Entrepreneurial activities are key factors driving regional economic growth, creating job opportunities, and promoting economic transformation. However, with the intensification of global economic pressures and the entry of the Chinese economy into a "new normal", traditional drivers of economic growth are gradually weakening, posing significant challenges to the entrepreneurial environment. Potential entrepreneurs not only need to contend with increasingly fierce market competition but may also face lower success rates, leading to a decline in entrepreneurial activities across various regions.
As the core driving force of the new wave of technological revolution and industrial transformation, artificial intelligence (AI) is rapidly integrating into various fields in China, spawning new industries, new technologies, new business forms, and new business models, thereby demonstrating its tremendous potential to drive a new wave of innovation and entrepreneurship. This rapid integration has transformed traditional industries, leading to breakthroughs in automation, data analysis, and customer engagement, which in turn accelerates economic growth. However, existing research indicates that the impact of AI on regional entrepreneurial activities has dual characteristics, with both positive creation effects, such as the emergence of new markets and job opportunities, and negative substitution effects, including the displacement of certain jobs and sectors. The complex interplay of these positive and negative effects makes the direction and intensity of AI's impact on regional entrepreneurial activities still inconclusive, leaving uncertainty around the overall outcomes for different regions and industries.
Looking back at past technological revolutions, although technological advancements may temporarily replace certain jobs in the short term, they ultimately stabilize total employment by generating emerging industries and creating new job positions. Therefore, with the rapid development of AI technology and its profound impact on the economic landscape of China, accurately assessing the role of AI in regional entrepreneurial activities is of great significance for improving entrepreneurial theory, guiding entrepreneurial practice, and promoting high-quality regional economic development. To this end, this paper takes the construction of national new-generation AI innovation development pilot zones as a typical case, exploring the intrinsic connections and mechanisms between AI development and regional entrepreneurial activities from the perspective of entrepreneurial choices, utilizing county-level panel data from 2012 to 2022 and a double machine learning model.
The research findings indicate that the construction of national new-generation AI innovation development pilot zones has a significant effect on enhancing regional entrepreneurial activities. Mechanism analysis reveals that AI development indirectly promotes entrepreneurial activities by enhancing entrepreneurial capabilities, alleviating financing constraints, and increasing capital returns, with a synergistic effect between entrepreneurial capabilities and capital returns. Further analysis finds that AI can stimulate entrepreneurial activities in technology-intensive industries and productive service industries, effectively driving regional economic structural transformation and high-quality development. Heterogeneity analysis shows that the positive effect of AI on entrepreneurial activities only becomes apparent when the regional population reaches a certain threshold.
This study contributes to the literature in three aspects: Firstly, by revealing the experiments of AI policies and national new-generation AI innovation development pilot zones, it proposes an innovative research perspective to explain the impact of AI on regional entrepreneurial activities. This not only integrates theoretical explanations from the perspective of entrepreneurial choices but also provides empirical support for institutional theories in the AI era. Secondly, the paper introduces a new chain construction path, namely "entrepreneurial capability—financing constraints—capital returns", providing strong support for further clarifying the complex mechanisms between AI and regional entrepreneurial activities. On the basis of the synergistic mechanism between "entrepreneurial capability—capital returns", it proposes enhancing entrepreneurs' AI literacy and resource integration capabilities, forming a positive feedback loop of "capability enhancement-capital appreciation-reinvestment of capabilities", maximizing the synergistic effect of the mechanism, and further promoting the virtuous interaction between technological innovation and capital flow. Thirdly, methodologically, the paper adopts double machine learning methods to solve the problems of model mismatch and curse of dimensionality in complex analyses, thereby effectively improving the accuracy of the conclusions.

In recent years, the rapid development of artificial intelligence technologies, such as intelligent robots and voice recognition, has become a key force driving the transformation of China′s economy. Artificial intelligence technologies can enhance human intelligence by enabling autonomous learning, communication, interaction, and problem-solving. In order to reduce labor inputs and increase work efficiency, more and more organizations are introducing AI technologies into their work. With the application of AI technology in the workplace, on the one hand, employees recognize that AI usage can help them perform redundant tasks and process complex data, which leads to experience positive emotions . A number of studies have also shown that AI usage can promote employees′ sense of work enthusiasm, job engagement and organizational citizenship behaviors. On the other hand, it can impose new work requirements on employees. The complexity of the technology can induce negative psychological emotions and increase employees′ work stress and job insecurity, which could lead to emotional exhaustion, employees′ burnout, knowledge hiding, and intentions to leave.
Scholars have viewed AI usage as a form of technological stress that leads to anxiety and insecurity, which in turn affects employee performance, proactive behavior, and job crafting. AI usage as a stressor can significantly affect employees′ attitudes and behaviors at work. In addition, organizations that want to stand out in a complex and changing economic environment need to rely on the wisdom of their employees, for example, employee voice. Employee voice in the organization will be affected by work pressure. Therefore, AI usage as a stressor has an impact on employee voice and silence. However, few studies have focused on the double-edged sword effect of its impact on employee voice and silence from the perspective of the cognitive activation theory of stress. This theory posits that employees cognitively appraise the outcomes of stressors as positive or negative, which can spill over into non-work time and trigger adaptive or non-adaptive psychological responses. Regarding AI, such appraisals can lead to problem-solving adaptive responses or emotionally centered non-adaptive ones. In Chinese culture, the concept of "Zi Xing" is reflected in employees′ work as rumination, which is split into problem-solving pondering and affective rumination. AI usage can activate problem-solving pondering and affective rumination among employees, which ultimately influences employees′ voice and silent behaviors.
In Study 1, a scenario experiment was conducted with 240 employees who use AI technology in their daily work, recruited via the Credamo platform in China. A between-subjects experimental design with two factors (AI technology application: high vs. low; job autonomy: high vs. low) was employed. In Study 2 (questionnaire survey) , employees from organizations using AI were recruited via the WJX online data collection platform. A three-stage time-lagged data collection approach was adopted to mitigate the impact of common method bias. Research hypotheses were tested and the empirical results show that through the mechanism of problem-solving pondering, artificial intelligence usage has a positive influence on employee voice and a negative impact on employee silence; via the mechanism of affective rumination, the artificial intelligence negatively affects employee voice and positively influences employee silence; work autonomy strengthens the positive effect of artificial intelligence usage on problem-solving pondering and weakens the positive impact of artificial intelligence usage on affective rumination.
This study makes the following theoretical contributions. First, this study confirms that AI usage can affect employees′ voice and silent behaviors in terms of problem-solving pondering and affective rumination, respectively. This not only broadens the research horizons of the impact of AI usage, but also enriches the antecedent research of employees′ voice and silent behaviors from the perspective of stress cognitive activation theory of stress. Second, this study brings in job rumination in the research framework, which broadened the scope of the impact of AI usage on employees′ attitudes and behaviors, and also provided a novel explanatory framework for the research on the mechanism of the impact of AI usage on employees′ voice and silence. Finally, this study incorporates job autonomy into the impact of AI usage on employees′ voice and silence, further enriching the boundary conditions for the study from the perspective of job resources.

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.

