compensations to all executive compensations to measure managerial overconfidence, and the higher this value is, the higher the degree of managerial overconfidence.
The study finds that after the midstream enterprises are subject to the U.S. technology blockade, the supply chain spillover effect is generated, which not only promotes the collaborative innovation of upstream enterprises, but also promotes the collaborative innovation of downstream enterprises, thus highlighting the symmetry of the U.S. technology blockade supply chain spillover. Operational risk and managerial overconfidence are the internal impact mechanism of U.S. technology blockade on the collaborative innovation of upstream and downstream enterprises. The heterogeneity analysis shows that the spillover effect of the U.S. technology blockade on the supply chain is more obvious in the samples with closer supply chain distance, higher supply chain stability, lower supply chain concentration and higher market status.
According to the empirical results, the following countermeasures and suggestions are proposed respectively. For enterprises, on the one hand, when making collaborative innovation decisions, they not only need to assess the risks they face directly, but also need to comprehensively assess the potential threats in the entire supply chain network, especially the potential risks brought by other enterprises in the supply chain under the sanctions of the U.S. Entity List, so as to adjust the collaborative innovation strategy in a timely manner. On the other hand, enterprises should set up a full-time risk management department, strengthen the voice of independent directors and set up a special audit committee to conduct a third-party evaluation of managers ' major decisions, so as to curb managers ' overconfidence tendency and unblock the channels through which U.S. technology blockade affect the collaborative innovation of upstream and downstream enterprises. For the government, on the one hand, it should take into account the spillover effects on the supply chain caused by the U.S. technology blockade,and support not only the midstream enterprises that have been directly sanctioned but also provide appropriate resource allocation to the upstream and downstream enterprises within the supply chain, so as to maximize the effect of policy support by improving the overall competitiveness of the supply chain ; on the other hand, the government should tailor innovation support policies to different enterprises based on their unique circumstances, so as to enhance the level of enterprise collaborative innovation.

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 difference-in-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.

With generative artificial intelligence (AIGC) becoming deeply integrated into human life, people are increasingly unable to distinguish between AI-generated synthetic content and human-created material. China's Measures for the Identification of AI-Generated Synthetic Content (hereinafter referred to as the “Measures”) officially came into effect on September 1, 2025. In addition to specifying the identification obligations of providers of AI-generated synthetic services, the Measures also clarify the responsibilities of users of such content, online information dissemination service providers, and application distribution platforms. Through the “Exception to Explicit Identification” clause in Article 9, the Measures aim to strike a better balance between facilitating the use of generated content and maintaining oversight of the information ecosystem.
This study conducts an interpretive analysis of the regulatory cluster on identification obligations, including the "Measures", to address the urgent compliance needs arising from the current implementation of the "Measures". Moreover, it fills the gap in the current research regarding the lack of law and economics analysis, clarifies the functional limitations of the identification system in terms of efficiency, and expounds that the interpretation of identification obligations should follow the principle of "cost reduction and efficiency improvement". What's more, it reflects on some theoretical viewpoints that currently support the legitimacy of identification obligations and examines the interpretive limits of "the theory of product information disclosure obligation". Lastly, it conducts a detailed analysis of two interpretive approaches to the "Exception to Explicit Identification" in Article 9 of the "Measures", providing solutions for the implementation and optimization of the "Measures".
At present, there is an overly optimistic perception of the functional effectiveness of the identification system, leading to a tendency to expand the scope of supervision. However, the progress of substantive transparency in China's identification system remains limited. The identification of generated content is prone to problems such as devaluation of information value and weakening of the contributions of human co-creators, and undermines users' and platforms' willingness to comply with identification requirements. Meanwhile, the identification of generated content has functional limitations: it cannot replace judgments on information quality, authenticity, or copyright, and is easily tampered with, which affects the function of traceability supervision. From the cost-benefit perspective, if the social welfare brought by identification is limited by "formal transparency" and "watermark tampering", and is lower than social costs such as technology construction, value devaluation, and dispute resolution, the legitimacy of the system will be difficult to justify. In an environment of "prevalent non-compliance", high rigid penalties are not only ineffective but also incur high law enforcement costs.
To promote the long-term development of the identification system, it is necessary to establish a legal and economic analysis mindset, adhere to the principle of maximizing functional effectiveness, promote the transition to substantive transparency, and conduct substantive labeling of the extent and methods of AI participation. It is essential to reflect on the limitations of the product information disclosure theory and emphasize the collaborative fulfillment of identification obligations by multiple subjects. The principle of differentiated interpretation for cost control should be upheld: requirements should be adjusted according to differences in subjects and risk scenarios, and explicit identification can be reduced or canceled in low-risk scenarios. The identification obligations of platforms shall be supervised by administrative authorities, relying on the four-dimensional framework of "legal provisions - departmental regulations - national standards - platform rules"; the obligations of users shall be mainly supervised by platforms, which need to provide convenient tools and prompts, and establish a proportional sanction and appeal mechanism. The interpretation of Article 9 of the "Measures" needs to shift from the "supervision-oriented perspective" to the "development-oriented perspective". As a common exception to relevant provisions, explicit identification should only be mandatory in high-risk scenarios, while in other scenarios, it may not be mandatory and can be determined in accordance with user agreements.
Further efforts should be made to improve empirical research and quantitative analysis on the implementation effects of the AIGC identification system, refine the standards for AIGC risk classification, and improve the specific provisions of penalties such as administrative penalties for identification obligations applicable to platforms and users. In addition, the relationships between AIGC identification, copyright, and fair use of data need to be further clarified.

