Along with the Sino-U.S. trade dispute , the complex geopolitical situation and other variable factors, supply chain friction has become the norm. The risks associated with the flow of capital, information, and logistics in the supply chain have dramatically increased, making it urgent to improve the chain's resilience in order to mitigate disruption risk and maintain the chain's security and stability. At this stage, the importance of data elements in empowering supply chain governance is becoming more and more prominent, and the scale of data assets owned and the ability to transform them into productivity have become important factors in gaining new competitive advantages. From the perspective of supply chain network characteristics, the penetration and application of data elements in the subject and structural elements of the supply chain have significantly revolutionized the operation mode of the supply chain, and become a key driving factor to enhance risk-taking and ensure the continuity of supply and demand.
This study investigates the enabling mechanisms of data elements on the subject and the structural resilience of the supply chain in order to reveal the supply chain resilience enhancement paths in a more thorough and detailed manner. As a result, given the network characteristics of the supply chain, the subject element and structural element may show different resilience mechanisms against risks. By employing the OLS regression model, the study collects data from Chinese listed companies between 2015 and 2022 and uses text analysis to create indicators that show the application level of enterprise data elements. It then uses supply chain subject resilience and structural resilience to characterize the overall resilience of the supply chain and empirically investigates the impact of data elements on resilience.
The research results show that data elements can effectively enhance the level of enterprise risk-taking and the stability of supply-demand relationship, and the findings remain robust after a series of endogeneity and robustness tests, showing that data elements can effectively enhance both supply chain subject resilience and structural resilience. In addition, the application of data elements by enterprises can effectively enhance the innovation effect and the information effect, thus improving the innovation capability of enterprises and obtaining the information advantage to promote the resilience of the supply chain. At the same time, the enabling effect of data elements on supply chain resilience can be differentiated by the internal and external environments of enterprises, that is, the enabling effect is stronger when the enterprise is large, operates in a highly competitive industry, and is situated in an area with a poor supply chain operating environment and high marketization.
Compared with the results of previous studies, this study expands the research perspective of supply chain resilience, constructs enterprise-level supply chain resilience measurement indexes from the perspective of network characteristics, and enriches the research scope of supply chain in the era of digital economy; and it further empirically examines the two indirect empowerment mechanisms of innovation effect and information effect, which provides a useful supplement to the research on the empowerment mechanism of data elements on supply chain resilience. Through the analysis of the research findings, this study proposes to accelerate the construction of data element marketization, further activate the potential of data elements in enhancing supply chain resilience, and formulate targeted data elements application solutions according to the differences in the internal and external environments of enterprises, which will help the government optimize the policies on data elements and supply chain and support the scientific decision-making of enterprises, and have certain theoretical significance for the implementation of the strategic plan of "focusing on enhancing the resilience and security of industrial chain and supply chain".
Future research may integrate the two dimensions of supply chain entity resilience and relationship resilience into a comprehensive index for research, further combine the characteristics of the supply chain to reveal the mechanism path involving more data elements to empower supply chain resilience improvement, and the subsequent economic impacts of data elements empowering supply chain resilience improvement could be predicted.

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.

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.

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.

With the comprehensive acceleration of the global economic and social digital transformation and the rise of a new round of technological revolution and industrial transformation, the manufacturing industry is developing in the high-end, intelligent, and green trend. However, many underlying and key technologies in industries such as electronic information and advanced manufacturing in China are still confronted with bottlenecks. The competitive advantage of the ICT manufacturing industry is increasingly constrained by the resilience of the supply chain. The various links of the industrial and supply chain are closely interconnected, involving the complex integration of different production factors such as capital, technology, and talent. As society further develops, the connotation of production factors is gradually expanding and deepening. New quality productive forces, including data, knowledge, technology, and management, are important supports. Therefore, accurately grasping the connotation of new production factors, exploring their impact mechanism on the resilience of the supply chain, and cultivating new quality productive forces to maintain the security and stability of the supply chain are of great significance for the ICT manufacturing industry to accumulate competitive advantages with new factors.
This study synthesizes insights from various scholars, and proposes a comprehensive supply chain resilience framework that addresses three critical dimensions: pre-disruption response capabilities, intra-disruption adaptation capabilities, and post-disruption recovery capabilities. It takes 295 listed companies in the computer, communication, and other electronic equipment manufacturing industry on the A-share market from 2018 to 2022 as samples. Numerical values of new production factor variables are obtained based on the data collected through the CSMAR database, CNRDS database, and corporate annual reports to calculate the supply chain resilience of enterprises over a 5-year period for analysis. By defining variables and constructing a multiple linear regression model, the interaction mechanism among the four factors is further explored.
The results show that data and management factors have a significant positive impact on the resilience of the supply chain in the ICT manufacturing industry, while knowledge and technology factors have a significant negative impact. However, when combined, there is a certain substitution effect between the data and knowledge factors, that is, when the level of data elements is generally improved, the negative impact of risks such as the knowledge paradox on the resilience of the supply chain may be partially suppressed. There are differences in the resilience of the supply chain between upstream and downstream enterprises in the ICT manufacturing industry, with downstream enterprises having better resilience than upstream enterprises, and the new factors have different effects on the resilience of the supply chain between upstream and downstream. There is a significant differentiation in the competitive pattern among industrial regions, and there is regional heterogeneity in the impact of new factors on the resilience of the supply chain.
This paper expands the empirical research on supply chain resilience, and clarifies the connotation of new production factors and quantifies them in the context of the digital economy and new quality productive forces. It conducts an in-depth analysis of the relationship between new production factors and the resilience of the supply chain of ICT manufacturing enterprises from the perspective of factor combinations, providing support for ICT manufacturing enterprises to better cope with uncertainty shocks and maintain supply chain stability. To enhance the resilience and competitiveness of the ICT manufacturing industry's supply chain, it is imperative to foster the synergistic application of new production factors, including the robust safety management of data, knowledge, and technology. Concurrently, there is a need to bolster communication and collaboration with supply chain partners across the upstream and downstream sectors, ensuring the coordinated development of each link within the supply chain. This approach will facilitate the establishment of stable relationships and lead to the collaborative optimization of the supply chain. Furthermore, to capitalize on regional development opportunities, it is essential to seize the momentum of the central region's rise and the Western Development Strategy. This involves actively encouraging and supporting the growth of ICT manufacturing enterprises in the central and western regions.

Resource depletion, climate change and excessive pollutant emissions have led to the deterioration of the natural environment, seriously constraining high-quality economic development. Heavily polluting enterprises are an important source of environmental pollution, and improving the sustainable performance of heavily polluting enterprises is not only an intrinsic requirement to follow the high-quality development of the economy, but also a responsibility to actively address environmental challenges. Digital transformation has become the core driving force behind the development of enterprises. Therefore, it is necessary to study whether digital transformation can positively affect the sustainable performance of heavily polluting enterprises. However, few studies have explored the impact of digital transformation on sustainable performance from the perspective of heavily polluting enterprises. Then digital transformation empowers businesses to swiftly adapt their resources and competencies to the ever-shifting competitive landscape. It facilitates a dynamic alignment between a company's internal assets and the external environment, optimizing the allocation of existing resources and enhancing the efficiency of resource distribution. However, few existing studies have focused on the mediating role of resource allocation efficiency between digital transformation and enterprise sustainable performance from the integrated perspective of resources and capabilities. Finally, absorptive capacity is an important variable that affects the ability of firms to transform and utilize resources, but few studies have focused on the moderating role of absorptive capacity between digital transformation and sustainable performance of heavily polluting enterprises.
Therefore, this study examines the impact of digital transformation on the sustainable performance of heavily polluting enterprises based on the resource base view and dynamic capability theory, using data from A-share heavily polluting listed companies from 2008 to 2022. The results of the study show that digital transformation can enhance the sustainable performance of heavily polluting enterprises; digital transformation will promote the sustainable performance of heavily polluting enterprises through improving resource allocation efficiency, but resource allocation efficiency does not play a mediating role between digital transformation and the environmental performance of heavily polluting enterprises; absorptive capacity does not play a positive moderating role between digital transformation and the sustainable performance of heavily polluting enterprises; the state-owned nature as well as the positive effect of digital transformation on sustainable performance improvement is more significant for heavy polluters in the growth period.
This study has three theoretical contributions. First, this study further expands the theoretical logic of digital transformation for non-economic value creation. It verifies the key role of digital transformation in achieving sustainable performance of heavily polluting enterprises, and further proves the importance of digital transformation in high-quality economic development. Second, this study further expands the scope of application of dynamic capabilities theory between digital transformation and sustainable performance of heavily polluting enterprises. By identifying the mediating role of resource allocation efficiency in the relationship between digital transformation and sustainable performance of HIPCs, this study further refines dynamic capabilities and opens the "black box" of the process of digital transformation affecting the sustainable performance of HIPCs. Third, it expands the boundary conditions for the impact of digital transformation on the sustainable performance of heavy pollution enterprises. By identifying the weighted role of absorptive capacity in the relationship between digital transformation and sustainable performance of heavily polluting enterprises and regressing it into groups according to the nature of the enterprise and the life cycle of the enterprise, this study enriches the contextualized research from the perspective of resources and capabilities.
Future research could explore if there is a nonlinear relationship between digital transformation and sustainable performance of heavily polluting enterprises. Then, the mechanisms of other factors, such as green innovation and business model innovation are worthy of further analysis, for it will generate valuable insights into the mechanism linking digital transformation and sustainable performance of heavily polluting enterprises under different circumstances. Furthermore, there may be differences in the impact of digital transformation on the sustainable performance of heavily polluting enterprises with the same cultural characteristics, so future research could use data from other countries to determine the applicability of the findings.