Coordinated regional economic development is a fundamental requirement for achieving high-quality and sustainable growth, with innovation serving as its primary driving force. In traditional development paradigms, high-end production factors such as skilled labor, advanced technology, and capital tend to concentrate in regions characterized by strong industrial agglomerations and rich innovation resources. This uneven spatial distribution of key inputs places underdeveloped and peripheral regions at a significant disadvantage in accessing essential production factors and participating in innovation-driven growth processes. Consequently, regional disparities are exacerbated, hindering inclusive national development. However, the deep integration of data elements and artificial intelligence (AI) technologies has begun to reshape this landscape. This integration has effectively reduced spatial and institutional barriers to the dissemination of knowledge and technologies, dramatically lowering the costs of cross-regional technology diffusion and enhancing the fluidity of innovation resources. In doing so, it contributes to more efficient allocation of resources across regions and elevates regional innovation capacities, thereby offering new and potentially transformative pathways for narrowing long-standing economic development gaps between regions. In addition, the productive value of data elements cannot be realized independently. Their economic potential is maximized only through synergistic interaction with other production factors. Particularly, the convergence of data with AI technologies generates multiplier and complementary effects that significantly enhance innovation efficiency and technological productivity. These effects have drawn growing attention in both theoretical and empirical studies, becoming a focal point in discussions of digital transformation and spatial equity. Against this backdrop, the present study adopts the Regional Innovation System (RIS) theoretical framework to investigate the role of data-intelligence integration in shaping regional economic disparities. The RIS framework emphasizes the systemic and interactive nature of innovation, underscoring the importance of collaboration among multiple actors within a region,including governments, firms, universities, and research institutions. Such multi-agent collaboration facilitates knowledge exchange, accelerates technological diffusion, and enhances the integration of innovation resources. Thus, it provides a solid theoretical basis for understanding the regionalized effects of data-AI integration.
To conduct this analysis, the study constructs a multidimensional indicator system to systematically evaluate the development and application levels of data elements and AI technologies across Chinese provinces. A coupling coordination model is applied to measure the extent and quality of integration, which is then introduced into the empirical model as a key explanatory variable. This methodological approach helps address two notable gaps in the literature: the insufficient attention to the synergistic relationship between emerging digital factors and traditional inputs, and the lack of empirical research on how digital transformation influences regional economic convergence. Empirical analysis is conducted using panel data from 30 Chinese provinces (excluding Xizang, Hong Kong, Macao, and Taiwan) covering the period from 2012 to 2023, yielding 360 valid observations. Regional economic disparity is quantified using average income gap indicators. The study employs both multiple linear regression and threshold effect models to assess the impact of data-AI integration on regional disparities. Furthermore, four potential mechanisms are analyzed: industrial upgrading, resource misallocation, regional innovation capacity, and innovation infrastructure.
The results demonstrate that higher levels of data-AI integration significantly reduce regional economic disparities. Regional innovation capacity, industrial structure upgrading, and improvements in resource allocation function as mediating pathways, while innovation infrastructure exerts a moderating influence. Heterogeneity analysis reveals that these effects are more pronounced in the eastern and western regions, but are not statistically significant in the central region. Additionally, the threshold effect model identifies a critical financial development level (6.173), beyond which the positive impact of data-AI integration on narrowing disparities reverses, possibly due to diminishing marginal returns or crowding-out effects in overly mature financial systems.
In sum, by integrating insights from the RIS framework, Schumpeterian innovation theory, and industrial structural evolution theory, this study provides a systematic investigation of how emerging production factors—especially data and AI—interact to influence regional economic coordination. Unlike previous studies that focus narrowly on digital technologies or innovation inputs in isolation, this research emphasizes the systemic value of factor integration. The findings offer not only theoretical contributions but also practical implications for policymakers seeking to promote high-quality, balanced, and innovation-driven regional development in China.

The Chinese government has been endeavoring to have carbon dioxide emissions peak before 2030 and achieve carbon neutrality before 2060, which has heightened governmental and societal attention to corporate ESG (Environmental, Social, and Governance) performance. Agricultural listed companies, as the core carriers of the green technology innovation system, not only construct mechanisms for enhancing multidimensional environmental benefits through green technology research and application innovation but also create synergies along the industrial chain via innovation. In this context, it is essential for agricultural listed companies to prioritize ESG performance, enhance the quality and transparency of ESG disclosures, ensure standardized and sustainable high-quality development pathways, and continuously improve green technology innovation capabilities. Existing research indicates that ESG practices in China remain nascent, with no consensus on how its three dimensions (environmental performance, social responsibility, and corporate governance) impact corporate performance. Few studies have directly linked corporate ESG performance, green technology innovation, and corporate performance, particularly exploring the moderating role of green technology innovation in the "ESG-performance" nexus. Notably, specialized research on agricultural listed enterprises is scarce. Therefore, this study focuses on agricultural listed companies to investigate three core relationships: the impact of ESG performance on corporate performance, the effect of green technology innovation on corporate performance, and the moderating role of green technology innovation in the relationship between ESG performance and corporate performance.
This paper examines ESG performance among China′s agricultural listed companies, exploring the interplay between ESG performance, green technology innovation, and corporate performance. The study employs initial samples of agricultural firms listed on China′s A-share market from 2013 to 2023, selecting 821 valid observations following rigorous screening. It utilizes descriptive statistical analysis, correlation analysis, and regression modeling to test the hypotheses. Additionally, robustness checks are conducted to address potential endogeneity issues, omitted control variables, and alternative sample intervals. Further analysis examines the distinct impacts of the environmental, social, and governance dimensions of ESG on firm performance. Heterogeneity tests are also performed based on ownership structure (state-owned versus non-state-owned) and regional distribution (eastern versus non-eastern regions).
According to the empirical analysis, it is concluded that (1) agricultural listed companies with superior ESG performance exhibit significantly higher corporate performance; (2) green technology innovation exerts a positive impact on corporate performance; however, this facilitating effect not only exhibits a time-lagged characteristic but also demonstrates a progressively declining trend over time; (3) investments in green technology innovation act as a significant moderator, amplifying the positive influence of ESG performance on corporate performance; (4) heterogeneity analysis further reveals that non-state-owned and eastern-region agricultural listed companies demonstrate stronger ESG-driven performance enhancements, particularly under the moderating effect of green technology innovation. Thus, in regions with relatively weaker economic foundations and imperfect market mechanisms, it is necessary to formulate and actively utilize ESG strategies to promote sustainable agricultural development and modernization in these areas. In contrast, in the eastern regions, the focus should be on strengthening ESG disclosure and regulatory oversight, and driving agricultural sustainability to a higher level. Non-state-owned enterprises should closely monitor their performance in environmental protection, social responsibility, and corporate governance on an ongoing basis; while state-owned enterprises should gradually increase their investment in the ESG domain, continuously optimize their internal control mechanisms, and improve efficiency levels to resolve existing issues and achieve sustainable development.
The theoretical contributions of this study are threefold. First, it uncovers the significant positive impact of ESG performance on agricultural listed companies' corporate performance, enriching ESG-related research and providing empirical evidence for understanding how agricultural enterprises can optimize ESG practices to enhance economic benefits within a sustainable development framework. Second, it highlights the dynamic, time-decaying nature of green technology innovation's performance-enhancing effects, deepening scholarly insights into the temporal evolution of green innovation's economic consequences. This offers policymakers and corporate managers theoretical guidance for designing innovation incentives and strategic investments with temporal considerations. Third, it underscores the catalytic role of green technology innovation in advancing corporate sustainability strategies, opening new theoretical perspectives for research in related fields.

