Under the guidance of China's "dual carbon" goals, green innovation has emerged as a core driver for the low-carbon transformation of the economy and society, while the traditional pollution control model is shifting toward a more value-creating green development paradigm that emphasizes sustainable growth and innovation. The intensification of global trade frictions has hindered the international flow of green technologies, making the enhancement of independent innovation capabilities a critical pathway to achieving carbon neutrality. However, green innovation is characterized by high investment, long cycles, and significant risks, and these characteristics frequently lead to unsustainable corporate investment decisions, particularly under short-term operational pressures, thereby creating persistent bottlenecks in innovation advancement. The rapid adoption of digital technologies in recent years offers a new breakthrough to address this challenge. Against the backdrop of digital transformation, data, as a core resource, is permeating various industries, becoming the key link between digital industrialization and industrial digitization. Data assets, with their attributes of infinite supply, low-cost reuse, and cross-spatiotemporal sharing, can effectively reduce the trial-and-error costs of green innovation, enhance technological synergies, and drive breakthroughs in energy conservation, emission reduction, and circular economy.
Meanwhile, the transformation of data assets into green innovation outcomes is influenced by multiple factors. High-quality digital talent is essential for unlocking data value, while robust digital infrastructure improves data allocation efficiency. Moderate market competition drives corporate innovation, and strong ESG performance steers data resources toward sustainable development. Existing research has examined the impact of digital finance, the digital economy, and digital policies on green innovation. However, studies on data assets remain largely theoretical, focusing on accounting, valuation, and economic effects, and empirical research in this domain is still in its infancy, particularly concerning the intricate relationship between data assets and corporate green innovation. A deeper understanding of how data assets empower green innovation is crucial for enriching the theoretical framework surrounding data marketization but also for providing actionable insights to policymakers. By better understanding this relationship, policymakers can more effectively align digital economy strategies with sustainability objectives, which is of paramount importance in the context of achieving China's “dual carbon” goals.
Against this backdrop, this study employs the double machine learning model to examine the causal effects and mechanisms through which data assets influence corporate green innovation capabilities. Empirical results demonstrate that data assets significantly enhance green innovation, a finding that remains robust across a series of tests. The analysis reveals that data assets improve green innovation by optimizing human capital structure, particularly in firms with higher ESG performance, where the effect is stronger due to their emphasis on sustainable development and technological advancement. Additionally, digital infrastructure strengthens this relationship by improving the allocation efficiency of data resources, while intensified industry competition further amplifies the effect by increasing firms' sensitivity to technological innovation. Nevertheless, the magnitude of this impact varies across different firm types and regions due to differences in resource endowments and technological readiness: enterprises in megacities, high-tech firms, state-owned enterprises, and large corporations benefit more significantly, likely due to their greater access to resources and technological infrastructure; whereas small and medium-sized city enterprises, non-high-tech firms, non-state-owned enterprises, and smaller businesses exhibit weaker effects due to differences in resource endowments and technological readiness. This study not only uncovers the mechanisms driving green innovation through data assets but also offers policy insights for facilitating corporate green transformation through optimized data resource allocation.
The contributions of this study lie in its integration of data assets as a new production factor and its methodological innovation through the application of double machine learning, addressing limitations in traditional econometric approaches. By clarifying how skilled labor mediates the relationship between data assets and green innovation, it demystifies the role of human capital in fostering technological breakthroughs. Moreover, by examining contextual moderators such as ESG performance, industry competition, and digital infrastructure, the study provides actionable guidance for firms across diverse sectors and regions, informing strategies for leveraging data assets to advance sustainable development objectives.

In today′s era, data has become a new factor of production and is driving the development of the global economy. How to realize the value of data elements has become a major issue that urgently needs to be addressed in today′s era. Data elements have no inherent value due to their virtual nature. Only when combined with other production factors can they play a certain role, improve social efficiency, promote innovation in product research and development, technology application, and other aspects of enterprises, and generate actual value. Although a small number of studies have preliminarily explored the process of realizing the value of data elements, they have not fully considered that it is a complex process involving the interaction of technology, organizational capabilities, and environment, making it difficult to fully explain and guide how enterprises as micro subjects should realize the value of data elements. Therefore, the mechanism for realizing the value of data elements urgently needs to be condensed from a new theoretical perspective.
This study employs a case analysis of Unicom Digital Tech′s 'Jin Yi Lian' platform—a pioneering application of blockchain and privacy computing technologies in China—to explore its mechanism for realizing the value of data elements. This platform is the first successful delivery and implementation of "blockchain technology & privacy computing technology" in China, which enables the secure circulation of data elements. It not only achieves breakthroughs in the combination of the two technologies, but also innovates in the digital element circulation mode, and is the best interpretation of the action plan for implementing big data innovation applications. In addition, this study aims to extract scientific issues and management value by reviewing the process of building a digital platform. The case study is based on the practice of enterprises, proposing theoretical propositions, filling research gaps, constructing and developing new theories, which is very suitable for the needs of this study. Finally, the case study method is more conducive to clearly presenting the mechanism of the entire process of collecting, analyzing, and realizing the value of data elements in the specific context of China, and improving the internal validity of the research. The selected objects meet the principle of typicality and follow the theoretical sampling principle.
The 'Jin Yi Lian' financial anti-fraud platform of Unicom Digital Tech is a channel connecting data sources and users. Scientific and standardized platform construction can improve the utilization efficiency of multiple parties and realize the value of data. To summarize this implementation mechanism, this article introduces the theory of technology affordance and explores how enterprises can anchor, generate, and extend the value of data elements based on data characteristics and their own capabilities in different contexts. (1)In the context of data aggregation, based on the multi-source and independence of data, Unicom Digital Tech has achieved physical affordance and anchored the value of data elements through its own unified ability. (2)In the context of a data alliance, Unicom Digital Tech has achieved functional affordance and generated data element value through its own encryption and collaboration capabilities based on the privacy and dissemination of data. (3)In the context of data development, Unicom Digital Tech has achieved sustainable affordance and extended the value of data elements through its own migration capabilities, based on the scale and openness of data.
The theoretical contribution of this article is twofold. (1)It identifies the characteristics and value of data elements in different contexts. (2)This study introduces a novel theoretical framework centered on technological affordance, integrating the process model of "physical affordance, functional affordance, and sustainable affordance" to systematically explore the value realization mechanism of data elements. By proposing the "value anchoring, value generation, and value extension" trilogy, this study conceptualizes how data elements transition from foundational availability to functional utility and, ultimately, to sustainable impact. This framework not only clarifies the dynamic interplay between data characteristics and organizational capabilities but also offers actionable insights for enterprises and policymakers.