With the integration of digital economy with technological innovation, organizational innovation and industrial innovation, more and more enterprises have been introducing artificial intelligence technology to replace some low-end repetitive work. The emerging workforce, nurtured during the era of AI's swift advancement, is acutely aware of its influence on their professional competitiveness. This rapid technological evolution has instilled a sense of unease among these young professionals regarding potential job displacement and the necessity to master novel technologies. This phenomenon is commonly referred to as artificial intelligence anxiety.
From the perspective of the cognitive evaluation theory of stress, the study analyzes the impact of AI anxiety on the innovative behavior of the new generation of employees. The paper proposes the following hypothesis that, first, AI anxiety can promote innovative behavior of the new generation of employees, and fully tapping the characteristics and potential of the new generation of employees can better stimulate their innovative vitality under the anxiety of AI. Second, job crafting plays a mediating role between AI anxiety and the innovation behavior of the new generation of employees. AI anxiety can stimulate the new generation of employees to actively think about the changes in work content and work mode. Third, organizational attachment not only moderates the relationship between AI anxiety and job crafting, but also moderates the mediating effect of job crafting between AI anxiety and the innovation behavior of the new generation of employees. It is difficult for employees with high organizational attachment to have innovative behaviors in the context of insecurity. On the contrary, employees with low organizational attachment have an attachment style corresponding to secure organizational attachment. Therefore, employees with low organizational attachment are better able to carry out innovative practices and carry out innovative behaviors at work, and AI anxiety has a stronger effect on stimulating their innovative behaviors through job crafting.
The study conducted a survey in two phases with data from November 2023 to February 2024 through a multi-time point survey methodology. The target population consists of employees across various sectors, including the Internet and Technology (IT) industry, manufacturing, catering services, and education. There are 343 valid responses out of 435 questionnaires, which corresponds to a robust response rate of 78. 85%. The study employs SPSS 23. 0 and Mplus 8. 0 software to assess the reliability and validity of the research model. To evaluate the discriminant validity among the four key variables of artificial intelligence anxiety, organizational attachment, work remodeling, and employee innovative behavior, it conducts the confirmatory factor analysis (CFA) using Mplus 8. 0. Additionally, to ensure that common method bias was not a significant concern in this research, Harman's single-factor test was applied to an exploratory factor analysis of the 40 items pertaining to these four variables. It delves into the mediating effect of job crafting, and the moderating effect of organizational attachment; and then it explores the boundary of the relationship among AI anxiety, job crafting and the innovative behavior of the new generation of employees under different levels of organizational attachment.
The results show that artificial intelligence anxiety positively affects the innovation behavior of the new generation of employees through job crafting, and the degree of organizational attachment of employees plays a moderating role in this process. When employees' organizational attachment is low, the positive mediating effect of AI anxiety on the innovation behavior of new generation employees through job crafting is significant. When employees have a high degree of organizational attachment, the mediating effect above is not significant.
Therefore,it is essential for managers to acknowledge the influence of AI anxiety on the new generation of employees, and harness this anxiety constructively to steer, inspire, and energize employees to proactively redefine their roles and to embrace innovation with confidence. While employees should maintain a secure level of organizational attachment, which is characterized by a healthy balance between commitment and autonomy. To facilitate this, managers are tasked with cultivating an organizational environment that serves as both a "safe haven" and a "security island", providing the new generation of employees with a robust sense of security.

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 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 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.

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.

ESG information disclosure is regarded as a systematic and comprehensive way of releasing corporate non-financial responsibilities such as ecological environmental responsibilities,social responsibilities,and corporate governance responsibilities to the stakeholders. More and more listed companies have taken ESG information disclosure as an important means of establishing their green corporate image and gaining momentum for achieving high green innovation performance,and how the companies can improve green innovation performance through ESG information disclosure motivation has attracted attention from the research field. Many scholars have found that internal factors such as corporate financial performance,values,risks,and investment evaluation,and external factors like pressure from governmental environment regulation,public environmental concern,and consumers' green consumption intentions,have a great impact on the corporate motivation to disclose ESG information to outsiders. ESG information disclosure is beneficial for corporate reputation management,exerting green spillover effects and value transmission effects to generate altruistic social value. However,there is still controversy about whether ESG information disclosure motivation can positively affect green innovation. Some scholars argue that corporate ESG information disclosure motivation is restricted by macro policy and institutional factors and may have no significant impact on green innovation performance; besides,the impact mechanism of ESG report information disclosure motivation upon corporate green image is still unclear and remains an unsolved “black box” for the present researchers. Therefore,it is of significance to have a comprehensive understanding on the correlation between ESG information disclosure motivation,green image and green innovation and thus know how to help companies promote green development.
Following the motivation theory,the signal transmission theory and the environmental psychological value-belief-norm theory,this study constructs a conceptual model of the relationship among the dual motivations of corporate ESG report information disclosure,green image,green value cognition,and green innovation performance,taking green image as a mediating variable and green value cognition as a moderating variable,to explore the impact mechanism of ESG information disclosure motivation upon green innovation performance. It divides the motivators of corporate ESG information disclosure into egoistic ones and altruistic ones,in which the egoistic motivators mainly refer to those generated by the corporates to meet the needs of self-growth reputation,resource needs,etc. ,while the altruistic motivators mean those generated by the corporates to meet the needs of social responsibility and social value realization. The study adopts the questionnaire survey method of 254 samples from the listed companies that have released ESG reports between 2018 and 2022 and finds that ESG information disclosure motivators,no matter egoistic ones or altruistic ones,both have a significant positive impact upon corporate green innovation performance; green image plays a mediating role in the relationship between ESG information disclosure motivation and green innovation performance; and the top managers' green value cognition positively adjusts ESG information disclosure motivation and green innovation performance. This study also discovers that ESG information disclosure motivation can help companies solve the problems of information asymmetry and agency conflicts,convey environmental,social and governance information to stakeholders,and meet the corporate green development needs. The more attention corporates have paid to ESG information disclosure motivation,the higher the green innovation performance they are likely to achieve.
In summary,by adopting the environmental psychological value-belief-norm theory,this study expands the field of interdisciplinary theoretical research and enriches theoretical research perspectives; it uncovers the impact mechanism “black box” of ESG information disclosure upon green innovation performance,discusses the role and significance of green image construction from multiple dimensions,and provides basic theoretical support for improving the self-utility and social utility of corporate green image,enhancing the top managers' green image awareness,and thus improving the corporate green innovation performance.
In order to gain continuous momentum for improving green innovation performance,the companies should actively promote ESG information disclosure motivation,improve corporate green image and the top managers' green value awareness. Besides,in the current context of China's comprehensive green transformation of economic and social development,the government should strongly enforce the corporate ESG information disclosure laws,insistently strengthen the supervision and technical guidance on ESG information disclosure,and stimulate corporates' enthusiasm for their ESG information disclosure by providing green subsidies and other preferable policies.

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.

An innovation ecosystem (IE) is the evolving set of actors, activities, and artifacts, along with the institutions as well as complementary and dependency relations. Artificial intelligence (AI) is increasingly adopted by organizations for innovation,giving rise to platform-based innovation ecosystems for collaborative innovation and value co-creation. However, most research has focused on the co-evolution of platform architecture and platform governance and the role of boundary resources provided by the platform owner, while heterogeneous artifacts provided by complementors are rarely explored. Specifically, the industrial and managerial literature still lacks information about how heterogeneous artifacts can foster collaborative innovation in the AI innovation ecosystem.
Thus, this paper presents a framework for the collaborative innovation process of technological complementarity by using a comprehensive research model to examine the impact of collaboration in the AI innovation ecosystem. The conceptual framework termed “elements-process-performance” from the perspective of complex system, captures the intrinsic mechanisms of collaborative innovation between platform owners and complementors. The elements of innovation refer to the technological components within the ecosystem, comprising both core components and complementary technologies. The process of innovation encompasses the way complementors integrate these technologies to foster innovation. The innovation performance assesses how the empowerment of core technical components affects collaborative innovation performance.
After reviewing the relevance of studying the innovation system, the study proposes a bipartite network (BN) model to describe the relationships between complementors and technology components based on real-world data from “Baidu Brain” which is an AI open platform in China; then through analyzing the properties of the projected network of the bipartite network, it quantifies the structural features of component interdependency at the node-level, group-level, and whole network-level; the process of innovation is modeled as a bounded, iterative, trial-and-error search over a complex landscape using an NK model, and several simulation experiments are conducted to observe the complex relationship patterns between innovation performance and components' interdependency; finally the study manages to predict the ecosystem innovation performance based on empirically observed patterns from the Baidu Brain ecosystem.
The analysis reveals that complementary component strategies can affect collaborative innovation in ecosystems in four ways: (1) the level of technological interdependence formed by embedding complementary components into the ecosystem through component strategies has a negative impact on the potential for collaborative innovation; (2) the higher the proportion of core components in the system (C-Ratio), the lower the performance of collaborative innovation; (3) it hampers collaborative performance by raising the ratio of core components above a threshold, indicating over-reliance on the platform's core elements without enough complementary components impedes synergy;(4) an inverted U-shaped relationship exists between component interdependency E-I index and innovation performance. These important findings further emphasize that collaboration among different actors in the ecosystem plays a critical role in achieving higher innovation outcomes.
The findings provide insights for management innovation in platform enterprises. Platform enterprises as ecosystem builders and leaders, should effectively manage complementary innovation. As a coordinator, it is necessary to pay attention to the network pattern among collaborative innovation entities and effectively manage and coordinate the actions of internal complementarity through institutional design. In addition, platform enterprises also need to pay attention to the management and coordination of modular components. When the E-I Index of a partition of the complementary dependency network deviates from the optimal level, adjustments can be made by enhancing the standardization and flexibility of core components, encouraging participation from complementary enterprises, increasing the diversity of complementary components, reducing the proportion of core components, and modifying the complementarity of technical components.
This study focuses on how platform enterprises manage and coordinate with complementor enterprises. Given the complexity of technology dependence, integrated application of innovation ecosystems and collaboration innovative theory, it utilizes data-driven and simulation modeling methods to explore the complex relationship logic between technological dependence and collaborative innovation. This paper advances the understanding of the intersection of innovation and AI and provides suggestions for complementary innovation and the ecosystem development.

