At the moment when AI technology is fully penetrated into all fields of social life, the rise of AIGC(Artificial Intelligence Generated Content) represented by ChatGPT, Sora and ERNIE Bot marks the leap of AI technology to a new stage of development, and indicates the approach of the era of "artificial general intelligence". However, while AIGC greatly amplifies human innovation potential, it also presents considerable challenges to the existing ethical governance system. Against this backdrop, this article examines the current state and future trends of AIGC in China, analyzing its ethical risks and generative logic. Drawing from the ethical risk governance trends of AIGC globally, the article proposes a governance framework that is not only attuned to China's national context but also forward-looking. This is aimed at providing robust theoretical guidance and practical paths for the standardized development of AIGC.
This study commences with an analysis of the potential ethical risks associated with AIGC, considering its technical attributes. Initially, it addresses the risk of ethical value imbalance, primarily evident in the intensification of algorithmic discrimination. Following this, it examines the risk of ethical norms being beyond control, which is predominantly showcased in the challenge of accountability. Lastly, it explores the risk of ethical relationship imbalance, which is chiefly characterized by a diminution of human agency. From the perspective of technological risk, the root cause of AIGC ethical risk lies in the complexity and uncertainty of technology. Particularly under the 'black box' phenomenon of algorithms, the behavior of AIGC becomes challenging to anticipate and manage, thereby amplifying ethical risks. At the same time, the tension between technological rationality and value rationality, as well as the limitations of human risk perception and response, further deepen the complexity of ethical risks.
Considering the trends in AIGC governance, this article advocates "trusted governance" as the core strategy to mitigate AIGC ethical risks. It underscores the importance of technology being controllable, accountable, fair, reliable, interpretable, and secure, with the goal of ensuring that technological advancements are transparent, equitable, and contribute positively to society. At the same time, the Artificial Intelligence Law (scholars' proposal draft) also provides important governance basis and guides the legal path of AIGC trusted governance.
Under the principle of 'trusted governance, this article proposes three core strategies. First, it calls for the establishment of a robust data risk governance framework. By integrating cross-border collaborative governance and internal fine governance, it emphasizes adherence to 'reliability' standards. This includes enhancing regulations for cross-border data flows and promoting data ethics and compliance within enterprises to ensure data usage is reliable and secure. Second, it advocates for an optimized liability attribution mechanism to address AIGC infringement risks. This involves assessing subject obligations, product defects, and infringement liability in light of AIGC's technical characteristics to enforce the 'accountability' standard. Third, it recommends integrating a 'people-oriented' approach into the AIGC ethical governance system. This aims to find technological solutions that are tailored to China's context and capable of addressing its unique challenges. In practice, it shall involve two aspects: first, at the organizational level, creating a dedicated AIGC ethical governance body to oversee ethical governance and supervision, thereby enhancing AIGC's controllability; and second, at the normative level, harnessing the 'complementary advantages' of policies, laws, and technology to uphold standards of 'fairness,' 'safety,' and 'interpretability,' guiding AIGC towards positive development.
In the future, it is essential to further integrate market development with China's national conditions, adopting 'trusted governance' as the core and the 'Proposal Draft' as the foundation to construct a comprehensive ethical risk governance system for AIGC. Moreover, there is a need for ongoing exploration in the realm of ethical governance, focusing on the organic integration of technological ethics with legal governance. This approach aims to devise AI ethical governance strategies that are distinctively Chinese, thereby equipping to adapt to and potentially spearhead the high-quality development of the new round of the global digital economy.

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.

Elevating the level of intelligence is a pivotal measure for enterprises to expedite the formation of new quality productive forces. As a new generation of digital technology, artificial intelligence has realized subversive innovation in production methods, production processes and models, accelerated the transformation of production factors to production capacity, and provided new momentum for developing new quality productivity. However, most existing research on artificial intelligence focuses on the macro level. More literature needs to be examining how artificial intelligence technology empowers the development of enterprises' new quality productive forces.
Therefore, this paper delves into artificial intelligence's empowerment path for enterprises' new quality productive forces. This study focuses on A-share listed companies from 2013 to 2022 to explore the impact of artificial intelligence on the new quality of productive forces within enterprises and the mechanisms that facilitate this influence. Drawing on the theoretical insights, it develops a regression model to assess the effects of AI and further examines the mechanisms through three key lenses: technology empowerment, efficiency empowerment, and information empowerment. To ensure the robustness of the findings, the study performs a series of rigorous checks. These include substituting variable indicators, applying the Propensity Score Matching (PSM) technique, conducting Two-Stage Least Squares (2SLS) regression analysis, and employing a quasi-experimental design centered on the smart transformation of pivotal industries. The results consistently demonstrate the robustness and reliability of our conclusions. Lastly, the empowering effect of artificial intelligence on new quality productive forces can significantly differ because of the distinct internal characteristics and external environment of enterprises. Consequently, the study conducts a heterogeneity analysis of the main effects across three core dimensions of enterprise-level,industry-level,and regional differences.
The study finds that artificial intelligence significantly enhances the development of enterprises' new quality productive forces. Mechanism analysis further reveals that this enhancement is achieved by strengthening digital innovation capabilities, improving the efficiency of supply chain, and mitigating the information asymmetry. Heterogeneity analysis demonstrates that there exist notable disparities in the enabling effects of artificial intelligence on new quality productive forces among different types of enterprises. Specifically, state-owned enterprises, labor-intensive enterprises, those situated in high-tech industries, and those located in regions with substantial fiscal support all exhibit more prominent improvements in their quality new productive forces, being empowered by artificial intelligence. This paper enhances the comprehension of the role of artificial intelligence in the production process at the micro-enterprise level, thereby providing valuable insights for promoting the efficient development of enterprises' new quality productive forces.
The innovations of this paper lie in the following three aspects: Firstly, this paper studies the impact of artificial intelligence level on enterprise development in micro-enterprises. Existing research on artificial intelligence primarily focuses on macro-level effects, such as its influence on regions, industries, and labor markets, with relatively scarce studies at the micro-enterprise level. Furthermore, previous works often measured the artificial intelligence level of enterprises using industrial robot penetration rates, whereas this paper employs text analysis and machine learning methods to construct an artificial intelligence index for enterprises. Secondly, this paper enriches the research on new quality productive forces in enterprises. As a newly proposed concept, the new quality productive forces have predominantly been studied qualitatively, exploring its connotations, characteristics, and formation logic. By leveraging data from listed companies, this paper empirically analyzes the influence of artificial intelligence technology on enterprises' new quality productive forces. Thirdly, this paper provides theoretical support for promoting the enhancement and development of enterprises' new quality productive forces. This paper delves into the mechanisms through which artificial intelligence empowers new quality productive forces from the perspectives of technology, efficiency, and information. The heterogeneous effect on the main effect is further discussed in relation to the differences at enterprise ,industry and regional levels. It offers valuable insights for upgrading enterprises' new quality productive forces.