In the context of rapid advancements in big data technologies, cloud computing capabilities, and artificial intelligence, data has emerged as a pivotal engine driving economic and social progress. Data, as an emergent factor of production, is categorized as an intangible asset. Unlike physical goods, it lacks tangible form and clear boundaries of holding. As data's value grows exponentially across various sectors, its intangible nature allows for instantaneous and limitless replication and distribution in the digital realm. This capability complicates the original creators' or proprietors' ability to govern the spread and application of their data.Consequently, it amplifies the challenge of establishing and maintaining clear ownership rights over data, and the issue has become a major bottleneck, hindering the full potential of data. Traditional property rights theories, while extensively studied, struggle to adequately address the unique challenges posed by data’s intangible, replicable, and easily distributable nature.
To address these complexities, this paper proposes a novel data ownership solution rooted in the concepts of holding and access rights. Drawing from both theoretical frameworks and practical considerations, the study endeavors to bridge the gap between the evolving digital economy and existing legal constructs. The abandonment of the static notion of ownership in favor of a more dynamic understanding of usufruct forms the cornerstone of this approach. By focusing on the rights of data holders and the naturally extended access rights, the paper outlines a framework that not only recognizes the multifaceted nature of data usage but also fosters efficient and equitable data circulation.
The methodology employed in this study involves a thorough examination of existing academic and theoretical explorations within China’s scholarly community, categorizing them under the “subject-object dichotomy” paradigm. On the object regulation front, it evaluates schemes rooted in traditional property rights theories alongside those aimed at balancing interest distribution and rights protection. Conversely, subject regulation focuses on behavior-based ownership recognition, drawing heavily from regulatory frameworks such as the Personal Information Protection Law, Cybersecurity Law, and Data Security Law.
Key findings highlight the inadequacy of relying solely on ownership as a means of defining data rights, given its intangible and easily replicable attributes. Instead, the paper advocates for a structured approach that prioritizes data holding rights and the accompanying access rights. The proposed solution emphasizes “factual control” as the basis for determining rights over data, ensuring that various stakeholders share these rights equitably and efficiently. The principles of fairness and efficiency guide the allocation of data rights, promoting both individual incentives and societal welfare.
Furthermore, the paper delves into the personalized rules governing data access, aligning access rights with the distribution status of data resources. Establishing data access rules involves ensuring legal and ethical compliance, protecting rights, and maintaining market fairness. Transparency in data resource allocation and access rules is crucial, guaranteeing data subjects' information rights. Rules must allow secure and private data access for subjects and necessary technical means and management measures must be taken to ensure the security and confidentiality of data during transmission, storage, and use. Security measures and risk controls are mandatory to prevent data breaches and misuse. By categorizing data based on its scarcity, importance, and innovativeness, the framework facilitates setting of varying access levels and permissions. This approach not only safeguards data privacy and security but also promotes data liquidity and efficient utilization.
The innovation of this paper lies in its departure from traditional ownership-centric frameworks and the development of a usage-based rights model tailored to the unique characteristics of data. By embracing the concepts of holding and access rights, the proposed solution aims to unlock the full potential of data as a strategic asset in the digital economy. It offers a practical and adaptive legal framework that can evolve alongside technological advancements, ensuring that data continues to drive innovation, economic growth, and societal progress.

As a new quality of labor, human-AI collaboration is of great significance in improving production efficiency and liberating and developing social productivity.However, human-AI cooperative decision-making faces several challenges, including consistency and compatibility issues, low levels of collaboration, and unsatisfactory outcomes.How can humans and AI make collaborative decisions based on mutual understanding and each doing their best? This question remains largely unexplored.The results in the field of psychology provide evidence for this paper to explore the mechanism of human-AI knowledge coupling affecting human-AI cooperative decision-making.However, these findings are often vague, decentralized, and expository, lacking conceptual, centralized, and empirical support.In contrast, studies in the field of management have focused more on the differences and roles of human-AI intelligence in cooperative decision-making and the impact of collaborative intelligence on decision quality.Yet, the complex mechanisms of synergizing human-AI intelligence have not been thoroughly explored due to the lack of in-depth interpretation of the management changes it brings about.Although the research on human-AI collaboration under the cognitive perspective of psychology can provide evidence for the fusion of human-AI knowledge to explain human-AI cooperative decision-making, these theories are too messy and complex to explain the linkage of human-AI “knowledge coupling to intelligent collaboration”, and even more unable to reveal the mechanism of human-AI “doing their best”.In the field of management science, ignoring the co-production aspect of human-AI knowledge hybrid and directly discussing human-AI “hybrid intelligence” makes it difficult to uncover the relationship between knowledge coupling and intelligent collaboration in human-machine collaborative decision-making. Moreover, hybrid intelligence cannot clearly explain the operational rules of different human-AI collaborative decision-making.
This study utilizes 346 valid questionnaires from 30 types of AI application cases in 15 manufacturing enterprises, and explores the "modes and patterns of human-AI cooperative decision-making influenced by knowledge coupling and intelligence collaboration" by taking advantage of the NCA and fsQCA methods to explore the multiple concurrent causal relationships.This study identifies three distinct human-AI cooperative decision-making modes: the human intelligence-driven mode, the human-AI intelligence hybrid-driven mode, and the AI intelligence-driven mode.(1) Human intelligence-driven mode: In this mode, human intelligence surpasses AI intelligence in efficiency.Humans take the lead in decision-making, leveraging AI as a tool to assist and enhance their capabilities in completing tasks.The AI operates under human direction, providing support rather than autonomy.(2) Human-AI intelligence hybrid-driven mode: This mode is characterized by a balance of intelligence between humans and AI, where both parties collaborate as equals.They complement and empower each other, working together to achieve consistent and effective decision-making outcomes.The synergy between human intuition and AI computational power is central to this mode.(3) AI intelligence-driven mode: In this mode, AI intelligence outperforms human intelligence in efficiency.The AI takes the primary role in decision-making, operating autonomously, while humans provide support and oversight to ensure the successful completion of tasks.Furthermore, the study emphasizes that the human-AI relationship should be rooted in mutual understanding across three key dimensions of knowledge coupling: information processing, knowledge application, and intelligent activities.This mutual understanding reinforces the concept of "co-production" in human-AI knowledge hybrids, highlighting that the integration of human and AI knowledge leads to a progressive and unified advancement of knowledge.Additionally, the principle of "each doing their best" manifests differently across the three decision-making modes.In the human intelligence-driven mode, human efficiency is the primary driver of success.In the human-AI intelligence hybrid-driven mode, the novelty generated through human-AI collaboration becomes the critical factor.Finally, in the AI intelligence-driven mode, human experience plays a pivotal role in guiding and supporting AI-driven decisions.
