Large SOE groups in China have widely established internal R&D platforms, yet subsidiaries frequently bypass these resources to collaborate with external partners like universities, research institutes, and private firms, even when relevant internal capabilities exist. This "nearby resources available yet external cooperation favored" phenomenon weakens intragroup knowledge flows and innovation efficiency. Existing theories including transaction cost economics, the resourcebased view, social network theory, and behavioral theory offer partial but insufficient explanations, as they neither adequately address why internal cooperation becomes highcost and highrisk in the SOE context nor capture subsidiaries' dynamic decisionmaking under multiple constraints including accountability, autonomy, and performance pressure. To address these gaps, this study asks why SOE subsidiaries prefer external partners, how boundary extension emerges in project decisions, and how different external cooperation models are formed and reinforced. Integrating the four theoretical perspectives and incorporating SOE governance features, the study employs a multicase design with three groups (CASIC, CREC, CETC) representing administrationdriven, marketdriven, and hybrid governance. Within each, one core and one noncore subsidiary were selected, and for each, an internal and an external cooperation project were paired, yielding 6 subsidiaries and 12 projects. Data came from 52 semistructured interviews, archival documents, and onsite observations, analyzed through grounded theory procedures. The findings reveal that cooperation boundary choice is conditional. Internal cooperation is sustained when technological fit and resource irreplaceability are high, market window pressure is weak or task mandatoriness is strong, or longterm trust exists-operating via taskdriven or resourcedriven mechanisms. When these conditions are absent, institutional constraints, network positions, and performance pressure do not directly determine the boundary extension; instead, they activate three perceptions (risk-cost, resource-autonomy, relationship-trust). Through perceptual prioritization, decision-makers form dominant logics: risk-avoidance, efficiency-first, or identity-construction, which respectively lead to risk-transfer, capability-acquisition, or symbol-dominated cooperation, with the last representing peripheral subsidiaries' strategic use of external reputation for organizational visibility. The deep drivers are rooted in three mutually reinforcing structural dilemmas: institutional transaction cost (internal cooperation "not worthwhile" due to hidden costs and blurred responsibilities), network position disadvantage (internal cooperation "unattainable" for peripheral subsidiaries), and performance pressure transmission (external cooperation "more desirable" under shortterm evaluation systems). These dilemmas are selfreproducing across projects. Furthermore, external cooperation outcomes reinforce the outward tendency through a feedback loop of experiencing, attribution, routinization, and reproduction, creating path dependence. This dependence can be weakened, however, when the group provides clear responsibility boundaries, stable resource access, or longterm collaborative mechanisms. This study contributes by revealing the conditional nature of boundary choices, constructing a cognitive transformation pathway that moves from contextual conditions to perceptions, then to decision logic, and finally to external cooperation models, and by reframing intragroup synergy failure as a systemic problem shaped by institutional, network, and performance constraints. In terms of practical implications for management, SOE groups should adopt combined governance interventions that standardize internal protocols and accountability rules to lower approval burdens and synergy costs, while expanding peripheral subsidiaries' access to core networks through merit-based bidding and rotating leadership. Performance appraisals should incorporate fault-tolerance and stage-based evaluation for cross-unit and exploratory R&D. They should also reshape managerial perceptions through clear responsibility delineation, menu-based resource autonomy, trust-building via personnel secondment, and differentiated guidance for risk-averse, capability-seeking, and symbol-seeking decisions. Finally, external collaborations should be categorized into risk-transfer, capability-acquisition, and symbol-oriented types, with scrutiny of formalistic arrangements, technology absorption metrics, and feedback systems to convert external outcomes into shared knowledge assets.

Against the macro backdrop of mounting uncertainties in the global external environment, China has deeply integrated the resilience philosophy into the overall planning of urban development. Accelerating the integrated development of the digital and real economies acts not only as a critical pathway to construct a modern industrial system, but also as a core driving force for enhancing urban economic resilience. Extant scholarship has explored the impacts of digital elements including single digital technology adoption and digital platforms on economic resilience. Nevertheless, empirical research concerning the net effect and transmission mechanisms through which digital-real integration boosts urban economic resilience remains insufficient. This study takes 19 national urban agglomerations from 2010 to 2023 as research samples and employs the Technology-Organization-Environment (TOE) framework to unpack the mechanisms through which digital-real integration influences urban economic resilience. The empirical analysis proceeds through a baseline regression model incorporating city and time fixed effects, with standard errors clustered at the city level. A mechanism model is further specified to examine transmission channels, namely digital technological innovation, entrepreneurial vitality, and industrial structure upgrading. The core explanatory variable, digital-real integration, is operationalized through patent co-classification, while urban economic resilience is evaluated using the entropy method to capture recovery and improvement capabilities. The core conclusions are as follows: Digital-real integration can significantly improve urban economic resilience, and this finding withstands a series of robustness tests. Mechanism analysis demonstrates that digital-real integration facilitates the improvement of urban economic resilience through a three-dimensional synergistic transmission path rooted in the TOE framework. From the technology dimension, it generates enabling effects by unlocking the dividends of digital innovation. From the organization dimension, it delivers promotional effects by stimulating market entrepreneurial vitality. From the environment dimension, it exerts supporting effects by advancing industrial structure upgrading. Heterogeneity analysis reveals that the promoting effect of digital-real integration varies markedly across groups distinguished by innovation resource endowments, geographical locations and spatial organizational structures. Specifically, the positive effect is more prominent in urban agglomerations with concentrated leading digital enterprises, inland central-western urban agglomerations with huge development potential, and polycentric urban agglomerations. Extended analysis further verifies that digital-real integration improves the economic resilience of both central and peripheral cities within urban agglomerations without aggravating the internal digital divide. This study extends the existing literature in three dimensions compared with prior studies. First, it centers its research perspective on national urban agglomerations and introduces the TOE framework to elaborate the intrinsic mechanism linking digital-real integration to urban economic resilience from the perspectives of technological empowerment, organizational restructuring and environmental support, thereby enriching the transmission channels identified by previous studies. Second, from the urban agglomeration perspective, this study empirically estimates the overall effect of digital-real integration on economic resilience and its heterogeneous performance across urban agglomerations with differentiated characteristics. It provides empirical evidence for policy-makers to fully exploit the dividends of digital-real integration and implement targeted policies to strengthen regional economic resilience in light of local realities. Third, it further investigates whether digital-real integration can break the spatial monopoly of traditional production factors and its differentiated impacts on economic resilience between central and peripheral cities. Through the above empirical investigations and mechanism analyses, this study expands the research frontier of literature regarding digital-real integration and economic resilience, and offers vital references for advancing digital-real integration and elevating urban economic resilience.

The transformation of scientific and technological achievements serves as a crucial bridge linking scientific and technological innovation with industrial upgrading. Since the revision and implementation of the Law of the People's Republic of China on Promoting the Transformation of Scientific and Technological Achievements in 2015, China has undergone more than a decade of systematic construction and has preliminarily established a multi-level, full-chain system for technology transfer. However, from an international perspective, China's technology transfer level still lags considerably behind that of leading innovative nations such as the United States, Germany, and Japan. This gap does not stem from weaknesses in any single link or deficiencies in local capacities, but rather represents a structural shortfall in overall system effectiveness. Therefore, in the face of the inherent complexity of technology transfer, there is an urgent need to transcend the patchwork mindset and reshape the understanding of transformation from a holistic perspective. Existing studies are mostly confined to deep exploration within a single dimension. Some dissect transformation bottlenecks from the practical field, some construct causal frameworks from the theoretical field, and others analyze transformation policies from the governance field. The three exhibit a relatively independent mode of knowledge production, making it difficult to enhance the holistic comprehension of technology transfer as a complex operational system. In view of this, this study seeks to address the following questions: What thematic landscapes are respectively presented by the practical demands, theoretical responses, and governance measures of China's technology transfer? How do the three-dimensional fields form full-chain consensus in agenda-setting, and at which critical junctures does structural disembedding occur? What intrinsic mechanisms drive the convergence of consensus and the emergence of disembedding? This paper constructs a “Practice – Theory – Governance” analytical framework, employing the BERTopic model and cosine similarity algorithm to conduct topic identification and cross-domain association analysis on multi-source heterogeneous data of technology transfer. The findings are as follows: (1) In terms of topic identification, the practice dimension identifies twelve practical bottlenecks, which, according to the logic of transformation implementation, can be summarized into three major practical issues of insufficient endogenous motivation, poor supply-demand linkage, and weak support guarantee. The theory dimension identifies fifteen theoretical hotspots, which, according to the logic of knowledge production, can be categorized into four major research directions: historical context review, practical issue response, institutional design support, and high-quality development guidance. The governance dimension identifies fourteen governance focal points, which, according to the logic of governance implementation, can be categorized into four major governance modules—strategic direction guidance, factor resource guarantee, operational standardization, and risk-compliance constraints. (2) In terms of topic association, topic consensus represents the collective cognitive accumulation of the three fields of practice, theory, and governance. Five types of consensus are identified: organization-synergy consensus, factor-support consensus, rights-incentive consensus, scenario-undertaking consensus, and tool-hub consensus. As the antithetical category of consensus, topic disembedding tends to trigger structural failure and synergistic efficiency loss within the technology transfer system. Two types of disembedding are revealed: governance-blind-spot disembedding and policy-suspension disembedding. Consensus and disembedding do not represent a static and oppositional end-state picture, but rather exhibit an evolutionary pattern of coexistence and mutual transformation. Finally, in response to the two types of disembedding, this paper proposes the countermeasures and suggestion that efforts should be made to resolve the response lag in governance blind spots and effectively bridge the practical gaps of policy suspension.

Advancing green productivity is essential for reconciling high-quality economic development with the comprehensive green transition. Digital technologies such as artificial intelligence, big data, cloud computing, and the industrial Internet are increasingly embedded in technological innovation, industrial upgrading, resource allocation, and public governance, giving rise to digital innovation ecosystems characterized by interactions among heterogeneous actors, diversified resources, and supportive environments. These ecosystems can promote knowledge creation, improve efficiency in resource matching, accelerate green technology diffusion, and transform traditional production modes. However, Chinese regions differ markedly in resource endowments, industrial foundations, innovation capacity, market development, and institutional conditions. It is therefore necessary to examine how multiple ecosystem elements work together and how their effects vary across time and space. Green productivity are neither a simple "greening" of traditional productive forces nor equivalent to green total factor productivity or eco-efficiency. The latter concepts mainly evaluate economic efficiency under resource and environmental constraints, whereas green productivity refer to a systemic transformation that internalizes ecological constraints, environmental carrying capacity, and sustainability objectives into the production system. Based on the Marxian framework of laborers, means of labor, and objects of labor, the vertical and horizontal scatter degree method is used to measure green productivity across 30 provincial-level regions of China from 2015 to 2024. Guided by systems theory and synergy theory, the study applies dynamic qualitative comparative analysis from the perspective of the digital innovation ecosystem to examine the configurational effects, temporal evolution, and differentiated pathways through which digital innovation actors, resources, and environments jointly enhance green productivity. Robustness is tested by adjusting the consistency and proportional reduction in inconsistency thresholds. The findings show that none of the seven antecedent conditions, whether present or absent, constitutes a necessary condition for high levels of green productivity, indicating that the outcome results from the coordinated interaction of multiple conditions rather than the isolated effect of any single factor. Four equifinal configurational paths are identified and grouped into three types according to their core conditions and synergistic relationships: the digital-innovation-actor-led funding-market dual-linkage type, the digital-innovation-actor-led funding-market-innovation-infrastructure linkage type, and the actor-resource-environment multidimensional driving type. The effects of these configurations also vary over time, with consistency generally following a pattern of relatively high levels in the early period, fluctuations in the middle period, and decline in the later period. Considerable provincial heterogeneity is also observed, as some provinces are align with multiple paths, while others depend more strongly on particular combinations. Spatial comparisons further reveal that coastal regions rely more on broad coordination among innovation actors, resources, market mechanisms, government support, and innovation platforms, whereas inland regions place greater emphasis on technological innovation and talent input. These differences suggest that regions should select development pathways suited to their resource endowments, industrial foundations, and innovation conditions rather than uncritically adopting a uniform model. This study contributes by clarifying the conceptual boundary between green productivity and efficiency-oriented concepts such as green total factor productivity and eco-efficiency, developing a systematic measurement framework, and constructing an integrated actor-resource-environment explanation. By incorporating both temporal and case dimensions into configurational analysis, it also reveals dynamic and region-specific pathways that conventional regression analysis and static QCA may overlook. The findings suggest that coastal regions should strengthen cross-actor collaboration, market-based resource allocation, and innovation-platform connectivity, while inland regions should prioritize key green technologies, talent cultivation, sustained funding, and infrastructure that facilitates the translation of innovation outcomes into green industrial applications.

