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.

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.

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.

Opportunity has been central to entrepreneurship research, with scholars extensively discussing its nature, leading to two contrasting perspectives: the discovery view and the creation view. Recently, an increasing number of scholars have attempted to reconcile the tension by integrating the discovery view and the creation view. However, limited to its fragmented research status of the opportunity holistic view, the ambidextrous nature of opportunities, which encompasses both discovery and creation, remains obscured. This further constrains the research on the development process of ambidexterity opportunities, as well as comparative analyses across discovery, creation, and ambidexterity opportunities. Furthermore, opportunity evaluation (OE), a critical but under-explored stage in opportunity development, is often automatically presumed to transition into entrepreneurial action (EA), with limited inquiry into the underlying mechanisms or boundary conditions shaping this process. Existing research suggests that entrepreneurs' propensity to act can influence this transition, yet the factors affecting this propensity and the OE-EA relationship remain insufficiently studied. Regulatory Mode Theory (RMT) from psychology posits that two regulatory modes—locomotion mode (LM) and assessment mode (AM)—significantly shape individual action propensity, suggesting their potential influence on the OE-EA dynamic. Despite this, little attention has been given to applying RMT within entrepreneurship research, leaving the mechanisms of how regulatory modes influence the OE-EA relationship largely unexplored.
This study addresses these theoretical gaps by exploring the relationship between OE and EA across discovery, creation, and ambidexterity opportunities, grounded in the opportunity holistic view. By leveraging RMT, the study investigates how LM and AM moderate the OE-EA relationship across three different opportunity types (discovery, creation, and ambidexterity). First, the study clarifies the ambidextrous nature of opportunities based on the holistic view, theoretically categorizing opportunities into discovery, creation, and ambidexterity, elucidating their characteristics to provide a solid theoretical foundation for exploring the development processes of three different opportunity types (discovery, creation, and ambidexterity). Then, an empirical analysis of 756 Chinese start-up entrepreneurs is conducted. The K-means clustering algorithms are utilized to segregate 756 samples into three distinct clusters: discovery, creation, and ambidexterity. Following this classification, separate Ordinary Least Squares (OLS) regression analyses are conducted within each of the three sub-dimensioned opportunity contexts to test the hypotheses. Results show that OE plays a pivotal role in driving EA across all three opportunity contexts. With regard to the moderating effects, the empirical results highlight that the interplay between OE and EA varies markedly across different opportunity contexts. Specifically, in the discovery context, LM is found to exert a negative moderating influence on the OE-to-EA relationship, while AM demonstrates a positive moderating effect. In the creation context, a contrasting pattern is observed: LM positively moderates the relationship between OE and EA, whereas AM negatively moderates the same. In the ambidexterity context, only LM is revealed to have a significant positive moderating impact on the OE-to-EA relationship, while AM exhibits no moderating influence.
This study contributes to the literature in three ways. First, building on the opportunity holistic view, it clarifies the ambidextrous nature, typology, and characteristics of opportunities, further reconciling the tension between the opportunity discovery and creation views, and advancing theoretical and empirical research in the opportunity holistic view. Second, it elucidates the mechanisms underlying the OE-to-EA transition in the varied development processes of discovery, creation, and ambidexterity opportunities, addressing previous research gaps that treated this process as automatic, and expanding the micro-foundation research of EA. Third, by integrating RMT, the study uncovers the internal mechanisms through which different types of OE lead to EA, enriching the understanding of the boundaries of RMT and providing interdisciplinary insights at the nexus of psychology and entrepreneurship. This study also offers several practical implications: First, it helps entrepreneurs align their evaluations of opportunities with their regulatory modes, facilitating more effective entrepreneurial action. Second, it informs policymakers in designing industry-specific entrepreneurial support strategies to promote high-quality development. Third, for investors, the study provides insights for assessing the alignment between entrepreneurs and their opportunities, thereby enhancing investment decision-making and success rates.

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.