Innovation drives productivity, economic growth, and social advancement. Amidst business pressures, how to improve the innovation performance of enterprises has become the most concerning issue for entrepreneurs. Digital technology, as a key driving force in the era of the digital economy, is leading industrial change, reshaping enterprise production methods, and promoting the emergence and development of new business forms and new models. Digital transformation accelerates innovation and provides a clear direction for growth and value creation. Enterprise digital transformation refers to the process of triggering significant changes in physical attributes with the help of a combination of digital technologies, such as information, computing, and communication, to facilitate the evolution of the business entity. Most scholars have a more consistent view of the relationship between digital transformation and enterprises' innovation performance and believe that it can positively affect enterprise innovation performance. However, scholars widely agree it positively impacts innovation performance, though research often overlooks the digitization of production factors within enterprises and their influence.
A major feature of the current economic environment is the knowledge economy, in which knowledge is the most important production factor and core asset of the enterprise, and a key resource for enterprises to maintain strong innovation vitality. Knowledge digitization, by changing the existing form of knowledge resources and giving them new characteristics, may open up the existing barriers and provide a boost to the enterprises innovative behavior and performance. Therefore, this study focuses on the impact of enterprise knowledge digitization on enterprise innovation performance, and deeply explores the process and mechanism of enterprise knowledge digitization on enterprise innovation performance.
This research first proposes the concept of knowledge digitization, and establishes a mediation model with moderation, aiming to investigate the impact of knowledge digitization of enterprises on innovation performance, as well as the mediating role of R&D exploration capability and the moderating role of entrepreneurial orientation. Using the research data of 243 enterprises in China, this research uses SPSS for multiple linear regression tests and AMOS for structural equation modeling tests, and the results show that (1)knowledge digitization and its codability, convergence, and generativity have significant positive impacts on enterprise innovation performance; (2)R&D exploration capability plays a mediating role in those processes; (3)entrepreneurial orientation positively moderates the relationship between R&D exploratory capability and enterprise innovation performance. These conclusions are in line with the current knowledge management theory and enterprise innovation practice in the context of digital transformation.
When knowledge becomes a digital resource, it can break the time and space limitations in the rapid dissemination speed of digital technology,and greatly shorten the cycle of knowledge flow, increase the opportunity for knowledge integration and reorganization, thus helping enterprises to promote the development of new products, the construction of new business models, and the improvement of the benefits brought by innovation.
The contribution of this research focuses on the following points. First, it proposes the concept of knowledge digitization. In the field of knowledge management, the possibility of using digital technology to change the form of knowledge existence and its impact has not been considered; in the field of digital transformation, the mechanism of the impact of digitized enterprise production factors on enterprise innovation performance has not been clarified. This research proposes the concept of knowledge digitization by combining the knowledge management theory with the digital empowerment theory, which complements and improves the existing research in the field of digital transformation and knowledge management. Second, this research verifies the mechanism of knowledge digitization's impact on enterprise innovation performance. Knowledge digitization as a brand new concept, needs to be further excavated for its impact mechanism on enterprise innovation performance. This study chooses R&D exploration capability as the mediator variable and entrepreneurial orientation as the moderator variable, constructs and validates a model of the impact of knowledge digitization on enterprise innovation performance, which opens this “black box”. Finally, this study offers management insights on how to promote knowledge digitization, enhance R&D exploration capability and improve entrepreneurial orientation, providing a reference for innovation strategies in the knowledge and digital economy contexts.

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 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.

The integration of artificial intelligence across various sectors, including autonomous vehicles, finance, and biomedicine, has catalyzed significant shifts and advancements in both established and burgeoning industries.Building and developing an innovation ecosystem is an important strategy for countries to promote innovation and development. The application of artificial intelligence can drive technological innovation at the source, attract multiple stakeholders to participate in value creation, integrate innovation resources, and jointly build an innovation ecosystem. With the gradual deepening of artificial intelligence empowerment, it has promoted the dynamic evolution of innovation ecosystems and posed new challenges to the theory of innovation ecosystems. The particularity of artificial intelligence technology has led to a lack of in-depth and systematic research on its specific application process and characteristics in the innovation ecosystem, and the internal driving factors and paths for the evolution of the industrial innovation ecosystem empowered by artificial intelligence are not yet fully understood.This paper adopts a longitudinal single case study method with the biopharmaceutical industry as the case study object. The application of artificial intelligence is divided into a technology accumulation period, an integration period, and an industry empowerment period. From a dynamic perspective, the driving factors and paths of the evolution of the biopharmaceutical industry empowered by artificial intelligence are explored, providing theoretical support and practical guidance for promoting the continuous optimization and upgrading of the industrial innovation ecosystem.The results show that (1) policy guidance, technology promotion, market pull, and organizational change jointly drive the evolution of the innovation ecosystem, and the dominant driving factors that play a role vary in different stages of artificial intelligence technology. (2) Artificial intelligence promotes the evolution of innovation ecosystems through pattern innovation, and innovation patterns achieve a comprehensive transformation from point to end and then to automation. (3) The period of technological accumulation is driven by the survival pressure caused by market competition, achieving a single point breakthrough in technology; the technology integration period is driven by policy-guided innovation strategies, achieving an end-to-end research and development model; and the industrial empowerment period is driven by multidimensional synergy led by technology, achieving a model of "AI+automated experiments+expert experience".The novelties of this paper are as follows: Given the lack of industry analysis at the meso level under technological background in the existing research , this paper conducts case studies on the biopharmaceutical industry to analyze the driving factors and paths of the evolution of artificial intelligence enabled industrial innovation ecosystems, expanding the theoretical research on industrial innovation ecosystems. Moreover, it divides the technology empowerment stage into technology accumulation stage, technology integration stage, and industry empowerment stage, and expands the research on innovation ecosystems from a dynamic perspective. Finally, due to the particularity of artificial intelligence technology, there is a lack of systematic research on the driving factors and paths for the evolution of AI-empowered industrial innovation ecosystems domestically and internationally. This study analyzes the evolution process of the innovation ecosystem in the biopharmaceutical industry, and through model innovation, it facilitates the evolution of the innovation ecosystem.The deep empowerment of artificial intelligence technology and the deep integration of artificial intelligence and industrial innovation ecosystem are the fundamental guarantees for promoting the prosperous and orderly development of industries. At the government level, it is necessary to guide the application of artificial intelligence in different industries, considering the focus of different policies such as supply, demand, and environment. At different stages of technological development, efforts should be made to shift from encouraging technological development to emphasizing regulation, and then to achieving balanced development. At the enterprise level, it is necessary to actively layout artificial intelligence, adjust organizational structure in a timely manner according to the policies and market environment, and achieve a dual wheel drive of "technology+mode". At the same time, in the future, multiple case studies or large-scale empirical studies can also be used to test the research conclusions of this article, further tracking the laws and mechanisms of change, and exploring the empowerment of the biopharmaceutical industry by artificial intelligence from different perspectives.

With the in-depth development of digital economy, digital transformation has become an important force for enterprises to break through the dilemma of value creation. Scholars have analyzed the important impacts of digital transformation on enterprise value creation from multiple angles. In terms of the positive impacts, the existing literature has deeply analyzed how digital transformation promotes enterprise value creation through optimizing resource allocation, enhancing information matching, improving the integration of supply chain, promoting service-oriented transformation, and upgrading business modes. However, due to the long-term inputs and uncertain payoffs of digital transformation, enterprises are also confronted with a series of challenges. For example, the high level of investment and high possession of resources damage the welfare of employees, the resource competition against original businesses increases management costs, and the rent-seeking motivation of enterprises lowers the quality of innovation. The mismatch between digital transformation and business scenarios even leads to a "digital paradox", thus decreasing the value creation of enterprises. In addition, the product-oriented logic with technology innovation and the service-oriented logic led by service innovation are the two main paths of enterprise value creation, but the existing research mainly focuses on the single impact of digital transformation on product innovation or service innovation. Furthermore, whether digital transformation can promote enterprise value creation is influenced by many factors. Whereas, the existing research emphasizes more external factors with less attention on the internal ones of the enterprise. Therefore, it is urgent to explore the relationship between digital transformation and enterprise value creation in depth.
Following the theoretical framework of "behavior-process-performance", this paper takes the A-share listed companies from 2011 to 2022 in China as the sample, and sets enterprise value creation as the dependent variable and digital transformation as the explanatory variable to explore the effect, action path, and moderating conditions of digital transformation enabling enterprise value creation. Market indicators represented by Tobin's Q value reflect both the book value and market value of a company, and are less likely to be manipulated by humans, which can comprehensively reflect the value creation of the company. This article selects Tobin's Q value as the proxy variable for enterprise value creation. Referring to the practices of existing literature, it characterizes the degree of digital transformation in enterprises through text analysis.
The results show that digital transformation has significantly improved enterprise value creation, and it is more significant among small-scale enterprises, state-owned enterprises, mature enterprises, and enterprises in eastern China, but shows no significant impact on the value creation of large-scale enterprises, enterprises in the recession period, and enterprises in the central and western regions. The mechanism tests indicate that digital transformation promotes enterprise value creation through product innovation and service innovation, and product innovation is the main path of digital transformation to promote enterprise value creation. Moreover, the investment efficiency and absorption ability of enterprises provide a solid foundation for digital transformation to promote enterprise value creation.
The contributions can be concluded in three aspects. First, wider samples are selected to investigate the impact of digital transformation on enterprise value creation, which helps to more comprehensively reflect the general impact of digital transformation. Second, it is proposed to follow the product-oriented logic and service-oriented logic of value creation to build the bridge of product innovation and service innovation between digital transformation and enterprise value creation, which breaks through the limitations of previous research that discussed the value creation effect of digital transformation from the single perspective of technology innovation, and extends the application depth of innovation theory in the digital scenario. Third, from the integrated perspective of resource and capability, the moderating effects of investment efficiency and absorption ability between digital transformation and enterprise value creation are investigated, which enriches the scenario mechanism research of the economic consequences of digital transformation. The conclusions provide empirical evidence and theoretical support for enterprises to improve the positive impacts of digital transformation through the dual paths of product and service innovation, as well as from the integrated perspective of resource allocation and absorption ability.