The promotion of carbon emissions reduction has become an urgent priority. Currently, it is essential to transform the economic growth model from the traditional, resource-intensive approach to a more intensive, sustainable one. This shift relies heavily on green innovation to enhance economic quality and global competitiveness. While studies have demonstrated that China's carbon market has played a positive role in reducing carbon emissions, the extent to which it can improve the green innovation efficiency of micro-enterprises remains an area that requires in-depth exploration.To address this question, the theoretical mechanisms through which carbon emissions trading policies impact a firm's green innovation efficiency are examined. On one hand, the carbon allowance system has increased the operating costs for enterprises. To reduce carbon emissions, enterprises must undergo a green transformation and sell their surplus carbon allowances to offset their R&D investments. On the other hand, enterprise behavior is also influenced by the reward and punishment mechanisms associated with carbon emissions trading. First, the pressure of carbon allowance has forced some high-energy-consuming enterprises to transform or abandon their operations due to financial pressure. Thus, some high-energy-consuming industries are gradually replaced by low-energy-consuming industries. Second, under the incentive policy, to obtain resources tilted by the Government, enterprises take the initiative to improve the quality of infrastructure service units through green innovation and realize the quality improvement of processes and products. Third, the reward and punishment mechanism not only help to release investment but also increases the mobility of production factors such as personnel and technology, which helps to optimize the allocation of resources and coordination capacity among industries. Therefore, carbon emissions trading can also enhance the enterprises' green innovation efficiency by optimizing the transformation of industrial structure.
This paper selects industrial enterprises listed on China's A-share market from 2006 to 2023 as research samples, with 7 596 observations from 422 listed companies. The green innovation efficiency of industrial enterprises is measured by the Super-SBM model, the carbon emissions trading pilot policy is taken as an exogenous shock variable, and a multi-period double-difference model is constructed to explore the impact of the carbon emissions trading pilot policy on the enterprises' green innovation efficiency. This paper selects industrial enterprises listed on China's A-share market from 2006 to 2023 as research samples, with 7 596 observations from 422 listed companies. The green innovation efficiency of industrial enterprises is measured by the Super-SBM model, the carbon emissions trading pilot policy is taken as an exogenous shock variable, and a multi-period double-difference model is constructed to explore the impact of the carbon emissions trading pilot policy on the enterprises' green innovation efficiency. Meanwhile, industrial structure transformation is introduced into the model as a mediating variable to further explore the impact mechanism. In addition, the heterogeneity of the impact is discussed.
The findings are presented as follows. First, the carbon emissions trading pilot policy significantly enhances enterprise green innovation efficiency, and this conclusion remains robust across a series of sensitivity analyses. Second, both industrial structure upgrading and rationalization play a positive mediating role in the relationship between the carbon emissions trading pilot policy and enterprise green innovation efficiency. Third, heterogeneity analysis indicates that the impact of carbon emissions trading on green innovation efficiency varies across different contexts. Specifically, the effectiveness of the policy is influenced by factors such as whether the firm is located in a key environmental city, and the ownership structure of the enterprise. These factors contribute to differentiated policy outcomes. Collectively, these findings provide empirical evidence to objectively assess the green effect of the carbon trading market.
To further advance the green and low-carbon transformation of enterprises, several key measures are proposed. First, it is essential to continuously improve the safeguard measures for enterprises during their green and low-carbon transformation. This includes enhancing market stability, expanding financing channels, and providing macro-level information and technical support.Second, regions should further optimize carbon allowance pricing mechanisms to reduce market uncertainties caused by price volatility. When formulating carbon allowance allocation systems, regions must balance the survival and development of enterprises with the effectiveness of carbon emission governance. Third, mechanisms for incentivizing and supervising the green and low-carbon transformation of non-state-owned enterprises should be strengthened to ensure their active participation and compliance.Finally, policies should be continuously refined to promote industrial structure transformation, thereby enhancing the overall efficiency and sustainability of green innovation.

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 factors 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 (fsQCA), 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 FsQCA 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. Through training on vast amounts of data, ANNs are capable of capturing non-linear relationships between variables, thereby enabling the prediction of future trends and outcomes.This combination not only enhances the depth of causal analysis, but also improves prediction 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 transformation being the core condition for achieving high-level knowledge management model innovation.
The contributions of this study are as follows: First, 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. Second, 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 exploration of human-machine collaboration in existing research.

In the era of "big science", the scientific research paradigm is undergoing a profound transformation from decentralization to concentration, making scientific teams the core unit for addressing complex challenges and achieving major innovations. In response to the significant demand of China's "Organized Research Paradigm" strategy for building stable and highly efficient teams, systematically reviewing and integrating the vast body of global research on scientific team structure and innovation effectiveness, and clarifying its developmental trajectory and core themes, it is not only theoretically urgent but also a crucial prerequisite for providing a solid academic foundation for national strategic practice.
To this end, this study utilizes bibliometric tools to analyze 4 541 key publications from both domestic and international sources between 2000 and 2024, systematically delineating the development process of global research on scientific teams. The analysis indicates that this field has formed a global research landscape led and dominated by European and American scholars. International research started early, possesses a mature system, and continues to deepen its thematic focus, showing a clear evolution from surface-level characteristics to underlying mechanisms. In contrast, although China has experienced exponential growth since 2015 and become a significant research force, Chinese literature still shows noticeable gaps compared to the international frontier in terms of the systematic nature and depth of topics, as well as journal support. Overall, Chinese research exhibits certain characteristics of lag and fragmentation.
Further focusing on the analytical framework of "team identification - structural elements - innovation effectiveness", the study sorts out and reveals research hotspots and findings. In team identification research, the approach has evolved from relying on entity organization lists to using co-authorship networks for identification, forming two main identification paths: entity teams and virtual teams. The former offers reliable information but is difficult to obtain, while the latter is scalable and efficient but susceptible to interference from temporary collaborations. Regarding structural elements, research has expanded from explicit structural elements to implicit structural elements and gradually developed complex model relationships. For instance, implicit elements often act as key mediating variables, regulating the complex mechanism through which diversity affects innovation effectiveness. In terms of innovation effectiveness, research has constructed differentiated evaluation systems: academic teams focus on academic impact, while corporate teams prioritize market value. However, both types of evaluation face the common challenge that traditional quantitative indicators struggle to comprehensively measure the comprehensive contribution of teams in major scientific and technological breakthroughs. This systematic review reveals the development trajectory and core bottlenecks in research methods and theories within this field.
Finally, responding to the demands of China's "organized research paradigm" for stable and highly efficient scientific teams, future research should focus on the following areas: Regarding the focus of research objects, attention should shift from temporary virtual teams to teams with stronger entity attributes, such as those driven by academic leaders or research platforms, to align with the requirement for long-term, stable collaboration in major scientific and technological challenges. Concerning the optimization of structural elements, it is necessary to break through the superficial analysis of existing explicit structures by introducing topological and complex network methods, while simultaneously overcoming the quantitative bottlenecks of implicit structural elements to achieve the transformation from abstract concepts to operable parameters. Pertaining to the evaluation of innovation effectiveness, a multi-dimensional evaluation system aligned with strategic orientations should be constructed, incorporating strategic outputs like national science and technology awards and major engineering breakthroughs into core indicators, and establishing models linking resource input to innovation effectiveness. This will provide solid theoretical support and practical guidance for forging scientific teams capable of undertaking major national missions.
The significance of this study lies in its systematic review of global research hotspots and evolving trends in scientific teams. Building on this foundation, it addresses the new requirements for team building under China's " Organized Research Paradigm" strategy, integrating cutting-edge international topics with the national strategic context. This research pinpoints critical directions for future investigation,including research subjects, structural elements, and innovation effectiveness,thereby offering targeted insights for constructing a theoretical framework to support scientific teams tasked with major national S&T missions.