Strengthening urban innovation systems and optimizing the efficiency of innovation factor allocation are critical for overcoming the "middle-technology trap" and realizing a Chinese-style modernization. Against this backdrop, the digital revolution has positioned data as a crucial production factor. In China, public data accounts for over 80% of the national total data resources, with an estimated value of 10-15 trillion yuan in 2020. The nationwide open datasets have exceeded 2 million items, covering livelihood areas such as transportation, healthcare, education, and environmental protection. Open government data policies can break down information silos, enhance inter-departmental coordinated development, and expand socio-economic benefits. Both domestic and international research consensus holds that enhancing innovation capacity is a key impact of public data openness, though systematic empirical verification remains insufficient. Domestic research mostly focuses on the enterprise perspective, with less research on urban innovation.Additionally, existing literature varies significantly in its research angles, resulting in no unified conclusions regarding how public data openness specifically influences urban innovation. Therefore, it is essential to explore their mechanisms in promoting urban innovation.
This study utilizes annual panel data from 271 cities in China from 2009 to 2023 to construct a multi-period difference-in-differences model to explore the impact of open public data on urban innovation. The establishment of open government data platforms in cities is treated as a quasi-natural experiment, with the number of patent grants per city as the dependent variable. Mechanism tests are conducted via effective market and proactive government paths. In addition, the paper studies the moderating role of digital inclusive finance and human capital stock, and finally uses a spatial Durbin model to investigate whether the positive effect of open government data on urban innovation has spatial spillovers. The study tests the spatial autocorrelation of the dependent variable—urban innovation capacity. Following the existing literature, it computes the global Moran’s I for each year under three weighting schemes: the contiguity matrix (W1), the economic-distance matrix (W2), and the nested economic-geographical matrix (W3).
Empirical results indicate that the implementation of open government data policies significantly promotes urban innovation by stimulating market vitality and enhancing marketization levels to make the market effective, reducing government intervention, optimizing government-business relations, and improving the business environment to promote proactive government action, thus fostering urban innovation through the dual pathways of an effective market and a proactive government. Digital inclusive finance plays a positive moderating role by alleviating financing constraints and providing funding support, while human capital stock amplifies the innovative advantages of open government data by strengthening knowledge spillover effects. Moreover, there is heterogeneity among cities: cities southeast of Hu Huanyong’s line, cities with a higher level of industrial structure upgrading, and large cities exhibit a stronger innovation promotion effect, likely due to more complete digital infrastructure, a more advantageous industrial structure, and stronger human capital, which further confirms the positive moderating effect of human capital stock and digital inclusive finance. In addition, open government data has a significant spatial spillover effect on urban innovation, positively impacting other cities through spatial interactions, and this effect is jointly driven by geographic proximity, economic similarity, and the interaction between the two. Lastly, in the transmission mechanism of public data openness promoting urban innovation capacity, the synergy of an effective market and a proactive government has produced a significant promotion effect.
The theoretical contributions are threefold. First, by conceptualizing open government data as a quasi-experiment, we extend existing innovation theory to encompass data governance frameworks. Second, the integration of effective market and proactive government mechanisms addresses a critical gap in understanding state-market synergies within innovation ecosystems. Third, our spatial and moderating analyses provide nuanced insights into cross-city disparities and multiplier effects. Practically speaking, this study offers valuable guidance for policies related to data openness roadmaps, digital finance integration, and intercity innovation networks, providing an actionable blueprint for fostering high-quality urban development.

The report of the 20th National Congress of the Communist Party of China underscores the imperative to promote the development of “specialized, sophisticated, differentiated, and innovative”(SSDI) small and medium-sized enterprises (SMEs). As a key part of the innovation-driven development strategy, SSDI SMEs have gained new attention for their innovation capabilities. Yet, innovation requires heavy upfront investment and brings uncertain results. For SSDI enterprises—typically constrained by limited scale—technological innovation faces compounded challenges. They often face serious R&D funding shortages, structural talent deficits, and a common leaning toward imitative innovation. Consequently, many SMEs are compelled to adopt low-value-added innovation models that prioritize quantity over quality, creating a critical bottleneck in their innovative advancement.Thus, addressing financing constraints, optimizing talent allocation, and refining innovation mechanisms are urgent tasks for promoting the innovative growth of SSDI SMEs.
Against the backdrop of digital-real economy integration, the disclosure of data assets can effectively address the aforementioned challenges. As a form of voluntary corporate disclosure, data asset disclosure serves as a critical channel for the public to understand value creation from data assets, big data applications, digital transformation, and related corporate activities. It also holds significant implications for firms' own innovation development. Given the exponential growth of data as an “asset class” and the pressing need to enhance the innovation capabilities of SSDI SMEs, academia urgently needs to investigate the innovation effects of data asset disclosure. Such research would provide valuable insights for China to seize new opportunities in digital development and expand new frontiers for economic growth.
Existing literature has empirically demonstrated the positive effects of data asset disclosure, including lowering external financing costs, boosting market reactions, enhancing analyst forecasts, and increasing firm value.Yet, its impact on SME innovation quality is still unclear. This research gap takes on heightened significance when examining SSDI SMEs. Given their advanced digital maturity, richer data asset portfolios, and more comprehensive disclosure practices compared to conventional SMEs, SSDI enterprises present an ideal context to investigate whether data asset disclosure exerts a disproportionately stronger effect on innovation quality enhancement. Therefore exploring this is crucial for innovation policies and corporate digital strategies.
Adopting a micro-level perspective, this study examines the impact of data asset disclosure on innovation quality in SSDI SMEs, using a sample of China's “Little Giant” listed enterprises from 2011 to 2023. The empirical results demonstrate that data asset disclosure significantly enhances innovation quality among SSDI SMEs, with findings remaining robust across a series of sensitivity tests. Mechanism analysis reveals that data asset disclosure primarily improves innovation quality through three channels: alleviating financing constraints, optimizing human capital allocation, and reducing innovation conformity behavior. Furthermore, the study identifies significant moderating effects, showing that stronger intellectual property protection, intensified industry competition, and increased analyst coverage can amplify the positive influence of data asset disclosure on SMEs’ innovation quality.
Compared with existing literature, this study makes three key marginal contributions: First, it enriches micro-level research on data assets. Prior studies focused on macro-level aspects, neglecting micro-level analyses of how data asset disclosure enhances innovation quality. This work bridges the gap between information and innovation economics. Second, this study elucidates the mechanisms through which data asset disclosure improves innovation quality. Previous research has largely treated the relationship between data disclosure and SME innovation as a “black box”. Departing from conventional approaches, this study adopts an information transparency perspective to demonstrate how SSDI SMEs leverage data asset disclosure to enhance innovation quality along three dimensions: (1) improving capital accessibility, (2) optimizing labor structure, and (3) strengthening governance capacity. Third, existing studies predominantly rely on annual reports as the primary source of data asset information. Recognizing that corporate news coverage serves as an equally critical channel for information dissemination,and is indispensable for a comprehensive evaluation of data asset disclosure,this study systematically analyzes data-related disclosures in both annual reports and news articles. Methodologically, it employs Python-based text mining techniques and constructs a specialized data asset lexicon using Word2Vec neural network modeling. This dual-source approach not only enhances measurement accuracy but also pioneers a novel methodological pathway for future data asset research.