In the new wave of industrial digital transformation, Chinese enterprises are facing multiple challenges including accelerated technological innovation, constantly changing consumer preferences, and the volatility and restructuring of global supply chains. Therefore, enterprises need to re-examine their digital transformation vision and strategy, focusing on how to enhance and reshape their digital capabilities. As the dynamic capability foundation for enterprise digital transformation and upgrading, digital capability is the ability of enterprises to widely integrate digital resources and other organizational resources in the value network using digital technology to promote systematic digital transformation and achieve digital value creation. This enables enterprises to break free from traditional resource dependencies during the digital transformation wave, thereby achieving a sustainable competitive advantage. However, building and enhancing a company's digital capability is a complex system process, and existing research on the factors that affect a company's digital capability is relatively scattered. Previous studies mainly focus on the independent effects of a limited number of factors on digital capability, and the identification of key factors is not comprehensive enough, nor does it consider the possible interrelationships between various influencing factors.
To answer the fundamental question of how enterprises possess digital capabilities, this study adopts the Technology-Organization-Environment (TOE) framework and the ISM-MICMAC system dynamics approach to explore and clarify the hierarchical path of multiple factors affecting the digital capability of enterprises. This study uses literature research method and TOE framework to screen out 19 effective English articles and 23 articles in Chinese, and preliminarily identifies the influencing factors of technology, organization, and environment on the digital capabilities of enterprises. Then, through expert group consultation and opinion summary, it identifies the main 19 influencing factors of enterprise digital capability. Secondly, the ISM method is used to clarify the hierarchical relationship and correlation path between various influencing factors, and converges to enhance the digital capability of enterprises, constructing a multi-level hierarchical explanatory structure model of the influencing factors of digital capability. Finally, this study combines the MICMAC method to identify strong driving force and strong dependent force factors, revealing the importance and role of each influencing factor in the system.
It is concluded that, firstly, in terms of element categories, there are 4 technological factors, 10 organizational factors, and 5 environmental factors that affect the digital capability of enterprises. Secondly, in terms of hierarchical structure, the explanatory structure model of this study consists of eight levels, among which the root factors affecting the digital capabilities of enterprises include six factors such as demand transformation and digital economy level. The intermediate factors include 11 factors such as executive digital cognition and digital human capital, and the surface factors are digital organizational preparation and digital technology application and diffusion. Thirdly, in terms of factor correlation, there are six factors with high driving force and low dependence, among which the deep driving factors are demand transformation, policy orientation, digital economy level, and executive digital background. There are four factors that contribute to high dependence and low driving force. The intermediate factors are digital human capital and technology management, while the superficial factors are digital organizational preparation and digital technology application and diffusion.
The theoretical contributions of this study are threefold. Firstly, by sorting out multiple factors that affect the digital capabilities of enterprises within the theoretical framework of TOE, it advances the systematic understanding of how to enhance the digital capability of enterprises, and makes up for the lack of fragmented discussions on a limited number of antecedents in empirical research. Secondly, it uncovers the hierarchy and interplay of elements in building digital capabilities, offering a new lens on their formation and key components. Thirdly, this study expands the application of system dynamics in the field of enterprise digital research by constructing an explanatory structural model that affects the digital capabilities of enterprises. The exploration of multiple research paradigms to enhance the theoretical framework of enterprise digital capability is beneficial and provides valuable insights for enterprises on building and enhancing their digital capability during digital transformation.

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.

SRDI enterprises are characterized by “small yet specialized” and “focused yet refined” qualities, which endow them with high flexibility and significant innovation potential. However, compared to large enterprises, SRDI enterprises face multiple challenges, including financing difficulties, limited appeal to innovative talent, and weaker risk resilience. These issues introduce significant uncertainty and risk to their innovation resilience. Digital transformation has the potential to ignite innovation, boost dynamism, and enhance resilience through various facilitative effects. However,the way its influence on innovation resilience is characterized by a complex, long-term, and non-linear trajectory. Thus, a thorough research on the mechanisms of digital transformation and its impact on innovation resilience is essential for both theoretical and practical aspects. This is vital for fostering the digital transformation of SRDI enterprises and ensuring their stable and sustainable innovation-driven development.
Drawing on dynamic capability theory and ambidextrous innovation theory, this study analyzes the nonlinear processes and multiple mechanisms through which digital transformation influences the innovation resilience of SRDI enterprises from both dynamic and systematic perspectives. It then empirically examines the nonlinear impact of digital transformation on the innovation resilience of SRDI enterprises employing polynomial regression analysis on panel data from China's A-share-listed SRDI SMEs and "Little Giant" enterprises between 2008 and 2022, and an inflection point analysis is conducted to delineate the distinct stages of the effects of digital transformation. Meanwhile, three mediating variables, namely inter-firm learning ability, R&D investment and financing constraints, are introduced to empirically analyze the impact of digital transformation on the multiple mechanisms of innovation resilience by using the "Jiangting two-step method". Moreover, this study further explores the heterogeneity of these effects across firm classification, firm age, and economic cycle.
This study identifies a significant "decline-rise-decline" inverted-N relationship between digital transformation and the innovation resilience of SRDI enterprises. Mediation analysis reveals that the digital transformation impacts inter-firm learning ability, R&D investment, and financing constraints of SRDI enterprises in a non-linear manner, characterized by increasing, inverted N-shaped, and inverted N-shaped patterns, respectively. Inflection point analysis further identifies two critical thresholds within the digital transformation process of SRDI enterprises, segmenting its impact on innovation resilience into three distinct stages: the "break-in period", the "expansion period", and the "pain period". The "break-in period" is relatively short. The majority of SRDI enterprises are currently in the "expansion period". Additionally, 16% of SRDI enterprises have progressed into the "pain period", a phase characterized by substantial challenges associated with advanced digital transformation efforts. Heterogeneity analysis reveals that digital transformation exerts a more pronounced impact on the innovation resilience of national-level SRDI enterprises and those in the growth stage. Furthermore, during periods of economic downturn, the positive effects of digital transformation on innovation resilience are notably stronger, whereas its influence diminishes during economic upturns.
According to the above findings, this study proposes some management recommendations.First, SRDI enterprises should proactively identify the financing constraints during the "expansion period" and the potential R&D risks in the "pain period", thereby providing appropriate policies and resources to sustain the innovation resilience of SRDI enterprises. Second, SRDI enterprises in the start-up stage should take moderate digital transformation actions, and then shift towards more proactive steps in the growth stage, which can better enhance their dynamic innovation capabilities and innovation resilience. Third, as SRDI enterprises progress into the “pain period”, the government should actively support their digital transformation processes through comprehensive policy measures, ensuring that SRDI enterprises remain pivotal drivers of innovation. With the deepening of digital transformation, future research can focus on whether the impact of digital transformation on the innovation resilience of SRDI enterprises has shifted from a three-stage pattern of "decline-rise-decline" to a more cyclical pattern of four or five stages. It is also of great research value to delve into the formation principle of the inflection points in digital transformation, revealing the complex dynamic mechanisms by which digital transformation shapes innovation resilience.

With the new generation of technological revolution and the acceleration of industrial transformation, it is increasingly urgent to promote China's high-quality development through new quality productive forces. The innovation network is a more suitable platform for the repeated exchange of tacit and embedded knowledge than market transactions. It is effective in enhancing enterprise innovation capabilities, achieving breakthroughs in key technologies, and forming new quality productive forces. To promote the qualitative leap in the advanced productive forces of innovation network member enterprises and achieve high-quality development, this paper attempts to answer the following key questions: How does core enterprises' knowledge spillover drive network members' new quality productive forces? What roles do network homogenization and relationship capital play in this process?
On the basis of the knowledge-based view and resource-dependence theory, this paper analyzes the relationship between core enterprise's knowledge spillover and network member's new quality productive forces. The research adopts survey data from 392 enterprises and conducts an empirical analysis using SPSS and Amos. The results show that (1) there is a significant positive relationship between core enterprise's knowledge spillover and network member's new quality productive forces. The influence of core enterprises on network members mainly stems from knowledge spillover, and under its continuous influence, the new quality productive forces of network members can be improved. (2) Network homogenization negatively moderates the impact of core enterprise's knowledge spillover on network member's new quality productive forces. Network homogenization implies a lack of resource, skill, and capability complementarity among members, rendering enterprise resources less unique, valuable, and scarce. Consequently, core enterprise's knowledge spillover cannot provide deeper knowledge and technology interaction for network members, reducing the chance for network members to increase their knowledge diversity and ultimately affecting the development of network member's new quality productive forces. (3) Relational capital positively modulates the relationship between core enterprise's knowledge spillover and network member's new quality productive forces. Strong relationships among innovation network members benefit enterprises in obtaining more social capital, which promotes the exchange and sharing of knowledge and information, and fosters the growth of new talents and the accumulation of new labor materials.
Theoretically speaking, this paper firstly supplements the empirical study of new quality productive forces at the enterprise level. Then it constructs a measurement scale of network member's new quality productive forces, which provides ideas and methods for further empirical research related to new qualitative productive forces at the enterprise level. Secondly, from the perspective of the interaction of innovation network members, this paper explores the impact of core enterprise's knowledge spillover on network member's new quality productive forces, enrich the theoretical connotation of existing research and promote the development of knowledge spillover related theoretical research. Thirdly, by integrating the knowledge-based view and resource dependence theory into the research on the knowledge spillover effects of the core enterprise, the applicability of these theories is enhanced, offering a new perspective to understand the knowledge spillover effects of core enterprises. Lastly, it is a valuable addition to the research on the knowledge-based view and resource-dependence theory to incorporate network homogenization and relational capital into the research framework. This expands the scope of research on the knowledge spillover effects of core enterprises and responds to scholars' recognition of the core enterprise's knowledge spillover effects.
The results hold practical significance as follows: (1) The transformation and enhancement of productive forces quality in innovation networks can not be separated from core enterprise's knowledge spillover. If network members aim to develop new quality productive forces, they must realize the importance of core enterprise's knowledge spillover and expand the channels for enterprises to obtain new knowledge, technical information and other resources. (2)The findings affirm the importance of enterprise knowledge heterogeneity, and suggest that enterprises should select partners based on their own existing knowledge, in innovation networks, members should be clear about who to approach in order to gain more reconfigurable knowledge. (3) Strong relationship capital is the effective support to promote new quality productive forces, and the continuous accumulation of relationship capital can ensure that network members have an advantage in choosing partners for core enterprises. (4)In the process of high-quality development of innovation network, it is necessary to adopt tailored measures for different enterprises.