In the current global economic landscape, there has been a shift in the factors that drive enterprise development. Now, technology and innovation are becoming the core elements that drive enterprise competitiveness and sustainable development. SRUI(specialized,refined ,unique and innovative) enterprises focusing on innovation have injected new vitality into national innovation through their flexible market advantages, relentless pursuit of efficiency, and broad scope in terms of number and distribution. However, in the face of increasingly fierce international and regional trade competition, enterprises can gain competitive advantages in the highly competitive market environment only by focusing on a certain market segment. At the same time, SMEs (small and medium-sized enterprises) are faced with difficulties, such as high investment and low return, as well as limited resources. In order to disperse risks, enterprises often adopt diversified development strategies. This makes it difficult for SRUI enterprises to maintain their core competitive advantages in a particular segment. Since introducing the "SRUI" concept, regions have enacted policies to foster SME evolution into SRUI entities, aiming to boost quality growth. The effectiveness of these policies in enhancing SRUI innovation and transformation has become a key concern.
While existing research focuses on the key role of policy support in enhancing the innovation capability of SRUI enterprises, the specific mechanism by which policies can lead firms to focus on SRUI strategies and thus enhance innovation performance remains to be verified. At the same time, the SRUI enterprises’ policy environment is multi-level, and considering only a single policy does not fully capture their overall impact on innovation performance. Therefore, this study investigates the impact of different dimensions of SRUI policies (policy effectiveness, policy quantity, and complexity of policy combinations) on innovation performance, and reveals the mediating role of enterprises’ SRUI strategies. The heterogeneity tests are conducted based on regional distribution and enterprise type. There are 541 SRUI “little giant” enterprises in China selected as the research sample, covering the period from 2013 to 2023. The list of SRUI “little giants” enterprises comes from the WIND database, the enterprise patent data is sourced from the Patsnap database, the data related to policy measures is sourced from the PKULaw database, and the control variables are sourced from the CSMAR database. Considering the lag effect of policy measures, the policy-related variables are measured using a three-year rolling window period.
It is concluded that the policy effectiveness, policy quantity, and complexity of policy combinations have a positive impact on enterprises’ innovation performance; SRUI strategy plays a significant mediating role between SRUI policy and innovation performance. Further analysis shows that the promotion effect of SRUI policy is more significant in the eastern region, as well as among non-state-owned enterprises.
Drawing from the above findings,this study proposes some managerial insights. (1) The government should fully leverage SRUI policies to incentivize enterprise innovation and foster a conducive policy environment. It needs to tailor detailed, targeted measures to SRUI enterprises, while ensuring policy coherence and synergy to minimize overlap and conflict, thereby maximizing the policy’s impact. (2) Taking into account the type of property rights of enterprises and regional characteristics, the government should accurately grasp the local resource structure, industrial base and enterprise characteristics, and then formulate diversified support policies. (3) The government must rigorously oversee the review process and performance evaluation to guarantee the efficient execution of the policy, thereby facilitating the implementation of the SRUI enterprises’ strategy.
The research contributions of this paper are mainly reflected in three aspects. Firstly, it offers a more comprehensive and nuanced view of policy effects from three distinct dimensions, thereby enriching the research framework that examines the interplay between macro-policies and micro-enterprise innovation behaviors. Secondly, the study introduces the SRUI strategy, elucidating the specific mechanisms through which policies influence innovation performance and enhancing the analytical framework for understanding SRUI policy impacts. Lastly, it reveals that enterprises of different types or regions exhibit varied responses to policy initiatives. Consequently, when devising policies, the government should consider the specific property rights and geographical traits of businesses to support their development more effectively and in a targeted manner.

With the vigorous rise of artificial intelligence and other emerging technologies, the role of digital-intelligence transformation in promoting the new industrialization process is becoming more and more significant. With its high precision, high efficiency and relatively low cost, artificial intelligence technology has brought unprecedented improvement to production efficiency and product quality of enterprises. While the technological revolution has brought numerous benefits, it has also quietly transformed the labor market, particularly impacting traditional manufacturing jobs. In the manufacturing sector, workers have long relied on mechanized operations, with their tasks often centered on repetitive production processes. These roles typically require minimal application of intuition and innovation. This nature of the job makes manufacturing workers especially vulnerable to the impact of AI technology. As automation and intelligent equipment become more widespread, many production processes that once required manual labor are gradually being replaced by machines. This shift not only threatens the job security of manufacturing workers but also poses severe challenges to their career development paths.
More profoundly, the popularization of artificial intelligence technology may also have a negative impact on the psychology of manufacturing workers. In the face of the impact of technology, many workers may feel at a loss, which leads to negative emotions such as occupational anxiety and decreased self-efficacy, and adversely affects their work performance. However, it is worth noting that manufacturing workers possess a unique quality that AI cannot replace: craftsmanship. The spirit of craftsmanship is a professional attitude of pursuing excellence, which is the core element of quality improvement and innovation in the manufacturing industry. In the era of artificial intelligence, how to cultivate and stimulate the craftsmanship has become an important topic for manufacturing enterprises to achieve high-quality development.While existing research has largely concentrated on the impact of leadership styles and individual employee factors on craftsmanship, it has largely overlooked the role of technological disruption awareness. As a measure of employees' perception and response to technological change, technological disruption awareness significantly influences their work attitudes and behaviors. Although technological disruption awareness is often regarded as a negative factor in many studies—potentially leading to career anxiety and turnover intentions—some studies suggest that it may also have a positive impact on stimulating employees to improve their skills and work performance. Therefore, this study introduces promotion-prevention focus as the mediating variable to explore the dual influence paths of technological disruption awareness on the craftsmanship.
The study employs an online questionnaire(N=474 manufacturing workers)with subsequent data analysis and hypothesis testing conducted via SPSS, STATA, and Amos. The results show that the technological disruption awareness has a significant effect on the craftsmanship of manufacturing workers; the promotion focus plays a partial mediating role in the positive relationship between technological disruption awareness and craftsmanship. There is a U-shaped relationship between the prevention focus and the technological disruption awareness. Digital leadership plays a positive moderating role in the relationship between technological disruption awareness,prevention focus and manufacturing workers' craftsmanship.
The findings of this study enrich and deepen the research in related fields, and have certain theoretical innovation and practical significance. First of all, this paper discusses the positive impact of technological disruption awareness on craftsmanship, which makes up for the lack of previous studies focusing on the negative impact of technological disruption awareness and provides a new perspective for manufacturing enterprises to effectively cultivate craftsmanship in the process of digital transformation. Secondly, drawing on the theory of regulatory focus, this paper explores the dual action pathways of promotion focus and prevention focus. It particularly proposes a U-shaped relationship between prevention focus and craftsmanship, thereby broadening the research perspective on prevention focus.Finally, this paper discusses the moderating role of digital leadership, which provides a theoretical reference for manufacturing enterprises to play the role of leadership and stimulate the spirit of craftsmanship in the process of digital transformation.