In order to maintain the dominant position occupied by the United States in the global science and technology industry chain, the U.S. Department of Commerce has included Chinese firms in the Entity List, which has intensified the uncertainty of market competition and challenged the innovation capacity of Chinese firms. The Entity List is a coercive economic measure by which the United States attempts to decouple China's science and technology, aiming to curb China's ability to sustain innovation in the high-tech field through trade sanctions, export and investment controls, and a ban on cooperation between Chinese and American enterprises and universities, such as high-level talent training exchanges.
The great power game affects the global development pattern, and the capability of scientific and technological innovation has a direct impact on the competitiveness of both sides of the game, and enterprises are the main body of innovation. Therefore, an in-depth study of the impact of the U.S. Entity List on enterprise innovation can help reveal the opportunities and challenges embedded in the event, help enterprises identify bottlenecks in their own innovation and development, adjust their innovation strategies, and seek to develop key technological directions and entry points. At the same time, government departments can more accurately assess and predict the economic and technological risks that may arise from the incident, formulate response programs and countermeasures, and provide support and guidance for the innovative development of enterprises. However, there is inadequate literature that analyzes the impact on firms' innovation capabilities from the perspective of the great power game. Regarding the impact of the Entity List, the existing literature mainly studies the motivation of sanctions, the risk of decoupling and cutting off the industrial and supply chain, the legal logic, and the impact of enterprises' entry into the Entity List on their innovation capability is rarely discussed, and there is no consensus on the impact of the Entity List.
Taking 1216 Chinese A-share listed companies from 2017 to 2022 as samples, this paper adopts a staggered difference-in-difference model to test the impacts on the innovation capability of Chinese enterprises and the possible development paths against the backdrop of the Sino-US game. It uses the number of patent applications to characterize the innovation output, and examine the overall innovation capability and substantive innovation capability of enterprises through the "total number of patent applications" and "number of invention patent applications", respectively. The study then conducts the PSM equilibrium test to prevent the sample self-selection bias in the control group from affecting the robustness of the results.
It is found that (1) under the impact of entering the entity list, the innovation capability of Chinese listed companies appears to be enhanced with a lag and short-term; (2) the channel analysis reveals that firms included in the entity list enhance their innovation capability through the intangible asset reserve, growth capacity improvement, and value discovery channels; (3) the heterogeneity analysis shows that private firms, as well as access to government subsidies, low agency costs, moderate and less social responsibility, and control of management's base compensation, innovation capability is more significantly positively affected by the entity list. Further analysis reveals that access to the entity list enhances innovation efficiency but does not significantly affect innovation persistence, which may explain the short-term nature of the effect, and that organizational resilience is a potential motivator of firms' enhanced innovation capability.
Therefore, enterprises in the Entity List of should enhance continuous R&D investment, optimize the innovation output process with a focus on substantive innovation; optimize the management of intangible assets, and shape a good corporate image; it is also critical to enhance the efficiency and sustainability of innovation, and establish an emergency response mechanism to enhance organizational resilience under crisis events. While the government should promote cooperation among industries, universities and research institutes to accelerate the application and commercialization of innovation outputs, improve the regulation of corporate information disclosure, strengthen the construction of scientific research infrastructures, promote the efficiency and sustainability of innovation, and shorten the R&D cycle of enterprises.

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.

New quality productive forces refer to modern advanced productive forces that have been created through revolutionary technological breakthroughs, innovative allocation of production factors, and deep transformation and upgrading of industries, the core essence of which is the qualitative transformation of the three elements of workers,means of labor, and labor objects and their optimal combination, and the most significant symbol of which is a significant increase in total factor productivity. It significantly differs from the traditional productivity of high input and high energy consumption, emphasizes key and disruptive technological breakthroughs, and is characterized by high technology, high efficiency and high quality. New quality productive forces bring about a qualitative leap in productivity and serve as the driving force for facilitating high-quality development in the new era. The development of artificial intelligence (AI) provides new ideas and new impetus for fostering new quality productive forces and promoting high-quality development. AI's ongoing development will drive technological innovation, increase production intelligence, spawn new economic sectors, and integrate with traditional industries to enhance resource allocation efficiency and labor productivity. This integration will steer traditional production models towards greater scalability and specialization, moving industries up the value chain.
The examination of artificial intelligence's influence on new quality productive forces holds substantial theoretical and practical importance. The literature review indicates that existing studies predominantly concentrate on the essence, value, and developmental priorities of new quality productivity, as well as AI's effects on productivity, economic growth, employment, and industrial structure. However, there is a scarcity of research addressing the nexus between AI and new quality productive forces, and empirical evidence on AI's role in shaping these forces is limited. The reasons and mechanisms by which AI bolsters new quality productive forces are not well understood.
This paper aims to bridge this gap by theoretically and empirically analyzing AI's impact on new quality productive forces using provincial panel data, revealing the underlying mechanisms and regional disparities in AI's promotion of new quality productive forces, thereby enriching and advancing the discourse on AI and new quality productive forces. This study focuses on 30 provincial administrative regions in China. Data limitations preclude the inclusion of Hong Kong, Macao, and Taiwan, while Xizang is omitted due to incomplete data records. To ensure data consistency and completeness, linear interpolation is applied to estimate missing values in certain provinces; any unfillable gaps are addressed by listwise deletion, ensuring the reliability of the analysis.
The findings suggest that the development of AI can significantly contribute to the improvement of new quality productive forces. The robustness of the findings of this study is verified by conducting robustness tests,shortening the sample period, replacing the explanatory variable, adding control variables, and removing extreme values. In addition, the study employs the number of robots installed in the U.S. as the instrument variable to address potential endogeneity issues in the model and ensure that the estimates are unbiased. Through mechanism analysis, it reveals that AI contributes to new quality productive forces through three main channels: improving innovation, improving energy efficiency, and improving digitization. Through heterogeneity analysis, the study further finds that the driving effect of AI on new quality productive forces is more significant in regions with high marketization, high technology aggregation, and high industrial structure optimization.
This paper deepens the theoretical mechanisms by which AI promotes new quality productive forces on the basis of the existing literature and provides strong empirical evidence for the new quality productive forces effect of AI through empirical tests. Further, this study explores the fundamental question of the path through which AI mainly affects new quality productive forces within a unified framework, supporting the role of innovation capacity, energy efficiency, and digitization level in the path through which AI affects new quality productive forces, and deepens the existing literature and related studies. In accordance with the findings of the study, the paper puts forward policy recommendations conducive to the full development of AI and the promotion of new quality productive forces, which will provide important decision-making references for the development of new quality productive forces in various regions according to local conditions.