Enabled by digital technology, innovation ecology can efficiently realize the transfer, interaction, expansion and evolution of knowledge, and form the core competitiveness of the digital age. At present, China has initially explored an innovation ecosystem supported by "new infrastructure" such as 5G, Internet of Things, industrial Internet and artificial intelligence, with industry leading enterprises as the main body and "platform + a large number of small and medium-sized enterprises" as the main structure, which has become a prototype of the integration of China's digital economy and the real economy, and can meet the needs of future scenarios better. The long-term development of the innovation ecosystem led by the industrial Internet platform is the key to the integration of China's digital economy and the real economy.
However, the current academic research on industrial Internet platform dwells on connotation analysis, exploration and application, and business model analysis, and there is a lack of research on industrial Internet clusters and their ecology. In addition, current studies on the value co-creation of innovation ecosystems are mainly conducted from the perspectives of the innovation ecosystem subjects, and the network relationship formed between the subjects and the external innovation environment, but less attention is paid to the co-creation relationship between the platform and manufacturing enterprises, between intelligent devices and humans, and between machines and data to explain the value co-creation process mechanism of the innovation ecosystem led by the industrial Internet platform. The value co-creation mechanism of the innovation ecosystem led by the industrial Internet platform is yet to be revealed.
The innovation ecosystem led by the industrial Internet platform takes digital resources and digital infrastructure as key production factors, reshaping the win-win cooperation logic in the industrial value network, and reflecting unique characteristics different from the general innovation ecosystem. Therefore, drawing on the value co-creation theory of the innovation ecosystem, this study explores three mechanisms, including modularity, scenario, and complementarity from the intrinsic attributes, and analyzes their interactive utility. This paper selects the COSMOPlat Industrial Internet platform as the case study object; it adopts a structured data analysis method to analyze the case, encodes and abstracts the original data, realizes the analysis and induction of the theme, and finally extracts a rigorous theoretical theme, forming a data structure composed of first-order constructs, second-order themes and third-order categories.
It is found that (1) the innovation ecosystem led by the industrial Internet platform is a value creation and value acquisition system composed of multiple participants in different fields. It is an open, supply-and-demand-driven, loosely coupled innovation system based on a multi-platform interactive architecture, and the innovation system can improve the initiative of each subject to participate and the digital ability and innovation diffusion ability through the sharing and symbiosis of technology, resources and scenes, so as to achieve the goal of system innovation. (2) The innovation ecosystem led by the industrial Internet platform achieves a win-win situation of efficiency and collaboration within the ecosystem on the supply side through modularity and complementarity, and integrates the demand side into the ecosystem through the scenario mechanism to form a future-oriented co-creation system. The modularity mechanism effectively integrates information technology infrastructure, big data technology, blockchain technology, and artificial intelligence technology, and builds a multi-platform interactive architecture to promote the integration of data and reality. The scenario-based mechanism accesses the scalable new generation of intelligent technology to construct the user scenario-based experience, and activates multiple subjects to actively embed the ecology through the governance scene to create a win-win situation. The complementary mechanism and modular technology are nested in multiple layers to complete the industry technology complementarity and digital technology complementarity, match scenarios to iterate the solution covering the whole life cycle of users, optimize collaborative innovation among various entities in the ecosystem through the platform enabling process complementarity. (3) The innovation ecosystem led by the industrial Internet platform integrates resources efficiently to achieve value co-creation through the modular mechanism, scenario mechanism, and complementary mechanism, improves the ecological digitization ability, and forms the same side network effect, cross-side network effect and cross-domain network effect of the innovation ecosystem.

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.

Emerging technologies not only signify the cutting edge of technological innovation but also represent a pivotal aspect of international competition, and they are highly valued by nations, international organizations, and leading corporations worldwide. By swiftly and accurately detecting potential emerging technologies in target domains, they can grasp the opportunities of future technological and industrial development, break through the technological barriers in various fields, and thus enhance the competitive advantage of both nations and enterprises in strategic global competitions. In the context of China's aggressive pursuit of its "carbon neutrality and carbon peak" goals, innovation in power grid technology assumes exceptional importance. Power grid companies are compelled to deeply comprehend and master the evolving trends in emerging grid technologies, undertake crucial technological research, and guide precise research and development investments to secure a dominant position in the international arena.
As vital carriers of scientific information, academic papers and patent literature are predominantly used to evaluate the level of scientific research activities and the innovation in industrial technology, and have become the primary sources for detecting emerging technologies. An in-depth study and analysis of these documents, followed by the extraction and selection of innovative information within them, helps to uncover latent technological knowledge. Although single data source methods are operationally effective, they struggle to accurately reflect the complexity of scientific themes. Conversely, research on detecting emerging technologies through multi-source data remains relatively scarce. Currently, there is a lack of academic consensus on the definition of emerging technologies, leading to different indicators for assessing whether a technology topic is an emerging technology. Identifying these technologies accurately and delineating their quintessential characteristics—impact, growth, coherence, novelty, and uncertainty—is crucial. It is noteworthy that quantified research on these features, particularly uncertainty and ambiguity, is still scarce.
This paper introduces a method for integrating multi-source data, aiming to amalgamate academic and patent data to enhance semantic complementarity between varied data types, thereby boosting efficiency in identifying emerging technologies. The study utilizes the Topical N-Grams (TNG) model to extract technological themes from academic papers and patent documents, followed by manual selection to ascertain key technological themes. According to these themes, it computes five primary feature indicators: impact, growth, coherence, novelty, and uncertainty. These indicators are then amalgamated to calculate an emergence score. Subsequently, the study employs a support vector regression machine model for extrapolating these indicators, identifying emerging technologies with potential for future growth. Focusing on the grid sector, the study collects patent literature from the Derwent Innovation database and academic papers from the Web of Science core collection,limiting document types to "Article" and "Review" and setting the timeframe from 2015 to 2022, with a total of 743 344 academic papers and 1 247 235 patents. The analysis of the annual distribution of academic and patent papers published in the grid field reveals a steady increase in research interest; the emerging technologies in the grid sector for 2022 include research on low-carbon planning for new power systems, electric motor drive and control technology, intelligent inspection technology for transmission lines, smart operation and maintenance technology for digital grids, cooperative control technology in multi-unmanned systems, and internal combustion engine power system technology. Notably, research on low-carbon planning for new power systems has consistently ranked high in emergence in recent times. In the deep development strategy of the current power industry, low-carbon transformation and upgrading are deemed vital. Further indicator extrapolation indicates that research on low-carbon planning for new power systems, intelligent inspection technology for transmission lines, cooperative control technology in multi-unmanned systems, and internal combustion engine power system technology maintain their top-five status, while intelligent wind energy power system integration technology and ion battery and energy storage technology are gradually climbing the ranks, suggesting that these emerging technologies are poised for increased attention and development in the future. In addition to academic papers and patent literature, funding programs and policy texts are also very important sources of information. These resources can provide information about the direction of funding and support for S&T research, as well as the level of government attention and policy orientation towards emerging technologies.