In the era of the digital economy, data has evolved from an auxiliary information resource into a core production factor driving technological innovation and sustainable development. Understanding how data factor empowers firms to achieve balanced exploitative and exploratory innovation,thus enhancing their ambidextrous innovation capability,has become an essential question in both academic research and policy practice. Existing research primarily examines corporate innovation through the lenses of digital economy, digital transformation, and digital technology application, spanning macro, industrial, and digital capability levels. Recent studies have begun to explore the nexus between data factor and corporate innovation, highlighting their promotional effects, potential risks, and relevant theoretical models. However, significant deficiencies persist regarding mechanisms, contextual factors, and theoretical integration. Specifically, research on data factor and corporate ambidextrous innovation exhibits three major gaps: first, a lack of systematic characterization based on micro-level firm data; second, insufficient elaboration on how data factor embeds within the innovation process and exert differential impacts; and third, inadequate empirical testing that incorporates external environmental dynamics and firm heterogeneity, thereby limiting the generalizability of conclusions and practical implications.
Thus, this study aims to systematically explore the mechanisms and boundary conditions through which the application of data factor influences firms' innovation performance in both exploitative and exploratory dimensions. Grounded in the resource-based view and innovation management theory, this paper constructs a dual-path framework of “stock resource activation” and “incremental resource integration”. It argues that data factor application can stimulate exploitative innovation by identifying and activating slack internal resources, while promoting exploratory innovation by strengthening external R&D collaboration networks. To empirically test these mechanisms, a two-way fixed effects model is established using panel data from 3 076 Chinese A-share listed firms between 2008 and 2023, yielding 29 900 firm-year observations. The level of data factor application is measured through text mining of corporate annual reports across four dimensions: data stock, data development capability, data-driven business applications, and data monetization. Exploitative and exploratory innovation are distinguished by patent classification continuity, with slack resources and R&D collaboration as mediating variables.
Empirical results show that the application of data factor significantly promotes both exploitative and exploratory innovation, with a stronger effect on exploitative innovation, suggesting that data utilization primarily enhances incremental rather than radical innovation. Mechanism analysis confirms two distinct transmission paths: data factor application reduces slack resources by improving resource recognition, allocation, and utilization efficiency, thereby enhancing exploitative innovation; simultaneously, it facilitates exploratory innovation by optimizing partner identification, collaboration modes, and knowledge integration efficiency, underscoring the role of data in enabling heterogeneous resource synergy. Heterogeneity analysis shows that these effects are more pronounced under favorable institutional and regional conditions. Specifically, the establishment of national big data pilot zones and the economic advantages of eastern China significantly strengthen the positive relationship between data factor application and dual innovation outcomes.
This study makes three primary contributions. First, it advances micro-level research on data factor by integrating them into the analysis of firm-level innovation behavior, extending the theoretical frontier of data economy studies. Second, it deepens the understanding of how data-driven processes reshape firms' resource allocation mechanisms, providing empirical evidence that data applications can transform slack resources into innovation assets and foster cross-organizational knowledge recombination. Third, it identifies the heterogeneous effects of institutional environments and regional development levels, offering new insights into how data infrastructure and policy design can enhance firms' innovation capability.
From a managerial perspective, the findings suggest that firms should strengthen their data management and analytics capabilities to embed data-driven decision-making throughout innovation activities. For exploitative innovation, data can be used to optimize internal processes and accelerate incremental improvement; for exploratory innovation, big data and collaborative platforms can help identify emerging opportunities and reduce innovation uncertainty. Policymakers are advised to accelerate data market reforms, promote cross-regional data circulation, and build supportive institutional frameworks,especially in underdeveloped areas,to ensure equitable access to data-driven innovation opportunities.

Since China's dual-carbon goals were put forword, some regions have adopted radical and uniform carbon reduction measures without considering their economic development, leading to energy supply-demand imbalances and hindering the goal's orderly advancement.These actions have impacted economic and social development, creating a false trade-off between carbon goals and progress.National climate goals rely on regional actions, but regional inequality and heterogeneity can lead to increased inequality and economic losses if carbon reduction is not tailored to local conditions.The academic consensus is that there's an "inverted U-shape" trend in carbon emissions with economic resilience, but there's a need to explore regional differences and influencing factors.The paper examines the relationship between dual-carbon policies and economic resilience across China's 30 provinces.Using fuzzy set qualitative comparative analysis(fsQCA), it explores the grouped impact and development paths of these policies, aiming to provide practical solutions for classified carbon reduction actions and enhance economic resilience while reducing carbon emissions.
This study examines the pathways of carbon reduction at the provincial and municipal levels, focusing on the roles of science and technology, government capacity, and energy structure as key drivers.It decomposes the measurement indicators and takes 30 provincial administrative regions in China as the research object,and excludes Hong Kong, Macao, Taiwan, and Tibet due to data limitations.Then it employs the fuzzy set qualitative comparative analysis(fsQCA) method, leveraging panel data from statistical yearbooks, databases, and published research to assess the impact of science and technology.It further applies comparative analysis to explore the collective effects of these drivers on regional economic resilience.To ensure the robustness of the findings, the study incorporates robustness checks, case replications, and the distillation and generalization of path characteristics, aiming to validate the realism and theoretical validity of the results.
The results show that the types of high economic resilience in China's provincial-level administrative regions are generally categorized into the following four types:“industrial-type high-energy-consumption driven mode(group state 1)”, “non-energy-dependent high-carbon emission driven mode(group state 2) ”, “integrated multi-industry driving mode(group state 3)”, “low-carbonization high-tech industry driving mode(group state 4)”, the four major groups of economic resilience are composed of a variety of state paths, the characteristics and laws are different, of which The transition from group state 1 to group state 3 to group state 4 reflects the carbon-reducing characteristics of science and technology-enabled industries—financial level enhancement—reducing the dependence on traditional industries, presenting a “paroxysmal” process; the evolution from group state 2 to group state 4 is a “smooth” process, focusing on the enhancement of scientific and technological financial inputs.The evolution from configuration 2 to configuration 4 is a “smooth” process, focusing on the evolution logic of enhancing financial investment in science and technology, developing high-tech industries, and reducing carbon emissions from green industries.Both evolution patterns follow the two-factor quality enhancement strategy of “scientific and technological input—carbon reduction”.Therefore, strengthening the multi-principal scientific and technological investment of “government and society” can promote the balanced development of economic resilience and quality enhancement at the provincial level, which is also conducive to the realization of the carbon neutrality goal.
The innovation of this paper is that it reveals the configuration classification and path of economic resilience enhancement against the backdrop of China's provincial carbon reduction, highlights the synergistic nature of science and technology, government, and carbon neutrality, and makes a practical and systematic extension of the research in this field.The empirical analysis based on provincial panel data provides more objective and realistic guidance and significance for the study of China's dual-carbon goals and synergistic development strategies.To enhance economic resilience under the dual carbon goals, local governments should increase financial and tech investments, focusing on scientific and technological development to foster economic growth.They should align with the '30·60' decarbonization goal, prioritize ecological and green economy development, and avoid reckless carbon neutrality actions.High-energy-consuming regions should leverage government capacity to achieve low-carbon green economic development through financial subsidies and transfer payments.