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.

Since the outbreak of the China-American trade friction in 2018, China has faced increasingly severe challenges in key core technology fields. Despite a surge in the number of international patent applications, China's dependence on foreign intellectual property has not diminished, indicating an underlying weakness in innovation quality. Against this backdrop, the “Specialization, Sophistication, Differentiation, and Innovation” (SSDI) policy for small and medium-sized enterprises (SMEs), initiated by China's Ministry of Industry and Information Technology in 2011 and formally implemented in 2018, aims to cultivate “Little Giant” companies that excel in innovation and dominate niche markets. These firms are vital for driving technological innovation, enhancing industrial chain levels, and strengthening international competitiveness. However, while existing research suggests that the SSDI policy has encouraged increased R&D investment and innovation output, there is a paucity of in-depth analysis on whether it effectively improves the quality of corporate innovation. This research aims to contribute to a more comprehensive understanding of the factors that influence innovation policy outcomes. This is crucial for policymakers seeking to refine the SSDI policy and for firms looking to optimize their innovation strategies in the face of evolving market conditions and policy environments.〖HJ*2/5〗
This paper first proposes hypotheses on the impact and mechanisms of the SSDI policy on the technological innovation of “Little giant” enterprises, as well as the differential effects of the policy under varying levels of market transparency through a theoretical model. Further, this paper utilizes data from listed companies on the Shanghai and Shenzhen A-shares market from 2018 to 2023 which is sourced from the CSMAR and CNRDS databases, and employs a multi-period difference-in-differences (DID) method to test the above hypotheses. The research findings reveal that the SSDI policy significantly enhances both the innovation quality and quantity of “Little Giant” enterprises. Moreover, the policy has a gradient promotion effect, meaning that “Little Giant” enterprises outperform provincial and municipal SSDI enterprises in terms of technological innovation levels, and the latter, in turn, outperform other enterprises. By alleviating financing constraints, providing risk compensation, and promoting the accumulation of human capital, the SSDI policy substantially improves the innovation performance of “Little Giant” enterprises. In terms of human capital, it is the high level of human capital that is the key factor in improving the innovation quality of “Little giant” enterprises. Further research finds that the enterprises adopt different innovation strategies under varying levels of corporate information transparency. Specifically, when corporate information transparency is low, “Little Giant” enterprises receiving SSDI policy support tend to opt for strategies that increase the quantity of innovation rather than its quality, thereby engaging in “innovation pandering” behavior. In contrast, when corporate information transparency is high, the SSDI policy effectively promotes the innovation quality of “Little Giant” enterprises. These findings are pivotal as they not only substantiate the SSDI policy efficacy but also highlight the conditional nature of this effectiveness, contingent upon the level of corporate information transparency. By revealing the differential impact of the policy in various market conditions, this study offers valuable insights for policymakers aiming to refine the SSDI policy and for “Little giant” enterprises seeking to optimize their innovation strategies.
The possible marginal contributions of this paper are threefold. Firstly, current research on the effect of the SSDI policy mostly focuses on the quantity of innovation, this paper examines the impact of the policy on both the quantity and quality of enterprise innovation at the same time, which helps to comprehensively assess the effect of the policy. Secondly, most of the existing innovation policies only support technological innovation of enterprises from the aspect of financial support, and lack the design of human capital incentives. This paper not only analyzes the role of financial support of the policy, but also pays special attention to the role of different human capitals in the process of innovation, which provides an important idea for the incentive design of the innovation policy. Thirdly, the impact of innovation policy on the quality of enterprise innovation is controversial, and why the effect of the SSDI policy remains to be further analyzed. Following the information asymmetry theory, this paper analyzes the differentiated innovation strategies that enterprises may adopt under different market transparency conditions, and emphasizes the importance of constructing a transparent and efficient market information mechanism to stimulate the innovation vitality of enterprises.

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.

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.

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.

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.

Along with the strategy of innovation-driven development, public procurement has become an effective tool for supporting cutting-edge technological innovation and achieving sustainable development. However, existing studies lack a consensus on the connotation of innovation-oriented public procurement, resulting in inconsistent conclusions in terms of concept definition and measurement dimensions, and systematic research gaps on its influencing factors, mechanisms and policy effects. To further clarify the content, research scope, and boundaries of innovation-oriented public procurement, this study makes a comprehensive review of relevant domestic and international literature, and employs bibliometrics and content analysis to systematically summarize the research frontiers, development trends, and hot topics of this theme, aiming to construct an integrated research framework. Simultaneously, in accordance with the typical contextual characteristics of China and representative literature, the paper prospects the future research trends of innovation-oriented government procurement in the hope of providing effective references for both the academic and practical communities.
Firstly, this paper conducts a quantitative analysis of foreign research achievements on innovation-oriented government procurement based on the WOS database. Overall, there is a continuous increase in the volume of publications on innovation-oriented public procurement, with developed countries being the main research subjects, exhibiting a significant characteristic of interdisciplinary fusion. Then it elaborates on the relevant concepts, types, and evolutionary processes of the functions of innovation-oriented public procurement. This paper holds that innovation-oriented public procurement is a practical activity to meet public demand, stimulate innovation and create social value through public procurement in order to achieve specific policy goals in technology, industry or society. It is found that the end user of the product, the degree and form of innovation and the strategic orientation are the basis for the classification of innovation-oriented public procurement. In terms of functional positioning, the evolutionary process of government procurement supporting innovation can be roughly divided into four stages: commercialization, standardization, policy orientation, and strategic orientation. Thirdly, the implementation of innovation-oriented public procurement is influenced by individual, organizational and environmental factors. In order to effectively exert the innovation incentive function of public procurement, it is necessary for the transaction subjects to have a sense of change and a certain professional ability, put forward high requirements on factors such as organizational resource and innovation willingness, innovation-supporting policies, markets and cultural environments. In addition, innovation-oriented public procurement releases spillover effects on corporate performance, industrial upgrading and social development, but it is also accompanied by risks such as crowding out private R&D investment and exacerbating technology lock-in. The aforementioned process mechanisms are also influenced by contextual factors such as organizational and market levels. Given the above analysis, this study further constructs the integrated research framework of innovation-oriented public procurement.
Finally, in order to provide theoretical and practical guidance for future research, this study makes a comprehensive integration of the current situation of domestic research in this field and the unique situational characteristics of China, and proposes future development directions for innovation-oriented public procurement research in the Chinese context. Researchers should (1) conduct localized research on innovation-oriented public procurement in China , which involves typical Chinese contextual factors such as transition development stages, fiscal decentralization institutional background, and "guanxi culture"; (2) explore innovation-oriented public procurement from a multi-factor, multi-level interactive perspective,such as the studies on compound influencing factors and the synergistic effects of policies; (3) optimize the research methods and technical means of innovation-oriented public procurement from the aspects of qualitative research, large sample data and meta-analysis; (4) enrich the research objects and contents from the perspectives of procurement subjects, forms and effects; (5) carry out research on the mode reconstruction of innovation-oriented public procurement against the backdrop of digital economy, and how to utilize digital technology to restructure the standardized procurement processes, while leveraging public procurement to drive iterative innovation in the digital industry, will be the key issue in this field.