In summary, this study addresses the challenge of facilitating collaborative decision-making between humans and AI under conditions of high AI intelligence participation, grounded in the principle of "mutual understanding". The study not only elucidates the mechanisms underlying effective human-AI cooperation, but also contributes to the acceleration of the evolution of a diversified and hybrid workforce ecosystem and provides scientific basis for the transformation of the future work model.

At the critical stage of promoting high-quality development and addressing new challenges in international competition, the deep integration of technological innovation and industrial innovation has become a crucial strategic direction for enhancing national competitiveness. Industry-University-Research (IUR) cooperation, as a key component of the innovation-driven development strategy, has been at the center of national development priorities. By breaking down traditional barriers to innovation, it facilitates the flow and sharing of knowledge, technology, and talent, thereby achieving complementary advantages and allowing businesses, universities, and research institutes—the principal innovation agents—to pool their distinct strengths and jointly leverage shared resources. However, in practice, challenges such as information asymmetry, uneven resource allocation, and insufficient technological transfer capabilities often limit the effectiveness of IUR cooperation. Thus, exploring ways to deepen IUR cooperation and identifying new paths for integrating technological innovation and industrial innovation has become a pressing issue that needs to be addressed.
The continuous penetration of new-generation digital technologies, such as artificial intelligence and big data, across various industries has not only driven profound transformations in business models, operational processes, and organizational structures but also fundamentally influenced the construction and evolution of corporate technological innovation ecosystems. Existing literature has extensively demonstrated the positive impact of digital technology applications on innovation performance. Some scholars have begun to recognize that digital technologies can effectively promote knowledge sharing, drive open innovation, and ultimately achieve value co-creation. Yet, despite its promise as a powerful catalyst for collaborative innovation, the precise influence of digital technologies on the depth and effectiveness of IUR cooperation remains largely uncharted. Can digital technology applications effectively enhance IUR cooperation? The answer to this question is not only critical for determining the future development trajectory of digital technologies but also holds significant practical implications for the overall upgrading of China's innovation system.
Using the panel data from A-share listed companies in Shanghai and Shenzhen from 2013 to 2022, this study empirically examines the impact of digital technology applications on IUR cooperation and its mechanisms. Through a systematic empirical analysis process encompassing descriptive analysis, benchmark regression, endogeneity treatment, robustness tests, and heterogeneity analysis (including partner heterogeneity and time heterogeneity), as well as mechanism testing, the following conclusions are derived. The findings show that digital technology applications significantly promote IUR cooperation, primarily by reducing key costs such as information search and contract negotiation. Furthermore, under dual external pressures, media attention, as a form of social pressure, enhances the positive impact of digital technology applications on IUR cooperation, while market competition, as an economic pressure, exerts an inhibiting effect. Heterogeneity analysis reveals that under the influence of digital technology, "Double First-Class" universities (China′s initiative for world-class universities and first-class disciplines) stand out as preferred partners in IUR cooperation due to their significant branding effect. Additionally, IUR cooperation has emerged as a critical solution for companies in the growth and decline stages to achieve innovation breakthroughs and reshape their competitive advantages.
The potential contributions of this study are as follows: First, it empirically analyzes the positive effect of digital technology applications on IUR cooperation from a micro and industry-wide perspective, thereby overcoming the limitations of previous single-industry or single-technology research, and providing new evidence for understanding how digital technologies drive IUR cooperation. Second, it examines the roles of information search, contract negotiation, and enforcement costs from a transaction cost perspective, and explores the positive moderating effect of media attention (social pressure) and the negative moderating effect of market competition (economic pressure) under dual external pressures. This not only extends transaction cost theory but also reveals the complex impact of the external environment on digital innovation cooperation. Third, the study finds that "Double First-Class" universities have become the preferred partners for enterprises, and IUR cooperation is more likely to facilitate innovation transformation and competitiveness reconstruction during the growth and decline stages of enterprises. These findings provide important references for optimizing cooperation models and formulating differentiated policies in the digital era.

As the core carrier of resource integration in the process of digital entrepreneurship, digital platforms have become a crucial strategic tool for generating new kinetic energy and fostering superior economic growth. How to optimize the mechanism of digital platform-based entrepreneurship and improve the quality of digital platform-based entrepreneurship is an important issue. Current research often isolates single factors' impact on entrepreneurship, omitting a comprehensive framework for analyzing the multifaceted dynamics of digital platform entrepreneurship quality and the interplay among various elements. This study addresses this gap by employing the TOE (Technology-Organization-Environment) framework to delve into the quality of digital platform entrepreneurship.
With the help of TOE theoretical framework, this study adopts fuzzy set qualitative comparative analysis (fsQCA) and necessary conditions analysis (NCA) to explore the impact of different combinations of six antecedent conditions on digital platform entrepreneurship quality at the three levels of technology-organization-environment. The analysis focuses on forty online healthcare digital platforms. The variables include platform digital functionality, platform digital technology, human resources, financial security, platform policy environment, and platform public opinion environment, standardizes the data and assigns appropriate weights to each variable to ensure a balanced and comprehensive analysis. It establishes entrepreneurial quality as the dependent variable within the model, and subsequently calibrates both the antecedent and outcome variables to ensure accurate and reliable analysis, offering a nuanced view of the synergies among multiple factors and their collective impact on platform entrepreneurship quality.
The results of the study show that, first, from an overall perspective, no single factor is sufficient to ensure the high-quality entrepreneurship of digital platforms. In other words, the entrepreneurial quality of digital platforms is affected by the joint effect of multiple factors rather than a single factor. Second, there are 3 paths to achieve high entrepreneurial quality of digital platforms, namely, organization-environment synergistic policy environment-driven (H1), technology-organization-environment synergistic digital platform policy environment-driven (H2) and organization-environment synergistic human resource-driven (H3). Third, organizational conditions, especially financial security, play a central role in all three high entrepreneurial quality-driven paths and are the main conditions for digital platforms to achieve high entrepreneurial quality. Fourth, there are four non-high entrepreneurial quality realization paths for digital platforms. The digital technology and platform policy environment of the grouping NH1 platform are lacking; the digital functions and technology of the grouping NH2 platform are lagging behind; and the grouping NH3 and NH4 are lagging behind in the three dimensions of technology, organization, and environment. The four non-high entrepreneurial quality realization paths have an asymmetric relationship with the high entrepreneurial quality realization paths.