In the context of rapid advancements in big data technologies, cloud computing capabilities, and artificial intelligence, data has emerged as a pivotal engine driving economic and social progress. Data, as an emergent factor of production, is categorized as an intangible asset. Unlike physical goods, it lacks tangible form and clear boundaries of holding. As data's value grows exponentially across various sectors, its intangible nature allows for instantaneous and limitless replication and distribution in the digital realm. This capability complicates the original creators' or proprietors' ability to govern the spread and application of their data.Consequently, it amplifies the challenge of establishing and maintaining clear ownership rights over data, and the issue has become a major bottleneck, hindering the full potential of data. Traditional property rights theories, while extensively studied, struggle to adequately address the unique challenges posed by data’s intangible, replicable, and easily distributable nature.
To address these complexities, this paper proposes a novel data ownership solution rooted in the concepts of holding and access rights. Drawing from both theoretical frameworks and practical considerations, the study endeavors to bridge the gap between the evolving digital economy and existing legal constructs. The abandonment of the static notion of ownership in favor of a more dynamic understanding of usufruct forms the cornerstone of this approach. By focusing on the rights of data holders and the naturally extended access rights, the paper outlines a framework that not only recognizes the multifaceted nature of data usage but also fosters efficient and equitable data circulation.
The methodology employed in this study involves a thorough examination of existing academic and theoretical explorations within China’s scholarly community, categorizing them under the “subject-object dichotomy” paradigm. On the object regulation front, it evaluates schemes rooted in traditional property rights theories alongside those aimed at balancing interest distribution and rights protection. Conversely, subject regulation focuses on behavior-based ownership recognition, drawing heavily from regulatory frameworks such as the Personal Information Protection Law, Cybersecurity Law, and Data Security Law.
Key findings highlight the inadequacy of relying solely on ownership as a means of defining data rights, given its intangible and easily replicable attributes. Instead, the paper advocates for a structured approach that prioritizes data holding rights and the accompanying access rights. The proposed solution emphasizes “factual control” as the basis for determining rights over data, ensuring that various stakeholders share these rights equitably and efficiently. The principles of fairness and efficiency guide the allocation of data rights, promoting both individual incentives and societal welfare.
Furthermore, the paper delves into the personalized rules governing data access, aligning access rights with the distribution status of data resources. Establishing data access rules involves ensuring legal and ethical compliance, protecting rights, and maintaining market fairness. Transparency in data resource allocation and access rules is crucial, guaranteeing data subjects' information rights. Rules must allow secure and private data access for subjects and necessary technical means and management measures must be taken to ensure the security and confidentiality of data during transmission, storage, and use. Security measures and risk controls are mandatory to prevent data breaches and misuse. By categorizing data based on its scarcity, importance, and innovativeness, the framework facilitates setting of varying access levels and permissions. This approach not only safeguards data privacy and security but also promotes data liquidity and efficient utilization.
The innovation of this paper lies in its departure from traditional ownership-centric frameworks and the development of a usage-based rights model tailored to the unique characteristics of data. By embracing the concepts of holding and access rights, the proposed solution aims to unlock the full potential of data as a strategic asset in the digital economy. It offers a practical and adaptive legal framework that can evolve alongside technological advancements, ensuring that data continues to drive innovation, economic growth, and societal progress.

Artificial intelligence has been deeply embedded in the network of social operations, serving as the underlying architecture and infrastructure of the digital society. Academia has conducted in-depth typological analyses of data risks in generative artificial intelligence from various perspectives and methodologies, including the life cycle perspective, rights theory perspective, and data security perspective, and has proposed corresponding measures to address these data risks. However, a common limitation of the aforementioned life cycle perspective, rights theory perspective, and data security perspective lies in the disconnection between theoretical logic and practical dilemmas, specifically, they fail to fully recognize the unique characteristics of data risks in generative AI and systematically examine the complex feedback mechanism between training data and generated content. Consequently, these perspectives are trapped in an internal perspective of data risk governance, neglecting the spillover effects, transmissibility, and interconnection inherent to generative AI data risks themselves. Overall, academic analyses of generative AI data risks have long been confined to the cognitive limitation of "centering on governance objects",that is, overemphasizing the attribute characteristics of "data" and the morphological manifestations of "data risks" while ignoring the core attributes and process mechanisms of "governance" itself. This has rendered existing analytical approaches and models unable to respond to the complexity of governance practices, ultimately reducing governance schemes to mere "paper compliance" that cannot be effectively implemented. Therefore, it is necessary to take governance theory as the starting point, analyze the practical dilemmas in generative artificial intelligence data risk governance, and propose targeted optimization schemes for such governance.
Professor Gerry Stoker has further refined a "governance" theory that can provide an organizational framework. "Governance as theory" emphasizes four key aspects: the clarity of governance goals, the synergy of governance subjects, the completeness of governance basis, and the flexibility of governance means. The governance dilemmas in generative AI data risks can thus be categorized into four dimensions: ambiguity of governance goals, fragmentation of governance subjects, absence of governance basis, and rigidity of governance means. Accordingly, the governance dilemmas in generative artificial intelligence data risks can thus be categorized into four major types: the dilemma of misaligned governance goals, which includes difficulties in balancing security and development, reconciling national and corporate goals, and aligning short-term and long-term objectives; the dilemma of fragmented governance subjects, which involves complex games among public authorities, the binary opposition between the state and enterprises, and international competition for rule-making power; the absence of governance basis, which covers the lack of laws and regulations, industry standards, and ethical norms; and the rigidity of governance means, which includes the path dependence on traditional administrative tools, institutional obstacles to technological empowerment, and the breakdown of synergy among multiple tools.
In response, system synergy, dynamic adaptation, risk communication, and multi-stakeholder co-governance shall serve as core concepts. Goal calibration is to be achieved through a risk-classified dynamic balance mechanism, a synergistic coupling mechanism of government-enterprise interests, and a temporal cohesive mechanism for short-term and long-term goals; subject synergy is to be realized through a collaborative linkage mechanism among public authorities, a co-governance operation mechanism for government-enterprise symbiosis, and a collaborative mechanism for international rule-making; the governance basis is to be improved through a hierarchical legislative legal guarantee mechanism, a multi-stakeholder co-governance standard-setting mechanism, and an institutionally embedded ethical constraint mechanism; and governance means are to be innovated through a precision-oriented reform mechanism for administrative tools, an institutional activation mechanism for technological tools, and a synergistic coupling mechanism for multiple tools. Furthermore, through in-depth coupling of governance goals, subjects, foundations, and means, as well as systematic reconstruction of interrelated mechanisms, continuous innovation in the generative artificial intelligence data risk governance system and a qualitative leap in governance mechanisms can be ultimately promoted, laying a solid foundation for the healthy and sustainable development of generative artificial intelligence.