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

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.

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.

At present,new-generation information technology,high-end equipment manufacturing,and other strategic emerging industries have gradually become the core driving force of global industrial change. Compared to developed countries,China still encounters bottlenecks in certain critical technology sectors,such as high-end chips and industrial software. How to find a replicable and scalable breakthrough path for key technologies in China's emerging industries to tackle the bottleneck issues has become a pressing issue to resolve. The new generation of digital innovation technology represented by blockchain and artificial intelligence has become a new engine to promote high-quality economic development. Combined with the characteristics of key technology breakthroughs,exploring the role of digital innovation on key technology breakthroughs of strategic emerging industries in a dynamic environment is significant for China to establish industrial competitive advantages. Therefore,this study aims to elucidate the influence mechanism of digital innovation in promoting key technology breakthroughs and how industry-university-research co-operation can ensure the smooth progress of key technology breakthroughs.
This study argues that digital innovation can promote the key technology breakthroughs by improving the knowledge acquisition,knowledge absorption,and integration capabilities of enterprises,while industry-university-research co-operation can enhance the facilitating effect of digital innovation on the key technology breakthroughs. This study utilizes enterprise data from 2011 to 2023 to assess digital innovation levels through textual analysis and word frequency metrics. It constructs an indicator system for key technologies across innovation,dissemination,economic impact,and security. By integrating the technological catch-up perspective,the study evaluates key technology breakthrough capabilities. Employing multiple linear regression,mediation analysis,moderation effects modeling,instrumental variable approaches,and the Heckman two-stage method,the study investigates the direct impact of digital innovation on key technology advancements,the mediating role of knowledge acquisition,absorption and integration ability,and the moderating influence of industry-academia collaboration.
The empirical results show that digital innovation (including digital technology innovation and digital management innovation) has a significant facilitating effect on key technology breakthroughs,and the conclusion still holds after the robustness test and endogeneity treatment. Heterogeneity analysis finds that digital innovation has a stronger facilitating effect on key technology breakthroughs in non-state-owned enterprises compared with state-owned enterprises. Further analysis confirms that digital innovation can promote key technology breakthroughs by improving enterprises' internal knowledge integration ability,digital technology innovation can promote key technology breakthroughs by improving the enterprises' knowledge absorption ability,and there is a positive moderating effect of industry-university-research cooperation in the relationship between digital innovation and key technology breakthroughs. Therefore,enterprises are advised to establish a "technology + management" digital innovation framework to maximize the catalytic impact of digital innovation. They should also develop a knowledge management ecosystem to enhance the effectiveness of industry-university-research collaboration. Moreover,by implementing tailored digital innovation strategies,enterprises can invigorate their innovation potential.
The marginal contributions of this paper are as follows: Firstly,it expands the research on digital innovation from the enterprise level. Within the theoretical framework of dynamic capability and information processing,this study constructs the theoretical chain of "digital innovation - technological breakthrough" and expands the research on digital innovation. Secondly,it enriches the research on the influencing factors of key technology breakthroughs. This study empirically reveals that digital innovation can promote the breakthrough of key technology by improving the efficiency of information processing and the collaborative ability of multiple subjects,and reducing the risk,which provides a new perspective for tackling the bottlenecks in key technologies. Thirdly,utilizing the improved entropy weight TOPSIS method and the technological catching-up perspective,the key technology breakthrough ability is measured by combining patent data. From the perspective of technological catching-up,this study provides a new measurement of key technology breakthrough ability through the improved entropy weight TOPSIS method,combined with the patent data of the nine strategic emerging industries at the global level,which makes up for the insufficiency of the study on the measurement of key technology breakthrough ability at the micro level.