Against the backdrop of intensifying scientific and technological competition among major powers and the accelerating evolution of geopolitics, various sanctions against China have shifted from sporadic shocks to institutionalized and structural constraints, placing sustained pressure on the core operational components of China′s research system. How to scientifically evaluate and systematically enhance the resilience of the research system has become an important topic for achieving technological independence and strength.Existing research largely focuses on manifestations of resilience while neglecting its sources; it emphasizes fluctuations in output but lacks analysis of structural foundations; furthermore, the theoretical consistency of measurement tools is insufficient. Drawing on system resilience theory, the dynamic capabilities perspective, and risk governance theory, this study analyzes the connotation, formation mechanism, and structural sources of research system resilience from the perspective of the system's operational structure, and defines it as the systemic characteristic by which it maintains basic functions under external shocks, achieves recovery through structural adjustments, and accumulates new capabilities during the adaptation process.
This definition encompasses three progressive levels of capability. Basic maintenance determines whether the system can preserve essential functions under external shocks and constitutes the prerequisite of resilience; structural adjustment enables functional recovery through organizational coordination, network reconfiguration, and technological path switching, representing the critical link of resilience enhancement; and evolutionary development captures the process of forming new capabilities through institutional learning and continuous optimization, laying the foundation for transforming resilience into long-term competitive advantage. Research system resilience thus involves not only resistance and recovery but also learning and evolution. The formation of research system resilience exhibits distinct hierarchical and systemic features. Resource supply, infrastructure, and technological reserves constitute basic support; organizational coordination, collaboration networks, and technological path adjustment constitute structural adaptation; and institutional arrangements and risk governance constitute institutional regulation, with the three tiers functioning in synergy. Building on this foundation and following a constructional logic of mechanism abstraction, structural mapping, variable transformation, and system integration, this study identifies six key structural elements: resource assurance, organizational coordination, data infrastructure, technological substitution, institutional response, and risk management. Within a three-tiered mechanism framework comprising basic support, structural adaptation, and institutional regulation, the six structural elements are operationalized as six first-level evaluation indicators. Among them, resource assurance and data facilities mainly take on basic support functions, organizational coordination and technological substitution mainly take on structural adaptation functions, system response and risk management mainly take on regulatory functions, while still keeping elements like risk management's cross-level feedback features.Consequently, an evaluation indicator system for the research system resilience in the face of external shock scenarios has been established, comprising 6 first-level indicators and 18 second-level indicators. Within the three-tiered mechanism framework, this system enables the differentiated measurement and systematic mapping of the six first-level indicators across the three tiers of capability: basic maintenance, structural adjustment, and evolutionary development.
The study reinforces the structural analytical logic of research system resilience, and offers a theoretically grounded measurement framework ready for empirical application; in practical terms, it offers both a diagnostic basis for structural problems in research governance and risk management and a roadmap for phased enhancement through resource reinforcement, institutional optimization, and risk governance improvement.

With the deep integration of artificial intelligence into corporate R&D, decision-making, and innovation activities, innovation is increasingly shaped by the collaboration between human intelligence and artificial intelligence rather than by either human expertise or technological automation alone. This shift is particularly important for high-tech enterprises, which face rapid technological change, high R&D uncertainty, long innovation cycles, and increasing external shocks. Under such conditions, firms need not only innovation capability, but also innovation resilience, namely the ability to maintain innovation activities, recover from disruptions, and reconstruct innovation paths in uncertain environments. Human-AI collaboration provides a new mechanism through which high-tech enterprises can reorganize heterogeneous innovation resources and enhance innovation resilience.
Drawing on resource orchestration theory, this study constructs a theoretical model of "human-AI collaboration—knowledge coupling—innovation resilience" and introduces digital agility as a boundary condition. Resource orchestration theory emphasizes that competitive advantage does not come simply from resource possession, but from the effective structuring, bundling, and leveraging of resources. Following this logic, human-AI collaboration is regarded as a process of resource structuring, in which human experience, contextual judgment, algorithmic capability, and data resources are jointly configured. Knowledge coupling represents the process of resource bundling, through which dispersed knowledge elements are connected, recombined, and transformed into usable innovation resources. Innovation resilience reflects the result of resource leveraging, showing whether firms can transform reconfigured resources into sustained innovation under uncertainty.
This study further distinguishes between complementary knowledge coupling and substitutive knowledge coupling. Complementary knowledge coupling emphasizes the integration of different knowledge domains and the expansion of knowledge boundaries, while substitutive knowledge coupling focuses on replacing or reconstructing existing knowledge paths to reduce dependence on a single technological trajectory. Digital agility is introduced to explain when human-AI collaboration is more likely to enhance innovation resilience. Firms with high digital agility can sense changes more quickly, mobilize digital resources more effectively, and transform human-AI collaborative outputs into innovation actions more rapidly.
The empirical analysis is based on two-wave paired survey data from 328 employees of Chinese high-tech enterprises. To reduce common method bias, the first-wave survey measured human-AI collaboration, knowledge coupling, and digital agility, while the second-wave survey measured innovation resilience. The sample covers several high-tech manufacturing industries, including pharmaceutical manufacturing, aerospace equipment, electronic and communication equipment, computer and office equipment, medical instruments, and information chemicals.
The results show that human-AI collaboration significantly improves innovation resilience. Both complementary knowledge coupling and substitutive knowledge coupling play positive mediating roles between human-AI collaboration and innovation resilience. In addition, digital agility positively moderates the relationship between human-AI collaboration and innovation resilience, indicating that firms with stronger digital agility can better transform human-AI collaboration into resilient innovation outcomes.
This study makes three contributions. First, it extends resource orchestration theory to the digital intelligence context by conceptualizing human-AI collaboration as a new form of strategic resource configuration. Second, it reveals the knowledge transformation mechanism through which human-AI collaboration enhances innovation resilience. Third, it identifies digital agility as an important organizational condition shaping the effectiveness of human-AI collaboration. The findings also offer practical implications for high-tech enterprises seeking to build human-AI collaborative advantages, strengthen knowledge integration, and enhance sustained innovation capability.