The integration capability of complex product systems constitutes the core competence of system integrators, playing a pivotal role in breakthroughs in critical core technologies and the development of complex products. Unlike the technology catch-up patterns observed in mass-produced goods, complex product systems exhibit extended technology lifecycles and greater learning difficulties. Although significant research has examined paradigm shifts in systems thinking, technological adaptation, innovation ecosystems, resource orchestration, and organizational models within this context, less attention has been paid to the formation process and mechanisms of integrators′ system integration capabilities—an area that warrants further theoretical investigation.
This study adopts complex product system theory to conduct longitudinal exploratory case study on Cell-Bridge Biotech, examining the enterprise's technological substitution pathway in high-end equipment manufacturing for CGT upstream devices from 2011 to 2023, thereby extracting the enhancement mechanisms of complex product system integration capabilities during domestic substitution. Specifically, sustained technological breakthrough provides foundational support for domestic substitution. Through upgrading three dimensions of integration capabilities—technological integration, product integration, and system integration—the case enterprise achieved progressive technological substitution, critical module substitution, and ultimately full-process substitution, demonstrating an evolutionary trajectory characterized by gradualism. Technological integration capability refers to the ability to translate underlying technologies into tangible products by organically combining distinct technical components, systems, or software to achieve specific technological objectives. This capability emphasizes the process of technology development and transformation, typically resulting in the conversion of technological principles into laboratory products (product prototypes). At this stage, products are mostly in the internal testing phase, meeting basic requirements for consistency and stability. Product integration capability refers to the ability to rapidly and efficiently develop new products and bring them to market. This capability emphasizes swift responsiveness to market demands, iterative product refinement, and accelerated delivery to meet differentiated customer needs and maintain competitive advantage in fiercely contested markets. This process involves creating rapid prototypes, enabling user participation in evaluation to identify issues early and make adjustments, followed by iterative product development based on feedback. System integration capability refers to the ability to consolidate different information technology systems, hardware, and software components into a coordinated system or network, delivering comprehensive solutions to customers. This typically involves applying integrated technologies to unify disparate sub-product systems, applications, and data repositories, supporting business processes and requirements.Furthermore, the research reveals that the construction of complex product system integration capabilities emerges from the interplay between technological logic and market logic. The technological logic encompasses decomposition and linkage mechanisms, describing the interconnections among components, modules, or systems, while the market logic includes mechanisms of current market demand alignment and future demand traction, reflecting the market responsiveness at different stages of complex product development.
This study constructs a process model for enhancing the integration capability of complex product systems in domestic substitution. Theoretically, it constructs a dynamic process model for enhancing complex product system integration capabilities during domestic substitution, unveiling the mechanisms and pathways of critical core technology substitution from the complex product system perspective, thereby extending existing theories on both technology domestic substitution and complex product systems. Additionally, it proposes dual interaction logics—technological and market logics—for capability enhancement during substitution, enriching theoretical frameworks concerning complex product system development. Practically, the findings provide guidance for latecomer enterprises in critical technology domains to achieve technological leapfrogging. Continuous improvement of autonomous innovation capabilities through long-term fundamental technology breakthroughs is essential to reduce external dependence on core technologies, thereby establishing the technological foundation for enhancing system integration capabilities and realizing domestic substitution. Simultaneously, complex product system development must consider not only technological dimensions but also ensure alignment between technological and market dimensions.

In the rapidly evolving digital economy, data assets have emerged as a pivotal strategic resource, driving corporate transformation and playing an increasingly vital role in creating enterprise value. As businesses grow more reliant on data-driven decision-making, the disclosure of data assets has become a key factor influencing corporate performance and market competitiveness. Existing literature has explored either the direct impact of data asset disclosure on enterprise value or its indirect role through the lens of cost of equity capital in enhancing capital market efficiency in resource allocation. However, there remains a lack of systematic analysis regarding the underlying mechanisms through which data asset disclosure drives enterprise value creation. Most studies have yet to unpack the "black box" linking disclosure to enterprise value, failing to identify the specific pathways through which such disclosures exert their influence or the contextual conditions under which these effects are more pronounced.
To address this gap, this study conducts a comprehensive empirical analysis to investigate the impact of data asset disclosure on corporate value creation. The sample consists of 27 141 firm-year observations of Chinese A-share listed companies from 2008 to 2023, primarily drawn from the CSMAR database and firms′ annual financial reports. The dependent variable is Tobin′s Q, used to measure enterprise value creation, as it captures both current market valuation and expectations of future growth, and offers superior data availability and comparability in the context of China′s capital markets. Data asset disclosure constructed from firms′ annual reports is selected as the key explanatory variable.
The empirical findings yield several key conclusions. First, data asset disclosure exerts a statistically significant and economically meaningful positive effect on corporate value creation. This suggests that firms proactively disclosing their data assets are better positioned to unlock value, as such disclosures enhance transparency and signal the firm′s capacity to leverage data for competitive advantage. Second, a detailed mechanism analysis identifies two primary pathways: (1) improving surplus levels by optimizing operational efficiency and profitability via the strategic use of data assets; and (2) reducing the cost of equity capital, as increased transparency mitigates information asymmetry and lowers investor risk perceptions. These findings underscore the dual financial benefits of data asset disclosure, reinforcing its importance in corporate strategy. Furthermore, heterogeneity analysis reveals that the positive impact of data asset disclosure is not uniform across firms but varies with contextual factors. Specifically, the value-enhancing effect is more pronounced in firms operating in highly competitive industries, where data-driven insights provide a critical edge in sustaining market positions. Additionally, the benefits are stronger for firms located in regions with higher data marketization, where institutional support and infrastructure facilitate the effective utilization of data assets. Finally, firms with robust internal governance structures exhibit a more significant valuation premium from data asset disclosure, suggesting that strong governance amplifies the strategic benefits of data transparency.
This study makes several important contributions to theory and practice. Theoretically, it advances understanding of data asset disclosure as a strategic tool in the digital economy, highlighting its role in shaping enterprise value through financial and market-based channels. Practically, the findings offer actionable insights for corporate managers seeking to optimize disclosure strategies, emphasizing the importance of data asset transparency in enhancing competitiveness. Moreover, the study provides policymakers with empirical evidence supporting the promotion of data element marketization, suggesting that regulatory frameworks encouraging data disclosure can foster more efficient capital allocation and economic growth.
In summary, this study underscores the critical role of data asset disclosure in the digital era and provides a foundation for future studies exploring the intersection of data strategy, corporate finance, and market dynamics. Its implications extend beyond the Chinese context, offering valuable lessons for firms and regulators worldwide navigating the challenges and opportunities of the data-driven economy.