The high-quality development of China's economy necessitates the unlocking of the potential of data elements, the advancement of productivity reforms and innovation, and the creation of new forms of high quality productive forces. However, current research fails to elucidate the mechanisms by which data elements influence the development of new high-quality productive forces within enterprises. It also falls short in addressing how traditional production factors interact with these data elements. As a result, enterprises struggle to understand how to leverage both data elements and traditional production factors to foster the development of their new high-quality productive forces in their day-to-day operations Therefore, this paper comprehensively constructs a theoretical analysis model of the data elements on the development of enterprises' new quality productive forces from three aspects: direct effect, indirect effect and multiplier effect.
Using the panel data of Chinese listed companies from 2011 to 2022, this study examines the impact of the data elements on the development of new quality productive forces from the perspective of effect decomposition. The results show that (1) data elements can effectively empower the development of new quality productive forces of enterprises, and the conclusion is robust;(2) enterprises' new quality innovation and labor skill structure have a mediating effect in the development of enterprises' new quality productive forces empowered by data elements; (3) data elements has a multiplier effect in the process of new quality innovation and labor skill structure promoting the development of enterprise new quality productive forces; (4) the enabling effect of data elements is only obvious in highly competitive, high-tech and non-heavy pollution industries, and is more effective in regions with complete digital infrastructure, high level of data talent agglomeration and open data.
According to the derived conclusions, data elements exert multiple influences on the development of new high-quality productive forces within enterprises. In terms of direct effects, firstly, data elements can improve the decision-making efficiency and quality of enterprises by driving their management decisions. Secondly, through data sharing and integration, on the one hand, enterprises can improve the communication efficiency among various departments;on the other hand, enterprises can improve the collaborative production efficiency within their respective supply chains. Thirdly, the application of enterprise data elements can promote the transparency of enterprise information to alleviate the information asymmetry between enterprises and investors, thus improving the financing efficiency of enterprises. Finally, the intricacy of a company's production processes can be significantly streamlined through the application of data elements. In terms of indirect effects, first of all, on the one hand, enterprises can realize the efficient connection between innovation supply and demand through the application of data elements, reducing the cost and risk of R&D innovation, and thus improving the efficiency of new quality innovation of enterprises. On the other hand, the application of data elements can optimize the technological process and product design of enterprises to ensure the improvement of the quality of new quality innovation. Secondly, through the application of data elements, enterprises will create jobs of high-skilled labor and have a substitution effect on low-skilled labor, which can improve the skill structure of enterprises' labor and thus promote the formation and development of enterprises' new quality productive forces. In terms of the multiplier effect of data factors, the core logic is that it can help enterprises re-understand the traditional factors of production, expand the allocation space of traditional factors, improve the efficiency of traditional resource allocation, and thus magnify the role of other factors in the development of new quality productive forces. This study specifically focuses on the multiplier effects of data elements on two key traditional factors: technology and labor. It finds that the multiplier effect of data elements on technology is reflected in the expansion of innovation boundary and the acceleration of technology iteration, and the multiplier effect on labor is reflected in the further improvement of the comprehensive quality of labor. To summarize, by constructing the analysis model of “direct effect—indirect effect—multiplier effect”, this paper provides empirical evidence and policy implications for clarifying the enabling effect of data elements on enterprises' new quality productive forces and releasing the potential of data elements' new quality productive forces.

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.