In the era of "New Normal" with VUCA characteristics,how to enhance resilience has become the key for enterprises to avoid dangers and shift into safety and achieve sustainable development. As a strategic choice for knowledge expansion, technological diversification is beneficial for enterprises to diversify external risks, improve their perception and adaptability to the environment and then achieve resilient growth. Although existing research pays attention to the effect of technological diversification on enterprise resilience, the "bridge" between the two needs to be further explored. Continuous innovation related to operational efficiency and strategic flexibility is an important ability to achieve continuous development. According to the theoretical logic of "resource-capability-result", continuous innovation drives enterprises to change their mindset, continuously breaking through development bottlenecks and exploring new growth paths through the integration of diversified technologies. Therefore, continuous innovation may play a mediating role between technological diversification and enterprise resilience. In addition, the results of technological diversification depend on the combination of internal technological exploration and external technological relationships, making enterprises embedded in dual network relationships. In particular, the density of internal collaboration network characterizes the close relationship between internal inventors, which directly affects the effective transfer of diversified technological knowledge and information sharing; the heterogeneous resource advantages brought by the high degree of external collaboration network centrality can effectively alleviate the dilemma of knowledge homogenization in high-density internal cooperative networks, and then accelerate the process of diversified technological development and integration. However, existing research has not focused on the boundary effects of dual networks on the relationship between technological diversification and continuous innovation. Hence, this study aims to explore the internal mechanism between technological diversification and enterprise resilience, and investigate the boundary effect of internal collaboration network density and the three-way moderating effect of external collaboration network centrality from the perspective of dual network.
Following the resource-based theory and the theoretical logic of "resource-capability-result", this study conducts the empirical analysis using a sample of Shanghai and Shenzhen A-share listed enterprises in the new energy automobile industry. The resilience and control variables information of these enterprises is collected from CSMAR and patent data is collected from Incopat. Finally, an unbalanced panel dataset of 285 enterprises from 2010 to 2021 is constructed. The hypotheses are tested by the individual and time-fixed effect models using Stata 15.0.
The results show that (1) there is an inverted U-shaped relationship between technological diversification and enterprise resilience; (2) continuous innovation plays the mediating role in the inverted U-shaped relationship between technological diversity and enterprise resilience; (3) internal collaboration network density negatively moderates the relationship between technological diversification and continuous innovation; (4) external collaboration network centrality further mitigates the negative impact of internal collaboration network density on the relationship between overall technological diversification, unrelated technological diversification, and continuous innovation.
The theoretical contributions of the study are as follows: firstly,it clarifies the relationship between technological diversification and enterprise resilience in the context of a long-term and turbulent environment, and expands the theoretical space of technological diversification and enterprise resilience; secondly,the paper provides empirical evidence for the effect of continuous innovation on enterprise resilience and bridges the gap in the discussion of mechanisms underlying technological diversification and enterprise resilience; finally, from the perspective of dual network,it reveals the boundary effects of dual networks in the relationship between technological diversification and continuous innovation, and deepens the contextual mechanism for the transformation of technological diversification into continuous innovation. The practical contributions are proposed. Enterprises should first cultivate internal skills and moderately expand diversified technology fields based on risk assessment. Then it is necessary to seek change proactively. Enterprises should cultivate continuous innovation ability to leverage the positive effects of technological diversification on enterprise resilience. Finally, enterprise should pay attention to collaboration,encourage inventors to "go out", maintain loose partnerships internally and build a wide collaborative R&D network externally.

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.

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.

In the present fast-paced business world with high uncertainties, open innovation has become an indispensable strategy for firms seeking more heterogeneous information and staying competitive and thriving. However, there is still a critical need for a robust theoretical framework that can accurately predict and explain the realization of open innovation performance, and enlighten firms on how to cooperate and deal with internal and external knowledge partnerships in an open situation. The theory of HeXie (harmonious) management emerges as a promising avenue to address this need, especially the He principle and the two fields offer a conceptual lens that aligns with the complex demands of open innovation systems and provides insights into how firms can unlock their intrinsic value through open innovation initiatives. The purpose of this study is to delve into the mechanisms that underlie the realization of open innovation performance in high-tech manufacturing firms, utilizing the He principle as a guiding framework.
According to the He principle, the research constructs a comprehensive framework to explore the multifaceted pathways to open innovation performance in firms, and figure out how innovation openness exerts a positive influence on open innovation performance under the dual moderation of inclusive organizational culture and intellectual property protection. Empirical tests are made with the panel data from 662 high-tech manufacturing A-share listed firms spanning the period from 2012 to 2021 in China to validate the theoretical framework. Then, the model of fixed panel effects is built for data analysis: the study first examines the direct effect of innovation openness on financial and market performance, and then respectively analyzes the moderating effects of inclusive organizational culture and intellectual property protection policies; finally, it makes robustness analysis and heterogeneity analysis.
　The findings support the fact that the depth and breadth of innovation openness have a significant positive impact on firms' financial performance. However, it's noteworthy that the breadth of innovation openness does not exert a significant influence on firms' market performance, while the depth of innovation openness has a significant negative effect on market performance. This distinction between financial and market performance underscores the nuanced nature of open innovation outcomes. The study also reveals that the presence of an inclusive organizational culture strengthens the positive relationship between the depth of innovation openness and firms' financial performance. This finding underscores the importance of nurturing a work environment where diverse perspectives are valued and integrated into the innovation process. Lastly, it uncovers that the impact of the breadth of innovation openness on firms' financial performance is significantly enhanced in regions with well-developed intellectual property protection policies. This suggests that the interplay between openness and intellectual property protection can be leveraged to optimize financial performance outcomes. What's more, compared with the breadth of innovation openness, inclusive culture has a more significant moderating effect on the depth of innovation openness on financial performance; compared with the depth of innovation openness, the moderating effect of intellectual property policy on the breadth of innovation openness is more significant on financial performance. Intriguingly, the study further explores heterogeneity in the relationship between innovation openness and firms performance and finds that non-state-owned firms and those led by managers with overseas backgrounds experience a more pronounced impact of innovation openness on their performance. This nuanced insight emphasizes the importance of considering organizational characteristics and leadership profiles in the context of open innovation.
In conclusion, the study significantly contributes to the understanding of the pathways through which high-tech manufacturing firms realize open innovation performance. The theoretical framework of this paper is helpful to guide firms to achieve the dual pursuit of short-term and long-term goals and the balance of uniqueness and legitimacy in the context of open innovation. The conclusions provide valuable guidance for firms aiming to maximize the benefits of open innovation and adapt to the challenges in the dynamic business environment. Furthermore, it informs policymakers and practitioners about the significance of fostering inclusive organizational cultures and tailoring intellectual property protection policies to amplify the positive impacts of innovation openness.

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.

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.

Innovation can help SMEs reduce costs, improve efficiency, and enhance the quality of their products and services, bringing long-term growth. However, due to their own financial and technological constraints, SMEs generally have certain barriers to innovation. How to help SMEs ease financing constraints and enhance technological innovation capability has become an urgent issue to be addressed. Supply chain finance plays an important role in stimulating the innovation activities of SMEs. On the one hand, the adoption of supply chain finance can make it easier for SMEs to obtain lower-cost funds, thus alleviating their financing constraints. On the other hand, close partnerships based on supply chain finance ecological networks can promote business dealing and core technology sharing in the chain, thus accelerating the formation of collaborative innovation networks, which is conducive to improving the innovation capability of SMEs. Existing studies have verified that supply chain finance can alleviate financing constraints, while financing constraints can affect firms' R&D investment efficiency. However, the logical link between supply chain finance, financing constraints, and R&D investment efficiency has not yet been established.
Given the above research deficiencies, this paper aims to explore the effect of supply chain finance on the R&D investment efficiency of SMEs. It focuses on the external influence mechanism as well as the internal conduction path and verifies the role that financing constraints play between supply chain finance and R&D investment efficiency. The data of 109 GEM-listed SMEs from 2016 to 2022 is selected for empirical analysis. Firstly, the original data are subjected to descriptive analysis, a correlation test, and a variance inflation factor test to eliminate interfering factors. Subsequently, the data is analyzed based on three-stage DEA analysis, binary logistic regression, multiple linear model regression, and panel Tobit model regression to verify the suppressing effect and moderating effect among the variables. Finally, key variables were replaced for robustness testing to ensure the accuracy of the findings. The results show that supply chain finance can significantly enhance the R&D investment efficiency of SMEs, and financing constraints play a suppressing effect between the two (i. e. , by controlling for the factor of financing constraints, the effect of supply chain finance on the R&D investment efficiency will be more enhanced). Further, in firms with higher supplier concentration and customer concentration, the enhancement effect of supply chain finance on R&D investment efficiency is more significant.
This study makes a contribution in two ways. First, it introduces the concept of "efficiency" in measuring firms' innovation performance. Through the three-stage DEA method, the whole process of "injection of innovation resources", "incubation of innovation results", "realization and commercialization" is tracked, which is more comprehensive than other innovation performance measurement methods. In addition, three-phase DEA overcomes the disadvantages of traditional DEA, which ignores the environmental impact and cannot be adjusted. It is able to measure more realistic R&D investment efficiency, which provides a basis and guidance for the comprehensive assessment of SMEs' innovation performance and the long-term development of innovation activities. Secondly, it confirms the direct effect between supply chain finance and R&D investment efficiency, and verifies the suppressing effect of financing constraints and the moderating effect of supply chain concentration. This lays the foundation for further exploring the internal mediating variables and external moderating variables of the relationship between supply chain finance and R&D investment efficiency. The financing constraints and supply chain concentration variables selected in this paper can highly correspond to two important directions for the development of supply chain finance business: one is to continue to strengthen the role of support for SMEs' financing, and the other is to build a supply chain finance ecosystem that promotes SMEs to be embedded in the synergistic system of value co-creating and risk-sharing with supplier firms and customer firms. The research on suppressing and moderating effects in this study responds to the call of reality, and the conclusions are conducive to giving full play to the advantages of supply chain financing and collaborative innovation, so as to provide dual support for SMEs' innovation activities in terms of both finance and technology.

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.