Against the backdrop of “carbon peak and carbon neutrality”, China is accelerating the construction of a green, low-carbon and circular development economic system.This effort is designed to catalyze a shift in economic development paradigms through the lens of green innovation. While corporate investment in green innovation has seen a sustained increase, and the volume of green innovations has surged, and there are strategic green innovation behaviors funded by patent policies, resulting in enterprises focusing on the quantity of innovation while ignoring the improvement of innovation quality. Therefore, exploring how enterprises can improve the quality of green innovation and realize the "win-win" of economic and environmental benefits has become the focus of current research.
Environmental, social and governance (ESG), as a comprehensive framework for evaluating the sustainable development of enterprises, can significantly enhance investor confidence, improve consumer loyalty, and then bring economic benefits and social influence to enterprises. Its disclosure level is also increasing year by year. Does enterprise ESG performance have a positive impact on the quality of green innovation? Is the impact heterogeneous among different types of enterprises? What is the underlying mechanism of action? Answering the above questions is helpful to grasp the mechanism of ESG performance of enterprises as a whole, and improve the quality of green innovation of enterprises.
Following stakeholder theory, this study selects panel data of Chinese A-share listed companies from 2010 to 2021 as the research sample, and uses a two-way fixed effects model to empirically analyze the impact and mechanism of enterprise ESG performance on the quality of green innovation. It employs the patent knowledge breadth method to measure the quality of green innovation as the dependent variable enterprises, and uses the Huazhong ESG rating index to reflect the ESG performance of Chinese listed companies as the explanatory variable. The results show that enterprise ESG performance can significantly promote the green innovation quality. After a series of robustness tests such as instrumental variable method and PSM test, the conclusion still holds true. Moreover, the impact of enterprise ESG performance on green innovation varies across enterprises, industries, ESG levels, and green innovation quality. Corporate ESG performance has a more significant promoting effect on large-scale, non-state-owned enterprises, non heavy polluting enterprises, low ESG levels, and high green innovation quality enterprises. Furthermore, the ESG performance of enterprise promotes the improvement of green innovation quality by increasing internal funding acquisition, talent agglomeration, and strengthening external social supervision. Among them, the transmission effect of social supervision is the strongest, followed by fund acquisition, while the role of talent agglomeration is the weakest. Environmental regulations have a significant negative moderating effect on promoting the improvement of green innovation quality in ESG performance.
Drawing from the conclusions presented, this study offers a series of strategic recommendations. Enterprises are encouraged to, firstly, establish a sustainable development oriented ESG disclosure system, an ESG data collection and monitoring systems based on international standards, a special ESG management committee, and an internal evaluation mechanism. Secondly, differentiated strategies should be adopted according to the characteristics of different enterprises. For example, small-scale enterprises should strengthen cooperation with universities and research institutions; state-owned enterprises can further clarify property rights and management responsibilities; heavy polluting enterprises should develop and apply advanced emission reduction technologies and clean energy; enterprises with high ESG levels can participate in technical cooperation R&D projects and technical forums; low green innovation quality enterprises should establish an environmental and social responsibility office or team to focus on monitoring and improving ESG performance. The government needs to strengthen the synergy of environmental policies and encourage enterprises to improve their ESG performance. Finally, in order to improve the quality of green innovation, enterprises can increase environmental protection awareness to foster a culture of environmental stewardship, use multi-channel financing to reduce costs, and emphasize the utilization of talent agglomeration effect.

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, 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.

As a new economic form, the digital economy brings strong momentum to promote green technology innovation and support innovation-driven development. Meanwhile, the high-quality development of China's digital economy relies on the mutual promotion of efficient markets and capable governments. At present, the effect of the digital economy on green technology innovation has been widely recognized, but due to differences in research perspectives and insufficient consideration of market or government threshold factors, the relevant conclusions are highly controversial. Thus, this paper systematically studies the threshold effect of digital economy on green technology innovation under the synergistic support of market and government. This is of great significance for accelerating the emergence of green technology innovation and achieving the "dual carbon" goal.
In order to deeply analyze the complex relationship between digital economy and green technology innovation, this paper uses the panel data from 30 regions in China during 2011-2022, and then applies threshold panel regression techniques, taking the market support, government support, and their synergy as threshold variables, and empirically analyzes the threshold effect of digital economy on green technology innovation under the synergistic support of market and government. The results are as follows. The impact of digital economy on green technology innovation has a significant positive nonlinear effect based on the synergistic support of market and government. Under the conditions of marketization and opening-up level, the promotion effect of digital economy gradually increases, and the promotion effect is the largest under the high threshold of marketization. Under the conditions of government technology support and government governance intensity threshold, the positive effects of digital economy show the differential effects of first rising and then falling and declining, respectively. When market and government synergy surpasses a critical threshold, the digital economy’s growth momentum continues to accelerate. The positive nonlinear effects of digital infrastructure, digital industrialization and industrial digitalization on green technology innovation all show a significant upward trend, and the positive effects of digital infrastructure are stronger as a whole.
In light of the conclusions reached in this study, the following practical implications are proposed. Firstly, local governments should comprehensively deepen market-oriented reforms, promote the high-speed flow and effective allocation of green innovation resources, and reduce the cost of green innovation. Meanwhile, it is essential to further explore and deepen an open innovation environment, build a regional platform for green innovation exchange and cooperation, and accelerate the promotion of green technology innovation. Secondly, when utilizing government technology support, each region should identify the focus and foothold for enhancing green technology innovation, and enhance the efficiency of digital economy in empowering green technology innovation. At the same time, when using government governance measures, each region should pay attention to the scientific combination of green technology innovation in different cycles. Thirdly, each region should steadily improve the level of synergy between the market and the government, give full play to the leading role of the market in empowering green technology innovation in the digital economy, and supplement it with the guidance and supervision role of the government, thereby achieving an organic combination of effective market and proactive government. Fourthly, all regions should accelerate the development of the digital economy and do a good job of top-level design, including the vigorous development of digital infrastructure construction, the active expansion of the development of digital industries, and the strengthening of the strategic leadership of information and communications technology.
Compared with existing research, the theoretical contributions of this paper are as follows. First, this paper expands the threshold effect theory and further enriches the nonlinear research on the impact of the digital economy on green technology innovation. Second, it reveals the mechanisms by which different types of markets and governments affect green technology innovation in the digital economy, further improving the theoretical scope of empowering green technology innovation with the digital economy. Third, this paper promotes the research on the synergistic mechanism between the market and the government, further deepening the understanding of the relationship between digital economy and green technology innovation.