Digital transformation is a key driver of the current global economy, acting as a catalyst for industrial upgrading and enhancing corporate competitiveness to foster sustainable economic growth, which the Chinese government has prioritized as a core national strategy. By 2022, China’s digital economy reached an impressive scale of 50.2 trillion yuan, accounting for 41.5% of the GDP, underscoring the critical role of digital integration in economic advancement. The interplay between digital transformation and the real economy is profound, with substantial implications for corporate digitalization. This transformation enhances information flow, optimizes resource allocation, and stimulates innovation and productivity, thereby creating a favorable environment for high-quality development. However, the relationship between digital transformation and productivity is complex and multifaceted, warranting further investigation.
This paper centers on new quality productive forces, a concept emphasizing the urgency to enhance total factor productivity through technological innovation and the effective integration of resources. New quality productive forces represent a paradigm shift that not only focuses on quantitative growth but also emphasize qualitative improvements in productivity, reflecting the evolving demands of a modern economy. While existing literature recognizes the positive impacts of digital transformation—such as increased management efficiency, optimized specialization, and enhanced innovation capabilities—significant discrepancies remain regarding its overall effectiveness in different contexts. Some scholars have raised concerns about the multifaceted nature of the transformation process, noting that it is often fraught with complexities, including cultural resistance, inadequate infrastructure, and the need for substantial investment in skills and technologies. These factors can result in delayed effects, preventing the anticipated benefits of digital initiatives from being immediately realized.Consequently, it is essential to delve deeper into how digital transformation influences new quality productivity, as this understanding holds implications for both theoretical exploration and practical application. By analyzing the mechanisms through which digital initiatives contribute to productivity improvements, we can uncover valuable insights that guide policymakers and business leaders in crafting strategies that foster sustainable growth.
To explore this relationship, this paper adopts the Technology-Organization-Environment (TOE) framework, which posits that the success of technological innovation is influenced by three dimensions: technology, organization, and environment. From a technological perspective, the size of a firm and its type of technology dictate its capacity for transformation. Larger enterprises, with their abundant resources, typically exhibit a greater ability to adapt to new technologies, thereby enhancing new quality productivity. In contrast, high-tech firms leverage their technological advantages to achieve breakthroughs through digital transformation. Organizationally, the decision-making capabilities of management are crucial for the success of transformation efforts, as they significantly impact resource integration and efficiency. Additionally, the competitive environment plays a vital role; market pressures compel firms to rapidly adopt new technologies, enhancing resource allocation efficiency and driving productivity growth. This paper constructs a theoretical framework encompassing firm size, technology type, market competition, managerial capabilities, and resource allocation efficiency. This paper proposes hypotheses that will be empirically tested using data derived from A-share listed companies. Utilizing the entropy method, this paper measures new quality productivity through dimensions like innovation leadership, enterprise upgrading, technology-driven growth, and sustainability.
The results show that digital transformation positively impacts new quality productivity in a U-shaped manner. As the level of transformation increases, its beneficial effects amplify. Furthermore, the analysis suggests that larger firms, those in high-tech industries, and enterprises in competitive markets experience more significant advantages from digital transformation. Additionally, this paper identifies that managerial capabilities exert a nonlinear moderating effect, while resource allocation efficiency serves as a significant mediating factor, enhancing capital and labor allocation.
The contributions of this paper are threefold. First, it provides a nuanced examination of new quality productive forces, focusing on innovation leadership, enterprise upgrading, and sustainable development.Second, it enriches the existing body of literature by elucidating the nonlinear relationship between digital transformation and productivity. Lastly, the study explores the nonlinear moderating effect of managerial capability improvement and the mediating effect of resource allocation efficiency, revealing their key roles in digital transformation. It also addresses literature gaps and provides practical insights for businesses to leverage digitalization for enhanced productivity and sustainable growth.

The intensification of anti-globalization and protectionism has led the world into a new period of turbulent transformation. At this stage, new quality productive forces is a key factor for China's high-quality development. New quality productive forces is characterized by high technology, efficiency, and quality, fundamentally transforming productivity through technology, resource allocation, and industrial transformation. Regional disparities exist in China in terms of economy and industry due to varying development conditions, necessitating tailored development strategies as a primary governmental task. It is of significant theoretical and practical importance to analyze the evolution and disparities in new quality productive forces across regions, study influencing factors, identify obstacles, and explore enhancement paths for driving high-quality development so as to promote industrial upgrading and transformation, and lead global economic development.
A new quality productive forces development level index system is constructed based on data from 30 Chinese provinces between 2011 and 2022. The entropy method is used to measure the current state of new quality productive forces development at the national level, in each province, and within the 8 major economic zones. Markov chains and Dagum's Gini coefficient are employed to analyze the dynamic trends and regional development disparities. The DEMATEL-ISM model is utilized to conduct a structural analysis of the influencing factors of new quality productive forces, identifying intrinsic key factors and the logical relationships and pathways between these factors. Main obstacles hindering the development of new quality productive forces are identified through obstacle degree calculations, followed by targeted path analysis for enhancing new quality productive forces in each region, integrating all analytical indicators.
The results show that (1) in terms of the development of new quality productive forces, China has experienced a consistent upward trajectory from 2011 to 2022. Provinces with high development levels include Guangdong, Beijing, Jiangsu, Zhejiang, and Shandong. When divided by region, the overall level of new quality productive forces is highest in the eastern coastal regions. (2) According to Markov chain and Dagum analysis, significant differences exist in the levels of new quality productive forces development across various regions in China. These differences primarily stem from disparities among the 8 major economic zones. The largest regional disparities are between the northwest region and the eastern coastal, southern coastal, and northern coastal regions, with the lowest disparities between the middle reaches of the Yellow River and the middle reaches of the Yangtze River. Within various regions, the southern coastal areas exhibit the greatest disparities, while the eastern coastal areas show lower and more balanced differences. Additionally, there are spatial spillover effects, where regions with high development levels can drive development in regions with lower levels without hindering the development of higher-level regions. (3) Utilizing the DEMATEL-ISM model and obstacle degree analysis, the study reveals that within the 15 primary indicators of new quality productive forces, 8 indicators are identified as root factors, which include aspects related to the human capital structure, while 7 indicators are classified as outcome factors, encompassing elements such as human capital investment. Root factors affecting the structure include levels of technological cooperation flow, development of emerging industries, and future industrial layout. Intermediate influencing factors include human capital investment, telecommunications infrastructure, and human capital structure, among 7 indicators. Surface influencing factors include human capital assurance, transportation infrastructure, and five other indicators. The main obstacles in most regions are inadequate development in technological cooperation flow, future industrial layout, structure of the digital economy, and level of technological innovation. (4)Through path analysis, the study identifies four strategic development paths to bolster the new quality productive forces. The first path is dedicated to surmounting core barriers and igniting the catalyst for their advancement. The second path is geared towards navigating intermediate obstacles and establishing a systematic framework for growth. The third path underscores the importance of regional collaboration to amplify joint development initiatives. The last path concentrates on the pivotal role of core leadership in fostering balanced development of new quality productive forces.

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.

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.

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.