R&D investment is vital for innovation-driven development. The National Equities Exchange and Quotations (NEEQ), serves as the primary platform for innovative companies. With its large and diverse pool of NEEQ listed companies and their substantial contributions to R&D, the NEEQ represents a significant force in China's innovation. The multi-level capital markets play an essential role in optimizing resource allocation and promoting high-quality development of the real economy. The policy of NEEQ listed companies' switch to higher-level capital markets constitutes a major initiative through which the multi-level capital markets support R&D activities in the real economy. As the system for NEEQ listed companies' switch to higher-level capital markets continues to improve and its positive effects on corporate development become increasingly evident, the NEEQ's crucial role in linking different levels of capital markets and fostering innovative companies within the multi-level capital markets is becoming more prominent. Consequently, a growing number of NEEQ listed companies are anticipated to pursue switch to higher-level capital markets. As this system matures, its effects on both switching companies and their NEEQ listed peers warrant closer investigation. Drawing upon a sample of NEEQ listed companies from 2016 to 2024, this study empirically investigates the effect of NEEQ listed companies' switch to higher-level capital markets on the R&D investment of peer NEEQ listed companies. Research results show that the NEEQ listed companies' switch to higher-level capital markets significantly promotes the R&D investment of peer NEEQ listed companies. The growing number of NEEQ listed companies that switch to higher-level capital markets in the industry exhibits an inverted U-shaped relationship with the R&D investment of peer NEEQ listed companies. The quality of NEEQ listed companies' switch to higher-level capital markets significantly promotes R&D investment of peer NEEQ listed companies. The mechanism test reveals that the NEEQ listed companies' switch to higher-level capital markets improve the external financing ability of peer NEEQ listed companies, intensifies industry competition, and optimizes the industry information environment, thereby promoting R&D investment of peer NEEQ listed companies. Heterogeneity tests indicate that the NEEQ listed companies' switch to higher-level capital markets exerts a statistically significant positive effect on the R&D investment of peer NEEQ listed companies characterized by low technology intensity, non-state ownership, high risk-bearing capacity, as well as those with high market power. This paper makes a seminal contribution by being the first to uncover the effect of NEEQ listed companies' switch on R&D investment of peer NEEQ listed companies, providing important empirical evidence for the effectiveness of the policy of NEEQ listed companies' switch to higher level capital markets. The contributions of this study are threefold. First, the study is the first to theoretically and empirically examine the spillover effect of NEEQ listed companies' switch to higher-level capital markets on peer companies' R&D, shifting the analytical lens from switchers to industry peers. Second, it extends and deepens the literature on what drives and triggers R&D investment of NEEQ listed companies. Existing research on industry R&D investment largely focuses on peer companies' R&D text disclosure, MD&A tone, and other related aspects. Some scholars have studied the impact of IPO approval on peer A-share non-financial listed companies' innovation, yet have overlooked its effect on companies that were in a lower level of capital market prior to their IPOs. This study investigates the effect of NEEQ listed companies' switch to higher-level capital markets on peer NEEQ listed companies, to which prior industry-level R&D investment research has paid scant attention. Third, this study furnishes novel theoretical explanation and empirical evidence to guide government agencies in cultivating innovative companies and facilitating innovation-driven growth, and it highlights the imperative to carefully assess the effects of NEEQ listed companies' switch to higher-level capital markets on their peer NEEQ listed companies. The findings offer actionable policy insights for optimizing the NEEQ cultivation system and advancing innovation-driven development.

In the intelligent economy, the massive data generated by users' daily interactions with platforms constitutes the value foundation for algorithm training. However, platforms capture all the benefits while users receive nearly zero returns. This imbalance in distribution can no longer be simply explained by traditional notions of unclear property rights or market failure. This paper proposes and defines the analytical concept of the "distribution paradox", arguing that this phenomenon fully embodies three characteristics: contradiction, interdependence, and persistence. Its root cause lies not in incidental institutional flaws but in the structural features inherent to the production mode of the intelligent economy itself. In terms of theoretical approach, this paper integrates the dual perspectives of digital labor and algorithmic power. The digital labor perspective, grounded in Marx's labor theory of value, argues that users' unpaid labor is the true source of data value, establishing the moral premise for users' participation in distribution. The algorithmic power perspective reveals that algorithmic black boxes conceal value, information asymmetry leads to an imbalance in bargaining power, and the combination of increasing marginal returns and human-machine symbiosis allows platforms to consolidate their dominance. The convergence of these two perspectives indicates that the essence of the distribution paradox lies in the systemic separation of value creation and value allocation. Algorithmic power is not a product of technological autonomy, but rather results from platform capital actively exploiting technological complexity in an institutional vacuum to construct information barriers, locking the distribution structure into a unidirectional pattern that favors itself. Research has found that the generation of the allocation paradox follows a complete chain of mechanisms: users produce data through unpaid labor; the data aggregation creates value; the algorithmic black box obscures contributions; informational asymmetry leads to an imbalance in bargaining power; and ultimately, the platform monopolizes profits, solidifying the paradox. The three particularities of the intelligent economy respectively amplify the non-competitiveness, strong penetration, and increasing marginal returns of data elements, and their interplay forms a self-sustaining positive feedback loop. In terms of corrective pathways, this paper proposes three institutional concepts. Algorithmic transparency requires platforms to disclose to users the role of data in value creation, breaking the algorithmic black box; the right to informed allocation suggests adding statutory provisions on the right to be informed of data value, with the National Data Bureau leading the formulation of management and disclosure measures; and the Data Contributor Public Trust Fund uses collective inclusive sharing as the unit of distribution, piloting first in industries such as e-commerce and online advertising. Together, these three measures form a systematic corrective plan that moves from breaking information barriers to establishing an inclusive allocation mechanism. The marginal contributions of this paper are reflected in four aspects. First, it elevates the phenomenon of the divergence between user contribution and rewards from being merely "unfair" to being a "paradox", providing more precise theoretical targeting for subsequent research. Second, it constructs the dual perspectives of digital labor and algorithmic power, addressing the dual questions of why users should receive a share of distribution and why they actually cannot, thereby bridging the theoretical gap between the property rights approach and the distribution approach. Third, it reveals the specific operational logic of algorithmic power as a deep mechanism of distributive injustice, noting that it has surpassed unidirectional suppression and manifests a dialectical form of domination through empowerment. Fourth, it incorporates the issue of distributive justice into the core agenda of intelligent economy research, filling the academic gap created by the mainstream research's insufficient attention to distribution issues.

Digital transformation offers traditional manufacturing clusters a critical opportunity to break free from low-end lock-in and achieve value leapfrogging. Yet the heterogeneity of digital platforms poses a fundamental paradox: a single platform, limited in functional scope, cannot cover the full range of production, transaction, and innovation needs, while multi-platform adoption often creates data silos and fragmented operations. This study delves into how heterogeneous platforms can be horizontally coupled to overcome this dilemma and drive cluster upgrading. It develops an integrated theoretical framework explaining how traditional manufacturing clusters can leverage platform coupling to achieve innovation-driven, high-quality development, providing both theoretical grounding and practical guidance for cluster transformation. The study adopts a longitudinal single-case design, selecting the Keqiao modern textile industrial cluster as its empirical setting. As one of the largest and most complete textile clusters in China, Keqiao has undergone a full evolutionary trajectory from scale expansion to ecosystem restructuring and then to value innovation. Its digital platforms, covering design, manufacturing, and trading, were introduced sequentially over more than a decade, gradually moving from standalone operation to systematic coupling. This clear temporal sequence offers rich material for investigating the co-evolution of multiple platforms. Data were drawn from multiple sources. Primary data were collected through semi-structured interviews with government officials, platform operators, enterprise managers, and research and development personnel. Secondary data included policy documents, corporate annual reports, media coverage, and field observations. Through an iterative process of structured coding and constant theoretical dialogue, the study systematically traces the cluster's three-stage leapfrogging process and distills the co-evolutionary patterns among digital platform coupling, cluster dynamic capabilities, and institutional adaptation. The findings reveal an evolutionary logic in which shifting cluster challenges drive platform coupling evolution, which in turn enables stepwise capability upgrading and ultimately value leapfrogging. As the cluster advances, challenges evolve from generic issues such as information asymmetry and resource misallocation during the scale expansion stage, to more specific bottlenecks such as cross-chain collaboration barriers and innovation resource fragmentation during the ecosystem restructuring stage, and finally to systemic challenges including weak innovation leadership and green transition pressure during the value innovation stage. Correspondingly, platform coupling progresses through three patterns: foundational construction, which builds a single core platform to unblock critical bottlenecks; functional coupling, which connects multiple platforms through data interfaces and business rule alignment while preserving platform independence; and structural coupling, which establishes an industrial brain as a central hub, creating a deeply embedded and interdependent platform ecosystem. Platform coupling does not produce value leapfrogging directly; rather, it operates through cluster dynamic capabilities as a mediating mechanism. Specifically, foundational construction strengthens technological adaptation capability, driving efficiency-driven leapfrogging; functional coupling fosters resource integration capability, leading to innovation-efficacy leapfrogging; and structural coupling cultivates pathway creation capability, achieving systemic reconfiguration leapfrogging. Throughout this process, institutional adaptation plays a vital supporting role, progressing from foundational institutions that lower transformation thresholds, to coordinating institutions that reduce collaboration costs, to guiding institutions that anchor development direction. Together, these three institutional functions of reducing coordination costs, activating endogenous drivers, and anchoring leapfrogging direction form a temporally coordinated technology-capability-institution mechanism that sustains value leapfrogging. The theoretical contributions are threefold. First, the study extends the analytical unit of platform research from vertical collaboration between a platform and its complementors to horizontal interaction among platforms themselves. By identifying three progressive coupling patterns, it moves beyond the platform-centric unilateral perspective. Second, it reveals the co-evolutionary path of digital platform coupling and the stepwise upgrading of cluster dynamic capabilities. This shifts the focus of dynamic capability theory from a static analysis of capability composition to a dynamic process of capability generation, illuminating the inner mechanism through which digital platforms serve as capability incubators. Third, it reconceptualizes institutions not as exogenous environmental variables but as endogenous advantages woven into the entire leapfrogging process. The study offers an integrated framework that provides both theoretical depth and practical implications for understanding the digital transformation of traditional manufacturing clusters.