In the era of artificial intelligence, data has emerged as a new factor of production that underpins technological innovation, industrial transformation, and socio-economic development. However, while cross-border data flow is increasingly vital for global collaboration, innovation, and the value multiplication of data, the existing academic literature lacks a systematic framework to explain how such flows can be effectively accelerated. This study addresses the core bottlenecks that hinder cross-border data circulation—namely, the scarcity of applicable scenarios and the conflicts among institutional logics—by integrating context-driven innovation theory and institutional logic theory to construct a novel "Subject-Context-Institution" analytical framework. Through theoretical deduction and empirical analysis, it elucidates the mechanisms by which context drives the cross-border flow, transaction, and valorization of data elements, thereby offering both theoretical advancement and policy guidance for enhancing the global competitiveness of China′s digital economy.
The study first reviews the evolution of data governance and identifies two key structural challenges: insufficient scenario development, where global cross-border data exchange remains concentrated in traditional areas such as digital services, corporate operations, and personal information transfers while high-value application scenarios like medical research and technological cooperation are underdeveloped; and significant divergence in institutional logics among jurisdictions, particularly the U.S. favoring free data flow, the EU prioritizing rights and privacy, and China balancing openness with security, and these differences result in high compliance costs and fragmented governance that severely constrain efficient cross-border data utilization. To address these challenges, the study advances a context-driven mechanism that operates through a dynamic loop of "demand traction-data valorization-feedback circulation". In this model, scenario-specific demands act as a catalyst that aligns heterogeneous data resources across borders; the embedded application of data generates new derivative assets, which in turn stimulate new demands and reinforce continuous value creation. Simultaneously, the study proposes the concept of compound regulatory actors which comprise sovereign governments, digital trading platforms, international organizations, and industry self-regulatory bodies as a multi-layered institutional mediation mechanism that bridges conflicting legal and cultural logics. The synergy among these actors mitigates institutional friction, lowers transaction costs, and transforms regulatory divergence into a driver for optimizing global data allocation.
Building on the theoretical foundation, the study develops a process mechanism model for the full cycle of context-driven cross-border data flow,covering context identification, data acquisition, standardized processing, compliance verification, supply-demand matching, and iterative optimization. The model embeds institutional adaptation and collaborative governance to shift from "passive compliance" to "proactive empowerment". To validate the framework, the study conducts an in-depth case study of Beijing Friendship Hospital′s medical data cross-border cooperation with Amsterdam University Medical Center under the COLOR project. This case exemplifies how high-value medical data scenarios can achieve compliant and efficient cross-border circulation through context-driven mechanisms. By defining 72 core data fields and developing a "dual necessity list" with traceable justifications for each data item, the project establishes a replicable compliance model recognized by both Chinese and European regulators. Moreover, the application of international standards (e.g., ISO/IEC 27017) and ethical co-governance frameworks enables the transformation of institutional friction into interoperable governance interfaces. The project boosts research efficiency and clinical outcomes, generated tradable standardized data assets via the Shenzhen Data Exchange, and demonstrated cross-border data′s potential for cumulative value growth and international collaboration.
Policy recommendations are as follows: (1) building cross-border data ecosystems that integrates government, enterprise, and academic collaboration; (2) improving the compound regulatory framework to harmonize domestic and international rules and reduce compliance burdens; and (3) developing trusted data spaces through privacy-preserving technologies such as secure multi-party computation and homomorphic encryption. These measures can enhance the efficiency, safety, and inclusiveness of global data circulation, accelerate the transformation of data into new productive forces, and support digital economy sustainability.

The rapid iteration of artificial intelligence has triggered profound changes in global technological competition and innovation paradigms. General-purpose, vertical, and edge large AI models are developing in parallel, and their industrial application is accelerating. As a disruptive technology, large AI models overcome the technical constraints of traditional AI, fuel breakthrough innovations across diverse sectors, and expedite the deployment of new technologies, products, and business models. However, some enterprises fail to grasp the logic of paradigm restructuring enabled by large AI models, and blind adoption may lead to practical predicaments. Hence, exploring the AI-driven technological innovation paradigm has become a key academic research focus. This paper addresses two core issues: how large AI models drive the restructuring of innovation paradigms, and what approaches and processes underpin this transformation. The linear thinking of traditional innovation can no longer meet the needs of complex, diversified and sustainable innovation. Moreover, current research pays limited attention to AI-enabled innovation paradigms, and relevant theoretical discussions are still inadequate.
Drawing on paradigm theory, knowledge production theory and knowledge evolution theory, this paper explores the connotation of technological innovation paradigm restructuring induced by large AI models, and establishes a corresponding four-dimensional theoretical framework covering driving mechanisms, organizational models, innovation models and process mechanisms. In terms of driving mechanisms, large AI models are categorized into general-purpose and vertical types. General-purpose models, trained on massive data with vast parameters, possess strong learning and reasoning capabilities, offering high flexibility and broad applicability across various domains, yet they lack domain-specific depth. Vertical models, developed for specific fields, compensate for this by providing more precise and professional results. The synergy between the two forms a "general-purpose + vertical" dual-driving mode, which generates complementary strengths of broad coverage and deep penetration. The research findings reveal that the "general-purpose + vertical" dual-driving mode generates complementary strengths featuring broad coverage and in-depth penetration, breaking the constraints of insufficient general capabilities and poor scenario implementation in traditional innovation. In terms of organizational models, large AI models drive innovation subjects toward tripartite collaboration among humans, machines and scenarios. Rigid organizational structures evolve into fluid organizations, and experience-oriented decision-making is transformed into human-machine collaboration that combines experience and data, effectively eliminating organizational inertia. In terms of innovation models, a four-level progressive hierarchy of autonomy, cooperation, synergy and ecosystem is established, achieving multi-level integration from individual innovation to cross-domain ecosystem innovation. In terms of process mechanisms, large AI models empower the entire innovation chain ranging from emerging technology insight and technological R&D to the transformation of research achievements. Critically, the study also offers a balanced dialectical assessment of large AI models' inherent limitations, such as data biases, hallucination risks, and inadequate domain-specific reasoning, alongside potential risks including algorithmic lock-in, over-reliance on automated outputs, privacy erosion, and exacerbated digital divides. These challenges necessitate robust governance frameworks and human-centric oversight.