With the rise of the intelligent revolution, artificial intelligence (AI) came into being. AI refers to the process of simulating and extending human intelligence, enabling computer systems to possess cognitive abilities similar to those of humans. Achieving this goal typically relies on core technologies such as machine learning, deep learning, natural language processing, and pattern recognition. Human-computer interaction (HCI) is a technology that involves the mutual influence and interaction between humans and AI, focusing on the design and implementation of user-friendly interfaces to facilitate interaction between users and computer intelligent systems. HCI encompasses important aspects such as user experience, human-computer interface, and interaction design. The human-computer interaction between leaders and AI refers to the communication process where leaders interact with AI based on common goals.
In the traditional context, leaders play a key role in decision-making and commanding in organizations, and their behavioral characteristics usually reflect subjectivity and personal experience. However, on the basis of data analysis and model training, AI can quickly extract and analyze valuable information, reflecting the objectivity of technology. In contrast, leaders' behavior is limited by personal resources and cognitive bias, while AI's data-driven decision-making provides objective and unbiased analysis results, forming a sharp contrast. Yet, there is a lack of research on the interaction between leaders and AI, so it is necessary to explore it in depth.
First of all, this paper focuses on the evolution of the relationship between leaders and AI under human-computer interaction, including the stages of "command execution", "mirror symmetry", "intelligent leadership" and "interactive symbiosis", as well as the new requirements faced by leaders, including re-examining the leadership role, reshaping leadership skills, adhering to the unique characteristics of human beings, and demonstrating leadership empathy. Secondly, taking decision-making, empathy and innovation as dimensions, this study discusses the role and function of leaders and AI in various dimensions and the interaction between them. In the aspect of decision-making, the differences between leadership decision-making and AI decision-making, the advantages and limitations of AI decision-making, and how to enhance the effect of decision-making between man and machine are discussed. In the aspect of empathy, it discusses whether artificial intelligence is emotional, related theories, and how to establish an emotional connection between man and machine. In terms of innovation, this paper discusses the innovation interaction between man and machine from the generation stage, elaboration stage, defense stage and implementation stage. Finally, this paper proposes corresponding practices, including shaping AI ethics to ensure that algorithms are good, leaders play the role of responsible subjects, and data governance to promote the development of organizational intelligence.
In summary, this study offers theoretical significance in three aspects. Firstly, previous studies have mainly focused on how a particular leadership behavior can use AI to enhance effectiveness or improve the relationship between superiors and subordinates. There is a lack of research on the human-machine interaction between leaders and AI. This study enriches the literature on human-machine interaction and expands the development of leadership research in the context of organizational intelligence, while also providing a new research perspective for the field of human-machine interaction. Secondly, this study utilizes the “leader-follower” research framework to deeply explore the evolution of the relationship between leaders and AI. It applies traditional interpersonal interaction patterns to the field of human-machine interaction, revealing the dynamics of the interaction between leaders and AI, and further promoting the development of human-machine interaction theory. Finally, by analyzing the interactive processes between leaders and AI in decision-making, empathy, and innovation, this study reveals the leadership transformation in the context of organizational intelligence. It analyzes the impact of AI on the roles and functions of leaders, helping the theoretical community to re-examine the new changes in leadership and grasp the leadership needs and development direction in the era of intelligence. This provides a foundation for constructing leadership theories for the intelligent era.

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.

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.

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

In 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 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.

The digital-intelligent transformation of manufacturing enterprises is an inevitable choice to realize the deep integration of the digital economy and the real economy,and to promote the high-quality and sustainable development of enterprises. It is also an important starting point to accelerate the development of new quality productive forces. With the deep integration and development of artificial intelligence,big data and other technologies,the digital and intellectual transformation and upgrading represented by digitalization and intelligence provides a new power source for the scientific and technological progress and innovative development of manufacturing enterprises,which has become the core engine to boost the total factor productivity of enterprises. Therefore,how to seize the opportunity of the era of digital intelligence,how to enable manufacturing enterprises to comprehensively reshape,transform and upgrade,and how to promote the improvement of total factor productivity of manufacturing enterprises have become a hot topic in academic and practical circles.
This paper takes China's A-share listed manufacturing companies from 2015 to 2022 as the research sample,and uses a two-way fixed effect model to empirically analyze the impact and mechanism of digital-intelligent transformation on the total factor productivity of manufacturing enterprises. From the perspective of combining digitalization and intellectual,this paper explores the role of digital-intelligent transformation in improving the total factor productivity of enterprises. At the same time,it clarifies the intermediary role of the transformation of resource allocation efficiency,the innovation capability into log-intelligence,and the improvement of total factor productivity of manufacturing enterprises. At last,it analyzes the moderating effect of external financing constraints and environmental competitiveness on the two,which provides a new perspective for understanding the improvement of total factor productivity under the digital-intelligent transformation.
The results show that digital-intelligent transformation has a significant impact on the improvement of total factor productivity of manufacturing enterprises. The mechanism test finds that digital-intelligent transformation helps to promote the total factor productivity of manufacturing enterprises by improving the efficiency of resource allocation and innovation ability. Financial constraints negatively regulate the relationship between the digital-intelligent transformation and the total factor productivity of manufacturing enterprises,while environmental competitiveness positively moderates the relationship between them. The results of heterogeneity analysis show that the improvement of TFP by digital-intelligent transformation is more significant in large enterprises,state-owned enterprises and enterprises in the central region. Further research finds that digital-intelligent transformation can significantly promote manufacturing enterprises to accelerate the development of new quality productivity by improving total factor productivity.
This paper provides empirical evidence and policy implications for the digital-intelligent transformation of manufacturing enterprises to boost the high-quality development of manufacturing enterprises and accelerate the development of new quality productive forces. The government should issue relevant policies and regulations to promote enterprises' digital-intelligent transformation,and reduce their transformation costs and risks by means of tax incentives,financial subsidies and financing support. According to the needs and characteristics of small and medium-sized enterprises,the customized digital-intelligence solutions should be provided for them,so that they can make better use of digital and intellectual technology to improve the responsiveness of supply chain and to cope with the rapid changes of the market and the fluctuation of customer demand. Manufacturing enterprises should give full play to the enabling role of data elements,actively use the digital and intellectual technologies to promote the transformation and the remodeling of manufacturing enterprises,and effectively play the positive role of resource allocation and innovation ability in improving total factor productivity. While it is essential to fully leverage the regulatory role of financing constraints and environmental competition,explore various financing methods such as equity financing,debt financing,government subsidies,and venture capital to reduce the impact of financing constraints on the intelligent transformation. The management should fully recognize the uncertainty of the external macro environment,and maintain the sensitivity and strategic insight to the external environment,so as to minimize the impact of the uncertainty of the external environment and ensure the stable development of the enterprises.

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.

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.

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.

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.