The emotional economy has rapidly emerged with the upgrading of residents' consumption, becoming an important track to drive service consumption and cultivate new quality productivity. As a future industry with a forward-looking national layout, embodied intelligence provides a new technological foundation for identifying, responding to, and commercializing emotional needs with its technological characteristics of embodied interaction, multimodal perception, situational embedding, and continuous feedback. Exploring the empowerment of embodied intelligence in the emotional economy has important theoretical and practical significance. Existing studies on embodied intelligence empowering the emotion economy have established a solid foundation, yet several avenues remain open for further inquiry. First, current research focuses largely on the overall effects of this empowerment, while systematic examination of micro-level antecedent processes, such as emotional symbol output and users' empathetic transformation of emotion, remains underexplored. Second, existing studies emphasize technical and institutional constraints, such as ethical misconduct, technological limitations, and fragmented application scenarios, yet pay inadequate attention to humanistic obstacles emerging in human-machine interaction, including subject adaptation problems, cultural adaptation gaps, and the alienation of emotional trust; these hidden barriers, including false trust, emotional dependence, the erosion of human agency, and the ethical problem of "empathy illusion", are more covert and harmful than explicit technical risks and have not been adequately discussed. Third, existing solutions largely follow general-purpose AI governance paradigms, such as technological innovation breakthroughs, institutional regulation, industrial chain upgrading, and governance system optimization, without fully incorporating the industrial characteristics of the emotion economy to construct a governance system adapted to its business patterns. This study analyzes the internal mechanism of embodied intelligence empowering the emotional economy from three aspects: the reconstruction of new quality production factors, the expansion of new service models, and the emergence of new business models. Among them, embodied intelligence reconstructs emotional economic computing power, algorithms, and data core production factors, iterates product extension services, holistic solutions, and functional services into three service models, and promotes the integration of "emotion+leisure", "emotion+cultural tourism", and "emotion+socializing" into new business models, promoting the deep transformation of emotional value production, service supply, and industrial form. Existing research identifies three practical obstacles in current empowerment practices. First, the conversion of new quality productive factors, from computing power to algorithms to data, remains inefficient; second, the delivery of product extension services, overall solutions, and functional services is obstructed; third, emerging business formats suffer from vague standards, homogeneous competition, and unclear regulatory boundaries. To address these obstacles, it is necessary to upgrade emotional application standards and build an industrial ecosystem of "product + service + scenario"; enhance human-machine collaborative service capabilities and unleash the vitality of composite talent supply; strengthen whole-process governance of emotional data and promote coordinated allocation of industrial factors; and refine tiered supervision while allocating responsibilities among multiple actors, so as to strike a dynamic balance between industrial innovation and ethical risk prevention and control through coordinated action across all four fronts. This study deepens the theoretical understanding of how embodied intelligence empowers the emotion economy at the micro-mechanism level and foregrounds the humanistic dimensions of this process, thereby offering theoretical insights and practical guidance for the high-quality development of China's emotion economy and its future industrial applications.

Digital platform ecosystems centered on Industrial Internet platforms have undergone rapid evolution and have emerged as a critical pillar underpinning both platform ecosystem maturation and enterprise innovation capacity building in the global digital economy. Yet existing research has not yet systematically elucidated the elemental structure of these cross-level ecosystems, nor the differentiated causal pathways through which ecosystem elements collectively shape platform enterprises' innovation efficiency. This leaves a critical theoretical gap regarding how digital platform ecosystems interact with core platform enterprises to translate ecological resources into tangible innovation outcomes. Grounded in innovation ecosystem theory and integrated with the Technology-Organization-Environment (TOE) analytical framework, this study deconstructs the digital platform ecosystem as a nested cross-level system formed around Industrial Internet platform enterprises-the core organizers and resource orchestrators of the ecosystem. This cross-level system is hierarchically divided into three interconnected layers: platform enterprises' internal capabilities (T), inter-organizational ecological collaborative relationships (O), and macro external supporting conditions (E). Specifically, these layers encompass five core elements: platform enterprises' digital and intelligent empowerment capability, industry-university-research collaborative symbiosis, digital intelligence infrastructure support, regional industrial foundation support, and government policy incentive support. On this basis, it builds a complex mediating model that embeds these cross-level ecosystem elements into the causal chain "digital platform ecosystem-Industrial Internet platform development level-platform enterprises' innovation efficiency", allowing for both direct and indirect effect mechanisms. This study develops a mixed approach that combines fuzzy-set qualitative comparative analysis (fsQCA) with regression-based mediation analysis. FsQCA is used to identify multiple equivalent configurations of technological, organizational, and environmental conditions associated with high industrial Internet platform development, capturing the multi-factor coupling and causal asymmetry of digital platform ecosystems. Enterprises' membership scores in high-development configurations are then transformed into configuration-level variables and entered into regression and mediation models to estimate their total, direct, and indirect effects on innovation efficiency. The empirical analysis is based on 38 listed industrial Internet platform firms selected from the Ministry of Industry and Information Technology's "Top 100 Industrial Internet Platforms 2023" list, with core variables constructed from authoritative databases and enterprises' annual reports. The results reveal that the coupling of different elements within digital platform ecosystems forms three distinct pathways for promoting high-level development of Industrial Internet platforms: the industrial foundation-supported pathway, the multi-factor-driven pathway, and the digital-intelligence-industry linkage pathway, which exert heterogeneous effects on platform enterprises' innovation efficiency. Specifically, the industrial foundation-supported pathway enhances innovation capability solely by elevating platform development level; the multi-factor-driven pathway not only directly promotes innovation but also indirectly improves innovation efficiency by driving platform development; the digital-intelligence-industry linkage pathway primarily improves platform development but has a limited impact on innovation efficiency. From a theoretical perspective, the study refines the dimensional structure of digital platform ecosystems from a TOE perspective, embeds them into a concrete "ecosystem-platform-firm" framework, and reveals configuration-specific mediating mechanisms, thereby extending innovation ecosystem theory to the industrial Internet context. The methodological contribution consists of proposing an integrated fsQCA-mediation approach for analyzing complex mediating relationships in digital innovation research. Practically, the findings provide guidance for policymakers and platform operators to identify prevailing ecosystem configurations, treat Industrial Internet platforms as core "innovation infrastructures", and design coherent bundles of technological, organizational, and policy interventions along the "ecosystem-platform development-innovation efficiency" chain, while recognizing that different configurations yield heterogeneous direct and indirect effects on innovation efficiency and that context-specific ecosystem design is therefore essential.

Enhancing urban economic resilience comprehensively is the core task of advancing urban governance modernization and a pivotal strategy for satisfying residents' aspirations for a higher quality of life. Confronted with a volatile external landscape and mounting domestic economic headwinds, China's urban economies face latent risks including shifts between old and new growth engines and restructuring of growth patterns—challenges that impose rigorous stress tests on urban economic resilience nationwide. As an outgrowth of technological revolutions and industrial shifts, the development of the intelligent economy propels deep integration between artificial intelligence and traditional industries, accelerates their digital and intelligent transformation, cultivates competitive strategic emerging sectors, and boosts urban economic resilience from an industrial perspective. Beyond industrial restructuring, the intelligent economy drives widespread deployment of digital infrastructure and research platforms, bridges the gap between R&D and commercial application, speeds up the translation and rollout of innovative outputs, buffers against external shocks, unlocks greater economic growth potential, and continuously lifts urban economic resilience. Existing scholarship has produced abundant separate studies on the intelligent economy and urban economic resilience. Nevertheless, theoretical inquiries predominantly center on the intelligent economy itself, with few systematic examinations of its multi-dimensional impacts and transmission mechanisms, which limits full recognition of its enabling value. Meanwhile, extant literature mostly investigates how the digital economy and artificial intelligence shape urban economic resilience, yet overlooks the intelligent economy's unique function in mitigating external risks and elevating economic quality and efficiency. Against this backdrop, this study constructs an integrated analytical framework and conducts an in-depth investigation into how the intelligent economy affects urban economic resilience, drawing on panel data covering 287 Chinese prefecture-level cities from 2012 to 2024. This study yields four core findings. First, the intelligent economy can significantly enhance the resilience of urban economy, and this conclusion has been tested through changing the measurement method of explanatory variables, tail reduction, removing special samples, and endogeneity testing. Second, mechanism testing indicates that industrial synergy and innovation and entrepreneurship vitality are important mechanisms for enhancing urban economic resilience through intelligent economy. Third, intellectual property protection positively moderates the enabling effect of the intelligent economy on urban economic resilience. Fourth, the resilience-enhancing effect of the intelligent economy displays pronounced heterogeneity across city population scales, geographic zones, and digital infrastructure endowments. Specifically, the effect follows clear hierarchical patterns: large cities outperform small and medium-sized cities; eastern regions lead central, western, and northeastern regions in sequence; cities with advanced digital infrastructure achieve stronger resilience gains than those with underdeveloped digital foundations. The marginal contributions of this article are as follows: First, from the perspective of intelligent economy research, this study systematically studies the relationship between intelligent economy and urban economic resilience by constructing a two-way fixed effect model, a two-step causal mediation effect model, and a moderation effect model. On the basis of enriching existing research, it provides empirical evidence for constructing new forms of intelligent economy and enhancing urban economic resilience. Second, this study incorporates industrial collaborative agglomeration and innovation-entrepreneurship vitality into the analytical framework to dissect the underlying transmission channels, broadening the research scope of relevant fields. Third, the study introduces intellectual property protection as a moderator to identify its contingent effect on the intelligent economy-resilience nexus, thereby refining the existing theoretical framework. Fourth, the study dissects heterogeneous effects across city population sizes, geographic locations, and digital infrastructure levels. These multi-angle examinations deliver tailored policy references for cities of varied characteristics to improve economic resilience in light of local conditions.

Corporate venture capital (CVC) has become a crucial strategic mechanism through which established corporations access emerging technologies and entrepreneurial firms obtain strategic resources for innovation. Unlike independent venture capital, CVC investors possess unique technological expertise and complementary assets that significantly shape portfolio firms' innovation outcomes. However, these benefits depend heavily on the technological distance between the two parties. Drawing on organizational learning theory, this study argues that technological distance simultaneously generates knowledge complementarities and coordination costs. While moderate distance provides access to novel knowledge, excessive distance creates severe cognitive barriers, and overly close proximity causes redundancy. Despite its importance, existing literature lacks a consensus on the optimal technological relationship between CVC investors and investees. This study addresses this gap by investigating the non-linear relationship between technological distance and portfolio firm innovation performance, explicitly balancing the synergy and friction effects to identify whether an optimal distance exists. Furthermore, this study examines how internal governance and external institutional environments shape this relationship by introducing parent firm opportunistic tendency and intellectual property (IP) protection intensity as key moderating factors. To test these propositions, this study constructs a large-scale panel dataset covering Chinese CVC investments from 2000 to 2024, comprising 2 254 CVC parent firms and 8 343 portfolio companies. Moving beyond traditional IPC classification codes, this study develops a fine-grained, continuous measure of technological distance based on patent text analysis. Specifically, this study employs text-mining techniques, including word segmentation, TF-IDF weighting, and cosine similarity calculations on patent titles, abstracts, claims, and keywords from the Innojoy database, defining technological distance as one minus the similarity score calculated via a five-year moving window. Portfolio firm innovation performance is evaluated across three dimensions: innovation quantity, innovation quality, and breakthrough innovation. Fixed-effects models are utilized for baseline estimations, alongside robust endogeneity checks, including propensity score matching (PSM), Heckman two-stage estimation, lagged independent variables, and interactive fixed effects. The empirical results confirm a significant inverted U-shaped relationship between technological distance and investee firm innovation performance, demonstrating an optimal technological distance that maximizes innovation outcomes. When technological distance is too small, substantial overlap in knowledge bases limits the potential for knowledge complementarity and innovative recombination. In contrast, when technological distance becomes excessively large, increasing cognitive barriers and communication costs reduce the efficiency of knowledge transfer and collaborative innovation. Crucially, the calculated inflection points vary significantly across dimensions, standing at 0.847 for innovation quantity, 0.890 for innovation quality, and 0.908 for breakthrough innovation. This upward trend indicates that breakthrough innovations require a substantially higher degree of technological heterogeneity and broader exploratory knowledge compared to routine, quantitative outputs. The moderating effect analysis further reveals that internal governance and external institutions significantly alter the trade-offs of technological collaboration. Specifically, parent firm opportunistic tendency significantly strengthens the inverted U-shaped relationship, making the curve steeper and shifting the inflection point leftward. This indicates that opportunistic behaviors amplify knowledge integration frictions, undermine trust, and narrow the effective range of innovation-enhancing technological distance. In contrast, the moderating role of IP protection intensity exhibits substantial heterogeneity across dimensions. For innovation quantity, stronger protection flattens the inverted U-shaped curve and shifts its inflection point leftward, reducing firms' reliance on precise technological matching to achieve higher output. For innovation quality, IP protection primarily shifts the inflection point leftward without altering the curvature. For breakthrough innovation, however, stronger IP protection steepens the curve and shifts the inflection point to the right, suggesting that robust external institutions encourage firms to engage in more distant and risky technological exploration by mitigating concerns regarding knowledge leakage and expropriation. In terms of managerial implications, the findings advise aligning technological distance with innovation goals by seeking proximity for routine tasks and distance for breakthroughs. Furthermore, policymakers should strengthen intellectual property rights and develop technological matching platforms to reduce transactional frictions and foster a more robust innovation ecosystem.