　　This theoretical framework extends the scope of conventional innovation theories centered solely on human innovators, and offers a new lens for understanding paradigm restructuring driven by large AI models. In practice, governments should coordinate the layout of large AI models and ensure equitable access to computing resources. The industry shall develop a collaborative innovation system led by chain-leading enterprises. Enterprises need to build human-machine-scenario collaborative organizations, encourage employees to apply large AI models with a heuristic mindset, and integrate individual innovation into organizational operations.

As a representative form of new quality productive forces,the rapid development of the low-altitude economy has placed regulators across the globe into the Collingridge Dilemma. Specifically,this manifests in three dimensions: in the temporal dimension, a paradox emerges between the swift deployment of technology and the lag in regulatory response; in the knowledge dimension,both regulators and developers fall into a state of "shared ignorance" due to information asymmetry; and in the power dimension,structural challenges arise from imbalanced allocation of authority and responsibility,along with poorly coordinated governance tools.A vertical comparison with other industries in China reveals that the low-altitude sector faces more acute governance challenges,intensified by accelerated technological iteration and great power competition. There is an urgent need to systematically address core issues such as disordered values,rigid management structures,and legal gaps.
From a horizontal perspective,the EU,the U.S.,and Japan have developed distinct governance models rooted in their respective legal traditions and industrial conditions. The EU adopts a unified legislative framework and proactive regulation,constructing a rigid model that prioritizes safety and rights protection. While this forward-looking regulatory design provides a stable,transparent,and predictable institutional environment, which embodyies the principle of "regional coordination + regulation first",it also constrains internal innovation vitality. The United States emphasizes market drive and incremental adaptation,adopting a flexible regulatory paradigm characterized by "layered oversight + market competition" to foster technological and industrial advancement. Although this approach grants significant latitude for innovation in early stages,it also introduces regulatory uncertainty and potential conflicts due to legislative delays and fragmented rules. Japan has developed a co-governance model based on national planning and social participation. While this model demonstrates strong strategic impetus and enables rapid resource concentration and technological breakthroughs, it also suffers from insufficient innovation diversity due to deep administrative intervention. Each of these three models presents distinctive strengths and limitations.
The manifestation of multidimensional safety risks in the low-altitude economy under the Collingridge Dilemma fundamentally stems from insufficient synergy among ethical values, policy guidance, and legal norms. To resolve this dilemma,China should critically learn from international experience and adopt a holistic approach to construct a trinity governance pathway integrating "ethics-policy-law": At the ethical level, guided by the holistic view of national security,a shift should be made from pursuing "zero-risk" absolute safety toward managing "tolerable risk", and from rigid control to a modern paradigm of "resilient governance". At the policy level,the government should transition toward a service-oriented role,empowering industrial leapfrogging through equitable subsidies, institutional innovation in central-local and civil-military coordination, and the construction of a nationwide integrated digital governance platform. At the legal level,a dual-track strategy of "interpretation and legislation" should be pursued, which involves adapting the existing legal system while advancing forward-looking legislation, with particular focus on tackling complex issues such as data classification,grading,and cross-border transmission.
The trinity synergistic governance pathway proposed in this study not only addresses practical challenges in China′s low-altitude economic development but also offers a systematic and operable Chinese solution to global low-altitude governance. Looking ahead,as technology continues to evolve and application scenarios expand, the governance system must keep evolving dynamically. It is expected that through deep collaboration among ethics, policy, and law, China will be able to foster industrial innovation while ensuring robust safety safeguards. This will ultimately achieve a virtuous cycle between high-quality development and high-level security, thereby establishing the low-altitude economy as a genuine new engine for future economic growth.

Currently, empowering the real economy with digital technologies and establishing a modern industrial system stand as the core driving forces for high-quality economic development. To this end, the Outline of the 15th Five-Year Plan for National Economic and Social Development of the People's Republic of China clearly puts forward the goal of advancing the in-depth integration of the digital economy and the real economy, so as to drive profound transformations in production modes and revolutionary leaps in productivity. Nevertheless, traditional enterprises, especially micro, small and medium-sized enterprises (MSMEs), are generally confronted with practical bottlenecks including insufficient willingness to invest in digital technologies and limited digital transformation capabilities. Against such a backdrop, systematically sorting out the underlying mechanisms behind the technological integration of digital and real industries and identifying policy instruments that can effectively facilitate enterprises to realize such integration carry profound strategic and practical significance. As a typical fiscal-financial synergy policy, a form of government procurement contract financing, the "Government Procurement Loan Policy"(a form of government procurement contract financing), is adopted as a quasi-natural experiment to thoroughly investigate the functional pathways and actual effects of fiscal-financial synergy on enterprises′ technological integration of digital and real industries.
Existing literature has identified influencing factors of digital-real integration from multiple perspectives such as digital infrastructure construction, cultivation of specialized, sophisticated, unique and innovative enterprises, smart city pilot programs, and supply of technology finance. However, few studies have specifically dissected the internal mechanism through which the fiscal-financial synergy mechanism acts on enterprises′ technological integration of digital and real industries. For this reason, this study constructs an analytical framework based on the Motivation-Opportunity-Ability (MOA) theory, incorporates fiscal-financial synergy into the research system of enterprises′ digital-real industrial technological integration, and conducts comparative tests to verify the value-added effect of this synergistic policy relative to standalone fiscal subsidies and inclusive financial instruments.
This study selects listed firms on ChiNext, the STAR Market and the Beijing Stock Exchange from 2015 to 2024 as research samples. After excluding financial firms, delisted companies, and observations with missing key variables, a final sample of 9 286 firm-year observations is obtained.A difference-in-differences (DID) model is constructed for baseline empirical tests, and a series of endogeneity and robustness approaches including parallel trend test, placebo test and instrumental variable method are adopted to further validate the research conclusions.
The study yields the following findings: the Government Procurement Loan Policy significantly boosts enterprises′ technological integration of digital and real industries, and this positive effect is more pronounced among enterprises located in regions with sound digital infrastructure, operating in highly competitive industries, led by long-term-oriented executives, and exposed to large-scale shadow banking activity. Mechanism analysis reveals two transmission channels. From the digital technology perspective, the policy promotes the integration of digital and real industrial technologies by accelerating enterprises′ digital and green transformation. From the real business perspective, the policy guides enterprises to shift capital from speculative financial activities to real economic operations and diversify their business portfolios, thereby advancing the technological integration of digital and real industries. Extended analysis shows that enterprises in the growth and maturity stages are more responsive to the policy than those in the decline stage, which aligns closely with the industrial support orientation of "investing in early-stage and small-sized entities". Furthermore, by advancing the technological integration of digital and real industries, the policy further alleviates low-end cutthroat competition among enterprises and promotes their high-quality development.
Drawing on the MOA framework and the literature on digital-real industrial integration, this study examines how fiscal-financial synergy shapes enterprises' technological integration of digital and real industries. Distinct from prior literature, this paper applies the MOA theory to the evaluation of fiscal-financial synergy policies, decomposes policy transmission paths from the three dimensions of corporate motivation, opportunity and ability, and expands the application boundary of the MOA theory in the field of industrial digitalization. The findings provide empirical support for the implementation of policies related to the integration of digital and real economies during the 15th Five-Year Plan period.