In the digital era, digital transformation has become an important driving force to promote technological innovation in manufacturing enterprises. It can affect the innovation performance of manufacturing enterprises from the following aspects: first, digital transformation facilitates real-time sharing of information and knowledge, enabling enterprises to identify new development opportunities, which promotes the formulation of novel value propositions and the realization of value creation; second, manufacturing enterprises with advanced digital transformation are more likely to harness digital technology as a robust technological underpinning for their innovation efforts, thereby transforming their innovation development models; third, digital technology can bolster the innovation development model of enterprises, making it more agile and responsive to market demands;fourth, digital transformation can reduce the innovation cost of enterprises: it significantly inhibits cost stickiness by lowering adjustment costs and management expectations, with this inhibitory effect being long-lasting; moreover, digital transformation enables full-process monitoring of the innovation process; finally, digital transformation can improve the quality and quantity of innovation knowledge that manufacturing enterprises can obtain. Therefore, digital transformation exerts a profound influence on the manufacturing industry, and its significance demands the attention of industry stakeholders.
In addition to the close relationship between enterprise digital transformation and its technological innovation, the dynamic capabilities are also crucial in the specific process of building a comprehensive digital and intelligent environment for manufacturing enterprises. According to the study of dynamic capabilities, the implementation of digital transformation can help enterprises respond to changes in the external environment, and acquire and reshape resources to accelerate the adaptation to the external environment, which in turn enhances the dynamic capabilities of the enterprise; at the same time, in the context of digital transformation, the demand for research and development personnel in the manufacturing enterprises has increased dramatically. However, the relationship between the proportion of R&D personnel in these enterprises and their innovation performance remains a subject of debate among scholars. Some researchers posit that the presence of R&D personnel positively influences a company's innovation performance, while others argue that the correlation is not as straightforward and may involve more complex dynamics. Despite this, the literature seldom delves into the role of the proportion of technical R&D personnel in the nexus between a company's digital transformation and its innovation performance. Against this backdrop, in order to explore how digital transformation improves firms' innovation performance and its specific mechanisms, the study selects the Chinese manufacturing companies listed on the A-share market from 2009 to 2022. Following winsorization and other data cleaning procedures, the final sample comprises 1 207 companies, yielding a total of 15 338 observations for analysis. The study applies the Bootstrap method to test the hypotheses, and applies the stepwise method as well as the sequential method to further test the robustness of the results.
The final results show that the digital transformation of manufacturing enterprises positively affects their innovation performance; the absorptive capacity of manufacturing enterprises plays a partial mediating role between their digital transformation and innovation performance; the proportion of R&D personnel in manufacturing enterprises has a moderating effect on the mediating role played by absorptive capacity, i.e., when the proportion of R&D personnel in manufacturing enterprises to the total number of employees is higher, it can effectively enhance the role of absorptive capacity in the digital transformation of enterprises, which will be more effective in enhancing the mediating effect of absorptive capacity between enterprise digital transformation and innovation performance.
In practice,enterprises should tailor their digital transformation strategies to their resources, setting clear 3-5 year digital goals and establishing support systems for talent, standards, and culture. They should leverage professional digital teams for integration and cooperation, creating efficient digital workshops. Since it is crucial to design future digital team structures and platforms, enterprises should focus on absorptive capacity, build open innovation networks, and foster a knowledge-sharing culture to enhance innovation. In addition,in order to strengthen talent development, it is essential to establish performance evaluation systems, increase R&D personnel number to drive innovation and prevent talent loss.

Globally, economies are facing multiple challenges from trade frictions, geopolitical tensions, climate change, etc., which not only pose a direct threat to the economic growth and development of countries, but also have a great impact on the stability and sustainability of the global economic system. The regional digital innovation ecosystem serves as a crucial driver of economic growth and innovation, for it augments the region's resilience against external disturbances and hazards and simultaneously mirrors its adaptability and innovative capability as it undergoes digital transformation. Therefore, an in-depth study of the pathways to enhance the resilience of regional digital innovation ecosystems is significant importance for addressing various challenges in global economic development and promoting sustainable economic growth.
While existing research has explored the resilience of regional digital innovation ecosystems, several gaps remain. First, current studies primarily focus on the concepts, measurement, and governance of ecosystem resilience, with insufficient attention to its antecedents and a lack of comprehensive exploration of influencing factors. Second, the enhancement of regional digital innovation ecosystem resilience is influenced by complex mechanisms across multiple dimensions and levels. A single-perspective approach is inadequate for explaining the variability in ecosystem resilience. There is a noticeable absence of research examining the driving pathways for enhancing resilience from a holistic perspective. In fact, building a resilient regional digital innovation ecosystem is an extremely complex task. It requires not only market entities with innovative capabilities and ample resources such as data, technology, and information, but also an environment conducive to innovation and sustainable development. Future research should focus on achieving effective coordination among various innovation elements, exploring the complex relationships between multiple antecedents and system resilience, and assessing potential substitution effects among different innovation factors.
This study first identifies the factors influencing the resilience of regional digital innovation ecosystems based on the "subject-resource-environment" framework and analyzes the mechanisms of these factors. Next, using a configurational perspective and the fsQCA method, it examines the diverse pathways for enhancing ecosystem resilience. The main findings are as follows. Firstly, compared to innovation resources and environments, innovation entities play a more universal role in enhancing regional digital innovation ecosystem resilience. A high level of industry-university-research coupling coordination and digital economy development is a necessary condition to promote the resilience of regional digital innovation ecosystems. On the contrary, a low level of industry-university-research coupling coordination is a necessary condition to restrict the enhancement of the resilience of regional digital innovation ecosystems. Secondly, there are four driving paths to enhance the resilience of regional digital innovation ecosystems, which can be further categorized into three types: subject-driven digital economy empowerment, government-industry-academia-research collaborative symbiosis, and subject-environment-driven platform support. The potential substitutive relationships among antecedent conditions indicate that, in specific contexts, innovation entities, resources, and environments can equivalently substitute each other to enhance resilience through different routes. Finally, there are four paths that constrain the enhancement of the resilience of regional digital innovation ecosystems, which can be further categorized into three types, namely, total factor deficient, digital economy-dependent and innovation factor dysfunctional. These constraining pathways highlight the main challenges faced by regional digital innovation ecosystems and demonstrate an asymmetric relationship with the driving pathways for resilience.
The research novelties are threefold. First, it pinpoints elements influencing regional digital innovation ecosystem resilience under a subject-resource-environment framework, and further deepens the cross-fertilization between resilience theories and regional digital innovation ecosystem theories. Second, the study unveils diverse resilience-enhancing pathways from a configurational view, addressing academic calls for using configurational perspectives and qualitative comparative analysis methods in studying complex management systems. Finally, it identifies multiple pathways that constrain ecosystem resilience, offering a more comprehensive understanding of resilience enhancement. This not only uncovers the differentiated roles of innovation elements within specific regional innovation ecosystem contexts but also effectively avoids the bottleneck effect of relying on single elements for resilience enhancement, ensuring sustained development and innovative capability.

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.