According to the research findings, this paper proposes that, firstly, in order to attain a high degree of entrepreneurial quality, digital platforms should adopt a holistic approach to system coordination, integrating both internal and external factors to develop comprehensive competencies. Secondly, recognizing the importance of resources, platforms must rationally integrate and reorganize them in alignment with their strategic objectives and operational realities. Given the finite resources, platforms should also leverage external environmental resources to strengthen their entrepreneurial activities. Finally, to align with the specific circumstances and the current situation of the digital platform itself, a suitable entrepreneurial development path should be sought. Managers should consider the actual situation of the platform in depth, judge the advantages and disadvantages of the enterprise in terms of technology, function, scale, resources, policy, public opinion, etc., and choose an entrepreneurial development path that matches the platform's resource endowment and external environment. The research contributions are threefold. First, the paper reveals the effective path for digital platforms to achieve high-quality entrepreneurship. Second, it constructs a model of the group effect of digital platforms on the high-quality development of entrepreneurial enterprises, which enriches and expands the application context of the TOE theoretical framework. Finally, the hybrid method combining fsQCA and NCA is introduced into digital platform research, which expands the theoretical explanation of the multi-factor combination effect behind high-quality entrepreneurship on digital platforms

Since China's dual-carbon goals were put forword, some regions have adopted radical and uniform carbon reduction measures without considering their economic development, leading to energy supply-demand imbalances and hindering the goal's orderly advancement.These actions have impacted economic and social development, creating a false trade-off between carbon goals and progress.National climate goals rely on regional actions, but regional inequality and heterogeneity can lead to increased inequality and economic losses if carbon reduction is not tailored to local conditions.The academic consensus is that there's an "inverted U-shape" trend in carbon emissions with economic resilience, but there's a need to explore regional differences and influencing factors.The paper examines the relationship between dual-carbon policies and economic resilience across China's 30 provinces.Using fuzzy set qualitative comparative analysis(fsQCA), it explores the grouped impact and development paths of these policies, aiming to provide practical solutions for classified carbon reduction actions and enhance economic resilience while reducing carbon emissions.
This study examines the pathways of carbon reduction at the provincial and municipal levels, focusing on the roles of science and technology, government capacity, and energy structure as key drivers.It decomposes the measurement indicators and takes 30 provincial administrative regions in China as the research object,and excludes Hong Kong, Macao, Taiwan, and Tibet due to data limitations.Then it employs the fuzzy set qualitative comparative analysis(fsQCA) method, leveraging panel data from statistical yearbooks, databases, and published research to assess the impact of science and technology.It further applies comparative analysis to explore the collective effects of these drivers on regional economic resilience.To ensure the robustness of the findings, the study incorporates robustness checks, case replications, and the distillation and generalization of path characteristics, aiming to validate the realism and theoretical validity of the results.
The results show that the types of high economic resilience in China's provincial-level administrative regions are generally categorized into the following four types:“industrial-type high-energy-consumption driven mode(group state 1)”, “non-energy-dependent high-carbon emission driven mode(group state 2) ”, “integrated multi-industry driving mode(group state 3)”, “low-carbonization high-tech industry driving mode(group state 4)”, the four major groups of economic resilience are composed of a variety of state paths, the characteristics and laws are different, of which The transition from group state 1 to group state 3 to group state 4 reflects the carbon-reducing characteristics of science and technology-enabled industries—financial level enhancement—reducing the dependence on traditional industries, presenting a “paroxysmal” process; the evolution from group state 2 to group state 4 is a “smooth” process, focusing on the enhancement of scientific and technological financial inputs.The evolution from configuration 2 to configuration 4 is a “smooth” process, focusing on the evolution logic of enhancing financial investment in science and technology, developing high-tech industries, and reducing carbon emissions from green industries.Both evolution patterns follow the two-factor quality enhancement strategy of “scientific and technological input—carbon reduction”.Therefore, strengthening the multi-principal scientific and technological investment of “government and society” can promote the balanced development of economic resilience and quality enhancement at the provincial level, which is also conducive to the realization of the carbon neutrality goal.
The innovation of this paper is that it reveals the configuration classification and path of economic resilience enhancement against the backdrop of China's provincial carbon reduction, highlights the synergistic nature of science and technology, government, and carbon neutrality, and makes a practical and systematic extension of the research in this field.The empirical analysis based on provincial panel data provides more objective and realistic guidance and significance for the study of China's dual-carbon goals and synergistic development strategies.To enhance economic resilience under the dual carbon goals, local governments should increase financial and tech investments, focusing on scientific and technological development to foster economic growth.They should align with the '30·60' decarbonization goal, prioritize ecological and green economy development, and avoid reckless carbon neutrality actions.High-energy-consuming regions should leverage government capacity to achieve low-carbon green economic development through financial subsidies and transfer payments.

As China transitions from a stage of high-speed growth to one of high-quality development, the traditional development model driven by factors and investment is becoming increasingly inadequate. The single-handed innovation paradigm also fails to meet the inherent requirements of high-quality economic development. The "innovation ecosystem," characterized by symbiotic cooperation and dynamic evolution among various innovation entities and their interactions with the innovation environment, is gradually emerging as a new paradigm and driving force for promoting high-quality economic development. In this context, digital intelligence, which integrates digitalization and intelligence, plays a significant role in accelerating the digital transformation and intelligent upgrading of traditional industries. Digital intelligence enhances the resilience and recovery of innovation ecosystems, thereby contributing to the construction of a resilient innovation ecosystem. Against the backdrop of accelerating the construction of a digital powerhouse,and addressing key technology bottlenecks, core component supply disruptions, and the decoupling of traditional international partnerships, studying the local neighborhood effect of digital intelligence on the resilience of innovation ecosystems is of great significance. Because it is not only beneficial for promoting the deep integration of digitalization and intelligence, accelerating the formation of new productive forces, and providing new effective approaches for the digital transformation and upgrading of traditional industries, but also of significant theoretical and practical value for building an efficient innovation ecosystem, empowering high-quality economic development in China, and accelerating the construction of a digital China and achieving high-level technological self-reliance and strength.