With the rapid advancement of science and technology, the world has entered the era of big science, where the scale, complexity, and influence of research activities have expanded, making research integrity a key issue. Ensuring research integrity is not only about regulating researchers' behavior but also about balancing scientific freedom with public interest. The effectiveness of the external governance system for research integrity plays a crucial role in maintaining the health of the research ecosystem and the sustainable development of science. However, the current governance system shows a clear fragmentation in practice, posing significant challenges to improving research integrity.
Drawing on holistic governance theory, the study conducts the systems analysis and employs interdisciplinary methods, to deeply analyze the shortcomings of the current governance system. First, in terms of concept, the definition and scope of research integrity lack clarity and consistency, leading to differing interpretations among governance bodies. Issues like illegal gene editing and helicopter science are not fully covered, resulting in incomplete governance of research misconduct. Second, the liability system is unclear, with an over-reliance on civil and administrative remedies. Civil penalties lack deterrence, while administrative measures, though central, often fall short in addressing serious misconduct. Lastly, governance tools are fragmented and homogenized, leading to repetitive sanctions, increasing costs, and reducing flexibility. Furthermore, current governance focuses too much on post-incident punishment, lacking preventive measures and innovation.
This study argues for the need and feasibility of reconstructing the external governance system. First, the research community's self-regulation is weak, requiring a governance system that combines external oversight with internal self-regulation. Second, given the significant public interest in research outcomes, research integrity should be viewed not just as an internal issue but as one that affects broader public interest. Finally, the challenges in policy implementation necessitate applying holistic governance theory to research integrity. Traditional administrative control models struggle to address the complexities of modern research, and holistic governance offers new solutions.
Consequently, three key shifts for reforming the governance system are proposed. First, governance objects should be redefined using a unified approach that integrates horizontal two-dimensionality and vertical four-dimensionality. Horizontal identification should be based on the criteria of "deviation from norms as the core connotation" and "adverse outcomes as the essential extension",while vertical identification should focus on "integrity of research, reliability of results, respect for ethics, and primacy of responsibility". Integrity in research underscores that adherence to scientific norms should be the cornerstone of scholarly endeavors, transcending the mere focus on individual integrity. Reliability of results highlights the importance of ensuring the safety and validity of research outcomes. This principle rejects the 'results-at-all-costs' mentality and acknowledges the inherent uncertainty and unpredictability of scientific research within the modern risk landscape. Respect for ethics places the ethical considerations of science and technology at the forefront, aiming to minimize the risk of dehumanization in research. While the primacy of responsibility emphasizes the convergence of external accountability with an internal sense of duty, for scientists are both answerable to the public and committed to their professional responsibilities, thereby jointly mitigating the risks associated with scientific research conduct. Second, a triple responsibility system is proposed, layering the framework for research misconduct based on “scientific norms, civil contracts, and significant legal interests”, thereby integrating governance with the rule of law. Lastly, governance mechanisms should shift from punishment to a balance between prevention and discipline, using education and preventive measures as core objectives. Leveraging big data, AI, and blockchain can modernize governance and improve efficiency.
This study promotes the transformation of the external governance system of research integrity from a traditional administrative control model to a more synergistic and diversified governance model by introducing holistic governance theory. Through these transformations, the governance capacity of the research integrity system will be significantly enhanced, providing strong support for the healthy development of the research ecosystem and the sustainable advancement of scientific research.

The development of the new energy vehicle industry and its technological innovation have received widespread attention in the context of the ongoing global technology revolution and the growing importance of sustainable development as a strategic focus. New energy vehicles are a technology-intensive emergent industry, and their basic technologies cannot be developed and implemented without the combined efforts and cooperative innovation of businesses, academic institutions, and research centers. However, the existing literature still lacks empirical studies on the long-term collaborative innovation behaviors of various innovation agents in the collaborative innovation network of the new energy vehicle industry. Using the exponential random graph model (ERGM), this study empirically examines the variables influencing the dynamic relationship changes in the collaborative innovation network of the new energy automobile industry.
Using the collaborative invention patent data of the new energy vehicle industry from 2017 to 2021 in China, during which the new energy vehicle industry developed rapidly, this study generates collaborative innovation networks year by year. The networks show obvious core-edge features, that is, a small number of members in the center of the network are closely connected, while the connections between edge nodes and other nodes are sparse. Specifically, the networks exhibit a spatial trend of decentralization and certain agglomeration, indicating that some innovation entities form some small-scale innovation clusters, and the innovation cooperation within the clusters is relatively close. The large number of star structures and closed triangle structures in these clusters indicate that the network structure variable plays an important role in the formation of the network. Through the analysis of the network nodes, the study finds that the core of this kind of cohesive sub-group is dominated by the leading enterprises, such as the leading automobile enterprises and large state-owned enterprises. This phenomenon is consistent with the overall situation of the new energy vehicle industry. The new energy vehicle industry has entered a stage of rapid development, and the capital and manpower invested by enterprises far exceed those of universities or research institutes. As a result, enterprises occupy a dominant role in technology research and development. Meanwhile, enterprises also need the research capabilities of universities and research institutes to support research and high-tech development; thus, with the leading enterprises as the core, a group of universities, research institutes, and small enterprises have gathered to form a collaborative innovation sub-group.
The study further applies ERGM to the networks generated by collaborative patents. It is found that in the development of the current network, organizations with different types of cooperation, such as university-industry cooperation, are more likely to generate invention patent creation, for the new energy vehicle industry is an emerging technology-intensive industry in need of diversified kinds of resources. In addition, it is found that organizations with high-level innovation capability would attract more collaboration, and organizations are willing to seek cooperation with others with similar innovation capabilities. As for the network structure, star structures are more likely to be formed in the current networks. According to the dynamic network analysis, the comparison results reveal that the effects mentioned above hold as time goes by, while the effects of differences in innovation capabilities between entities become weaker over time. The robustness checks, including TERGM analysis and analysis with alternative samples, also show consistency with the main analysis. Besides, the results of heterogeneous analysis based on organizational type indicate that, compared with enterprises, universities, and research institutes are more willing to generate collaboration within the network.
This study explores the collaborative innovation network in the new energy vehicle industry from a dynamic network perspective. It offers an overview of the development of collaborative innovation in the new energy vehicle industry in China, which contributes to the knowledge and provides insights into the development of the new energy vehicle industry in China. Additionally, the study has limitations. While several key influential factors to the network are identified, there are still more features that might be explored from a network analysis using larger datasets to advance the understanding of collaborative innovation.

In the era of digital technology, data, as a key and unique production factor, provides a possible opportunity for enterprise innovation to break through the constraints of factor endowment. However, many enterprises have difficulties reaching the digital innovation integration stage and have failed to improve their innovation performance through data resources. Therefore, in order to unravel the contradiction between the potential value of digitalization and its practice and grasp the unique points of digital reforms compared with general reforms, it is of great practical significance to systematically study the impact of digitalization on the innovation performance of enterprises from the microperspective. The conclusions of the existing studies linking digitalization and innovation performance are mixed, and the root of the controversy lies in the inconsistency of the measurement methods of digitalization and innovation performance, the contradictory nature of the action mechanism, and the differences in the contexts in which they work. Also,the controversy raises the question of whether there is a non-linear relationship between digitalization and innovation performance.
Digital proximity is a new integrative concept for capturing the digital condition of action subjects from a result-oriented network perspective.This paper aims to fill in the above-mentioned research gap by verifying that the relationship between digital proximity and innovation performance is U-shaped.Drawing on Hanns et al.’s research template,it builds a theoretical framework that deconstructs the U-shape into two forces,namely the blocking force and the driving force. It argues that the blocking force and driving force are respectively manifested in the two states of "dormant period" and "enabling period" of the digital proximity on the innovation performance of enterprises, and the combination of the two potential states makes the interaction between the two present a kind of strategic paradox, which is reflected in the significant U-shaped relationship.That is, at the early stage of product and service digitalization, the degree of digital proximity is low, and the rigidity of resources and practices in the process of digitalization is strong, at this time, digital proximity is negatively correlated with innovation performance; on the contrary, when the enterprise has a high digital proximity, the innovation spillover and promotion effects are gradually released, and the enhancement of the enterprise's digital proximity reaches the threshold of positive stimulation of enterprise innovation, presenting a negative correlation between the early stage of digital proximity and innovation performance and a negative correlation between the later stage and innovation performance. The trend toward a positive correlation between digital proximity and innovation performance in the later stage. In addition, executives' traits (rational factors such as political connection background, R&D background, and irrational factors such as overconfidence) affecting the strategic choices and target performance of enterprises have become one of the important clues in the study of enterprise performance, and may constitute an important boundary condition for the influence of digital proximity on the innovation performance of enterprises, so it is necessary to include executives' traits in the theoretical framework.
On the basis of data from Chinese listed companies, the distance between enterprises and digital industries is used to calculate enterprises' digital proximity, the stochastic frontier model (SFE) is used to measure enterprises' innovation efficiency, the binary variables of executives' political connection background and executives' R&D background are collected and computed, and the composite scores of the executives' personal characteristic indexes are used to assess the executives' overconfidence. Following the organizational inertia theory and the upper echelons theory, the study uses 2 580 observations of 1 044 listed companies from 2010 to 2022 to empirically analyze the influence of enterprises’ digital proximity on innovation performance, and examine the moderating effects of three types of executive traits, namely, executives' political connection background, R&D background, and overconfidence. It is found that, overall, there is a U-shaped relationship between digital proximity and innovation performance, and several robustness tests and endogeneity tests confirm the conclusion. Further, the rational factors in executives' traits (politic connection background, R&D background) positively moderate the U-shaped relationship between digital proximity and innovation performance; while the irrational factor in executives' traits (overconfidence) negatively moderates the U-shaped relationship between digital proximity and innovation performance.