China's data three rights separation has moved from policy design to a stage in which its legal form must be tested through registration, contracting, disputes, and cross-border transactions. The Data Twenty Measures introduced data resource holding rights, data processing and use rights, and data product operation rights to overcome the limits of a single ownership model and promote data circulation. Yet these rights can't function if they remain within policy discourse. Local legislation, data exchanges, model contracts, and public data authorization have brought the issue into concrete legal settings. A lawful holder must be able to prove its entitlement. Use restrictions must be traceable when data is transferred or reused. Rules are also needed for the allocation of derived data and data products. In cross-border transactions, Chinese legal terminology must be made intelligible to foreign parties. The central issue is how this policy expression can be translated into a rule structure that can be verified and adjudicated. By synthesizing normative reasoning with key institutional materials, this study highlights a shift in China's data governance practice. The discourse has transitioned from abstract debates on data ownership to a functional approach emphasizing lawful source, authorization chain, contractual allocation, registration certificate, compliance review, audit trail, and liability distribution. The three rights separation is better understood as a bundle of rights shaped by mandatory norms and specified through contractual autonomy. A strongly exclusionary property model does not fit data, which can be copied, reused, processed in parallel, and controlled by multiple actors. A rigid ownership structure would increase transaction barriers and strengthen data silos. Mandatory norms should provide the baseline for legality, personal information protection, data security, trade secret protection, and competition order. Within these boundaries, contracts can translate holding, processing, use, operation, audit, benefit sharing, and liability into transaction terms. Its value lies in giving market actors a framework for allocating interests and risks. A stable rule system also requires a clearer expression of the legal object. Although the distinction among data resources, data, and data products helps describe stages of value creation, it creates unnecessary layers when applied to registration and transactions. A more workable approach uses “data” as the unified object and handles differences among raw data, derived data, important data, personal information, trade secrets, and data products through duties, risk classifications, contractual restrictions, and conflict rules. This also clarifies the boundary between data rights and intellectual property. Copyright, trade secret law, patent law, and trademark law protect objects that meet specific requirements, while the three rights separation regulates data as a production factor. The refinement of the three rights separation should be anchored in the dual circulation of data markets. In the domestic unified data market, the aim is to build trustworthy rights. Registration should record rights status, encumbrances, authorization chains, compliance evidence, and responsible parties, rather than declare ownership. Its present function should be evidentiary, with limited opposability explored for visible registered restrictions. Conflict rules should distinguish disputes involving personal information, trade secrets, copyright, competition order, public data, important data, and national security. Relevant factors are lawful source, purpose limitation, necessity, proportionality, substitutability, market harm, confidentiality measures, and auditability. In cross-border markets, the decisive issue becomes whether these rights can be understood and enforced across jurisdictions. Modular contracts can express holding, use, operation, derived data allocation, audit obligations, onward transfer limits, security measures, liability, and dispute resolution without requiring foreign parties to accept Chinese terminology in full. Security assessment, standard contracts, certification, regional arrangements, and mutual recognition of standards can translate the domestic rule structure into an internationally intelligible compliance framework. The normative transformation of the three rights separation should proceed from policy expression to rule construction. Its theoretical contribution lies in shifting the basic question from data ownership to the verifiability and adjudicability of data interests. It reconstructs the three rights separation as a mixed rule structure composed of mandatory baselines, contractual allocation, registration effects, and conflict rules, and connects domestic trustworthy rights with international dialogic rights to show how a Chinese innovation may acquire legal stability and communicability in cross-border data circulation.

The market-oriented allocation of data has become central to China's digital economy. Although the regulatory framework covers data security, personal information protection, utilization of public data, and data circulation, the relationship between regulation and innovation remains unsettled. Excessive caution may raise compliance costs and narrow experimentation, while insufficient control may expose national security, public interests, and individual rights to risks. This article examines how encouraging innovation can be recognised as a normative objective, positioned within the value hierarchy, and translated into workable rules and regulatory tools. It combines normative analysis, case analysis, and law-and-economics analysis, drawing on policy documents, legislation, and local practices. The normative basis for encouraging innovation is reconstructed through the Twenty Measures for Data, the Data Security Law, the Personal Information Protection Law, and experiments in Beijing, Shanghai, Zhejiang, and Chengdu. It has taken shape through the separation of data resource holding, processing and use, and product operation rights, as well as through negative lists, regulatory sandboxes, privacy-enhancing technologies, tolerance and correction mechanisms, and layered public data development. National security, public interests, and personal rights define the boundaries of innovation, while efficiency and development call for regulatory restraint that is necessary and proportionate, preventing excessive control from suppressing innovative data applications where risks remain controllable. Inclusive and prudent regulation adjusts the weight of security and innovation across technologies, stages, and scenarios. The current framework contains gaps in rule supply and friction costs of implementation. The three-rights separation model has yet to generate stable legal expectations, and rules linking data entitlement, circulation, valuation, registration, licensing, and cross-border transfer remain incomplete. Divergent local and sectoral standards increase costs. Regulators lack the capacity to assess algorithmic systems, complex data flows, and privacy-preserving computation, which often leads to conservative supervision. Compliance obligations weigh heavily on small and medium-sized enterprises, while innovation returns are dispersed through copying and reuse. Regulatory sandboxes and exemption arrangements lack clear admission criteria, exemption lists, testing procedures, exit mechanisms, and sufficient legal authorization, leaving tolerance for experimentation symbolic. Law and economics provide the basis for reconstructing innovation-oriented regulation. Clearer allocation of data-related interests can reduce transaction costs. Properly calibrated liability and incentive rules can internalise the negative externalities of data misuse and the positive externalities of innovation. Public data should be governed by openness and public-service obligations, with authorised operation introducing market expertise under security constraints. Enterprise data should be protected as proprietary interests protecting competitive value, stabilising innovation expectations without creating excessive exclusivity. Personal data should remain anchored in the principles of dignity and individual control, while controlled data portability and lawful-use exceptions support mobility, competition, and socially beneficial use. The practical path should proceed on three levels. National legislation and policy should adopt a negative-list approach, clarify departmental responsibilities, strengthen cross-regional coordination, and unify rules for classification, circulation, and compliance. Sectoral standards and local rules should translate general principles into criteria for data classification and grading, risk assessment, asset valuation, registration, settlement, and compliance review. Enterprises should conduct scenario-based risk assessments, embed compliance into product design and operations, and use privacy-enhancing technologies to enable data use while reducing exposure of raw data. Meanwhile, regulatory tools must be refreshed. An agile model should combine prohibitions and administrative sanctions with administrative guidance, compliance guidelines, certification, and experimental mechanisms. Strict controls should focus on high-risk conduct, flexible tools should guide mainstream innovation, and regulatory sandboxes should provide controlled opportunities for testing new technologies and models. The application should proceed through context identification, rule matching, and regulatory impact assessment, covering security, compliance costs, market entry, innovation investment, and data circulation. The study concludes that encouraging innovation should be treated as a purpose-oriented principle with implications for interpretation, enforcement, and regulatory design. It requires regulation to remain risk-based, proportionate, adaptive, and open to revision, so that regulatory burdens correspond to risks and lawful experimentation is afforded sufficient institutional space.

Innovation ecosystems have emerged as the primary arena for technological competition and industrial upgrading in the 21st century. The strategic resources embedded within these interconnected, multi-agent systems have gradually replaced traditional firm-specific assets as the critical foundation for enterprises seeking to build sustainable competitive advantages. Despite extensive scholarly attention to ecosystem architecture and governance, research has yet to develop a systematic, quantifiable framework for identifying and evaluating the heterogeneous resource base that underpins ecosystem performance. This gap not only constrains theoretical advances in resource orchestration within ecosystem contexts but also leaves practitioners without actionable guidance on how to identify, acquire, and leverage ecosystem resources effectively. To address this gap, this study unpacks the multidimensional resource architecture of corporate innovation ecosystems, gauges resource endowments systematically, and identifies the principal obstacle to resource enhancement. Drawing on innovation ecosystem theory and resource orchestration theory, the study first develops a comprehensive four-dimensional resource identification framework encompassing relational resources, environmental resources, ecological niche resources, and system state resources. Building on this theoretical framework, it constructs a rigorous evaluation index system to quantitatively measure each dimension of ecosystem resources. To empirically validate the framework, 60 representative listed companies from five major high-tech industries in China, such as automotive manufacturing and biopharmaceuticals, are selected as the research sample. These industries are chosen for their high innovation intensity, strong ecosystem dependencies, and strategic importance to China's national innovation-driven development strategy. The analysis is based on balanced panel data spanning 13 years from 2012 to 2024, a period that covers the full implementation of China's innovation-driven development strategy and major global economic and technological shifts. The methodological design integrates three complementary quantitative approaches. First, the CRITIC method is used to objectively assign weights to evaluation indicators, effectively avoiding the subjectivity inherent in traditional expert scoring. An improved grey-TOPSIS model is applied to assess the relative levels of innovation ecosystem resources, addressing the uncertainty and incompleteness common in panel data. Then, the obstacle degree model is adopted to diagnose the key factors impeding the improvement of enterprise ecosystem resource levels. The empirical analysis yields three key findings. First, the overall level of innovation ecosystem resources among Chinese enterprises exhibits an upward trajectory, yet the internal structure remains notably imbalanced. Environmental resources and system state resources serve as primary drivers of this growth, yet they display significant regional disparities and sectoral heterogeneity owing to their sensitivity to external conditions. Conversely, relational resources and niche resources emerge as widespread bottlenecks, constrained by insufficient corporate conversion capabilities and escalating competitive intensity. Second, substantial inter-industry heterogeneity is evident in resource levels, developmental trends, and structural priorities. Within specific industries, intra-sectoral resource gaps are widening, indicative of a clear "Matthew effect" wherein dominant firms with stronger ecosystem embeddedness accrue disproportionate advantages. Third, obstacle diagnosis reveals that relational constraints and environmental constraints currently constitute the primary impediments. Specifically, supply chain instability, inadequate inter-firm collaborative innovation, deficient regional innovation milieu, and macroeconomic volatility are the top four obstacle factors affecting resource improvement. Consequently, the optimization of relational networks and the enhancement of environmental adaptability are pivotal for firms aiming to translate ecosystem resources into genuine innovative competitiveness. The marginal contributions of this study lie in three dimensions. First, by identifying and evaluating enterprise innovation ecosystem resources, it quantifies the advantages gained by enterprises through system embeddedness and interaction into concrete, measurable multidimensional resource variables, uncovering a new mechanism for enterprises to acquire system resources. Second, the findings enrich the interdisciplinary research between enterprise resource orchestration theory and innovation ecosystem theory, offering a fresh theoretical perspective for understanding the sources of enterprise innovation advantages. Third, the systematic evaluation system reveals the evolutionary trends and developmental constraints of ecosystem resources, providing a solid academic basis and practical guidance for enterprises to assess their innovation ecosystem status and formulate effective embeddedness strategies.