This study utilizes panel data from 30 provinces spanning 2012 to 2022 to investigate the "local-neighborhood" effect of digital intelligence on the resilience of innovation ecosystems. Specifically, the spatial Durbin model is employed to analyze this impact, while the mediation effect model is used to elucidate the underlying mechanisms. Additionally, the study explores the heterogeneity of these effects across different regions and various dimensions of innovation ecosystem resilience.The results indicate that (1) digital intelligence can significantly improve the resilience of local innovation ecosystem, but has negative spatial spillover effect on the resilience of innovation ecosystem in neighboring areas; (2) digital intelligence mainly promotes the resilience of the innovation ecosystem by adopting the transmission path such as optimizing the allocation of innovation resources and improving the level of scientific and technological innovation; (3) digital intelligence can significantly improve the resilience of the local innovation ecosystem in the eastern, central and western regions, with negative effects on the resilience of the innovation ecosystem in the eastern neighbors, and no significant spatial spillover effect on the central and western regions. In addition, there is an obvious heterogeneity in the "local-neighborhood" effect of the influence of digital intelligence on each dimension of innovation ecosystem resilience.
Compared with existing research, the marginal contributions of this paper are mainly reflected in three aspects: (1) Building on the concept of evolutionary resilience, this study constructs an evaluation index system for the resilience of innovation ecosystems from four dimensions: buffering, diversity, liquidity, and evolvability. The study employs a combined evaluation method, VHSD-EM, to conduct dynamic and comprehensive measurements and analyses. This approach enriches the existing body of research on measuring innovation ecosystem resilience by providing a more nuanced and multidimensional assessment. (2) From the "local-neighborhood" perspective, this study employs a spatial Durbin model to analyze the direct and spatial effects of digital intelligence on the resilience of the innovation ecosystem. Additionally, a mediation effect model is used to test whether digital intelligence can enhance the resilience of the innovation ecosystem by optimizing the allocation of innovation resources and improving the level of scientific and technological innovation, revealing the mechanism and transmission paths of the impact of digital intelligence on the resilience of the innovation ecosystem. (3) In accordance with the regional levels of east, central and west, and the resilience dimensions of the innovation ecosystem, this study explores the heterogeneity of the "local-neighborhood" effect of digital intelligence on the resilience of the innovation ecosystem across different regions and resilience dimensions of innovation ecosystems. The findings aim to provide insights for implementing differentiated strategies of digital-intelligent integration development, empowering the overall enhancement of the resilience of innovation ecosystems.

Collaboration is an essential means for technological innovation in modern enterprises, yet inevitable competition also triggers a series of conflicts, embedding enterprises in multiple networks. Both positive and negative networks drive or constrain the development of enterprises. The evolution of these networks has become a widely discussed topic. Due to limitations in perspectives and methods, existing studies primarily focus on positive collaborative relationships, with scant attention to negative networks, and even less on the dual embedding of both positive and negative networks. This paper employs the embeddedness theory as its core analytical framework to delve into the structure and dynamics of both positive and negative networks within high-tech enterprises. It focuses on the supply chain collaborations and litigation relationships among Chinese listed biopharmaceutical companies, utilizing a rich dataset spanning from 2018 to 2023 to provide a comprehensive analysis.
Specifically, this study employs multi-source and heterogeneous data on collaborations and litigations. The sample focuses on listed companies in China's biopharmaceutical industry, with these companies serving as the focal enterprises. For the collaboration network, the study utilizes the enterprise supply chain network. Direct and indirect collaboration relationships among 306 biotechnology and pharmaceutical listed companies from 2018 to 2023 were collected through Python web scraping and text mining, totaling 2 485 collaboration ties. The litigation network data is derived from litigation relationships among enterprises. Leveraging the China Judgment Online database, this paper collects data on litigation opponents and opponents' opponents (A-B-C relationships) among these 306 enterprises from 2018 to 2023, totaling 3 020 litigation ties. Both positive and negative holistic networks beyond the individual network level are constructed, and both are directed and dynamic. By the temporal exponential random graph model (TERGM), the study analyzes the five endogenous structures of reciprocity, non-closed transitivity, activity, popularity and dyad-wise shared partner configurations in positive and negative networks, as well as the three attribute factors of the region of the enterprise, ownership rights structure and registered capital, and explores the dynamic mechanism of network evolution. The TERGM incorporates network characteristics at different time points into the analysis, overcoming the limitations of the static network analysis of ERGM and providing a better exploration of the dynamic mechanisms underlying network evolution.
The results show that enterprise networks exhibit the following characteristics: (1) reciprocity in the collaboration network and retaliation in the litigation network are reflected in "return a favor with a favor" behavior in the collaboration network and "eye-for-an-eye" behavior in the litigation network; (2) the evolution of positive and negative networks maintains a balance in the number of relationships, with enterprises choosing the optimal relationship scale; (3) the same-origin supplier and litigation alliances between enterprises play a significant role in network evolution; (4) in terms of exogenous attributes, firms in the same province are more inclined to establish network relationships, and the nature of ownership in collaboration networks is more heterogeneous, while the opposite effect occurs in negative networks.
The study introduces the perspective of negative ties in the evolution of high-tech industries and compares the evolution mechanisms of positive and negative networks based on the dual network embedding perspective. It further discusses the interaction between positive and negative dual networks, such as "from litigation to collaboration","promoting collaboration through litigation", "replacing litigation with collaboration" and "replacing collaboration with litigation", and their practical significance. The findings provide significant insights into corporate strategies concerning positive and negative networks and enterprise management.
The marginal theoretical contributions of this study are as follows: First, it explores the significant impact of litigation, a less studied negative relationship, on corporate development. Second, by considering the dual embedding of both positive and negative networks, it provides a more comprehensive portrayal of organizational relational networks. Third, the inclusion of time variables in the analysis through TERGMs offers deeper insights into the dynamic mechanisms underlying the evolution of social networks.

Technological capital and knowledge capital are the crucial factor capitals for cultivating new quality productivity, which are the products of technology and knowledge successively after property registration, buying and selling transactions, and reinvestment in production activities. The balanced allocation of the two kinds of emerging capital is beneficial to the development of companies. Research and development (R&D) activity is the source of technology and knowledge, and knowledge is mainly generated by scientific research activity, while technical factors mainly come from experimental development activity and are driven by knowledge resources in the research stage. The imbalance between research expenditure and development expenditure in enterprises would easily lead to the unbalanced stock of technology and knowledge, and subsequently affect the allocation of technological capital and knowledge capital.