Since the implementation of the "Made in China 2025" strategy in 2015, the size of China's manufacturing industry has grown substantially, and its overall capacities have greatly improved. China has held the title of the world's largest manufacturing nation for 13 straight years. Meanwhile, the world is undergoing the most significant changes, characterized by a cooling global economy, the emergence of new trade protectionism and deglobalization trends, increasing geopolitical conflicts, and other global economic uncertainties. China's manufacturing industry is facing a progressively complex external environment. Enhancing its manufacturing resilience is an inevitable requirement for China's manufacturing industry to resist external uncertainties, promote high-quality development of the manufacturing industry, and smoothly realize the transition from a "big manufacturing country" to a "powerful manufacturing country". Digital transformation is the process by which enterprises utilize digital technologies to enhance efficiency, improve business processes, and innovate value creation methods. It can enhance manufacturing resilience through various means, such as promoting production process reengineering, enhancing collaborative innovation capabilities, and improving resource allocation efficiency and total factor productivity. Therefore, there are significant theoretical and practical implications for exploring how digital transformation can boost the resilience of manufacturing.
There is limited research on manufacturing resilience through the lens of digital transformation.Using panel data samples from 30 provinces from 2010 to 2022, this study adopts the dynamic threshold effect model, the moderated mediation effect model and the differential GMM model to analyze the impact of digital transformation on the development resilience of the manufacturing industry. It is found that (1) from a perspective of evolution, the resilience of China's manufacturing industry has slightly declined. Although the difference in manufacturing resilience varies among provinces, it shows an overall shrinking trend. (2) The regression results of the benchmark model demonstrate that digital transformation has a significant enhancing effect on manufacturing resilience. Furthermore, this effect is not linear. There exists a dynamic threshold effect, and when the level of digital transformation meets or exceeds the threshold, its impact on manufacturing resilience will exhibit a marginal decline. (3) The results of heterogeneity test indicate that the impact of digital transformation on manufacturing resilience varies among enterprises, industries, and regions. Compared to private enterprises and small and medium-sized enterprises, digital transformation has a more significant impact on enhancing the manufacturing resilience of state-owned or large enterprises; in low threshold areas, except for textiles, there is a significant enhancing effect on the manufacturing resilience of various subsectors of the manufacturing industry, while there are differences in effects within high value areas; compared to eastern and central China and low human capital areas, digital transformation has a more significant impact on enhancing manufacturing resilience in the western region and high human capital areas in China. (4) During the process of enhancing manufacturing resilience, digital transformation has a moderated mediating effect.Technological innovation is the mediating variable, and financing constraints and external financing are the moderating variables. Simultaneously, this digital transformation has significant industry and regional homogeneity effects. Hence, China should pay more attention to enhancing manufacturing resilience, and continuously strengthening the foundation of digital transformation by vigorously promoting a "new infrastructure". To enhance targeted digital transformation and manufacturing resilience from a heterogeneous perspective and consider technological innovation and financing convenience, it is necessary to fully leverage the peer effects of digital transformation and accelerate the process of digital transformation in the manufacturing industry through a digital transformation resource sharing platform.
The marginal contribution of this article lies in three aspects. The study first adopts a single indicator method to measure manufacturing resilience, which avoids the causal inversion problem that may occur when using a multi-factor indicator system to measure manufacturing resilience. Then it analyzes the nonlinear relationship between digital transformation and manufacturing resilience based on a dynamic threshold effect model, which enriches the theoretical connotation of the relationship between digital transformation and manufacturing resilience. Lastly, it advances the understanding of the relationship between digital transformation and manufacturing resilience by clarifying the internal mechanism of the relationship between digital transformation and manufacturing resilience, and the peer effects of digital transformation on manufacturing in different industries and regions.

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.

Data elements are strategic and fundamental resources for China's development and serve as a powerful driving force for the digital economy's growth. As a new type of production factor, the data element is unique because it represents an advanced productive force-new productive force. Data elements are of fundamental significance for realizing data value, digital industrialization, and digitalization of governance, and have the characteristics of high transformability, high innovativeness, and high integrability. The exploration of the linkage path to achieve the marketized high-level development of data elements significantly improves new-quality productive forces.
At present, China is in the initial exploration stage in promoting the marketization of data factors. At this stage, research predominantly begins with theoretical analysis, often focusing on a single factor. There is a need for more detailed, stage-by-stage identification of the complex causal relationships and the diverse concurrent paths involved in the marketization of data elements. Regarding research methods, few scholars use fuzzy qualitative comparison to analyze the configuration effect of market-oriented development of data elements, and few works of literature use artificial neural network models to analyze their relative importance. In addition, most existing studies construct an index system from a single stage or link of data element marketization and measure its development level, failing to consider the entire stage of data element marketization development and the identification of related variables. In different stages of data factor marketization allocation, are there core and necessary conditions for the realization of high-level development of data factor marketization? Which paths can be combined to improve the level of data factor marketization? What is the relative importance of each path? These questions still need to be further explored.
Utilizing the theoretical framework of the Technology-Organization-Environment (TOE) strategy typology, this study examines 30 provinces in China as case studies spanning from 2020 to 2022. It applies a fsQCA-NCA-ANN methodology to discover the multiple linkage paths of China's data element marketization and high-level development from the two phases of data element marketization construction and valorization-allocation, as well as the three levels of technological strategy, organizational resources, and external environment based on the grouping perspective.
The conclusions and practical implications are presented. First, more than the technical strategy, organizational resources, and external environment of the two stages must constitute the conditions for the high-level market-oriented development of data elements. Therefore, the high-level development of China's data element marketization results from the synergistic and supporting effects of various antecedents in the two stages. Second, the high-level development of data element marketization is formed by the joint action of multiple factors, and there are two realization paths in the stage of data element marketization construction, i.e., technology-organization-market diversified infrastructure-driven and technology-government-market diversified competition-driven.There are four realization paths in the data element valorization-allocation process, i.e., technology-policy binary endowment-driven, technology-industry binary openness-driven, technology-government-industry multiple openness driven, and technology-organization-environmental balance driven. These paths reflect the various ways in which the high-level development of the marketization of data elements can be achieved across different provinces and under varying development conditions. Configuration analysis reveals a variety of realization paths to achieve high-level development in the two stages of market-oriented construction and value-oriented allocation of data elements. The diversity of these paths reflects the complexity of the two stages. Each province should flexibly choose the appropriate implementation path according to the specific situation to achieve the high-level development of marketization of data elements. Finally, the relative importance of each antecedent condition in the two stages is ranked by ANN sensitivity analysis. In the stage of market-oriented construction, infrastructure maturity is the most influential antecedent condition. In areas with relatively complete information infrastructure construction, paths and combinations such as organizational soundness, government support, and significant data market demand can realize the high-level development of data element marketization. The information commercialization level is the most critical antecedent condition in the value allocation stage. Strengthening the construction of new information facilities and improving the level of information commercialization are still the top priorities at this stage. The balanced development of technology strategy, organizational resources, and external environment can achieve a higher level of marketization of data elements. Therefore, the antecedent conditions of the technological strategy dimension contribute more to the high-level marketization development of data elements.