Under the guidance of Chinese-style modernization, enterprises in China are increasingly called upon to transcend profit-driven logic and embed national strategic missions into their core innovation activities. However, existing management literature lacks systematic analysis of what constitutes Chinese-style enterprise innovation, how it differs from conventional Chinese or Western enterprise innovation, and through which mechanisms it fulfills its dual mission of economic viability and national service. Addressing this theoretical gap is critical for advancing China-specific management theories and guiding enterprises toward high-quality development pathways. This study pursues three interconnected objectives. The first is to clarify the conceptual boundaries of Chinese-style enterprise innovation relative to other innovation paradigms. The second is to explain how enterprises pursuing Chinese-style innovation identify, transmit, and accomplish their missions. The third is to specify practical pathways that enable Chinese-style enterprise innovations to translate mission exploration into tangible outcomes. To achieve these objectives, this conceptual study adopts a qualitative, theory-driven approach. It integrates Chinese-style modernization theory with value logic theory, drawing on comparative analyses of institutional contexts, mission tasks, development models, and corporate cultures. The analysis is structured around an original "mission identification-mission transmission-mission effectiveness" framework, illustrated with representative cases including BYD, Huawei, and Contemporary Amperex Technology Co., Limited (CATL), thereby grounding theoretical distinctions in observable practices. The study yields three principal findings. First, Chinese-style enterprise innovation is not merely a geographically or ownership-defined category but represents a distinctive innovation logic. Unlike conventional Chinese enterprise innovation, which often emphasizes imitation and regulatory compliance, Chinese-style enterprise innovation exhibits four boundary conditions: Institutionally, it proactively leverages the socialist market economy. Mission-wise, it internalizes national strategic tasks such as resolving critical technological bottlenecks. Developmentally, it prioritizes indigenous innovation over catch-up imitation. Culturally, it synthesizes traditional Chinese humanistic values, notably a people-centered orientation, with modern management principles. In contrast to Western enterprise innovation, which privileges private capital interests and individual performance, Chinese-style enterprise innovation emphasizes collective well-being, long-term strategic value, and a symbiotic state-market relationship. Second, through a value-theoretic lens, the study deconstructs the mission logic. Mission identification is co-driven by endogenous factors and exogenous factors. Mission transmission operates bidirectionally: a top-down "mapping" where the state communicates missions via binding policies, and a bottom-up "responding" where firms decode these missions, orchestrate resources, and embed them into their organizational identity. This interaction generates value outcomes across a hierarchical spectrum. Third, the study specifies a three-stage implementation pathway. At the identification stage, institutional anchoring and systematic responsiveness enable the translation of value consensus into institutional arrangements and responsive capabilities. During transmission, organizational identification and action transformation enable value consensus to diffuse from managerial cognition to organization-wide consensus and translate into concrete innovation actions. At the effectiveness stage, instrumental accumulation and purposive expansion enable firms to accomplish the value leap from economic value to social and national strategic value while maintaining economic sustainability. The theoretical contributions are threefold. This study extends value logic theory from the commercial context to the national strategic context, revealing the value generation and transformation mechanisms in firms' transition from capital logic to mission logic. It constructs a systematic analytical framework of mission identification, mission transmission, and mission effectiveness for Chinese-style enterprise innovation, providing an operable analytical tool for understanding its operational mechanisms. Furthermore, through systematic comparisons with Western and traditional Chinese enterprise innovation, it clearly defines the theoretical boundaries of Chinese-style enterprise innovation, responding to the academic call for in-depth contextual exploration in innovation research and offering a new perspective for constructing indigenous Chinese innovation management theories. In practical terms, these findings offer enterprises a clear roadmap for aligning innovation with national strategy without sacrificing competitiveness. They also equip policymakers with criteria for designing differentiated innovation policies and evaluation systems that reflect the distinctive logic of Chinese-style enterprise innovation.

The establishment of the World Data Organization marks a shift from "explosive growth" to "regulated governance" in the global digital economy. Cross-border data flow regulation has become a core issue in the strategic competition among major powers and the reconfiguration of regional economic and trade rules. Currently, the world has evolved into three typical regulatory paradigms: the "market-oriented" model of the United States, the "privacy protection" model of the European Union, and the "data security" model of China. The "trusted data free flow" model represented by Japan and ASEAN exhibits a strong pragmatic compromise color. Given this, this paper adopts a comparative analytical approach combined with a macro-micro research framework to advance its argument. On the one hand, it conducts a macro-level environmental scan and establishes a theoretical foundation by systematically reviewing the principle-based provisions and exception clauses in international economic and trade agreements such as RCEP, and thoroughly analyzing the legislative paradigms and underlying legal rationales of major global economies, including the United States, the European Union, ASEAN, and Japan, regarding data governance. This provides a comparative legal reference for subsequent analysis. At the same time, the study shifts its theoretical perspective to regional governance practices, reflecting on and evaluating the current state of data governance across different economies, with a particular focus on China's data governance system, thereby revealing the shortcomings of China's existing regulatory model in terms of international interoperability. On the other hand, at the micro level, the paper proposes institutional reconstruction and a Chinese solution. Grounded in a dual-track theory that fosters data flows while balancing legal interests, it suggests specific legal reforms, such as improving the adaptability of China's current standard contractual frameworks, exploring dynamic whitelist mechanisms, and deepening mutual recognition with regional multilateral certification systems, with the aim of providing intellectual support for achieving a balanced development and security strategy and enhancing China's institutional voice within the global digital governance architecture. Specific paths include: at the macro level, relying on the establishment of the World Data Organization to output appropriate standards and build a regional trusted data circulation circle centered on China; at the mechanism level, exploring a transformation from "single security assessment" to a "classified and graded + dynamic white list" tiered exemption system; at the tool level, enhancing the modular compatibility of standard contracts and deepening substantive mutual recognition with international certification systems. Through the refined reconstruction of the system, the aim is to achieve a dynamic balance between national security interests and the global benefits of data elements. Under the "promotion-balance" value framework, the paper argues that governance should give equal weight to flow-driven and balance-oriented approaches. Building on data classification and grading, this dual emphasis can foster a trusted data circulation network internationally while establishing whitelist and adequacy assessment mechanisms demestically. By starting from legal attributes and adopting comparative research methods among major global economies, this approach offers theoretical and methodological innovation in advancing the study of cross-border data flows. It should be acknowledged that, due to limitations in article length, the paper has not fully elaborated on institutional design for China's regional multilateral mutual recognition framework with foreign countries. Looking ahead, the World Data Organization, as the first global professional international platform for data governance, marks a transition beyond fragmented data rules and establishes a hub for multilateral coordination. Future research could focus on how the World Data Organization can leverage its role in technology governance by harmonizing cross-border data standards, balancing security and data flow, bridging the digital divide, and particularly supporting the development of regional multilateral frameworks for data interoperability, thereby facilitating global dialogue on digital technology rules.

Amid the accelerating evolution of profound global changes unseen in a century and the continuous escalation of China-U.S. trade friction, the security of global industrial and supply chains has suffered unprecedented shocks. Multinational corporations construct exogenous technological barriers through strategic patent layouts, while the path dependence and network effects within industries cause domestic enterprises to fall into endogenous technological lock-in. In view of this, this paper aims to clarify the internal logic between endogenous technological lock-in and the capability for independent and controllable industrial chains, identify its transmission pathways and boundary conditions, and provide theoretical support and decision-making references for resolving strategic bottleneck predicament and achieving secure development of industrial chains. Existing literature has accumulated insights into the formation mechanisms, economic effects, and unlocking pathways of technological lock-in, and has also examined the influencing factors and measurement methods of the capability for independent and controllable industrial chains. Nevertheless, theoretical dialogue remains notably insufficient. Current research exhibits three critical limitations: (1) analyses and measurements of technological lock-in predominantly focus on macro-level perspectives, neglecting micro-level firm investigations; (2) studies on the capability for independent and controllable industrial chains rarely examine technological lock-in as an influencing factor, and the measurement dimensions seldom simultaneously address both "dependence" and "controllability"; and (3) the two research streams have developed independently without establishing systematic theoretical linkages or transmission mechanisms between technological lock-in and the capability for independent and controllable industrial chains. Consequently, constructing an integrated analytical framework to reveal the mechanisms through which technological lock-in affects the capability for independent and controllable industrial chains represents a crucial academic endeavor for breaking through the strategic bottleneck predicament and safeguarding economic security. In terms of data, this study uses data of A-share listed manufacturing enterprises in China from 2016 to 2024, and integrates multi-source databases including CSMAR Database, CEPII-BACI Database, UN Comtrade Database and National Standards Information Database. After data screening, the study obtains 7 018 firm-year observations covering 875 enterprises. In terms of research design, firstly, from the two dimensions of "dependence" and "controllability", this study applies the entropy weight method to establish a comprehensive evaluation system for the capability for independent and controllable industrial chains. Meanwhile, it adopts the non-radial and non-oriented Super-Efficiency SBM Model under constant returns to scale to measure the degree of endogenous technological lock-in. In terms of econometric strategy, a fixed-effects model is constructed for baseline estimation. Furthermore, Bartik instrumental variables, Lewbel instrumental variables and propensity score matching are adopted to address endogeneity issues, and multiple approaches are used to verify the robustness of the empirical results. The findings are as follows. First, endogenous technological lock-in significantly inhibits the capability for independent and controllable industrial chains, a conclusion that remains robust after various endogeneity treatments and robustness checks. Second, heterogeneity analysis reveals that this inhibitory effect is more pronounced in high-tech manufacturing, strategic emerging industries, and "chain-master" enterprises. Third, mechanism tests and extended analyses indicate that endogenous technological lock-in undermines the capability for independent and controllable industrial chains by breeding innovation inertia, triggering supply chain disruption risks, and causing technological evolution failure. Meanwhile, firms can mitigate the adverse effects of endogenous technological lock-in through stronger adaptive capabilities.