The current study on the configuration of R&D expenditures is predominantly concentrated on statistical analyses or the derivation of mathematical models at the meso- or macro-levels, with a paucity of empirical examinations pertaining to micro enterprises. This dearth of empirical research is attributable to two primary factors. Firstly, there exists an inherent ambiguity in the quantification of corporate R&D expenditures, which poses challenges for micro-level analysis. Secondly, the academic inclination is often towards examining the broader implications of corporate R&D investment on technological or knowledge advancement from a holistic standpoint. Moreover, the academic community continues to grapple with significant disparities in the conceptual frameworks surrounding technological capital and knowledge capital, with a notable absence of consensus that exacerbates the issue of terminological confusion. Consequently, the literature that delves into the allocation of these two forms of capital is scant, and there is even less that endeavors to correlate them with the structural aspects of corporate R&D expenditures. This oversight in the research underscores the need for a more nuanced and integrated approach to understanding the intricate dynamics between R&D investment structures and the concomitant capital allocations within the context of micro enterprises.
In light of the deficiencies, this study explores the influences of balanced R&D expenditure structure on the allocation of technological capital and knowledge capital, using the sample of Chinese A-share listed companies from 2015 to 2021. To examine the impact of enterprise R&D expenditure structure on emerging capital allocation, a benchmark regression model is constructed;then, models are constructed to sequentially regress and verify the mediating role of innovation performance in the impact of enterprise R&D expenditure structure on emerging capital allocation. The study expects to provide favorable reference for encouraging firms to increase their investments in basic and applied research and accelerate their accumulation of emerging capitals.
Empirical research findings are as follows. First, balancing research expense and development expense is conducive to the quantity growth and even allocation of emerging capitals. Second, balanced R&D expenditure structure of enterprises could increase the amount of total emerging capitals by improving firms' innovation quantity, and meanwhile promote the equilibrium between technological capital and knowledge capital by enhancing innovation quality. Third, the beneficial effect of balanced R&D spending on emerging capital allocation is more pronounced for non-state-owned firms and those in technology-intensive industries.Fourth, on the premise of adequate R&D investment, enterprises optimizing R&D expenditure structure would strengthen the advantageous role of emerging capital allocation in raising total factor productivity.
There are three novel contributions. In terms of study perspective, this paper focuses on the proportionate structure of R&D investment rather than its scale, and conducts a more in-depth analysis of the transformation and application of innovative achievements to explore the impact of balanced R&D spending on the accumulation and distribution of emerging capital. In terms of index construction, it defines new measurement indicators of enterprise-level research expenditure and development expenditure, and refines the measurement standards of technological capital and knowledge capital based on the intrinsic connotation of technology and knowledge. In terms of research content, following the logic of innovation input-output, this paper verifies the mediating role of enterprise innovation performance, and then considers the different impacts and economic consequences of property rights nature and industry environment, thus establishing a comprehensive framework on how R&D expenditure structure influences emerging capital allocation.

The positive interaction and simultaneous improvement of innovation quantity and innovation quality of hidden champion manufacturing enterprises help to overcome weak links in key areas of manufacturing, ensuring industrial security. Therefore, it is of great practical significance to explore how to effectively drive the improvement of innovation quantity and innovation quality of hidden champion manufacturing enterprises. Currently, the studies on the innovation of hidden champion manufacturing enterprises mainly pay attention to aspects such as independent innovation and focusing on technological innovation, without simultaneously exploring both the innovation quantity and innovation quality. According to the resource-based theory and the institutional theory, there is an urgent need to study the key driving factors on the innovation quantity and innovation quality of hidden champion manufacturing enterprises in terms of both organizational characteristics and institutional environment. Addressing this gap will not only advance theoretical understanding but also provide actionable insights for fostering sustainable competitive advantages in hidden champion enterprises.
In view of this, this study uses the fsQCA method to discuss the configuration effects of organizational characteristics (enterprise scale, enterprise experience, enterprise profitability, enterprise human capital) and institutional environment (formal institutional environment, informal institutional environment) on the innovation quantity and innovation quality of hidden champion manufacturing enterprises. Unlike traditional regression methods, fsQCA is based on Boolean algebra and set theory, enabling the exploration of complex causal relationships among multiple factors, and is suitable for medium-sized samples while accommodating continuous data. This study selects 75 enterprises from the four batches of single-item champion lists published by the Ministry of Industry and Information Technology (excluding non-listed enterprises and those with missing data) as research samples. Innovation quantity and innovation quality data can be obtained from the China Listed Companies Patent Database, while other condition variable data can be obtained from the CSMAR database and the "MARKETIZATION INDEX OF CHINA′S PROVINCES: NERI REPORT 2021". To ensure the accuracy of the analysis, variables are calibrated using quartile anchor points to assign set-theoretic meanings, and adjustments are made to fuzzy membership scores.
The conclusions are presented in three aspects. Firstly, no single factor can be a necessary condition that leads to high innovation quantity and high innovation quality of hidden champion manufacturing enterprises. Secondly, there are four configurations that lead to high innovation quantity of hidden champion manufacturing enterprises, namely, human capital dominated type, scale-experience-profitability dominated type, scale-profitability dominated type, scale-human capital dominated type. Finally, there are three configurations that lead to high innovation quality of hidden champion manufacturing enterprises, namely, human capital and institutional environment synergy driven type, scale and formal institutional environment synergy driven type, scale-profitability dominated type. In addition, the scale-profitability dominated configuration enables hidden champion manufacturing enterprises to achieve both high innovation quantity and high innovation quality simultaneously. Thus, it effectively reconciles the dual objectives of innovation quantity and innovation quality.
The theoretical contributions include three aspects. Firstly, this study simultaneously incorporates innovation quantity and innovation quality into a research framework of the innovation of hidden champion manufacturing enterprises, thereby enriching the research field of the innovation of hidden champion manufacturing enterprises. Secondly, this study systematically analyzes the "multiple concurrency" and "equifinality" of the driving paths of high innovation quantity and high innovation quality of hidden champion manufacturing enterprises. Finally, introducing the fsQCA method helps compensate for the limitations in existing literature on the innovation of hidden champion manufacturing enterprises, where qualitative research lacks generalizability and quantitative research struggles to reveal complex causal relationships.
This study provides several suggestions for the hidden champion manufacturing enterprises. Firstly, in different institutional environments, enterprises need to have distinct organizational characteristics to achieve high innovation quantity. Secondly, different institutional environments require distinct organizational characteristics to be matched in order to achieve high innovation quality. Finally, in unfavorable institutional environments, hidden champion manufacturing enterprises can achieve a balance between innovation quantity and innovation quality by expanding their scale, improving profitability, and optimizing human capital.