Artificial intelligence (AI) has emerged as a pivotal driving force for scientific and technological breakthroughs. However, its application effects in corporate practices show significant differences:some enterprises have successfully integrated AI deeply into their core business, while others have made huge investments but failed to achieve substantial breakthroughs.
Existing studies mostly regard artificial intelligence as a passive technical resource or tool, ignoring its evolutionary role as a subject that participates in management activities through autonomous decision-making. From the perspective of resource orchestration theory, AI has shown the potential of a resource manager, being able to identify opportunities in real time, allocate elements, and collaborate or conflict with human decisions, which directly affects the pace, direction, and risk tolerance of innovation. Current research has three shortcomings: first, it fails to fully recognize the attribute of AI as a management subject; second, it lacks a systematic analysis of the path through which AI is embedded in the resource management process; third, it ignores the conflict mechanism between humans and intelligent agents in terms of resource dominance.
Drawing on the resource orchestration theory, this paper employs panel data from 3 131 Chinese firms over the period from 2014 to 2023 to investigate the dual roles of AI in shaping radical innovation—both as an innovation resource and as a resource orchestrator. Specifically, it addresses three critical questions: (1) Does AI as an innovation resource have a nonlinear effect on firms′ radical innovation? (2) How does AI, acting as a resource orchestrator, impact radical innovation through the resource management process? (3) How do human researchers moderate these relationships through interaction with AI?
The empirical findings reveal several important conclusions. First, AI as an innovation resource demonstrates an inverted U-shaped relationship with radical innovation. Specifically, the adoption and initial integration of AI resources significantly promote radical innovation by facilitating rapid knowledge recombination, reducing innovation costs, and enhancing the precision of decision-making processes. However, beyond a certain threshold, excessive reliance on AI tends to lock firms into existing technological trajectories, leading to diminishing returns and even inhibiting breakthrough innovation.Second, As a resource orchestrator, AI exerts its influence primarily through resource structuring and capability enhancement. Through automated data integration and analysis, AI strengthens firms′ resource structuring by accelerating and refining the identification of innovation opportunities and facilitating efficient knowledge recombination. However, the impact of AI on resource capability enhancement is nuanced. Initially, the introduction of AI resources may trigger internal resource allocation conflicts, temporarily undermining operational efficiency. Nevertheless, as firms progressively increase AI utilization, operational efficiency improves significantly, enhancing their capability to undertake radical innovation projects. Conversely,the study finds limited evidence of AI′s effectiveness in resource leveraging, suggesting current AI technologies remain inadequate for handling complex managerial tasks requiring sophisticated judgment, political negotiation, or contextual insight.Third, the study explores the interactive dynamics between AI and R&D personnel. The results highlight a clear substitution relationship: AI′s beneficial impact on radical innovation is stronger when the number of human researchers is limited, indicating that AI effectively compensates for insufficient human expertise in generating innovative ideas and strategic decisions. As the number of R&D personnel grows, however, this marginal benefit declines due to increased organizational complexity and potential resistance arising from perceived job threats, limiting AI′s incremental innovation benefits. Additionally, the strength of this substitution effect varies across firm lifecycle stages, being most pronounced during the growth phase and diminishing during maturity and decline phases.
This study contributes to the existing literature in several significant ways. Theoretically, it enriches the resource orchestration framework by explicitly considering AI transition from a passive, orchestrated resource to an active resource orchestrator. The study also expands current understandings of human-AI interactions by introducing the concept of "positional competition", suggesting that AI and human researchers may compete rather than collaborate in certain innovation processes. Empirically, this approach is novel in systematically analyzing
the complex nonlinear impacts of AI, considering the dual roles of AI and moderating factors such as human researchers and firm lifecycle stages.

Data elements have emerged as the pivotal force driving the latest wave of technological innovation and transformation. Advancing the market-oriented allocation of these elements is essential for maximizing the benefits of data and bolstering green innovation and development. Green innovation stands at the intersection of innovation-driven and sustainable development strategies, serving as a critical lever for enhancing economic performance and achieving sustainability goals. In the digital era, the data element marketization can promote the efficient circulation and standardized transactions of data elements, which helps to reduce the input costs and R&D risks of green innovation activities and provides diversified application scenarios for promoting the transformation of green innovation achievements. In this context, it is particularly important to clarify the impact and underlying mechanisms of data element marketization on green innovation efficiency, which contributes much to fully leveraging the empowering role of data element marketization in urban green innovation and accelerating the improvement of the urban green innovation system.
This study first constructs a theoretical framework to explore the mechanisms through which data element marketization impacts urban green innovation efficiency, focusing on the dimensions of element allocation effects and market scale effects. On one hand, data element marketization can facilitate the orderly flow of capital, labor, and other innovation elements via data trading mechanisms, which helps to alleviate their mismatch. On the other hand, data element marketization can break down market transaction barriers, broaden the sales and application channels for green innovation products, and drive the transformation of green innovation achievements, thereby enhancing the efficiency of green innovation. Then given that local government behavior plays an important role in the process of constructing data element market, this study analyzes the moderating effects of local government economic and innovation competition on empowering urban green innovation through data element marketization.Finally, the study undergoes an extended analysis of the spatial spillover effects in the relationship between the two, based on the correlation with digital technology.
Using panel data from 284 Chinese cities spanning the period from 2011 to 2021, this study conducts an empirical analysis of the establishment of a data trading platform as a quasi-natural experiment, employing the multi-period difference-in-differences (DID) method and spatial DID models. Three conclusions are drawn as follows. Firstly, data element marketization can significantly enhance urban green innovation efficiency, and this conclusion remains valid after a series of robustness tests. Meanwhile, the facilitating effect of data element marketization on green innovation efficiency is more pronounced in Southern cities and cities with higher levels of digital infrastructure and intellectual property protection. Secondly, data element marketization can promote urban green innovation efficiency through alleviating the mismatch of innovation elements and enhancing the market scale. Thirdly, the expansion analysis finds that under the competition role of local governments, the competition model of “competition around growth” strengthens the economic development motivation of local governments and weakens the promotion effect of data element marketization on urban green innovation; while the competition model of “competition around innovation” enhances the innovation development orientation of local governments and improves the green innovation driving effect of data element marketization. In addition, under the weight matrix of digital technology distance, data element marketization exerts a more significant spatial spillover effect on improving urban green innovation efficiency.
Policy recommendations are put forward from three aspects. First, through focusing on promoting the reform of data element marketization, a wide-coverage and deep-level data transaction and circulation model should be established to fully stimulate the development dividends of data elements empowering green innovation. Meanwhile, differentiated supporting development strategies should be formulated according to local conditions and in conjunction with the construction process of data trading platforms in different regions. Second, the allocation of regional innovation elements, including talents and capital, should be continuously improved to create diversified and innovative product markets and build a favorable environment for the green innovation of enterprises. Third, by establishing a variety of innovation competition assessment objectives and incentive mechanisms, inefficient technology competition behaviors among neighboring regions can be circumvented and cross-regional green innovation cooperation can also be promoted. In addition, digital technology network construction should be highlighted to fully leverage the benefits brought by the data trading platform, including information sharing and knowledge overflow.