Intensifying global competition and shortened technology cycles have established innovation as a cornerstone for the sustainable development of technology-based small and medium-sized enterprises (SMEs). Resource scarcity represents a fundamental constraint for these firms, compelling them to seek efficient innovation pathways. Micro-innovation, characterized by low cost, minimal risk, and rapid results, has consequently emerged as their strategic preference. However, the subjective perception of this scarcity can trigger divergent strategic responses, for it can either stimulate proactive innovation or induce defensive conservatism. Some studies suggest that the structural strain induced by the perception of resource scarcity can heighten an organization's willingness to take risks, thereby leading to greater innovation investment. However, whether this positive effect can be sustained requires further investigation. When an organization perceives that its resources are nearing depletion, it may enter a defensive mode, causing its innovation strategy to become more conservative or even irrational. Consequently, the relationship between resource scarcity perception and innovative behavior is likely non-linear.
Current academic research primarily focuses on the impact of objective resource conditions, with less attention given to subjective perceptions and to the internal organizational capabilities that these perceptions activate. Furthermore, existing studies often adopt a linear perspective, overlooking potentially complex non-linear relationships and the critical moderating role of organizational psychological resources. Grounded in Conservation of Resources (COR) theory, this study addresses this gap by constructing an integrated model that introduces organizational improvisation capability as a mediator and negative emotion management capability as a moderator. The study empirically tests this model using data from 315 valid questionnaires collected from technology-based SMEs across various provinces in China, employing hierarchical regression and bootstrapping methods.
First, this study examines the direct relationship between resource scarcity perception and micro-innovation. The results reveal a significant inverted U-shaped relationship. This finding highlights the dual nature of resource scarcity perception:at moderate levels, it heightens organizational vigilance and promotes micro-innovation as a resource investment strategy; however, when perception exceeds a critical threshold, it triggers defensive resource hoarding, thereby stifling micro-innovation vitality.Second, by incorporating organizational improvisation capability into the framework, the study analyzes its specific mediating role. The findings indicate that resource scarcity perception significantly enhances organizational improvisation capability. Moreover, organizational improvisation capability itself exhibits a significant inverted U-shaped relationship with micro-innovation, acting as a core mediator. Scarcity pressure drives firms to develop improvisational skills, which, within an optimal range, facilitate micro-innovation through flexible resource recombination and iterative learning. Conversely, excessive improvisation leads to cognitive overload and strategic fragmentation, ultimately hindering the sustained accumulation of micro-innovations. Bootstrapping analysis confirms the significance of this mediating pathway.Finally, the study investigates the moderating role of negative emotion management capability. Our analysis confirms that this capability significantly mitigates the inverted U-shaped impact of resource scarcity perception on micro-innovation. A high level of negative emotion management provides a psychological buffer against the strain of intense scarcity perception, helps maintain cognitive flexibility and psychological safety, and thus mitigates the decline in micro-innovation observed at high perception levels.
In conclusion, this study refines the conceptual understanding of resource scarcity perception, clarifying that its influence is not linear but follows an inverted U-shaped curve governed by the principles of resource investment and defense. It identifies organizational improvisation capability as a double-edged sword and confirms the critical moderating function of negative emotion management capability. These findings delineate the specific pathways through which perception translates into action, extending existing research on the antecedents of micro-innovation and the mechanisms linking resource scarcity perception to innovation outcomes. The study offers valuable insights for technology-based SMEs in China and similar contexts, guiding them on how to effectively manage scarcity perceptions, cultivate dynamic capabilities, and build emotional resilience within resource-constrained environments.