Against the dual backdrop of accelerated restructuring of global industrial and supply chains and the deep penetration of the digital economy into the real economy, breaking the low-end lock-in of enterprise value chains and promoting the leapfrogging of value creation capabilities have become core issues for high-quality development. As a key form of deep coupling between digital technologies and the real economy, digital-real technology integration provides an important path for enterprises to break through value chain bottlenecks. Although existing literature has extensively documented the industrial empowerment effects of digital transformation, there remains a paucity of research on the specific mechanisms through which digital-real technology integration drives value chain upgrading. Taking Chinese A-share listed companies on the Shanghai and Shenzhen Stock Exchanges from 2012 to 2024 as the research sample, this paper empirically tests the effect of digital-real technology integration on enterprise value chain upgrading, aiming to reveal the internal logic and practical path of digital-real technology integration empowering value chain climbing. To ensure the robustness of the findings, a series of tests are conducted; furthermore, mechanism analyses are performed to investigate the mediating roles of internal control quality, earnings volatility, supply chain resilience, and supply chain disruption risk; finally, heterogeneity analyses are conducted across dimensions of ownership, market competition, environmental regulation, and digital infrastructure, alongside an assessment of the moderating effect of the National Big Data Comprehensive Pilot Zone policy. The research findings are as follows: First, digital-real technology integration can significantly promote enterprise value chain upgrading, indicating that the penetration and integration of digital technologies into the real economy help enterprises improve their value-added creation capabilities and achieve a leap to the high-end links of the value chain. Second, mechanism tests show that digital-real technology integration exerts its empowering effect on value chain upgrading through four mechanisms: enhancing internal control quality, reducing earnings volatility, improving supply chain resilience, and mitigating supply chain disruption risks. Third, heterogeneity analysis indicates that the upgrading effect of digital-real technology integration is more significant in non-state-owned enterprises, low-market-postion enterprises, regions with low environmental regulation intensity, and eastern regions. Fourth, the policy of National Big Data Comprehensive Pilot Zones has a significant positive moderating effect, which can strengthen the boosting effect of digital-real technology integration on value chain upgrading. Policy support can optimize the external environment for digital-real technology integration and further amplify its promoting effect on enterprise value chain upgrading. The marginal contributions of this study are reflected in three aspects: First, it incorporates digital-real technology integration into the analytical framework of enterprise value chain upgrading, reveals the internal logic by which digital technology transformation and the integration of internal and external resources drive enterprise value chain climbing, and addresses the core proposition of how digital-real technology integration empowers the high-quality development of micro-enterprises. Second, it clarifies that digital-real technology integration helps enterprises break through the bottlenecks of value chain upgrading through four paths: enhancing internal control quality, reducing earnings volatility, improving supply chain resilience, and mitigating supply chain disruption risks, thereby enriching the theoretical research on the driving mechanisms of value chain upgrading. Third, it incorporates the National Big Data Comprehensive Pilot Zones into the analytical framework and empirically tests the moderating effect of regional policies, and confirms that regional institutional supply can significantly strengthen the boosting effect of digital-real technology integration on enterprise value chain upgrading. The research conclusions can provide empirical references for enterprises' transformation and upgrading practices and the design of relevant industrial policies.

The construction of world-important talent centers and innovation hubs is a strategic priority of China's talent-power strategy, yet how national talent endowments can be transformed into high innovation performance remains unresolved. Based on national innovation systems theory and the talent innovation and entrepreneurship ecosystem theory, this study develops a four-layer model of the national talent innovation and entrepreneurship ecosystem, comprising talent resources, innovation-supporting entities, key innovation resources, and an open and livable environment, and examines, from a configurational perspective, how these conditions jointly shape ecosystem effectiveness. This study applies fuzzy-set qualitative comparative analysis (fsQCA) to a cross-sectional sample of 94 countries drawn from the 2025 Global Innovation Index, the 2025 Global Talent Competitiveness Index, and the 2025 International Statistical Yearbook. Seven antecedent conditions are operationalized: talent resources, knowledge innovation entities, technological innovation entities, financial resource support, government policy support, openness to the outside world, and livability. The outcome is measured by the knowledge-creation score of the Global Innovation Index, capturing a country's talent-driven innovation effectiveness. The findings unfold in three steps. First, necessity analysis reveals that no single condition is necessary for high effectiveness, whereas the absence of technological innovation entities emerges as a quasi-necessary condition for low effectiveness, indicating that the lack of such innovation carriers is the most prevalent deficiency among underperforming countries. Second, configurational analysis identifies two equifinal pathways to high effectiveness, with an overall consistency of 0.898 and coverage of 0.568. Path S1, a talent-supported "knowledge innovation entity, government policy, open and livable environment" mode, characterizes 25 mostly small and medium-sized advanced economies such as Finland and Singapore, in which strong universities and stable policies combine with openness and livability to sustain innovation. Path S2, a talent-supported "technological innovation entity, key innovation resources, open and livable environment" mode, characterizes 12 large developed economies such as the United States, Germany, and the United Kingdom, in which dense corporate innovation carriers couple with developed financial systems and global talent networks. Talent resources, external openness, and livability constitute core conditions shared by both pathways, while knowledge innovation entities and the technological-innovation and financial-resource bundle exhibit a mutually substitutive, cross-configurational distribution. Third, the analysis of low-effectiveness configurations yields five paths classifiable into an innovation-foundation-weakness pattern, anchored in the joint absence of talent resources and technological innovation entities, and a resource and actor mismatch pattern, in which strong financial resources coexist with missing innovation entities and inadequate livability, as exemplified by resource-dependent economies such as Saudi Arabia. Robustness checks across alternative consistency thresholds, PRI thresholds, and calibration anchors confirm the stability of these results. This study makes two theoretical contributions. It extends innovation ecosystem research from the organizational and regional levels to the national level, demonstrating that the effectiveness of national talent ecosystems arises from multi-actor synergy rather than from the endowment of any single factor. It also uncovers the contingent role of technological innovation entities, challenging the practice of equating firm size with innovation capacity. The findings also carry practical implications. Countries should coordinate talent development, external openness, and livable-environment construction as an integrated undertaking, choose differentiated development modes according to their endowments, and avoid mistaking capital investment for innovation-capacity building when fundamental actors are missing. The results offer policy references for China's efforts to build world-important talent centers and innovation hubs, and for other economies seeking to enhance their national innovation ecosystems.

Cross-border flows of R&D factors are a vital support for fostering new quality productive forces. In recent years, however, escalating external technological restrictions and intensifying geopolitical rivalry have disrupted the global pattern of R&D factor mobility. Against this backdrop, standard connectivity under the Belt and Road Initiative(BRI) has increasingly become an important institutional interface for removing barriers to cross-border R&D factor flows and optimizing the global allocation of science and technology innovation resources. Existing studies have largely examined standard cooperation and cross-border R&D factor flows separately, focusing mainly on the trade-creating effects of standards coordination, technology diffusion, and international standards certification. Systematic and direct empirical evidence on how standard connectivity affects cross-border R&D factor flows in the BRI context, however, remains limited. Drawing on the knowledge spillover perspective and the multidimensional proximity framework, this study employs text-mining techniques to construct an indicator of standard connectivity for BRI partner countries based on project-related news texts published on the official Belt and Road Portal of China. This indicator is then matched with the OECD Inter-Country Input-Output Tables (OECD-ICIO) to examine the impact of standard connectivity on cross-border R&D factor flows. Given that the latest OECD-ICIO (2025 edition) covers data up to 2022, and taking into account the availability of BRI project-related data, the sample period is set from 2011 to 2022. Ultimately, the analysis yields a balanced panel of 696 observations across 58 countries. The empirical results show that standard connectivity significantly promotes cross-border R&D factor flows between China and its partner countries, thereby strengthening bilateral innovation linkages. From a directional perspective, the effect is mainly reflected in facilitating the outward flow of China's R&D factors to partner countries, while its direct linear effect on the inward flow of partner countries' R&D factors into China is not statistically significant. Further analysis reveals that the effect of standard connectivity is conditioned by multidimensional proximity in an asymmetric manner. Heterogeneity analysis shows that the promoting effect is more pronounced for countries along the 21st Century Maritime Silk Road, whereas it is not significant for countries along the Silk Road Economic Belt. Threshold analysis further indicates that the economic size of partner countries constitutes an important boundary condition for the effect of standard connectivity on R&D factor inflows into China. Specifically, using Hansen's panel threshold approach, the study identifies a significant threshold effect of partner countries' economic size on R&D factor inflows into China. Below this threshold, standard connectivity exerts a negative effect on partner-to-China R&D inflows; above the threshold, the effect turns significantly positive. This nonlinear reversal explains the baseline insignificance: smaller economies face binding constraints in R&D supply capacity and standard adaptation costs, whereas larger economies possess the absorptive capacity to leverage harmonized institutional interfaces and accelerate R&D resource allocation toward China. This study makes three main contributions. First, it extends research on the economic consequences of standard connectivity beyond trade performance, export quality, and technology diffusion to the domain of cross-border R&D factor flows, thereby filling an important gap in the literature at the intersection of standard connectivity and cross-border innovation resource allocation. Second, drawing on knowledge spillover theory and the multidimensional proximity framework, it identifies the contingent mechanisms through which standard connectivity affects cross-border R&D factor flows and highlights the heterogeneous effects on outward and inward R&D flows. Third, by constructing a country-year measure of standard connectivity from BRI cooperation project news texts, this study offers a context-sensitive quantitative approach for capturing institutional "soft connectivity" under the BRI, going beyond conventional measures that primarily rely on firm-level data, single-industry standards databases, or formal standards records dominated by developed countries.

Accelerating the transformation of scientific and technological achievements into real productivity and strengthening basic research capabilities are powerful drivers for the growth of emerging and future industries, and are essential requirements for cultivating and developing new quality productivity. The 2024 Central Economic Work Conference identified technology? led innovation to advance new quality productivity as a key task, and the 15th Five-Year Plan further called for more efficient transformation and application of scientific and technological achievements. The transformation of such achievements serves as a bridge linking research with markets and innovation with industries, and is therefore crucial for enhancing new quality productivity and promoting high-quality development. However, China's innovation system still suffers from a low commercialization rate of research outputs and inefficient technology transfer channels. With the rapid development of the digital economy, data factors have become a key driver of economic growth and technological innovation. As core infrastructure of the data factor market, data trading platforms bridge supply and demand, increase the supply of high-quality data, and facilitate the cross-entity circulation of data factors, thereby alleviating information asymmetry, reducing transaction costs, and accelerating knowledge spillovers. Thus, the platforms provide new momentum for technology transfer. Hence, in-depth investigation of the effects and mechanisms through which data factor marketization influences technology transfer holds significant theoretical value and practical implications for promoting high-quality development and fostering new quality productivity. Using panel data from 216 Chinese cities spanning 2011-2023, this study treats the establishment of data trading platforms as a quasi-natural experiment and employs a staggered difference-in-differences model to examine the impact of data factor marketization on technology transfer. The findings show that data factor marketization significantly promotes technology transfer. This conclusion remains robust after a series of robustness checks, including excluding the influence of other policies, accounting for heterogeneous treatment effects, using alternative variable measures, excluding municipalities directly under the central government, winsorizing extreme values, excluding the impact of the COVID-19 pandemic, and employing instrumental variable estimation. Mechanism analysis reveals that data factor marketization facilitates technology transfer primarily through three channels: alleviating information asymmetry, enhancing industry-university-research collaboration, and reducing R&D costs. Heterogeneity analysis further indicates that the positive effect of data factor marketization on technology transfer is more pronounced in regions with better information infrastructure, more developed institutional environment for data, stronger intellectual property protection, and higher levels of fintech development. Building on these findings, this study offers policy implications including accelerating the deployment of data trading platforms, promoting the precise matching and efficient utilization of data resources in R&D, and tailoring differentiated strategies for data factor market development to local conditions. The marginal contributions of this study are threefold. First, in contrast to existing studies that focus on the impact of public data on technology transfer, this study focuses on the role of data factor marketization, which deals primarily with commercial data. This expands the research boundaries of both the economic effects of data factor marketization and the drivers of technology transfer, offering a new perspective on how data factor marketization promotes technology transfer. Second, by identifying three mechanisms - alleviating information asymmetry, enhancing industry-university-research collaboration, and reducing R&D costs - this study reveals how data factor marketization empowers the transformation of scientific and technological achievements, providing novel empirical evidence on the underlying pathways. Third, through heterogeneity analysis along the dimensions of data infrastructure, intellectual property protection, and fintech development, this study identifies the key conditions under which data factor marketization takes effect, providing policy insights for precisely matching the development of the data factor market with regional development environments.