In the contemporary scientific and technological context, brain-computer interface (BCI) technology has become a cutting-edge area attracting extensive attention. By enabling real-time sharing and deep integration of neural information among individuals, BCI offers more efficient and precise tools for group decision-making, knowledge innovation, and social practice; however, it also intensifies group differentiation and integration, challenging the conventional operation and power structure of social organizations and presenting new issues in social equity and ethics. Thus, it is crucial to probe the social relationship changes caused by BCI .
This study focuses on exploring the profound influence of BCI technology on social relations, with the aim of comprehensively understanding its implications and providing guidance for the harmonious development of technology and society. The research method mainly involves a comprehensive review of literature and in-depth cross-disciplinary analysis. By collecting and analyzing a large number of research materials from fields such as neuroscience, information engineering, sociology, and ethics, this paper systematically expounds on the relationship between BCI technology and social relations. These materials include academic papers, research reports, and relevant case studies, which form the basis for in-depth exploration.
The research concludes that BCI technology brings about far-reaching changes in social relations. At the individual level, it remolds self-cognition. For example, through BCI-controlled devices like brain-controlled prosthetics, individuals' perception of their body's functions and boundaries is transformed, affecting their self-identity and mental health. Interpersonal interaction patterns also change. BCI-based emotion recognition and subconscious information transmission enable more direct and in-depth communication, but they also raise issues such as privacy infringement. In terms of social behavior norms, the combination of BCI and VR technology creates new virtual social experiences, changing social interaction habits and norms, and blurring the line between virtual and real social interactions.
At the group level, BCI technology innovates collaborative models. In complex projects, it allows for the real-time sharing of members' neural-cognitive patterns, improving collaboration efficiency and knowledge synergy. It also reconstructs social network structures. The connection basis in social networks shifts from traditional relationships to neural-activity-based similarities and complementarities, which has a significant impact on information dissemination and social power structures. Moreover, it reshapes group identity and class relations. The different levels of BCI technology application among groups lead to identity differentiation and class-related changes, exacerbating social inequality.
The innovation of this paper lies in its multi-perspective and in-depth analysis. This paper provides a comprehensive analysis of BCI technology, covering not only the technical aspects but also delving into its social, ethical, and philosophical implications. Such a holistic approach allows for a more thorough understanding of the technology's potential impacts on various aspects of society and human life. In addition to the in-depth analysis, this paper also proposes a series of regulatory and guarantee mechanisms to ensure the responsible development and application of BCI technology. For instance, it suggests the establishment of robust ethical review mechanisms. These mechanisms would play a crucial role in ensuring that BCI research and applications adhere to ethical standards, thereby preventing potential ethical dilemmas and misuse of the technology. Moreover, the paper emphasizes the importance of constructing a comprehensive legal regulatory system. This system would aim to protect personal privacy and rights in the context of BCI technology. Given the intimate connection between BCI and personal neural data, ensuring the security and privacy of this highly sensitive information is of paramount importance. The proposed legal framework would help safeguard individuals from potential privacy violations and other related risks. Furthermore, the paper proposes the formulation of social fairness guarantee systems. The benefits of BCI technology should be distributed in an equitable manner across society. This requires the implementation of policies and measures that can promote equal access to and benefits from BCI technology, particularly for disadvantaged groups.
In summary, through its multi-faceted analysis and the proposal of practical regulatory and guarantee mechanisms, this paper makes a significant contribution to the responsible and sustainable development of BCI technology. It provides valuable insights and suggestions for policymakers, researchers, and other stakeholders in the field, helping to ensure that BCI technology develops in a direction that is beneficial to all of society.

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.

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.

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.

The deep integration of technological innovation and high-tech industry innovation is crucial for achieving a high level of technological self-reliance. It also helps secure a competitive edge in international technology. However, the current integration in China faces several challenges, including a disconnect between fundamental and applied research, and difficulties in commercializing research outcomes. Therefore, exploring practical pathways to resolve existing issues and promote deeper integration, grounded in the intrinsic patterns and developmental realities of these innovations, is of significant importance. Current studies have begun to explore the theoretical aspects of the deep integration of technological and industrial innovation, yet they have not adequately addressed the importance of their mutual promotion or provided a clear explanation of their integration mechanisms. Additionally, the current levels of integration across different regions remain unclear, and there is a lack of in-depth exploration into the characteristics and driving factors of this integration.
This paper aims to elucidate the coupling mechanisms of technological innovation and high-tech industry innovation. Based on data from 30 provinces in China from 2012 to 2022, a coupling coordination model is employed to measure the real levels of integration in various regions. Using the Dagum Gini coefficient, Moran′s I, and spatial Markov chain method, the study reveals the spatiotemporal evolution patterns, distribution characteristics, and regional disparities. Subsequently, the CatBoost algorithm is utilized to analyze the importance proportions of the driving factors behind the coupling coordination degree.
The research reveals the following key findings: Firstly, the coupling coordination level of scientific and technological innovation and high-tech industrial innovation in China has been escalating year by year. On the whole, it has ascended from a state of mild imbalance to a primary coordination state. The eastern, southern and central China have reached the primary coordination state, while the remaining regions still remain in an imbalanced state. The provinces with better coupling coordination degree are primarily distributed in the coastal areas, and there exists a notable "Matthew effect" in the changes of coupling coordination degree among provinces. Secondly, the regional gap in coupling coordination degree is trending towards expansion. The intra-regional disparities are relatively small, and the imbalance among regions is the principal cause for the existence and widening of the gap in coupling coordination levels. Thirdly, the spatial correlation and agglomeration effect of coupling coordination degree have been continuously intensified. The evolution of coupling coordination degree types exhibits obvious path dependence, and the higher the coupling coordination level of the neighboring areas, the stronger the positive spillover effect on the local area. Fourthly, the high-tech industrial innovation system exerts a stronger driving force on the coupling coordination degree than the scientific and technological innovation system. Among them, the expenditure on new product development by high-tech enterprises is the most crucial driving factor, the commercialization subsystem plays the most significant role, and the characteristic importance of indicators related to R&D expenditure and personnel input accounts for a relatively high proportion.
Finally, the following countermeasures and suggestions are proposed: First, in response to the low level of integration between scientific and technological innovation and high-tech industrial innovation in China, it is necessary to improve the dynamic matching and collaborative system of scientific and technological innovation and industrial innovation to facilitate effective integration from research to industrialization. Second, against the intensifying imbalance in regional innovation and integrated development, it is essential to promote regional coordinated development of the integration of scientific and technological innovation and high-tech industrial innovation, and strengthen the cross-regional integration of innovative resources. Third, given the important role of enterprises′ R&D investment, new product development, and technology transfer capabilities in innovation integration, it is critical to focus on cultivating the independent research and development capabilities of high-tech industries and strengthening the dominant position of enterprises in innovation. Fourth, it is important to improve the market-oriented transformation mechanism for research and development technology achievements and build a full-chain market transformation ecosystem.

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.

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.