In the context of green development, enhancing green competitiveness has emerged as a core objective for the high-quality development of manufacturing enterprises. However, in practice, significant discrepancies exist in both the processes and outcomes of enterprises establishing a green strategic orientation and subsequently advancing their green competitiveness. Only a small fraction of manufacturing enterprises have adopted a proactive green strategic orientation and successfully translated it into distinctive green competitiveness. In contrast, while most enterprises express a willingness to pursue green development, their green strategic orientation fails to translate into tangible green competitiveness,even severely impeding the achievement of their original goals.
　Existing literature explains this phenomenon through institutional theory, stakeholder theory, and the resource-based view. Institutional and stakeholder theories emphasize external pressures driving "conformity". However, these frameworks cannot fully explain performance disparities and fail to systematically address how enterprises dynamically balance "conformity" and "differentiation". Successful green competitiveness cultivation requires balancing external compliance with internal unique advantages. Optimal distinctiveness theory, with its "strategic balance perspective", provides a new theoretical lens by addressing the dual motivations of "conformity" and "differentiation" and their dynamic balancing process.
　 Within the classic "orientation-behavior-outcome" logical framework, this study explores the mediating role of green strategic behavior in the relationship between green strategic orientation and green competitiveness. From the perspective of the "Porter Hypothesis", green innovation constitutes the primary green strategic behavior of enterprises. Meanwhile, a small number of scholars have categorized green innovation into heterogeneous innovation behaviors that embody the dual motives of "conformity-seeking" and "differentiation-pursuing"—a classification that is highly consistent with the propositions of the optimal distinctiveness theory. Thus, as the core green strategic behavior of enterprises, exploring the complex mediating role of green innovation in the relationship between green strategic orientation and green competitiveness holds significant theoretical and practical value. The study conducts an exploratory multiple-case analysis of three manufacturing enterprises to investigate the process mechanism through which their green strategic orientation is transformed into green competitiveness. The main findings are as follows: (1) The impact of green strategic orientation on green competitiveness is transmitted through the legitimacy and differentiation mechanisms of green innovation, with the differentiation mechanism reflected by strategic green innovation exerting a more pronounced positive effect. (2) Compliant and strategic green innovation play alternating roles in enhancing green competitiveness, meaning these two types of green innovation behaviors occur sequentially over time. (3) Green innovation undergoes three phases: legitimation, integration, and differentiation.
The innovations of this study are manifested in the following aspects. First, from the dual-motive perspective of the optimal distinctiveness theory, this study unpacks the theoretical "black box" of how green strategic orientation influences green competitiveness, providing a novel theoretical perspective for deconstructing the implementation process of green strategies. It classifies green innovation into compliant and strategic types, which reflect the dual motives of "conformity-seeking" and "differentiation-pursuing" respectively, and further analyzes the heterogeneous mediating effects of green innovation behaviors driven by different motives in the relationship between green strategic orientation and green competitiveness. Second, this study adopts the "strategic balance view" of the optimal distinctiveness theory, and addresses the theoretical gap in the existing literature, which has paid insufficient attention to the interaction between strategic behaviors driven by different motives. Second, this study addresses the theoretical gap in the existing literature regarding insufficient attention to the interaction between strategic behaviors driven by different motives. Through case studies, it deduces the interaction between compliant and strategic green innovation in affecting green competitiveness, and validates the "punctuated equilibrium" perspective of the optimal distinctiveness theory. This fills the existing theoretical gap and enhances the explanatory power and applicability of this theory in the field of enterprise green strategies. Third, this study explores the different phases of green innovation and its transition process from a dynamic perspective, directly shifting the research dimension of green innovation from static to dynamic and making a substantive contribution to the green innovation literature. It identifies three developmental phases of enterprise green innovation, analyzes their internal evolutionary mechanisms based on the optimal distinctiveness theory, and clarifies the phase transition process underlying enterprise green innovation behaviors.

Achieving high-level synergy between digitalization and greenization (dual-transformation synergy) is a critical strategic imperative for manufacturing enterprises to achieve high-quality development and meet China's national "dual carbon" goals. While existing literature has examined the individual effects of technological, organizational, and environmental factors on either digital or green transformation, it has neglected the nonlinear mechanisms through which multidimensional conditions interact to drive dual-transformation synergy. Emerging configurational studies have yet to systematically integrate the core dimensions of the Technology-Organization-Environment (TOE) framework, provide insufficiently nuanced characterizations of diverse driving paths, and focus predominantly on high-level synergy while overlooking the configurational logic behind non-high-level outcomes. To address these research gaps, this study examines the complex causal mechanisms underlying high-level dualization synergy in manufacturing firms, offering theoretical insights and practical guidance for enterprises and policymakers alike.
Drawing on the Technology-Organization-Environment (TOE) framework and a configurational perspective, this study uses fuzzy-set qualitative comparative analysis (fsQCA) to analyze survey data from 331 Chinese manufacturing enterprises collected between July and September 2025. Six antecedent conditions are identified: (1) technology dimension: digital-green technological innovation capability, digital-green technology integration capability; (2) organization dimension: top management support for dualization, organizational slack resources; (3) environment dimension: government support for dualization, market competition pressure. The fsQCA method is well-suited for this study because it captures conjunctural causation, equifinality, and asymmetric relationships among conditions, thereby transcending traditional net-effect analyses.
Necessity analysis shows that, given the complexity and multiplicity of driving pathways, no single condition is necessary for high-level dualization synergy.Sufficiency analysis identifies five distinct equifinal configurations that lead to high-level dualization synergy: (1) Technology-Environment Driven: technological innovation capabilities complemented by government policy support; (2) Technology Driven: synergistic technological innovation and integration capabilities, coupled with top management support and market competition pressure; (3) Technology-Organization-Environment Synergy Driven: tripartite alignment of technological capabilities, organizational slack resources, and government support; (4) Technology-Organization Driven: strong internal technological and organizational resources compensating for external environmental pressures; (5) Organization-Environment Driven: technological integration capabilities, organizational slack, and government support compensating for relatively weak indigenous innovation capabilities. Additionally, two configurations result in non-high-level dualization synergy:technology-organization deficient configuration and organization-environment-deficient. These findings highlight that a lack of organizational support and resources is a common underlying condition for transformation failure.
Theoretically, this study moves beyond conventional net-effect analyses by integrating the TOE framework with configurational theory, constructing a systematic analytical framework that clarifies the inherent interconnectedness of dual-transformation synergy. The identification of multiple equifinal pathways challenges the dominant "best practice" view and enriches organizational transformation theory by demonstrating how the "different paths, same destination" phenomenon operates in the digital-green context. The identified substitutive and complementary mechanisms among antecedent conditions advance dual-transformation research from asking whether individual factors matter to elucidating how they combine to exert influence. Meanwhile, the use of fsQCA effectively captures the conjunctural and asymmetric causality inherent in complex organizational transitions, allowing for the identification of multiple causal recipes while retaining case-specific complexity.The findings offer differentiated strategic guidance for enterprise managers to tailor transformation strategies to their unique resource endowments and suggest that policymakers shift from one-size-fits-all approaches to targeted interventions, developing diversified policy toolkits for heterogeneous enterprise contexts.