Releasing innovation potential and stimulating innovation vitality have become a key task for the innovation-driven strategy. The 20th National Congress report emphasizes unlocking innovation vitality as crucial for China's modernization and innovation-driven development. It advocates for deepening tech reforms, enhancing evaluation systems, increasing investment, and strengthening IP protection to foster a comprehensive innovation system. Cultivating an innovative culture, promoting scientific spirit, and expanding international cooperation are also highlighted to create a globally competitive open innovation ecosystem, underscoring the importance of improving the tech evaluation system and stimulating innovation vitality in the new development phase.
This study focuses on the innovation vitality status and selects data from 31 provinces in China. It adopts an integrated approach that merges technology assessment with innovation ecology. Through the application of configurational thinking and qualitative comparative analysis (QCA), the study thoroughly considers the interplay of multiple factors and the intricate causal relationships among seven key conditional variables that contribute to the regional disparities in innovation vitality. This comprehensive methodology allows for a deeper understanding of the underlying dynamics driving innovation across different provinces in China.
The study concludes that (1) among the seven conditional factors included in technology evaluation and innovation ecology, high innovation production and high innovation decomposition are necessary conditions for stimulating high-level innovation vitality, and these two conditions have significant importance in explaining high innovation vitality. (2) There are three driving paths for high innovation vitality, namely, the path dominated by innovative consumption and environmental synergy, the path dominated by innovative decomposition, and the path dominated by innovative production. The collaborative leading path of innovative consumption and environment refers to the linkage and matching of high-innovation consumption and high-innovation environment; the innovation decomposition dominant path refers to the linkage and matching of non-high policy measures and high-innovation decomposition;the innovative production-led path refers to the linkage and matching of high-innovation production and non-high-innovation consumption. (3) There are four types of driving paths for non-high-innovation vitality, and there is an asymmetric relationship with the driving paths for high-innovation vitality. (4) High policy intensity, high innovation production, and high innovation decomposition have relatively common impacts on high innovation vitality.〖HJ*2/7〗
In light of the research findings, with the aim of more effectively stimulating regional innovation vitality, it is imperative to concentrate efforts and allocate innovation resources in a rational manner. On the one hand, rich, innovative production and efficient innovation decomposition are necessary conditions for stimulating high innovation vitality; on the other hand, among the three paths that lead to high innovation vitality, strong policy intensity, rich innovation production, efficient innovation decomposition, and a good innovation environment have played a core role. Therefore, with limited resources, it is crucial to enact and introduce comprehensive laws, regulations, and policy provisions concerning scientific and technological evaluation at the provincial level to enhance innovation vitality. Secondly, by directing enhanced financial and infrastructural support to higher education institutions, research institutions, and research-oriented enterprises, it is likely to enhance the diversity and caliber of research outputs, and enrich the innovative production. The third is the reinforcement of support and management for technology intermediary organizations. By enhancing their operational efficiency and service standards, it is possible to expedite the process of innovation diffusion and ensure that novel ideas could be translated into tangible economic and social benefits. The fourth is to improve innovation infrastructure, build a new system for sharing innovative resources, create a culture of integrated and symbiotic innovation, and promote inclusive innovation systems to create a conducive environment for innovation.
Furthermore, it is essential to enhance collaboration and refine innovation systems and mechanisms. The study indicates that high levels of innovation vigor result from the synergistic effects of multiple factors in technology evaluation reform and innovation ecosystem development.Different regions can choose the most suitable path for stimulating innovation vigor based on their unique conditions and emphasize the integration and alignment of these factors to enhance the mechanisms for boosting innovation vitality.

With the deep integration of information technology and economic society, data as a new factor of production can be quickly integrated into the social reproduction links of production, distribution, exchange and consumption, playing a multiplier effect of amplification superposition and attachment multiplication, and has become an important factor to enable new quality productive forces. As the largest share of data resources in China, the opening and sharing of public data is an important channel to increase the high-quality supply of data elements and leverage the multiplier effect of data elements. It may become a key measure to break through the obstacles to the development of new quality productive forces and empower its rapid growth. However, although existing studies have extensively explored the value creation effect of public data openness and the influencing factors of new quality productive forces, they have not explored the enabling effect of public data openness on new quality productive forces from the perspective of social reproduction. Then, as a major national strategic measure, can public data openness empower the development of new quality productive forces? If so, what is its internal mechanism of action? Furthermore, what factors can enhance or limit the enabling effect of the opening of public data? The analysis of the above-mentioned issues is conducive to a deeper understanding of the practical value of the public data openness strategy, and will also provide experience and inspiration for accelerating the development of new quality productive forces in China from the perspective of strengthening the supply of data elements and giving full play to the value of data elements.
From the perspective of social reproduction, this paper uses the panel data of 287 prefecture-level and above cities in China from 2009 to 2023 to construct a multi-period difference-in-differences model to study the enabling effect, mechanism of action and influencing factors of public data openness on new quality productive forces.The research conclusions are as follows: (1) Public data openness has effectively played a role in empowering new quality productive forces at the city level, and has become an important starting point to promote the rapid development of new quality productive forces; (2) The transformation effect of the production link, the optimization effect of the distribution link, the efficiency improvement effect of the exchange link and the upgrading effect of the consumption link are important paths for the public data openness to empower new quality productive forces; (3) The empowerment effect of public data openness on new quality productive forces is more pronounced in cities with higher government governance efficiency, more complete digital infrastructure construction, and in the eastern and southern regions of China; (4) The quality and transformation efficiency of public data openness play a significant role in the process of public data openness empowering new quality productive forces. That is, the higher the quality and transformation efficiency of a city's public data openness, the higher its new quality productive forces level will be.
The marginal contributions of this paper are as follows: (1) It verifies from the urban level the empowering effect of public data openness on new quality productive forces, which not only enriches the value creation effect of public data openness but also provides policy inspiration and new entry points for strengthening the high-quality supply of data elements and accelerating the development of new quality productive forces; (2) From the perspective of social reproduction, the mechanism of public data openness empowering new quality productive forces is revealed, which not only helps to deepen the understanding of the internal law of public data openness empowering new quality productive forces, but also provides empirical evidence that data elements can act on social reproduction and thus empower new quality productive forces; (3) This paper explores the impact of factors such as government governance effectiveness, digital infrastructure, data opening quality and transformation efficiency on the empowerment of new quality productive forces by public data openness, and deepens the research on the influencing factors of public data openness empowering new quality productive forces.