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

Innovation is the first driving force behind high-quality economic development. The key technology bottlenecks and the low-added value of products have been restricting China′s high-quality development. Therefore, it is necessary to adopt appropriate industrial policies to support key technological innovation and the innovation and development of strategic emerging industries. At the same time, the government should improve the competition policy and continuously optimize the environment for industrial development. Industrial policy is an important tool for China′s economic transformation and development, and both the central and local governments have actively implemented different types of industrial policies to support or stimulate the development of specific industries, including financial subsidies, tax incentives, and other policies. Competition policy refers to the various economic policies implemented by the government to protect, promote, and regulate market competition. Existing studies have not fully explained whether the joint implementation of various policies has a synergistic effect on innovation or hinders innovation.
Although some scholars have analyzed the relationship between industrial policy and competition policy, few have demonstrated it from a micro-empirical perspective. To improve policy synergy, optimize policy tools, and ensure the smooth development of innovation, this study explores the impact of industrial policy and competition policy on enterprise technology. It investigates whether the selective industrial policy represented by government subsidies and tax incentives and the functional industrial policy represented by the provision of public services and competition policy can produce policy synergy and achieve the purpose of stimulating enterprise innovation. First, the study finds that, except for functional industrial policies, other policies can effectively improve the level of technological innovation in enterprises. After placing industrial policy and competition policy in a unified analytical framework, it is found that the selective industrial policy represented by the subsidy policy and the competition policy produces significant contradictions and conflicts, which inhibit enterprise innovation, while the conflict between preferential tax policies and functional industrial policies and competition policies is not significant. This suggests that it is necessary to avoid direct subsidy-type industrial policies. From the heterogeneity analysis, we concluded that the level of industry competition can affect the synergistic effect of industrial policy and competition policy. The competition policy and functional industrial policy are more constrained in industries with low competition levels. Further analysis shows that the spatial Durbin model at the city level shows that selective industrial policy and functional industrial policy can have policy spillover effects on neighboring cities and promote innovation in neighboring cities, while competition policy has a policy siphoning effect on neighboring cities and inhibits innovation in neighboring cities.
The possible marginal contributions of this paper are threefold. Firstly, from a micro-empirical perspective, the positive role of industrial policy and competition policy on enterprise innovation is verified, and industrial policy and competition policy are incorporated into the unified analytical framework to study the superimposed innovation effects of different industrial policy tools and competition policy. It is concluded that the policy combination of the two has a conflict inhibition effect on innovation. Secondly, the study uses the spatial Durbin model to verify the spatial spillover effect of industrial policy and competition policy, which has certain policy reference significance for the overall planning of regional innovation and development. Thirdly, by evaluating the innovation effects of industrial policy and competition policy, this study concludes that China′s selective industrial policy still has a strong position, and the competition policy is in a weak position, and there is an obvious conflict and inhibition relationship between industrial policy and competition policy for industries with different levels of competition. Therefore, it is necessary to optimize the combination of policy tools for industries with different levels of competition, steadily promote the transformation of selective industrial policies to functional industrial policies, and establish the fundamental role of competition policies.

Organizational strategy and dynamic capability are crucial factors in the process of enterprise digital transformation, as they greatly impact the achievement of enterprise technological innovation goals. Existing theoretical studies have primarily focused on the relationship between path-dependent strategy and technological innovation from the perspectives of knowledge dependence, technology locking, and technology path dependence. These studies are relatively independent, and scholars have not yet realized the complementary and restrictive effects of these factors. Moreover, although few studies examined the potential synergistic effects of organizational strategies and dynamic capabilities, there is a clear need for further research to explore the matching difference perspective. It is evident that the realization of technological innovation performance by an enterprise should not be limited to consideration of the top-level design at the strategic level. Rather, it should also be matched with the dynamic capability of concrete implementation in the process of practice. The path-dependent strategy of an enterprise affects its ability to allocate resources to enhance dynamic capabilities. Organizational resilience reflects the enterprise's capacity to guarantee the implementation of its strategy based on its unique resources and capabilities. These two factors play a role in the enterprise's technological innovation performance. However, the existing research has not yet addressed this question.
Therefore, following strategic configuration theory and dynamic capability theory, this study discusses how the combination of path-dependent strategy and organizational resilience at different levels affects the performance of technological innovation in terms of growth effect, assimilation effect, and utilization effect. It examines the digital transformation of over 430 high-tech enterprises by sending questionnaires to 362 middle- and senior-level managers engaged in the digital transformation business. The hypothesis is verified using polynomial regression and the response surface technique.
It is found that in the scenario of consistency matching, the growth effect and assimilation effect of enterprises technological innovation are higher when there is a higher consistency of high path-dependent strategy and high organizational resilience, compared to low path-dependent strategy and low organizational resilience. Conversely, when there is a low path-dependent strategy and low organizational resilience matching, enterprises have a higher level of utilization effect (compared to highly path-dependent strategy and high organizational resilience). Additionally, a higher level of policy support moderates the relationship between the growth effect, assimilation effect, and utilization effect of technological innovation in enterprises with a low path-dependent strategy and low organizational resilience consistency level. With high-level policy support, the relationship between path-dependent strategy, organizational resilience consistency matching and enterprise technological innovation performance is not a simple linear one. Instead, it follows a 'U' shape (growth effect), an inverted 'U' shape (assimilation effect), and another inverted 'U' shape (utilization effect). Optimal technological innovation performance is achieved when an enterprise implements a low level of path-dependent strategy and has low organizational resilience, as opposed to highly path-dependent strategy and high organizational resilience. In the scenario of inconsistent matching, enterprises' technological innovation growth, assimilation, and utilization effects are becoming higher when the low path-dependence strategy-high organizational resilience matching has higher variability compared to highly path-dependence strategy-low organizational resilience. High-level policy support plays a moderating role in the low path-dependence strategy-high organizational resilience capability inconsistency match with enterprises' growth effect, assimilation effect, and utilization effect relationships. The relationship between path-dependent strategy and organization resilience incongruity match and the assimilation effect and utilization effect of technological innovation of enterprises show an inverted 'U' shape, and thus optimal performance is achieved when the enterprise implements a lower level of path-dependent strategy and possesses a high level of organization resilience, as opposed to high path-dependent strategy and low organization resilience.
Different from previous literature, this paper pioneers the exploration of the influence of consistency matching and difference matching of different levels of path-dependent strategy and organizational resilience on enterprise technological innovation performance, and extends the connotation of it. The empirical analysis supports the conclusions of this paper and strengthens the empirical research basis of technological innovation theory. The research results also have guiding significance for enterprises with different strategic activities and different levels of dynamic capabilities to improve technological innovation performance.