Amid intensifying global economic uncertainty, profound domestic structural adjustment, and accelerating technological change, enterprises face the dual challenge of enhancing innovation capacity and preventing default risk. While innovation strengthens long-term competitiveness, it is inherently characterized by high investment, long cycles, and substantial uncertainty. When firms bear the risk of innovation failure independently, capital constraints may deepen and default risk may escalate. Default risk reflects not merely the accumulation of debt, but the interplay of asset value, debt servicing pressure, and cash flow stability. In the context of innovation-driven development, such risk is further linked to the uncertainties, information asymmetries, and resource misallocations inherent in innovation activities. Therefore, this study examines the risk-mitigation effect of university-industry-research collaborative innovation on corporate default risk and its transmission mechanisms. As firms face pressure to improve innovation capacity while maintaining financial stability, collaboration among enterprises, universities, and research institutes may contribute not only to innovation performance but also to corporate risk governance.
Using Chinese A-share listed companies from 2013 to 2024 as the sample, this study develops a theoretical framework based on information asymmetry theory and agency cost theory. Corporate default risk is measured using a distance-to-default model. A two-way fixed-effects model is employed to estimate the impact of university-industry-research collaborative innovation on corporate default risk. To address potential endogeneity and sample-selection bias, the study applies an instrumental-variable approach and propensity score matching. To mitigate potential interference caused by the sensitivity of matching methods, this study adopts the entropy balancing method to re-estimate the model.This study further employs three additional methods to enhance the robustness of the conclusions:alternative variable measurement, exclusion of certain confounding factors, and the inclusion of high-dimensional fixed effects.
The findings show that university-industry-research collaborative innovation significantly reduces corporate default risk. The mechanism analysis reveals that this effect operates through three channels. First, collaborative innovation generates a technological effect by improving total factor productivity. Access to knowledge, research expertise, and technological resources enhances knowledge integration, technological absorption, and resource allocation efficiency, thereby strengthening operational performance and financial resilience. Second, collaborative innovation produces a certification effect by alleviating financing constraints. Cooperation with universities and research institutes enhances the credibility of firms′ innovation activities, reduces information asymmetry between firms and external capital providers, and improves financing accessibility, thereby strengthening debt-servicing capacity. Third, collaborative innovation creates a governance effect by restraining inefficient investment and excessive debt. The involvement of research organizations introduces expertise and external constraints into corporate decision-making, helping reduce resource misallocation, curb aggressive investment, and limit excessive debt accumulation. Further analysis shows that the risk-mitigation effect is more pronounced among non-state-owned enterprises, firms in technology-intensive industries, and firms in regions with stronger intellectual property protection. Findings indicate that the effectiveness of collaborative innovation varies with ownership characteristics, technological attributes, and institutional environments.
To optimize industry-university-research collaboration, a multi-stakeholder approach is recommended. First, enterprises should tailor strategies based on ownership; specifically, non-state-owned firms can leverage these partnerships to mitigate risks, improve information transparency, and reduce inefficient investments under financing constraints. Second, financial institutions should upgrade their credit risk assessment models to evaluate the quality and conversion efficiency of collaborative innovation, thereby offering better financial support to tech-intensive firms. Finally, governments should strengthen institutional environments by refining intellectual property protection and dispute resolution mechanisms, particularly in regions with weaker IP enforcement, to provide stable expectations and reduce innovation risks.
This study extends research on the economic consequences of university-industry-research collaborative innovation from innovation performance to corporate risk governance. By identifying technological improvement, certification effect, and governance enhancement as key transmission channels, it provides micro-level evidence on how collaborative innovation affects financial stability and offers support for firms seeking to optimize collaborative innovation decisions and improve credit risk management.

The deep integration of artificial intelligence(AI) has become a core strategic driver for enterprise productivity growth. However, a paradox has emerged at the micro-level within organizations: Contrary to expectations that AI would simplify workflows and enhance efficiency, employees commonly encounter a counterintuitive outcome where increased usage leads to greater exhaustion, ultimately impairing job performance. Existing literature predominantly focuses on the enabling effects of AI in sharing repetitive tasks, largely overlooking the potential psychological costs, cognitive intrusions, and subsequent efficiency declines it may trigger. To address this gap, this study aims to unravel the “black box” of how AI applications influence employee job performance, transitioning the academic focus from a single-effect analysis to a multiple-balance mechanism study.
Drawing upon the Job Demands-Resources(JD-R) theory, this study constructs a dual-chain mediation model to comprehensively examine the double-edged sword effect of AI applications. The JD-R framework allows for a dialectical perspective, revealing two distinct pathways. On one hand, AI serves as a job resource by elevating task automation, which theoretically mitigates resource depletion. On the other hand, AI can act as an intrusive job demand, significantly increasing cognitive load through its sheer volume of generated information and the constant need for human verification. Work burnout is conceptualized as the critical psychological mechanism linking these technological pressures to subsequent performance changes. Furthermore, perceived job complexity is introduced as a vital boundary condition that determines whether AI functions as a resource or a demand.
To empirically test the proposed hypotheses, this study employed a three-wave longitudinal research design, effectively minimizing common method bias. Data were collected over a three-month period across three distinct time points with one-month intervals. The sample comprised employees from 26 enterprises located in major Chinese economic hubs, including Beijing, Shanghai, Zhejiang, Jiangsu, and Guangdong. These firms operate in AI-intensive sectors such as information technology, high-end manufacturing, financial services, healthcare, and scientific research. After matching across the three waves, a total of 350 valid questionnaires were obtained, representing an 87.3% response rate. The data were systematically analyzed utilizing SPSS 26.0 and AMOS 24.0, while the PROCESS macro and Bootstrap resampling techniques were applied to verify the complex moderated mediation paths.
The empirical results reveal that the double-edged sword effect of AI applications on job performance is profoundly contingent upon perceived job complexity. First, when employees perceive a low level of job complexity, AI applications seamlessly align with highly standardized and predictable task requirements. In this context, AI exerts a significant positive impact by acting as a resource; it enhances the level of task automation, effectively alleviates work burnout, and ultimately promotes overall job performance. Conversely, in scenarios of high perceived job complexity, tasks are typically unstructured, dynamic, and uncertain. Under these conditions, AI generates knowledge-intensive outputs that require intense human scrutiny, thereby transforming the technology from an assistive tool into a high-pressure job demand. Consequently, AI applications significantly amplify employees' cognitive load, accelerate the depletion of psychological resources, exacerbate work burnout, and ultimately diminish job performance.
This research presents several theoretical innovations. Primarily, it shatters the prevailing linear cognitive assumption of AI's universal empowerment, uncovering a critical "functional reversal" of technological effects across varying task contexts. Additionally, this study successfully extends the traditional JD-R model into the digital era, expanding its theoretical boundaries to clearly define the motivation and depletion mechanisms in human-machine collaborative scenarios. Practically, the findings offer vital guidance for organizations navigating intelligent transformations. Management should abandon "one-size-fits-all" AI deployments, instead implementing differentiated strategies tailored to specific job complexities. Furthermore, organizations must empower employees with technical autonomy, which will potentially grants the right to refuse AI in highly complex tasks,while actively managing “cognitive surplus” and establishing dynamic early-warning systems to mitigate tech-induced burnout before it translates into tangible performance losses.