In the digital economy, platform algorithmic governance has become a critical determinant of users' sustained participation. While platforms enhance matching efficiency, issues such as algorithmic discrimination, opaque decision-making, and perceived unfairness have triggered public skepticism and undermined user trust, thereby threatening platforms' long-term viability. Existing research on algorithmic governance remains fragmented, largely focusing on single-dimensional factors such as transparency or efficiency, while overlooking the conjunctural mechanisms through which multi-dimensional governance attributes shape users' fairness perceptions and subsequently influence sustained participation. To address this gap, this study integrates the Stimulus-Organism-Response (SOR) paradigm with the Technology-Organization-Environment (TOE) framework to construct a complex mediation model. It positions algorithmic governance attributes(spanning technological, organizational, and environmental dimensions)as external stimuli that drive fairness perception and ultimately elicit sustained participation as the behavioral response. The research aims to identify heterogeneous configurational pathways, examine the mediating role of fairness perception, and analyze variations in direct and indirect effects under different intensities of external constraints. A mixed-methods design combining fuzzy-set qualitative comparative analysis and regression analysis was adopted. Data were collected through a structured online questionnaire on the Credamo platform, yielding 600 valid responses from active users of major Chinese e-commerce, social media, and short-video platforms. Regression models subsequently tested the direct effects of configuration membership scores on sustained participation and the mediating role of fairness perception, controlling for demographic variables. The results reveal that no single governance attribute constitutes a necessary condition for high fairness perception, confirming the conjunctural nature of effective algorithmic governance. Six sufficient configurations were identified and parsimoniously synthesized into four distinct types. Justice-Responsibility configurations feature high data bias control and algorithmic accountability as core conditions, with low transparency compensated through internal justice mechanisms, as observed in Meituan's dispatch system and Kuaishou's content recommendation. Transparency-Accountability configurations combine high data bias control and accountability cores with peripheral transparency and low controllability, exemplified by Douyin and Bilibili. The Stability-Constraint configuration integrates high data bias control, controllability, and dual external pressures (regulation and social opinion) as cores, reinforced by transparency, as seen in Pinduoduo under stringent oversight. Externally Oriented configurations are driven by high transparency alongside dual external constraints and low controllability, demonstrated by JD. com's rule disclosure practices. Regression analyses confirm that all four governance types exert significant positive effects on sustained participation, with the Stability-Constraint type displaying the strongest direct effect. Fairness perception significantly mediates these relationships across all types, serving as the central psychological bridge. Heterogeneity is evident with respect to external constraints: configurations operating under strong dual external pressures generate pronounced dual promotion effects through both direct activation of trust and system dependence and indirect fairness-mediated pathways, whereas those relying primarily on single external drivers operate predominantly through the indirect route. The study makes four theoretical contributions. It extends SOR theory into platform algorithmic governance by demonstrating fairness perception as a multi-dimensional mediator that transforms conjunctural TOE stimuli into behavioral outcomes. It advances TOE framework applications by revealing synergistic technological-organizational-environmental interactions rather than isolated effects. The fsQCA-regression mixed approach enhances methodological rigor for examining complex mediation in digital platform research. It also shifts algorithmic governance scholarship from efficiency-centric or single-attribute perspectives toward a justice-oriented configurational lens. The findings indicate that platforms should prioritize the synergistic integration of data bias control mechanisms and robust accountability systems, as this combination reliably elevates fairness perceptions even under conditions of lower transparency. Transparency interfaces must be carefully designed to avoid cognitive overload. Managers can further strengthen user retention by aligning internal governance with external regulatory compliance and social responsiveness, particularly under strong institutional pressures. Policymakers are advised to foster synergistic regulatory-social supervision frameworks that incentivize fairness-enhancing algorithmic practices.

As one of the new productive forces in the era of digital intelligence, computing power is regarded as an important internal driving force to promote the digital transformation of economy and society. With the in-depth application of computing resources, the development model of key areas such as artificial intelligence, government services, and biopharmaceuticals has been reshaped, further changing the production and lifestyle. However, while empowering digital construction, computing resources also bring monopoly worries that cannot be ignored. Against this backdrop, this paper explores the theoretical connotation, risk patterns, and governance challenges of computing power resource monopoly, aiming to build an inclusive, effective, and pragmatic governance order. This study first clarifies the theoretical connotation of computing power resource monopoly, categorizing it into "computing power production resource monopoly" and "computing power scheduling resource monopoly". In terms of the generation mechanism of computing power resource monopoly, the study identifies that the high input, technical standards and ecological lock-in effects of the computing power industry and the characteristics of economies of scale have become key driving factors. Building on this, the study analyzes how these factors, combined with the integrated control of software and hardware by leading enterprises, create high entry barriers and ecological lock-in effects. The incompatibility of computing power underlying technology and the competition restriction of infrastructure often manifests in anti-competitive behaviors like tying, M&A, and predatory pricing. When the data monopoly advantage is superimposed on the computing power monopoly advantage, the leading enterprises can further enhance the ecological user stickiness. Finally, the paper discusses the practical dilemmas in anti-monopoly governance, noting that traditional definitions of relevant markets and dominant positions are ill-suited for this emerging field, making it difficult to balance industrial development with competition policy. In the world's largest computing power consumer market, it is imperative to strengthen the anti-monopoly of computing power resources. On the basis of clarifying the basic concept and generation logic of computing power resources, this paper combines the specific risk patterns and practical dilemmas brought about by the dynamics of computing power competition, and proposes a targeted normative path : First, "inclusive and prudent" regulation should serve as the core governance principle to address the Collingridge dilemma and balance computing power innovation with oversight. As an emerging industry, computing power is highly susceptible to technological iteration, and the post-correction logic of traditional antitrust often fails to respond to dynamic competition risks in a timely manner. Under the concept of inclusive prudence, anti-monopoly efforts should scientifically establish an analytical framework for defining the relevant market and identifying market dominance, gradually building a preventive governance system. Second, coordination between industrial and competition policies must be promoted to ensure the fairness of market access and factor flow through fair competition reviews. In terms of specific industrial policy guidance, the upstream sector should focus on independent research and development and establish a fair licensing mechanism for results, the middle sector should focus on the construction of dispatching platforms, and the downstream sector should focus on promoting ecological multi-coordination. Third, to establish a public-private cooperative paradigm for anti-monopoly oversight, effective computing power regulation requires a multi-governance model involving government, enterprises, and society. The government should safeguard international competitiveness, implement differentiated domestic supervision, and enforce mandatory interoperability rules. Industry associations need to play a self-discipline function to promote standard unification and resource coordination. While establishing a compliance mechanism, computing power enterprises should assume social responsibilities according to their market control ability.

How latecomer countries can achieve technological catch-up has long been a key concern across various sectors of society. Against the backdrop of the deepening division of labor in global innovation and the increasingly networked nature of innovation activities, proactively integrating into the global innovation network (GIN) has become an important pathway for latecomer firms in open economies to break through technological bottlenecks, enhance innovation capabilities, and achieve leapfrog catch-up. Given this historical context, systematically exploring how Chinese enterprises can leverage the GIN to facilitate technological catch-up holds substantial theoretical and practical significance. Enterprises entering international markets often face high entry costs and resource constraints; as a result, not all firms can participate directly in global innovation activities or benefit from them. In this context, for the vast number of domestic firms unable to directly engage with the GIN, effectively acquiring advanced international knowledge and continuously improving their innovation capabilities are critical to their survival, development, and competitiveness. Currently, the domestic production system is becoming increasingly complex with a deepening division of labor, and supply chain networks have emerged as important carriers of knowledge diffusion. This leads to the core question of this paper: Can "star firms" that pioneer integration into the GIN propel local firms toward technological catch-up through supply chain linkages? What are the underlying mechanisms of this effect? Drawing on a dataset of 160 million patent records and a longitudinal complex network analysis of the GIN from 2009 to 2023, this study empirically investigates the impact of customer integration into the GIN on suppliers' technological catch-up, using data from Chinese A-share listed firms. The results show that customers' integration into the GIN significantly promotes their suppliers' technological catch-up. Mechanism analysis indicates that knowledge spillover effects, improved information accessibility, and enhanced supply chain stability play important roles in this process. Heterogeneity analysis reveals that this promoting effect is more pronounced in firms with high dynamic capabilities, lower information asymmetry, and higher supply chain concentration. The contributions of this study are mainly reflected in the following aspects: First, by taking the perspective of the GIN, it extends the research boundary of technological catch-up by latecomer firms in open economies. Second, from a supply chain spillover perspective, it reveals the mechanism through which customers' integration into the GIN promotes suppliers' technological catch-up, offering rich theoretical implications. Third, using massive global patent data, it measures, at a fine-grained level, the degree of firms' integration into the GIN and their technological catch-up performance, providing a new reference for related empirical research. The policy implications of this study are as follows: First, policymakers should accelerate enterprise integration into the GIN by encouraging local firms to establish strategic cooperation with foreign enterprises possessing core technologies, thereby enhancing their capacity to access and leverage global innovation resources. Second, they should promote synergistic development between local supply chain networks and the GIN by leveraging the unified national market to break down administrative barriers and industry monopolies and by deploying digital technologies to remove information barriers and facilitate knowledge diffusion; this enables enterprises to integrate indirectly into the global innovation system through local supply chains, thereby helping more local firms achieve technological catch-up. Third, they should adopt firm-specific policies and provide precise guidance tailored to enterprise characteristics. This includes encouraging firms to systematically develop dynamic capabilities to convert open resources in the GIN into tangible innovation outcomes, accelerating digital infrastructure construction to break down information barriers across supply chain links, and guiding highly concentrated enterprises to prevent external risks from over-reliance on core partners and to deepen technological cooperation, which transforms stable supply chain relationships into sustained momentum for technological catch-up.

The rapid iteration and full-scenario penetration of artificial intelligence (AI) have made endogenous value misalignment a critical global governance challenge. While major economies have adopted risk-based classification frameworks to balance innovation and risk control, China's existing governance systems primarily focus on physical safety and personal rights violations, failing to address the concealment, transmissibility and systemic impact of value misalignment risks. This structural flaw has produced a persistent regulatory paradox: high-risk systems remain under-regulated while low-risk applications face disproportionate compliance burdens, with legal frameworks and technical mechanisms operating in mutual isolation. This study aims to construct a precise AI governance framework tailored for value alignment, using risk classification as the core analytical lens and dual-dimensional legal-technical regulation as the implementation path. By employing normative analysis, comparative study and case analysis, the study examines regulatory practices in the EU and the US to identify transferable insights, and draws on authoritative data from the China Academy of Information and Communications Technology (CAICT) 2024 AI Governance Blue Book and the Cyberspace Administration of China (CAC) 2025 "Qinglang" Special Action Report to analyze compliance capacity gaps between leading enterprises and SMEs, as well as limitations of current regulatory mechanisms. The study first theoretically justifies the inherent coupling between risk classification and value alignment governance, demonstrating that a proportionality-based tiered model is the fundamental solution to current regulatory imbalances. It then identifies core obstacles: at the legal level, the absence of a unified national risk classification framework, fragmented sectoral regulations and homogeneous compliance requirements; at the technical level, the lack of standardized tiered specifications, uneven enterprise capabilities and insufficient full-lifecycle monitoring. The root cause is the structural exclusion of value misalignment risk from core evaluation dimensions in existing AI risk classification systems. To address this gap, the study develops a novel four-level AI risk classification framework for value alignment, based on a dual-dimensional indicator system measuring both the harm and probability of value misalignment. The framework defines clear criteria, typical scenarios and core requirements for each risk level, ranging from legally prohibited unacceptable risks to industry self-regulated low risks. Three supporting mechanisms of dominant risk determination, overlapping obligation application and dynamic adjustment with flexible exemptions ensure the operability of the system. Building on this framework, the study designs a tiered dual-dimensional governance system. For legal regulation, it establishes gradient compliance obligations: rigid prohibitions and full-chain accountability for unacceptable risks, full-lifecycle strict regulation and mandatory assessment for high-risk systems, targeted behavioral regulation and transparency obligations for medium-risk systems, and industry self-regulation with exemptions for low-risk systems. For technical regulation, it proposes differentiated full-lifecycle control measures, from closed physical isolation for unacceptable risks to voluntary compliance guidelines for low-risk applications. Finally, four synergistic mechanisms facilitate bidirectional interaction between legal and technical governance: standard coordination, information sharing, responsibility matching and remedy integration. The analysis yields three conclusions. First, a value alignment-oriented risk classification system is a prerequisite for transcending the "one-size-fits-all" approach and achieving regulatory precision. Second, the bidirectional co-construction of legal and technical regulation serves as the central mechanism for realizing AI value alignment, with legal norms defining operational boundaries and technical measures ensuring effective implementation. Third, adhering to proportionality in tiered governance is essential for balancing AI innovation and risk prevention. This study advances three key contributions. First, it innovatively incorporates value misalignment as the core evaluation dimension of AI risk classification, bridging the gap between general risk frameworks and value alignment objectives. Second, it establishes a differentiated dual-dimensional regulatory system anchored in risk classification, providing a systematic solution to current governance imbalances. Third, it clarifies the synergistic logic between legal and technical regulation and constructs four supporting mechanisms, forming a complete implementable governance framework. Limitations include the lack of in-depth industry-specific empirical research and quantitative validation of classification indicators, which will be addressed in future work.