The integration of digital technology and manufacturing offers substantial opportunities for industry development. SRDI (specialized, refinement, differential, innovative) "little giant" enterprises are the pioneers who overcome technological barriers and act as manufacturing powerhouses by addressing industry shortcomings. Empowering these little giants with digital technology is strategically vital and contributes to the independence, safety, and control of China's industrial and supply chain systems. The existing studies are mostly qualitative research that have explored factors influencing digital transformation, measurement of digital transformation levels, and economic performance resulting from digital transformation. Notably, there is a dearth of targeted evaluation research on the digital maturity of "little giant" enterprises.
To address these gaps, this study proposes six evaluation criteria for "little giant" enterprises. Utilizing annual report data from listed companies between 2016 and 2022, the study carries on the evaluation based on the four-level "Evaluation Indicators for Digital Level of Small and Medium-sized Enterprises" introduced by the Ministry of Industry and Information Technology in 2022. According to the "input-output" theory, input elements are categorized into digital management and digital foundation, while output processes are divided into digital operation and digital effect. The study extensively utilizes quantitative methods to establish six first-level indicators and thirteen second-level indicators for digital professional management, digital information system refinement, digital innovation efficiency, and product digitalization, etc. Regarding evaluation methods, static research approaches face challenges in assessing time-series three-dimensional data, while a singular "VHSD" evaluation model may overlook the wealth of indicator information. Thus, this study combines the entropy method (EM) and the "VHSD" model for a comprehensive evaluation. This approach overcomes the limitations of both methods, enhancing the credibility of the evaluation results. The study conducts a heterogeneity analysis based on the evaluation outcomes, considering industry and regional dimensions. Additionally, it identifies the primary influencing factors of SRDI "little giant" and performs empirical regression using the Tobit model.
The results unveil a stark polarization of digital maturity among SRDI "little giant" enterprises, highlighting substantial disparities between advanced and traditional manufacturing sectors. Geographically, this division extends across central cities, the southeast coast,the southern, western, and northern regions, exhibiting notable fluctuations over time. The study pinpoints government subsidies, financial technology spending, market competition intensity, and enterprise scale as pivotal factors influencing digital maturity, with varying impacts across samples with different maturity levels. Therefore, it is recommended to encourage "little giant" enterprises to increase digital investment, and apply the digital maturity evaluation system into scale matching, clear positioning;a longitudinal study on the digital transformation strategies of leading enterprises is expected for horizontal and vertical digital integration. On an industry level, the government and research institutions should explore the distinct challenges and opportunities that different industries encounter in their journey towards digital empowerment. Tailored policy support is crucial, especially for traditional manufacturing sectors. The government ought to guide businesses toward a focus of digital intelligence by offering resources, training, and financial assistance, encouraging collaborative, cross-industry digitization aligned with specific needs that can help build a digital ecosystem, enhancing efficiency, reducing costs, and propelling the entire industry towards comprehensive digitization. Regionally, measures such as talent introduction, increased capital investment, and enhancement of the digital level of social governance are proposed to achieve balanced development of digital maturity. Finally, the study proposes targeted policy support for "little giant" manufacturing enterprises at varying digital maturity levels. For traditional manufacturing industries with low digital maturity, they should focus on promoting internal digital construction and strengthening hardware investment;for advanced manufacturing industries with high digital maturity,financial subsidies and policy support are expected to facilitate scale expansion, promote digital technology updates, and effectively enhance overall digital maturity.
This study focuses on the distinctive strengths of "little giant" enterprises in evaluating digital maturity,enhances the existing research on the index evaluation model of digital maturity by employing text mining, the stochastic frontier method and annual report statistics. Moreover, it reveals substantial disparities in digital maturity between advanced and traditional manufacturing at the industry level, along with regional imbalances, and identifies crucial factors for SRDI ‘"little giant" enterprises at different levels of digital maturity, offering valuable insights for policy optimization.

The emergence of artificial intelligence-generated contents (AIGC) technology has posed great challenges to the economy and society. Most of the existing research discusses aspects of technical regulation and development history; few empirical studies are conducted on the patent layout of AIGC leading enterprises. This paper aims to direct its attention towards the comprehensive examination and scrutiny of the patent layout of enterprises at the forefront of AIGC in the United States. To accomplish this objective, this paper selects 14 leading startups and 4 technology giants in the field of AIGC in the United States to analyze their patent IPC network. Intricate network analysis methods and K-means clustering algorithms are employed.
It is found that the patent layout of AIGC emerging enterprises in the United States focuses on technical fields such as electrical digital data processing, graphic data reading and presentation. Patent layout is analyzed by knowledge width, knowledge depth, knowledge cohesion, knowledge fragment level and knowledge consistency clustering. Through this meticulous study, a profound understanding of the patent layout is achieved, leading to the identification of three distinct categories: professional players, technology giants and leaders, and innovators. Professional players refer to AIGC companies that have a focused and specialized patent layout, indicating a deep understanding and expertise in specific technical fields. Technology giants and leaders are AIGC enterprises that have established themselves as industry leaders, with a patent layout that reflects their dominance and influence in the field. Innovators are AIGC startups that demonstrate a unique and novel approach in their patent layout, showcasing their ability to introduce new ideas and technologies to the AIGC industry. These categories provide invaluable insights into the diverse strategies implemented by the aforementioned enterprises.
Furthermore, an in-depth exploration of the patent core knowledge areas and bridging knowledge areas is conducted, ultimately revealing the focal points of knowledge and the areas of expertise within AIGC companies. Core knowledge refers to the essential and fundamental knowledge areas that are central to the patent layout of AIGC enterprises. These knowledge areas represent the key technical fields in which the companies focus their patent filings. Bridging knowledge areas, on the other hand, are the knowledge areas that connect or bridge different technical fields within the patent layout of AIGC enterprises. These areas indicate the interdisciplinary nature of the companies' patent filings and their ability to integrate knowledge from multiple domains. The identification of core knowledge areas and bridging knowledge areas helps in understanding the strategic focus and innovation capabilities of AIGC companies, as well as the potential for cross-pollination of ideas and technologies across different technical fields.
The paper's pivotal contribution lies within its empirical analysis of the patent layout, which presents a wealth of valuable insights into the intricate clustering patterns and specialized knowledge areas of the leading enterprises in the field of AIGC. Firstly, it provides empirical insights into the patent layout of leading AIGC enterprises. Secondly, it employs complex network analysis methods and K-means clustering algorithms in machine learning to examine the patent IPC network of AIGC companies, offering a novel approach to investigating their patent layouts. Thirdly, it categorizes the patent layout for AIGC enterprises into three distinct groups: professional players, technology giants and leaders, and innovators. These categories are determined based on factors such as knowledge width, depth, cohesion, fragment level, and consistency clustering. Fourthly, it identifies the core knowledge areas and bridging knowledge areas within the patent layouts of AIGC companies, thereby shedding light on their technical focus, interdisciplinary integration, and innovation capabilities.
In summary, this analysis of the layout of patent networks for companies specializing in AIGC encompass enables a comprehensive understanding of the technical focus and specialization of AIGC companies based on the arrangement of their patents. Then it allows for the identification of prominent industry players and technology leaders within the AIGC sector based on the structure of their patent layouts, and facilitates an assessment of the innovation capabilities and novel approaches employed by AIGC companies, as reflected in their patent layouts. Furthermore, this analysis suggests policy measures for the development of the AIGC industry in China, with a specific focus on algorithm, computing power, and data.

With the rapid development and wide application of digital technology, the digital economy is promoting profound changes in the modes of production, living and governance, and has become a key force in changing the pattern of global competition. In order to improve their competitiveness and achieve sustainable development in the complex and changing digital environment, many enterprises are accelerating digital transformation. Manufacturing enterprises as the micro embodiment of the deep integration of the digital economy and the real economy, promoting their successful digital transformation not only helps to enhance their own competitive advantage, but also has great significance for the construction of a manufacturing power and digital China. Digital transformation is a transformation process characterized by dynamism, continuity and adaptability, which is essentially the use of digital technology to achieve insights into external stimuli and make agile responses, and most companies do not have a smooth ride in the digital transformation process. In this context, it is crucial to have an agile workforce adapted to the digital transformation and upgrading of manufacturing enterprises. Therefore, how to better cultivate and improve employee agility has become a focus of attention in both the academic and business worlds.
Existing research on the factors affecting employee agility mainly focuses on organizational and individual factors, and few scholars have explored its impact on employee agility from a leadership perspective. Existing research shows that leadership is an important antecedent variable that affects employees' psychology, attitude and behavior. Digital leadership is a concept developed for successful digital transformation and refers to the ability of leaders to create a clear and meaningful vision for their organization's digital transformation, to encourage employees to actively participate in their organization's digital transformation, and to lead their organization's members in executing and successfully implementing their digital transformation strategy. Digital leadership reflects the strong willingness of leaders to change, and leaders will actively promote the digital transformation of the enterprise. In addition, leaders focus on improving employees' digital capabilities during the transformation process, and are happy to provide employees with more information sharing and work autonomy. This helps to stimulate employees' sense of efficacy and passion for their work, encouraging them to take the initiative to learn new skills, adapt themselves to changes in the environment, and present greater flexibility in their work. Therefore, this study investigates the mechanism and boundary conditions of digital leadership on employee agility with employees in manufacturing companies as the research object.
This study uses the questionnaire survey method to collect data, and after screening and eliminating invalid data, a total of 570 valid samples are obtained. Descriptive statistical analysis, confirmatory factor analysis, homology bias test, etc. are conducted by SPSS and AMOS to verify the hypotheses. The results show that digital leadership has a positive effect on employee agility; digital self-efficacy and harmonious work passion play a partially mediating role in the relationship between digital leadership and employee agility; and openness to change has a positive moderating effect between digital leadership and the relationship between employee agility, digital self-efficacy, and harmonious work passion.
The results of this study have enriched and deepened research in relevant fields, and have certain theoretical innovation and practical significance. First, the antecedent research on employee agility is enriched from a leadership perspective. Combined with the context of digital transformation, exploring the impact of digital leadership on employee agility not only enriches the theoretical research results on employee agility, but also has important practical significance. Second, following the cognitive-affective personality systems theory, this study reveals how digital leadership impacts employee agility through the dual paths of digital self-efficacy and harmonic work passion, filling the theoretical caps in the existing literature. Third, the boundary conditions of digital leadership on employee agility are clarified by introducing individual-level openness to change as a moderating variable. Finally, this study provides managerial recommendations on how to improve employee agility, including cultivating digital leadership, stimulating employees' digital self-efficacy and harmonious work passion, and enhancing employees' openness to change.

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.