A surge of new ventures is reshaping the economy, fueling its transformation while creating jobs and anchoring social stability.However, these ventures face severe resource constraints,including limited capital, insufficient professional talent, lack of advanced technology, and weak market access, and these constraints significantly impede their competitiveness and long-term sustainability. Cross-border resource integration innovation, defined as the creative reconfiguration and repurposing of heterogeneous resources across domains, has emerged as a strategic response to such scarcity. Yet, the antecedents and boundary conditions of this innovation remain under explored, particularly in the digital era where network based resources are fluid and accessible.
Grounded in resource orchestration theory and the knowledge based view, this study develops a theoretical model that posits an inverted U shaped relationship between digital networking orientation and cross border resource integration innovation. The study argues that moderate network embeddedness facilitates access to diverse, non redundant information and complementary knowledge, thereby enhancing resource recombination. However, excessive networking triggers cognitive dependence (rigid thinking and reduced ability to recognise novel resource uses) and social dependence (over embeddedness, resource homogeneity, and relational maintenance costs), which eventually suppress innovation. Furthermore, the study proposes that knowledge field activity which refers to the dynamism, interactivity, and recombination potential of knowledge flows in the entrepreneur′s environment mediates this curvilinear relationship.
This study collects empirical data via a face-to-face questionnaire survey of 212 entrepreneurs of micro and small enterprises in the Yangtze River Delta region,yielding a valid response rate of 70.7%. The sample is predominantly male (79.7%) and concentrated in manufacturing and retail. All constructs are measured using 5-point Likert scales, adapting established scales for digital networking, knowledge field activity, and cross-border resource integration innovation, while developing and validating new items for entrepreneurial belief and policy availability through expert review. Rigorous psychometric testing confirms the instrument's robustness, demonstrating satisfactory internal consistency and a KMO value of 0.872. Furthermore, Confirmatory factor analysis indicates that the five-factor model fits the data well, with AVE values strongly supporting both convergent and discriminant validity. The results reveal that digital networking orientation exhibits an inverted U-shaped relationship with cross-border resource integration innovation, and knowledge field activity mediates this relationship. Drawing on the perspectives of cognitive and social dependence, this study examines the boundary conditions of this main effect. The findings indicate that entrepreneurial belief does not exert a significant moderating role, whereas policy availability exerts a negative moderating effect on this relationship. Furthermore, policy availability strengthens the moderating effect of entrepreneurial belief.
This study makes several theoretical contributions. First, it challenges the prevailing linear optimism regarding digital networks by uncovering an inverted U shaped effect, thus extending innovation network theory in digital contexts. Second, it identifies knowledge field activity as a critical mediating mechanism and, for the first time, demonstrates its diminishing mediating role as networking intensity increases, which offers a processual understanding of how network resources translate into innovation. Third, the non significant moderation of entrepreneurial belief cautions against oversimplified assumptions that psychological capital universally facilitates resource transformation, calling for finer grained stage specific investigations. Fourth, it reveals the double edged sword of policy availability: while formal support alleviates resource constraints, it may inadvertently substitute for informal network based innovation motivation, yet it can also activate the value of psychological capital through synergistic interaction.
From a practical perspective, entrepreneurs of micro and small enterprises should maintain a focused set of high value network ties, implement low cost knowledge exchange routines, and adjust their networking efforts according to the accessibility of policy support. Policymakers are advised to design innovation linked incentives rather than unconditional subsidies, and to offer digital literacy training to enhance entrepreneurs′ effective use of networks.

