Innovation plays a central role in driving China's high-quality development. However, China remains heavily reliant on international sources for key core technologies and components. In recent years, global economic fluctuations and geopolitical tensions have constrained technological innovation activities, with the risk of innovation disruption persisting and potentially increasing. It is urgent to optimize innovation practices and activate innovation resilience. Technological innovation often has high risks due to its large cost scale and high output uncertainty, and is easily constrained by corporate resources. Patient capital can provide long-term and stable financial support for corporate technological innovation. Furthermore, through its risk diversification function, patient capital can effectively mitigate the high-risk nature of technological innovation. Therefore, patient capital has the typical characteristics of highly matching with the intrinsic needs of corporate technological innovation, and is gradually becoming a new quality driving force to enhance corporate innovation resilience.
However, due to the inherent profit-seeking and risk-taking nature of capital, the excessive penetration of patient capital may intensify the “goal conflict” caused by investors chasing short-term self-interest. This could lead to an imbalance of interests between investors and innovative subjects and ultimately weaken the sustainability of the innovation ecosystem. Additionally, patient capital may also lead to the development of a comfortable mentality and innovation inertia among managers. These issues raise a series of key questions that deserve deeper inquiry. Can patient capital become the core element to stimulate corporate innovation resilience And how to effectively ensure that patient capital can continue to promote corporate innovation resilience and help enterprises overcome technological blockades and continuously realize technological breakthroughs.
Using the data of A-share listed companies in Shanghai and Shenzhen from 2009 to 2023, this study empirically examines the influence of patient capital on innovation resilience. Meanwhile, from the horizontal strategy dimension and vertical time dimension, this study constructs a framework for the transmission path of patient capital's influence on innovation resilience. Additionally, by analyzing the characteristics of internal innovation decision-makers within enterprises and the characteristics of the external innovation environment, this study explores how patient capital influences innovation resilience under different circumstances.
The results show that the influence of patient capital on corporate innovation resilience presents an inverted U-shaped relationship and the optimal allocation for patient capital is 34.36%. Mechanism tests reveal that patient capital affects corporate innovation resilience through three dimensions: innovation cooperativeness, innovation ambidexterity, and innovation sustainability. Further research based on innovation decision-makers and environmental characteristics shows that the inverted U-shaped impact of patient capital on corporate innovation resilience is further strengthened when managers exhibit higher levels of patience, innovation decision-making power is more concentrated, and intellectual property protection is higher.
Compared to existing research, the possible marginal contribution of this study is three-fold. First, this study reveals the inverted U-shaped relationship between patient capital and corporate innovation resilience from a nonlinear dual perspective, which not only deepens the theoretical understanding of the duality of patient capital, but also provides a scientific basis for corporate capital allocation by identifying the “optimal” threshold. Second,this study explores the role of patient capital in enterprise innovation resilience from three dimensions: innovation cooperativeness, innovation ambidexterity, and innovation continuity. It not only expands the connotation of innovation resilience research at the theoretical level, but also provides new ideas for enterprises to optimize capital allocation and enhance innovation effectiveness in practical application. Third, the study characterizes the innovation decision subjects within the firm in terms of the degree of managerial patience and the allocation of innovation decision-making power, and applies the degree of intellectual property protection to characterize the external innovation environment. From the perspectives of internal decision-making subjects and external environment, it can provide new perspectives and insights for academics to understand the role of the boundary of patient capital, and help promote the in-depth integration of relevant theoretical research and corporate practice.

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.

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.

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

With the 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.

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