The ESG framework emphasizes sustainable business growth across environmental, social, and governance aspects, aligning with the high-quality development goals of Chinese firms. Increasingly, scholars are examining its micro-level impact on enterprise growth. Studies suggest that robust ESG practices mitigate risks, ease financial strains, and enhance operational sustainability. Rating agencies, key capital market players, strive to offer impartial ESG assessments. Yet, disparities in ESG ratings arise due to the use of varied public and private data and divergent information-gathering capabilities among agencies, leading to inconsistent evaluations for the same company. China's nascent ESG rating system has caused significant stock volatility, impacting investor behavior and complicating corporate investment in value-adding and green innovation projects. This undermines the ratings' objectivity in economic activities and influences corporate default risk. Research is scarce on how rating discrepancies impact default risk and the mechanisms involved.
This paper investigates the impact of discrepancies in Environmental, Social, and Governance (ESG) ratings on the default risk of Chinese enterprises and elucidates the underlying mechanisms at play. The analysis utilizes data from Chinese A-share listed companies on the Shanghai and Shenzhen stock exchanges spanning from 2015 to 2022, alongside data from six prominent ESG rating agencies, to provide an in-depth examination of how ESG rating discrepancies affect corporate default risk.
The study reveals a significant inverted U relationship between ESG rating discrepancies and corporate default risk. Specifically, at lower levels of ESG rating discrepancies, an increase in such discrepancies is associated with a rise in default risk. This effect is primarily driven by two factors: market pressure and financing constraints. Firstly, discrepancies in ESG ratings draw considerable media and public attention, which generates external pressure on companies. This pressure compels firms to adopt short-term strategies to gain stakeholder approval, although such short-term actions often undermine long-term performance, thereby increasing default risk. Secondly, ESG rating discrepancies contribute to market uncertainty, which impacts investor decision-making. This uncertainty restricts external financing and intensifies internal financing pressures, further exacerbating the default risk faced by companies.However, when ESG rating discrepancies surpass a certain threshold, the dynamics change. Beyond this threshold, greater ESG rating discrepancies compel companies to enhance their practices. Specifically, firms improve the quality of their information disclosure to mitigate information asymmetry. This improvement helps reduce market pressure and alleviate financing constraints, thereby decreasing default risk. This finding suggests that, at elevated levels, ESG rating discrepancies can act as a catalyst for self-improvement within companies, encouraging them to focus more on ESG performance to enhance their market image and financing options.
Furthermore, the study highlights that external audit opinions play a significant negative moderating role in the relationship between ESG rating discrepancies and corporate default risk. Unqualified audit opinions strengthen investor confidence in the quality of corporate information disclosure, thus reducing the market asymmetry caused by ESG rating discrepancies and mitigating corporate default risk. This finding underscores the critical role of external audits in supervising corporate ESG disclosures and enhancing investor trust.The study also explores the heterogeneous effects of managerial green cognitive endowment, corporate environmental reputation, and individual ESG performance on the relationship between ESG rating discrepancies and corporate default risk. It finds that the impact of ESG rating discrepancies on default risk is more pronounced in companies with lower executive green cognitive endowment, poorer environmental reputation, and weaker individual ESG performance. This indicates that to improve ESG performance and reduce default risk, companies must consider the cognitive capabilities of their management and their environmental reputation,and ensuring that ESG ratings accurately reflect the company's true situation is crucial for lowering default risk.
The contributions of this paper are threefold. First, it provides a theoretical analysis of the relationship between ESG rating discrepancies and corporate default risk, uncovering an inverted U-shaped relationship and elucidating its underlying mechanisms.Second, it demonstrates the moderating effect of external audit opinions on this relationship, offering new insights into strategies for mitigating corporate default risk. Third, it examines the role of managerial green cognitive endowment, corporate environmental reputation, and individual ESG performance in shaping the impact of ESG rating discrepancies, providing valuable guidance for improving corporate ESG performance and reducing default risk.

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.

The energy crisis and environmental issues are two major challenges facing contemporary society, and existing studies have shown that new energy vehicles play an important role in sustainable development, for they are conducive to conserving energy, reducing carbon emissions, enhancing air quality, and improving climate conditions. With the support and guidance of China's industrial policy, China's new energy vehicle industry as an emerging industry has been developing rapidly. Although China has enacted many policies for the development of the new energy automobile industry to facilitate the rapid growth of production and sales, there are still controversies about the relationship between industrial policy and the technological innovation of enterprises. Some scholars have pointed out that the financial subsidy policy has a promotional effect on the level of technological innovation of new energy automobile enterprises, and some scholars believe that the government's subsidy policy has a crowding-out effect on enterprise R&D investment, which is not conducive to the technological innovation of enterprises. Whether China's current new energy vehicle industrial policy can really help new energy vehicle enterprises improve their technological innovation capability needs to be discussed. In addition, existing research on the relationship between industrial policy and technological innovation of enterprises mostly focuses on a single policy dimension, ignoring the totality of the policy and the mutual influence of the policies. What is the current trend of China's new energy vehicle industrial policy? Which type of policy has the most effective impact on the technological innovation capability of enterprises? Such questions need further research and discussion.
This paper supplements the research literature on the overall perspective of China's new energy vehicle industrial policy through a big data approach to provide suggestions and references for in-depth analysis of new energy vehicle industrial policy. The study utilizes the LDA model to calculate the theme intensity of new energy vehicle industrial policies, and combines the data of 165 new energy vehicle enterprises during the period of 2012-2020 with the double fixed effect model to examine the impact of industrial policies on the technological innovation of enterprises. The results show that China's new energy vehicle industrial policies mainly focus on green environmental protection, infrastructure construction and financial subsidy policies, and the three themes can enhance the quantities of invention, application and appearance patents, and promote technological innovation of enterprises; however, there are differences in the impact of different types of industrial policies on technological innovation of enterprises, among which the infrastructure type of industrial policy has the best effect on the promotion of technological innovation of enterprises. The best effect of infrastructure industrial policy on enterprise technological innovation is found in the infrastructure type of industrial policy. Further analysis of the mechanism reveals that there is a positive moderating effect of enterprise human resource integration capacity between the three themes and enterprise technological innovation, and that the improvement of enterprise human resource integration capacity can further promote enterprise technological innovation.
The conclusions help to improve the understanding and assessment of China's new energy automobile industry policies and provide effective suggestions for the future development of China's new energy automobile industry. The contributions of this study are mainly as follows: first, broadening the perspective of industrial policy research. It uses big data analysis methods and text analysis tools to conduct a scientific and holistic assessment of industrial policy, and it studies and discusses the relationship between industrial policy and corporate technological innovation from a holistic perspective. Second, new methods are introduced to assess industrial policy. The article leverages the Latent Dirichlet Allocation (LDA) model to conduct a thorough assessment of industrial policy, drawing insights from its textual framework. This approach introduces an innovative method for evaluating industrial policies. Furthermore, it contributes to the existing research on human resources by examining the role of human resources through the lens of core competencies, specifically focusing on the integration capabilities of human resources.

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.

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 digital era, businesses are increasingly facing unprecedented challenges and opportunities. Digital innovation has become a crucial driver for high-quality development and maintaining a competitive edge. Despite its importance, there is a significant gap in the literature concerning the comprehensive understanding and multidimensional deconstruction of the internal processes of digital innovation. This study aims to fill this gap by exploring the dimensions of digital innovation, developing a robust measurement scale, and investigating its impact on enterprise performance. The significance of this research lies in providing a deeper theoretical understanding of digital innovation and offering practical tools for businesses to enhance their innovation practices.
To achieve these objectives, the study employs grounded theory methodology, incorporating perspectives from recombination innovation, supply-demand interaction, and service-dominant logic. The interview data came from managers of four manufacturing enterprises, especially managers of information or digitalization departments. Through this rigorous process, two critical dimensions of digital innovation were identified: enterprise design recombination and user use recombination. Enterprise design recombination focuses on how enterprises reorganize resources on a large scale to enhance efficiency and innovation capacity. User use recombination emphasizes the reconfiguration of resources through use, highlighting the role of users as active participants in the innovation process. From two identified dimensions, a digital innovation scale comprising 28 items was developed. The scale underwent thorough validation processes, including exploratory factor analysis (EFA) and confirmatory factor analysis (CFA), to assess its reliability and validity. The scale demonstrated high internal consistency and construct validity, making it a reliable tool for measuring various aspects of digital innovation within enterprises.
The study further employs this scale to empirically examine the impact of digital innovation on enterprise performance. Data is collected from 254 enterprises across the manufacturing industry through questionnaires. The analysis utilizes regression analysis to test the hypothesized relationships between digital innovation dimensions and enterprise performance. The results show that both enterprise design recombination and user use recombination significantly contribute to improving enterprise performance. These results indicate a strong positive correlation between digital innovation and enterprise performance, validating the predictive validity of the developed scale.The study offers several key conclusions. Firstly, the reconceptualization of digital innovation as a recombination of resources provides a nuanced understanding of its internal processes. This perspective shifts the focus from merely applying digital technologies and developing digital products to emphasizing the reconfiguration and integration of digital technologies, data, and experiential knowledge. This aligns with identified characteristics of digital innovation such as reconfigurability, data homogenization, and self-referentiality.Secondly, the study underscores the importance of the interactive roles of both enterprises and users in the innovation process, incorporating the supply-demand interaction and service-dominant logic perspectives. This highlights the role of users as active participants and collaborators in digital innovation, addressing the call for greater focus on user innovation and extending the theoretical foundation for continuous innovation in the digital context.Thirdly, the developed digital innovation scale serves as a valuable tool for subsequent empirical research, offering a robust framework for assessing the multidimensional characteristics of digital innovation. By examining the relationship between digital innovation and enterprise performance, this study validates and extends existing discussions on the positive impact of digital innovation on enterprise performance, underscoring its importance as a strategic choice for future business development.
In conclusion, this study offers a comprehensive exploration of digital innovation dimensions, develops a validated measurement scale, and demonstrates the positive impact of digital innovation on enterprise performance. The innovative perspective on digital innovation’s internal processes and the interactive roles of enterprises and users significantly enriches the theoretical discourse and provides practical guidance for businesses navigating digital transformation. This study not only enhances academic understanding but also equips practitioners with valuable tools to leverage digital innovation for sustained competitive advantage.