Since the 18th Congress of the Communist Party of China in 2012, the national strategy has been that scientific and technological innovations serve as the driving force for development. One of the fundamental questions is how to innovate scientifically and technologically？ The upstream of innovations is scientific research, with empirical research being paramount. Among the empirical research projects, controlled experiments are the major form of scientific practice. Thus, the research question of this article emerges: Is the goal of scientific research, particularly controlled experiments, the acquisition of objectivity or certainty？
This research question and its answer are important to both science and technology management and the philosophy of science. This article starts with a literature review. It first reviews the literature on objectivity and then the literature on uncertainty and certainty. The review finds that many studies that specifically discuss objectivity mention replication of scientific studies as the means to achieve objectivity. In fact, it is the characteristics of objective certainty, not merely objectivity. Studies on uncertainty often overlook the goal of reducing it in scientific research, while those on certainty seldom address objectivity. In reality, objective things include both immutable and regularly changing parts, as well as irregularly changing parts. The immutable and regularly changing parts correspond to certainty, while the irregularly changing parts correspond to uncertainty. Most of the literature does not involve the specific operation of eliminating non-objectivity and uncertainty in scientific practice, so it is difficult to have an accurate and comprehensive understanding of the goal of scientific research from the literature only.
Different from the approaches taken by most of the literature, this article starts from scientific practice and focuses on controlled experiments in scientific inquiry and emphasizes that there must be an acting party and an acted party in a controlled experiment, and the former produces experimental phenomena for detection when it acts on the latter. The definition of replication for a single controlled experiment is clarified according to a consensus study report, Reproducibility and Replicability in Science, published by the U.S. National Academies of Sciences, Engineering, and Medicine in 2019. The report further states that one of the goals of science is to understand the overall effect of a set of scientific studies rather than strictly determining whether any one study replicated other studies. The report is more geared towards generating and assessing cumulative evidence on the same topic. There are many references to cumulative evidence in the literature, and this article chooses “conceptual replication” to show that it is both different from and similar to replication for a single experiment, i.e., “physical replication” or “direct replication.” When the complexity of the experimental system is high or the controllability is low, the advantages of cumulative evidence generated by conceptual replication are more prominent, while direct replication is difficult to achieve. The key is that the independence of the former is high, while the independence of the latter is low. Independence is equivalent to the degree of flexibility in subject, method, and sample selection.
After clarifying the difference between cumulative evidence generated by conceptual replication and direct replication of experiments, the article expresses the core characteristics of cumulative evidence in mathematical form, showing that experimental results include three parts: non-objectivity, objective uncertainty, and objective certainty. Among them, only the objective certainty of each experimental result together can form the consistency of multiple pieces of cumulative evidence, but neither the non-objectivity nor the objective uncertainty can. This consistency is the hard core of cumulative evidence. Therefore, scientific research pursues objective content with certainty, and the ultimate focus is on certainty, not just objectivity.
Most of today’s scientific research will eventually be transformed into technology, and technology has two core characteristics: repeatability and predictability. Under the “pressure” of technology, a scientific inquiry must pursue objective certainty since it is closely related to the above two core characteristics, while objectivity alone cannot achieve them with assurance. Therefore, the statement “science is a cause that pursues objectivity”, believed by quite a few researchers, is incomplete. The statement should be “science is a cause that pursues objective certainty.”

In response to the significant impact of ethical risks on quantum technology innovation in quantum-techno-future narratives, it is necessary to conduct forward-looking reflection and governance on potential issues in the process of quantum technology innovation. At present, quantum technology, as an emerging technology that utilizes quantum mechanics principles such as quantum superposition and quantum entanglement, is in the upstream stage of its innovation. Quantum technology innovation involves the creation and commercial application of quantum technology. This is a process of transforming existing theoretical quantum science research results (such as the principles, characteristics, and laws of quantum mechanics) into certain quantum technology principles, and then creating quantum products, quantum processes, quantum materials, and quantum methods through a series of quantum technology practices (such as conception, design, and research and development). With the gradual disappearance of the traditional boundary between technology oriented applied science and cognition oriented basic research, quantum technology presents novelty, enablement, systematicity, and uncertainty in its creative dynamic shaping process with quantum science and quantum society. Compared with classical technology innovation, the ethical risks of quantum technology innovation have the following prominent characteristics:a wider penetration range, greater coordination difficulty, and stronger speculative nature. In the narrative of the second quantum revolution, the ethical risks of quantum technology innovation are specifically manifested as the threat of quantum supremacy, worry of quantum race, imbalance of quantum divide, and dilemma of quantum hype. Due to the influence of various factors mentioned above, the ethical governance of quantum technology innovation still faces many practical problems. The main manifestations include an incomplete ethical system, weak ethical awareness,a low cognitive level, insufficient coordination ability, limited collaboration space, and a lack of democracy and transparency.
This paper draws on the theoretical content of "responsible research and innovation", "technology assessment", and "anticipatory governance", and integrates their theoretical perspectives, adhering to forward-looking governance principles such as "anticipation", "participation", "negotiation", and "action", further emphasizing the synergy and initiative among government, industry, academia, and the public. To achieve responsible or ethical quantum technology innovation, the study proposes the following strategies for ethical governance of quantum technology innovation. (1) At the institutional level, it is necessary to broaden the scope of laws and regulations to encompass quantum technology, reinforce legal oversight in this domain, and establish clear moral responsibilities and elevate ethical standards specific to quantum technology to ensure responsible innovation. (2) At the technical level, there is a need to carry out value design to promote the expected development of quantum technology, and conduct ethical assessments to reduce unexpected risks associated with quantum technology. (3) At the industry level, it is crucial to enhance diversified negotiation and expand the open space of quantum technology, and promote international cooperation and maintain the ecological balance of quantum technology. (4) At the public level, it is important to strengthen public education and enhance the level of understanding of quantum technology, and advocate public dialogue and promote the democratic process of quantum technology.
Since ethical research on quantum technology innovation is just beginning, the research conducted in this paper has a certain exploratory nature. According to the current research status at home and abroad, this paper analyzes the ethical governance of quantum technology innovation from four levels: institutional, technological, industry, and public. It not only provides a new research perspective for the theoretical construction of this field, but also offers more specific and actionable governance measures to improve the problem of existing research being too macroscopic. In addition, this paper further clarifies how quantum technology should proceed, in which areas, who should participate in innovation, and how to avoid irresponsible innovation by emphasizing the role positioning and forward-looking responsibilities of core stakeholders. It is precisely because of the above efforts that this governance strategy can provide guidance on how to reduce the ethical risks of quantum technology innovation, achieve safer, accessible, and fair quantum technology innovation, and ensure that its results can benefit human society in asustainable manner.