Led by the US, developed countries have modified supply policies towards China, seeking technological decoupling via a "de-Chinaized" supply chain. In response, China has introduced a “double-cycle” development strategy. Enterprises are the core forces to cope with changes in the trade pattern, and ensuring the resilience of enterprise supply chains and security is the top priority for realizing high-quality economic development. Currently, there are few studies in the academic community that utilize U.S. policy of technology decoupling from China as an empirical target to investigate the resilience of enterprise supply chains. This research gap could potentially hinder China's ability to manage the risks inherent in a volatile global market. Therefore, in the context of China's new round of opening-up policy, it is important to explore how U.S. policy of technology decoupling from China affects the resilience of enterprise supply chains, which provides a basis for how enterprises can enhance supply chain resilience in the complex international situation. This research is of great theoretical significance and practical value for the promotion of high-level opening to the outside world in line with the double-cycle development strategy.
To explore the impact of U.S. policy of technology decoupling from China on the resilience of enterprise supply chains, this study utilizes a sample of A-share listed firms from the period 2010-2022 to investigate this impact. The starting time of the US-China technology decoupling policy is identified as 2018. The impact of the US-China tech-nology decoupling policy on the cooperation between enterprises and their upstream and downstream partners is significant. Specifically, customer sales (Y1) and supplier procurement (Y2) are considered as the dependent variables.
The analysis yields some findings . First, the U.S. policy of technology decoupling from China significantly enhances the resilience of enterprise supply chains, and this finding remains reliable after the robustness test. Second, the heterogeneity analysis shows that the resilience of enterprise supply chains is more significantly positively affected by U.S. technology decoupling from China for the non-state-owned enterprises, eastern region, and high-tech industry categories. Third, further research has found that the U.S. policy of technology decoupling from China promotes corporate supplier procurement volumes by driving supply chain efficiency. Additionally, this policy enhances the resilience of enterprise supply chains by increasing supply chain concentration. However, the U.S. policy of technology decoupling from China also inhibits the improvement of resilience of enterprise supply chains through the complexity of supply chain collaborations.
This article makes novel contributions to the research on enterprise supply chain resilience. Firstly, it offers fresh empirical evidence, a significant advancement since the current literature is heavily reliant on qualitative analyses and lacks empirical studies on the relationship between the U.S. policy of technology decoupling from China and supply chain resilience. By integrating both qualitative and quantitative analysis methods, this paper uncovers this relationship, thereby providing valuable empirical evidence for management science research on supply chain resilience. Secondly, it expands and enriches the theoretical research on U.S. policy of technology decoupling from China and resilience of enterprise supply chains. Most of the existing studies examine this issue from the macro level of industry and national strategy. In this paper, from the micro level of enterprises, we discuss how the enterprise supply chains can cope with the unfavorable impacts brought by the changes in the international situation, so as to enrich the theoretical content of the relevant research. Lastly, the paper provides actionable strategies to bolster the resilience of Chinese enterprise supply chains. Given the frequent occurrence of unforeseen events in science and technology policies and international relations, and the scarcity of studies on the influence mechanisms and operational frameworks behind these uncertainties, this paper elucidates the mechanism of U.S. policy of technology decoupling from China affecting the resilience of enterprise supply chains, and provides effective practical insights for enterprises to enhance the resilience of supply chain.

Facilitating the transfer of tacit knowledge from users to artificial intelligence (AI) (referred to as "human-machine tacit knowledge transfer") is crucial for enterprises to address persistent challenges in AI implementation,including data scarcity,algorithmic constraints,and limited computational resources.This process is thus essential for unlocking the strategic value of AI and gaining a competitive edge.Consequently,how to promote human-machine tacit knowledge transfer has become an important issue that enterprises urgently need to address.
Although research on human-machine tacit knowledge transfer has been increasing,it is still scattered and lacks an effective framework.In addition,existing research emphasizes technological rationality,focusing on the technical implementation of human-machine tacit knowledge transfer from the perspective of AI as the knowledge transfer object,while largely overlooking the role of users as active knowledge contributors.This not only leads to an imbalance in research perspectives,but also results in a dual rupture of human-machine tacit knowledge transfer in terms of subject (users) and object (AI),as well as between behavior and technology,thereby hindering practical guidance for organizations seeking to facilitate effective knowledge transfer.
Therefore,this study investigates how human-machine tacit knowledge transfer unfolds from the user's perspective.Drawing on the theory of AI socialization,the study specifically examines how AI capabilities influence human-machine tacit knowledge transfer.It develops a research model in which AI capability serves as the independent variable,human-machine tacit knowledge transfer as the dependent variable,new professional role identity as a mediator,and perceived value and perceived threat as moderators.On this basis,this study collected data from 321 enterprises through a questionnaire-based survey and conducted empirical analysis and testing of research hypotheses using methods such as structural equation modeling and hierarchical regression analysis.
The results show that AI capabilities positively affect human-machine tacit knowledge transfer; furthermore,AI capabilities positively influence users' new professional role identity,through which AI capabilities positively influence human-machine tacit knowledge transfer.This also indicates that AI capabilities can not only directly affect human-machine tacit knowledge transfer,but also indirectly affect it through the mediating role of users' new professional role identity.The results also show that perceived value positively moderates the influence of AI capabilities on users' new professional role identity; whereas perceived threats do not hinder the influence of AI capabilities on users' new professional role identity.This also indicates that perceived value can indirectly enhance the impact of AI capabilities on human-machine tacit knowledge transfer.
This study contributes to the theory of human-machine tacit knowledge transfer:first,it promotes the diversification of research perspectives on human-machine tacit knowledge transfer by introducing a user behavior perspective; second,it provides a behavioral mechanism for the theory of human-machine tacit knowledge transfer,promoting the connectivity of research content in this field; third,it constructs an analytical framework of "capabilities (AI capabilities) - cognition (users' new professional role identity)-behavior (human-machine tacit knowledge transfer)" to study the behavioral mechanism of human-machine tacit knowledge transfer,providing effective theoretical support and a logical framework for subsequent research.
The findings offer actionable insights for organizations deploying AI on how to obtain the expected value of AI.Specifically, companies should prioritize building users' AI capabilities to facilitate tacit knowledge transfer between humans and machines. Additionally, aligning users' AI competencies with their evolving professional identities can further enhance this knowledge exchange. Organizations should also actively communicate the value of AI to encourage users to fully leverage these capabilities.The results also suggest that enterprises should not be fixated on whether to eliminate users' perceived threat of AI.Instead,they can guide users to turn perceived threats into motivation,thereby encouraging them to use their AI capabilities to carry out new work and transfer tacit knowledge to AI.