In recent years, as China transitions from a phase of high-speed economic growth to a stage of high-quality development, enhancing total factor productivity (TFP) has emerged as a critical lever for fostering sustainable economic progress. Amidst this backdrop, corporate innovation stands out as a pivotal engine driving productivity enhancements and long-term economic growth. Solow (1956) underscored the significance of TFP in economic advancement, sparking widespread exploration into the underpinnings of productivity growth. Drawing upon a rich tapestry of research that delves into various facets of corporate heterogeneity, including R&D investments, workforce demographics, and industry characteristics, this study aims to scrutinize the intricate mechanisms by which corporate innovation influences TFP. Specifically, it ventures beyond conventional linear perspectives, adopting a novel lens focused on the dimensions of innovation's breadth and depth, thereby providing a robust theoretical foundation for policy formulation and implementation.
This study harnesses a dataset comprising A-share listed companies in China spanning from 2003 to 2021. By meticulously curating a sample reflective of the broader economic landscape, this investigation offers a comprehensive overview of the nexus between corporate innovation and TFP within the Chinese context. The study employs a multi-dimensional approach to quantify corporate innovation, distinguishing between innovation's breadth and depth. The breadth of innovation, indicative of the diversity and cross-disciplinary nature of a firm's innovative endeavors, reflects the firm's ability to engage in innovation across various technological domains. Conversely, the depth of innovation encapsulates the intensity and commitment of a firm towards innovation, signifying concentrated efforts in core technological advancements and improvements in production processes. Utilizing sophisticated statistical techniques, including regression analysis and robustness checks, the study evaluates the impact of these innovation dimensions on TFP. The analytical framework is further enriched by exploring the moderating effects of ownership structure, technological intensity, marketization level, and intellectual property rights protection on the innovation-TFP relationship. The investigation uncovers an inverted U-shaped relationship between the breadth of innovation and TFP, suggesting that while diversifying innovation across different technological fields initially boosts TFP, beyond a certain threshold, it may lead to diminishing returns. In stark contrast, the depth of innovation demonstrates a consistently positive influence on TFP, underscoring the importance of concentrated innovation efforts. Furthermore, the study reveals nuanced insights into how non-state-owned corporates, compared to their state-owned counterparts, and high-tech firms, relative to lower-tech firms, exhibit distinct patterns in how innovation's breadth and depth affect TFP. These findings highlight the complexity of the innovation-productivity paradigm, influenced by corporate characteristics and the external economic environment. The research also illuminates the critical roles of marketization and intellectual property rights protection. In regions with higher levels of marketization and stronger intellectual property protection, the positive effects of innovation depth on TFP are accentuated, thereby reinforcing the argument for fostering conducive economic and legal conditions for innovation.
This study introduces an innovative lens for assessing corporate innovation, emphasizing the dual dimensions of innovation's breadth and depth. By unraveling the intricate dynamics between different facets of innovation and TFP, it extends the existing literature and offers new pathways for theoretical exploration. Moreover, the findings bear significant implications for policymakers and business leaders, advocating for targeted strategies to cultivate an ecosystem that nurtures innovation and drives productivity growth. In conclusion, this research sheds light on the multifaceted relationship between corporate innovation and TFP, contributing valuable insights into the drivers of high-quality economic development. By advocating for nuanced policies that recognize the complexity of innovation activities, this study underscores the imperative of fostering a supportive environment for innovation, tailored to the diverse needs of corporates across different sectors and developmental stages. In the following research, the sample could be expanded and the specific reasons for the formation of the inverted U-shaped relationship between firms' breadth of innovation and total factor productivity could be further explored, along with further exploration of how to optimize the depth of innovation in order to maximize its positive impact on productivity.

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.

Nowadays, China is accelerating the implementation of its innovation-driven development strategy. Cooperation between undertakings on research and development can not only help undertakings expand the scale of R&D and achieve technological complementarity, but it may also become a tool for undertakings to reach monopoly agreements on production and sales and a means to exclude other competitors. The purpose of the antitrust exemption from R&D agreements is to facilitate research and development while effectively protecting competition and providing adequate legal security for undertakings. The antitrust exemption is justified by protecting the dynamic efficiency of innovation at the cost of a relatively small static efficiency loss in price competition and not allowing the growth of long-term market power that hinders technological progress.
This paper takes the EU as a sample to analyze the antitrust exemption system for R&D agreements. In June 2023, the European Commission adopted revised Horizontal Block Exemption Regulations and accompanying Horizontal Guidelines. The new rules simplify the method of calculating market share and state that market shares shall be calculated on the basis of market sales value, or, if market sales volume data is not available, estimates based on other reliable market information, including expenditure on research and development, or research and development capabilities may be used. According to the new regulations, if the combined market share of the relevant parties does not exceed the relevant threshold at the end of the 7-year period but subsequently rises above that threshold, the exemption shall continue to apply for a period of two consecutive calendar years following the year in which the relevant market share threshold was first exceeded.
China's Anti-Monopoly Law provides antitrust exemptions for R&D agreements. The provisions of the monopoly agreement shall not apply where the undertakings can prove that the agreement reached is for the purpose of improving technology or researching and developing new products. The newly amended Anti-Monopoly Law does not stipulate the substantive conditions and procedural content for the exemption of R&D agreements, though it adds that “innovation is encouraged”. Moreover, the newly amended Anti-Monopoly Law establishes a safe harbor regime that only applies to vertical monopoly agreements and does not apply to horizontal R&D agreements between competitors. Enforcement practice mainly prevents joint ventures from restricting competition through merger review and does not rely more on the enforcement of monopoly agreements to regulate non-fully functional joint ventures similar to those in the EU.
The results show that the exemption from the Anti-Monopoly Law should not only consider the combined market share of the competing parties to the agreement on the relevant product and technology markets but also consider the content and duration of the agreement so as not to eliminate substantive competition among the undertakings. Access to the results by the parties should generally not be restricted where the agreement provides the joint use of the results. The compensation must not be so high as to effectively impede access to results where the research and development agreement provides that the parties compensate each other for giving such access for the purposes of further research and development or for the purpose of exploitation. The agreement must stipulate that each party shall be granted access to any pre-existing know-how of the other parties if such know-how is indispensable for the purposes of exploitation of the results. Where the agreement provides that the parties compensate each other for giving access to their pre-existing know-how, the compensation must not be so high as to effectively impede such access.
This paper comprehensively examines the legislative and judicial practice of the antitrust exemption system for R&D agreements in the EU and innovatively proposes a framework for the construction of the antitrust exemption system for R&D agreements based on the actual situation in China. China should formulate anti-monopoly guidelines for R&D as soon as possible and establish a safe harbor system according to market share. Meanwhile, it is important for China to explore the establishment of negative exclusion rules clarifying the core restrictions of illegality in the form of a negative list and study the market impact review standards in favor of innovation.

Artificial intelligence (AI) is a major driver of the fourth industrial revolution and is reshaping the landscape of many industries and sectors. The service sector is leading the way in the revolutionary wave of AI. AI is driving the transformation and upgrading of the labor structure,while it facilitates the work of service workers, it also brings enormous challenges. The challenges faced by service sector employees in terms of ‘machine replacement′ and learning and applying AI technologies have triggered widespread AI anxiety. For their job security, service industry employees can only be more creative in improving services or developing new services to capture and satisfy the diversified needs of customers through service innovation. Upon synthesizing the current body of research, it has become evident that previous studies have not adequately addressed the service industry′s perspective on the dual nature of AI anxiety—both its detrimental and beneficial aspects—when examining its varied effects on employees′ incremental and radical service innovations. Thus, it is imperative to adopt a comprehensive approach that considers both the negative and positive dimensions of AI anxiety within the service sector. Such an approach is essential for a thorough analysis of AI anxiety′s differential impact on employees′ engagement in both incremental and radical service innovations, as well as for understanding the intrinsic mechanisms that drive these effects. This comprehensive examination is crucial in the contemporary era of digital intelligence, where the implications of AI anxiety on service innovation are far-reaching and complex.　In this study, the questionnaire was developed using a well-established scale. It was meticulously adapted to ensure cultural and linguistic accuracy through a rigorous translation and back-translation process. The responses were measured on a 5-point Likert scale. Data collection was conducted at two distinct time points, spaced three months apart, yielding a total of 610 valid questionnaires after the matching process. The sample population was drawn from employees working in over 40 organizations across various cities in China, including major hubs like Shanghai, Beijing, Guangdong, Zhejiang, and Tianjin,with diverse service sectors such as tourism, hospitality, finance, and business consulting, providing a broad and representative dataset for analysis. Following the theory of workplace anxiety and the knowledge base view, this study takes into account the two sides of anxiety and uses SPSS26.0 and Mplus8.3 software to statistically test the mechanism of the effect of AI anxiety on employees′ dual service innovation. The results of the study show that AI anxiety has both inhibitory and facilitating effects on employees′ dual service innovation, but generally promotes employees′ dual service innovation. When AI anxiety leads to affective rumination, it primarily inhibits employees′ internal knowledge scanning, thereby hindering incremental service innovation. It also curbs external knowledge scanning, which impedes radical service innovation. Conversely, when AI anxiety prompts problem-solving pondering, it stimulates external knowledge scanning, thereby enhancing dual service innovation, with a more pronounced effect on radical service innovation. Employees with higher AI literacy are less likely to fall into affective rumination due to AI anxiety and are more inclined to engage in problem-solving pondering. This shift strengthens the facilitating effect of AI anxiety on dual service innovation and mitigates the inhibitory impact on incremental service innovation through affective rumination and internal knowledge scanning. However, high AI literacy does not diminish the inhibitory effect of AI anxiety on dual service innovation via affective rumination and external knowledge scanning.
The results of the study clarify the differential impact of AI anxiety on employees′ dual service innovation and its internal mechanism, taking into account both the negative and positive aspects of AI anxiety. By exploring the important role of employees′ AI literacy in the relationship between AI anxiety and work-related rumination, this study deepens the theoretical understanding of AI anxiety′s impact on employees′ proactive behaviors. Furthermore, the findings offer practical insights for organizations to guide employees in facing AI anxiety correctly and to foster dual service innovation as a response to AI anxiety.