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.

As China's strategy for independent innovation continues to deepen and the trend of "de-globalization" in the restructuring of industrial chains intensifies, the issue of "bottlenecks" in key industries is becoming increasingly serious. In the new stage of high-quality economic development in China, it is imperative to accelerate the breakthrough innovation capabilities of "Specialized, Refined, Differential, and Innovative" (SRDI) enterprises, to promote new drivers of development through technological innovation, and to achieve innovation-driven and intrinsic growth. In recent years, SRDI enterprises have made significant progress in the field of standard setting. Technical standards are gradually becoming a key guarantee to lead SRDI enterprises in breakthrough innovation, and to achieve autonomous control, security and stability in industrial and supply chains.
At present, the formulation of technical standards as an important way to integrate into the innovation network has attracted the attention of scholars. Most existing studies focus on the role of technical standards in improving information asymmetry, reducing business operational risks, and constraining technological diversity. Few literatures examine the innovative effects of technical standard formulation from the perspective of the innovation network, especially the impact on breakthrough innovation. Moreover, relevant studies are mainly based on theoretical analysis; due to the difficulty of collecting information on the technical standardization of microenterprises, there is a lack of empirical studies that systematically examine the relationship between technical standardization and breakthrough innovation of SRDI enterprises. From the perspective of innovation network, the innovation network formed by the formulation of technical standards can help SRDI enterprises achieve subversive goals through knowledge sharing and resource complementarity among innovation subjects.
Hence, in order to explore the impact of technical standardization on the breakthrough innovation of SRDI enterprises and its functioning mechanism from the perspective of innovation network, this study takes breakthrough innovation as the dependent variable and the formulation of technical standards as the independent variable, and selects enterprise size, growth rate of business income, cash flow, property rights nature, enterprise age, asset-liability ratio, and enterprise performance as control variables to establish the model. It then takes the listed SRDI enterprises announced by the state since 2019 as a sample, and the final sample obtained includes 960 SRDI listed companies, of which 668 samples have participated in the formulation of technical standards, and 292 samples have never participated in the formulation of technical standards, with a total of 7 723 observations.
It is concluded that, firstly, the formulation of technical standards can significantly promote the breakthrough innovation of SRDI enterprises. Secondly, the impact of technical standard setting is more significant in non-state-owned enterprises, enterprises with stronger absorptive capacity and enterprises with higher proportion of female supervisors. Thirdly, the formulation of technical standards can promote the breakthrough innovation of SRDI enterprises by broadening the knowledge breadth and improving innovation efficiency. Fourth, the supplier’s technical standard setting has significantly improved the breakthrough innovation of SRDI enterprises. However, due to the substitutability and specificity of input, the innovation spillover effect of customer technology standard setting is not significant.
This study has made contributions in three aspects. Firstly, following the theory of innovation networks, the study explores the relationship between standard setting and breakthrough innovation in SRDI enterprises, and verify its mechanism from the perspectives of knowledge breadth and innovation efficiency, which enriches and supplements relevant research. Secondly, the study attempts to shift the present research focus from incremental innovation to breakthrough innovation, actively exploring new technological paradigms and unknown knowledge areas, and deepening existing research on SRDI enterprise innovation. Thirdly, utilizing the supplier and customer data of SRDI enterprises, the study characterizes the innovation interaction effects of upstream and downstream enterprises in the ecosystem, and by the end it proposes to pay full attention to the technological changes of suppliers and customers, actively carry out strategic learning, promote upstream and downstream integration and collaborative innovation.

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.

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.

Although the number of patent applications and patent authorizations in our country has increased steadily, a large number of patents have not been implemented to gain technical advantages or economic benefits, which not only wastes social resources but also hinders the creation of a positive innovation atmosphere across society. Currently, our country is focusing on high-quality development as the thread of work, vigorously developing new quality productivity. This background underscores the importance and urgency of patent transformation and application, as the implementation of patents can supply technological achievements to the market and promote societal industrial transformation. The fourth amendment to the Patent Law of the People's Republic of China added a patent open licensing system. After preliminary pilot experiments, the implementation of the open patent licensing system is now being fully advanced.
The patent open licensing system is a normative system supported by administrative authorities to encourage patentees to actively participate in the transformation and application of patents through licensing, to complete the matching of technology supply and demand, and to achieve the goal of market-oriented allocation of factors. The system incentivizes patentee participation with reductions in annual fees, increases transaction efficiency with standardized licensing conditions, and ensures the smooth operation of the system through the intervention of public power. Universities and research institutes have great potential for patent technology innovation but a low rate of patent transformation, so they are a key target for incentives under the patent open licensing system. The establishment of the patent open licensing system is not only to meet the current needs of patent transformation, but also reflects China's determination to improve the quality of patents and further enhance the national overall innovation level.
In order to ensure the patent open licensing system can fully exert its incentive effects and at the same time make the system consistent with the national long-term development goals, this paper conducts a comprehensive and in-depth analysis of the content of the system: (1) The provisions regarding licensing fees do not conform to patent transaction practices, which may suppress the enthusiasm of domestic and international entities to participate. (2) Reductions in annual fees are an important means to attract patentees to make patent open licensing declarations, but legal provisions are still uncertain and may induce moral hazard. (3) The implementation of the patent open licensing system at every stage cannot be separated from the support and guidance of administrative authorities, but currently, the functions of administrative authorities lack clear stipulations, which may lead to improper government intervention in the market. To reduce conflicts between specific measures within the patent open licensing system, it is necessary to adjust and optimize the patent open licensing system from the perspective of systems theory, specifically focusing on three aspects: the system's philosophy, structure, and specific measures.
This paper advocates the following conclusions: First, the patent open licensing system undertakes the task of promoting the transformation and application of patents in the short term, but it should also aim for long-term goals of improving patent quality and fostering an ecosystem of high-quality innovation. Second, during the implementation of the system, administrative authorities should respect the laws of market operation, avoid interfering with the autonomy of the parties, and retain space for autonomous negotiation for the parties. There should be a formal review of open licensing declarations, an information disclosure system for ex post supervision, and a strengthening of the connection with the patent examination and patent invalidation systems. Third, the proportion of annual fee reductions should be set within a certain range, with differentiated reductions based on the implementation of patents. To prevent open licensing parties from speculating and arbitraging, and to filter out low-quality patents, a mechanism for the return of annual fees should be added, comprehensively examining the subject's subjective intentions, objective actions, and the implementation of patents, setting different amounts of annual fee returns, and pairing them with other types of punitive measures. Fourth, administrative authorities should strengthen their service functions and guide patentees to pay attention to the benefits through patent use through diversified measures, and better balancing the interests of patentees and licensees.