Data has emerged as a critical production factor for enterprise digital innovation, yet its replicability, non-excludability, and ambiguous ownership boundaries hinder investment protection across collection, processing, and application. Although existing studies have examined digital innovation through technology environments, organizational capabilities, infrastructure, data sharing, and traditional intellectual property (IP) protection, empirical evidence on how data IP registration pilots affect such innovation remains limited. Unlike conventional IP systems that rely on exclusivity and infringement remedies, China's data IP registration regime does not confer absolute monopoly control. Instead, it clarifies legal provenance, rights status, and circulation boundaries through registration and certification, reducing rights-verification and trust-formation costs in data transactions. This stabilizes revenue expectations and improves institutional conditions for data-driven innovation. Thus,this study leverages China's data IP registration pilot as a quasi-natural experiment to examine its impact on enterprise digital technology innovation.
This study draws on property rights theory and resource orchestration theory. From the perspective of property rights theory, the pilot policy reduces institutional uncertainty and strengthens firms' benefit expectations. The resource orchestration theory further holds that policy effects depend on enterprises' ability to absorb, integrate, and utilize resources rather than arising automatically. The study constructs an analytical framework of "resource absorption-resource integration-resource utilization", where absorptive capacity denotes the identification, assimilation, and transformation of external data resources and related knowledge; digital intangible asset configuration provides the infrastructural basis for processing and integrating data resources through software, databases, and information systems; and knowledge utilization captures the capacity to match, reorganize, and apply data resources across heterogeneous technologies and scenarios.
To test this framework, this study employs a multi-period difference-in-differences model on China's A-share listed companies from 2019 to 2024, using firm-level patent data to measure digital technology innovation and digital-real technology integration, with the core explanatory variable indicating provincial pilot implementation in a given year. Robustness is verified through parallel-trend dynamic event-study analysis, placebo tests, heterogeneous treatment-effect estimation, propensity-score matching, entropy balancing, alternative dependent-variable measures, lagged policy variables, and exclusion of regions newly added to the pilot in 2024.
The empirical results show that the data intellectual property registration pilot policy significantly promotes enterprise digital technology innovation. It not only increases digital technology innovation output, but also facilitates digital-real technology integration innovation. The moderating effect results further show that the policy effect has clear resource-capability boundaries. Absorptive capacity strengthens the positive effect of the pilot policy on digital technology innovation output, but weakens its positive effect on digital-real technology integration innovation. This may be because enterprises with stronger absorptive capacity are more likely to deepen innovation along the existing technology path, thereby improving the efficiency of digital R & D, while also strengthening path dependence and reducing their willingness to integrate cross-domain heterogeneous data resources. The allocation of digital intangible assets and knowledge diversification have strengthened the positive effect of the pilot policy on the digital technology innovation of enterprises. The heterogeneity test shows that the innovation promotion effect is more significant in large enterprises and enterprises facing lower financing constraints. The policy effect is different between high-tech enterprises and non-high-tech enterprises. For non-high-tech enterprises, the pilot policy mainly promotes digital-real technology integration innovation, while for high-tech enterprises, the pilot policy has a more obvious effect on the improvement of digital technology innovation output.
This study contributes to the literature in three ways. First, it shifts the analysis of enterprise digital technology innovation from conventional intellectual property protection to emerging data rights. Second, by distinguishing digital technology innovation output from digital-real technology integration innovation, it provides a more nuanced understanding of the heterogeneous innovation effects of the pilot policy. Third, the corporate policy response is incorporated into the "absorption-integration-utilization" framework to examine the moderating effects of absorptive capacity, digital intangible asset allocation and knowledge diversification. This helps to clarify the boundary conditions of policy incentives for innovation, and extends the resource orchestration theory to the context of digital innovation of data intellectual property institutions and enterprises.

The digital economy has placed digital technology at the core of national competitiveness. Universities occupy a dual role as strategic scientific forces and living laboratories of digital governance. Yet two decades of informatization have left persistent obstacles: system construction outpacing operational integration, entrenched data silos, and creeping digital bureaucracy, which together obscure the link between digital governance and research output. Empirical work on digital governance and innovation focuses largely on firms and government agencies, leaving universities under-examined. Open questions include whether the association turns rather than rises steadily, how regional ecosystems and authority structures bend the curve, and how far research administration informatization translates reform into bench-level practice.

To work through these puzzles, the study leans on four theories: organizational information processing, structural inertia, loose coupling, and institutional theory. Wider information-processing capacity ought to sharpen coordination, yet once business processes are hardwired into digital systems they can stiffen into inertia, and loose coupling paired with a gap between form and substance weakens whatever signal travels from governance reform down to actual scientific practice. The analysis rests on cross-sectional data covering 483 Chinese universities, pieced together from the Grendep University 360-Degree Panoramic Data Platform, the 2023 Report on Informatization Development of Chinese Universities, and details gathered from institutional websites. Six proxy indicators, among them system coverage, database applications, service portals, unified authentication, and cross-departmental data exchange, capture the level of internal digital governance, and the entropy weight method aggregates them into a single score. To capture potential nonlinearity, the baseline regression includes both a linear and a quadratic term. Endogeneity is addressed through two-stage least squares, with provincial internet penetration and the digital economy development index serving as instruments. Confidence intervals for the indirect component are derived from the bias-corrected percentile bootstrap. A series of robustness checks, encompassing broader outcome measures, principal component analysis, winsorization, and an extended set of controls, then tests whether the core structural parameters remain stable.
Internal digital governance and university innovation capability turn out to trace a significant inverted-U. Across a moderate band of governance the two climb together, but once a discernible turning point is passed the gains flatten and can slip into negative territory, a sign that piling on systems and the coordination they demand may cancel the very efficiency digitalization was meant to deliver. The two-stage least squares estimates keep both the sign and the significance of the quadratic term, and the curve holds its shape through the robustness checks. Splitting the sample by context sharpens the reading. The pattern stands out for universities in the eastern and central regions and for those whose informatization answers to a president-level leader, yet it never reaches significance in the western, northeastern, and Double First-Class subsamples, much as arguments about ecosystem maturity, loose coupling, and where strategic authority sits would lead one to expect. Unpacking the mediation confirms that research administration informatization operates as one channel among several: its indirect share is statistically real but small relative to the direct link.
This study makes three contributions. First, by incorporating a quadratic term, it moves beyond the linear specifications and provides evidence for a nonlinear relationship between digital governance and performance in higher education. Second, the combination of instrumental-variable estimation with robustness checks mitigates endogeneity concerns that otherwise undermine causal inference in cross-sectional designs. Third, bias-corrected bootstrap confidence intervals for the indirect effect advance mediation analysis from a binary significance test to a quantitative assessment of the intermediate channel's actual magnitude. For practitioners, the findings caution against accumulating digital systems indiscriminately. Universities benefit more from strategies tailored to their regional digital ecosystems, resource endowment, and internal governance structure, and from ensuring that redesigned research-administration processes genuinely penetrate researchers' daily workflows.