Manufacturing is the core sector of the national economic system and a key indicator of a country's comprehensive competitiveness. Since the reform and opening-up, China's manufacturing industry has made remarkable progress, yet the issue of being "large but not strong" persists. Its long-formed growth path is notably extensive, with prominent problems including low energy efficiency and a large surplus of low-end production capacity. Innovation is undoubtedly the core driver of transformation, and green innovation serves as a crucial engine for the sector's green transition. Helping Chinese manufacturing enterprises build a sustainable green ecosystem and advance high-quality green innovation has become a critical path for safeguarding the security of China's manufacturing industry and achieving the carbon peaking and carbon neutrality goals. To facilitate corporate green transformation, the government has launched a series of credit support policies at the fiscal level, guiding banks and other financial institutions to optimize credit structures and provide financial guarantees for enterprises' green transition. As corporate credit utilization links financial resource allocation and innovation, and Short-Term Debt for Long-Term Use is a typical issue in Chinese enterprises' investment and financing, it is necessary to systematically examine how credit utilization influences green innovation quality. This study takes China's A-share listed manufacturing enterprises as the research object, with the sample period ranging from 2010 to 2021. The reason why the cutoff year is set at 2021 is that the indicator measuring green innovation quality adopted in this study is derived from the citation counts of enterprises' invention patents by other entities, and the citation information in this database is updated to 2024. Therefore, on the premise of ensuring a three-year observation window, the research sample is accordingly intercepted up to 2021. The main research procedures are as follows: first, constructing a double machine learning causal inference model to empirically test the impact of Short-Term Debt for Long-Term Use on the green innovation quality of manufacturing enterprises; second, focusing on the mechanism of Short-Term Debt for Long-Term Use affecting the green innovation quality of manufacturing enterprises, investigating the mediating effects of ESG performance, financing constraints and human capital structure, as well as the moderating effect of industry competition; finally, conducting heterogeneity analysis based on differences such as the city tier where different enterprises are located and enterprise attributes to put forward targeted policy recommendations. The study finds that Short-Term Debt for Long-Term Use significantly inhibits the green innovation quality of manufacturing enterprises, and this conclusion still holds after a series of robustness tests. Mechanism analysis shows that Short-Term Debt for Long-Term Use inhibits the green innovation quality of manufacturing enterprises by weakening corporate ESG performance, exacerbating financing constraints and reducing the level of human capital structure, while the degree of industry competition strengthens its inhibitory effect on green innovation quality. Heterogeneity analysis shows that the inhibitory effect of Short-Term Debt for Long-Term Use exerts a significant inhibitory effect on the green innovation quality of manufacturing enterprises in non-state-owned, non-heavily polluting and high-tier cities. Its inhibitory effect on manufacturing enterprises in heavily polluting cities and general-tier cities is relatively weak, while no significant impact is observed on state-owned manufacturing enterprises. This study helps deepen the understanding of the effects of financial resource allocation, expand the existing research perspectives on green innovation, and clarify the mechanism of corporate green innovation behavior. It proposes that, first, government should optimize the financing supply structure by increasing the share of medium- and long-term credit in manufacturing firms, while improving bank assessment mechanisms to mitigate the over-reliance on short-term debt. Second, ESG performance should be explicitly linked to credit pricing and quotas, complemented by green tax incentives and talent subsidies to support green transitions. Third, differentiated policies should target non-state-owned, non-heavy-polluting, and high-tier city firms to prevent low-quality green innovation, by establishing quality-oriented evaluation mechanisms tied to financing access.

As large language models shift from content generation to task execution, a new product form has emerged in mobile terminals and personal computing environments: the system-level AI agent. Unlike plug-in assistants embedded in a single application, system-level agents operate at the operating-system layer. They can read screen content, identify interface states, invoke tools, and perform cross-application actions through simulated clicking, typing, and task chaining. This transition moves human-computer interaction from an application-centered mode to a task-centered mode. It also restructures the way data are accessed, circulated, and acted upon. Against this background, data security risks are no longer confined to one-off collection or isolated leakage. They become chain-based, cumulative, and processual, extending across data acquisition, data circulation, and task execution. The study is motivated by both industrial practice and regulatory signals. Products such as Doubao Mobile Assistant have demonstrated cross-application execution in searching, comparison shopping, ordering, and message coordination. OpenClaw has further shown how persistent memory, browser automation, file management, and script execution can be incorporated into open ecosystems. At the same time, regulators in China, including the MIIT threat and vulnerability information sharing platform (NVDB), CNCERT, and industry associations in the financial sector, have issued risk warnings concerning excessive privileges, weak default security configurations, prompt injection, malicious plugins, abnormal account control, and execution errors. These developments indicate that the problem has moved beyond a technical experiment and become a concrete governance issue. The research design is anchored in a lifecycle-based analytical framework. Drawing on the categories of the Data Security Law, the study reconstructs system-level AI agent data processing into three linked layers: acquisition, circulation, and execution. On this basis, the article combines doctrinal analysis with governance-oriented institutional analysis. It examines how system-level permissions, end-cloud collaboration, tool invocation, and continuous task execution reshape the boundaries of data collection and the structure of responsibility. It also reviews existing scholarship from three strands—technical security studies, personal-information and data-compliance research, and platform-governance studies—to show that current discussions remain fragmented and do not fully capture the integrated risk structure of system-level AI agents. The study reaches three main findings. First, system-level AI agents create a full-chain risk structure. At the acquisition layer, full-domain sensing and cross-application access turn local exposure into overall exposure and make over-collection and inadvertent collection more likely. At the circulation layer, end-cloud collaboration, plugin calls, and context retention generate hidden circulation risks, including re-identification after "desensitized" upload, contextual leakage, and ecological spillover across platforms. At the execution layer, automated agency amplifies the risks of unauthorized actions, cumulative deviations in multi-step task chains, and black-box loss of user correction capacity. Second, existing governance mechanisms are misaligned at three levels. Technically, the system is often operable but insufficiently visible, controllable, and verifiable. Normatively, responsibility attribution, the minimum-necessity principle, and informed consent rules are difficult to apply in multi-actor, cross-application, and continuous-execution environments. Economically, high compliance costs, strong incentives for data expansion, and the externalization of losses weaken firms' incentives to invest in sustained safety governance. Third, an effective response requires a three-tier governance framework rather than isolated fixes. The main contribution of this study lies in proposing a governance structure based on the sequence of rule-based boundary setting, technical embedding of constraints, and implementation support. On the rule side, the study argues for clarifying the boundaries of high-risk data, high-risk permissions, and high-risk tasks through the adjustment of the minimum-necessity principle, the optimization of informed consent, and the reconstruction of responsibility attribution. On the technical side, it proposes embedding those boundaries into system operation through visibility, controllability, and verifiability mechanisms, including layered tracking, dynamic markings, graded authorization, anomaly interruption, deletion proofs, and auditable records. On the implementation side, it recommends graded market access, pre-launch assessment, responsibility internalization, and risk-sharing mechanisms so that governance is not left to voluntary optimization. In this way, this study moves beyond traditional app-based or static compliance models and provides a more targeted framework for balancing operational efficiency with data security in the age of system-level AI agents.

The deep integration of new-generation information technologies with the manufacturing industry, exemplified by industrial Internet platforms (IIPs), has become an important force driving industrial transformation and upgrading. Although leading enterprises have achieved efficiency gains through digital transformation, many small- and medium-sized manufacturing enterprises (SMEs) still struggle to translate IIP adoption into measurable improvements in production efficiency. This raises an important question: under what configurations of conditions can resource-constrained SMEs leverage IIPs to improve production efficiency? Answering this question is essential for promoting the high-quality development of the manufacturing sector. Existing studies have predominantly focused on large incumbents or IIP technical architecture, neglecting heterogeneous outcomes among SMEs. IIP adoption is a complex strategic process shaped by technological, organizational, and environmental conditions. Moving beyond linear “net-effect” logic, this study adopts a configurational perspective to identify multiple equifinal pathways through which IIPs improve SME production efficiency, and examines how platform technology, organizational characteristics, and external environment interact to generate high efficiency. Grounded in the technology – organization – environment (TOE) framework, this study constructs an analytical model with three dimensions. Platform technology is represented by IIP capability. User organization is reflected in SMEs’ profitability, resource allocation capability, and managerial digital orientation. Platform environment is characterized by the regional industrial Internet ecosystem and market competition intensity. To test the model, this study employs fuzzy-set qualitative comparative analysis (fsQCA). The sample consists of 477 Chinese SMEs that have adopted IIPs, identified from the “2024 Top 100 Industrial Internet Platforms” and the “2024 Top 50 Featured and Specialized Industrial Internet Platforms.” Data on antecedent conditions were collected for 2022 from the official websites of the platforms, corporate annual reports, and the CSMAR database, with expert scoring used to evaluate platform capability. The outcome variable, production efficiency in 2023, was measured using the SBM-DEA model based on an input – output indicator system. The one-period lag between antecedents and the outcome helps establish temporal precedence. The results reveal four configurations leading to high production efficiency, and these are classified into three archetypal paths. First, the platform resource-deepening pathway, represented by Configurations S1a and S1b, emphasizes the deep coupling between strong platform capability and strong firm-level resource allocation capability. It reflects an internally driven growth model in which SMEs use IIPs to optimize existing resources. In this pathway, a strong external ecosystem is not indispensable; the strategic core lies in the joint effect of platform capability and internal resource allocation capability. Second, the platform ecosystem inclusive pathway represented by Configuration S2, reflects a compensatory mechanism. Firms with relatively weak resource allocation capability can still achieve high production efficiency by embedding themselves in a well-developed IIP ecosystem. By accessing platform-based data, algorithms, industrial collaboration, and resource spillovers, such firms compensate for internal capability deficiencies. Third, the market pressure-driven pathway, represented by Configuration S3, indicates that intense market competition can serve as a key external trigger. SMEs with strong profitability and resource allocation capability can respond rapidly to competitive pressure and optimize internal processes. In this configuration, high platform capability is not necessary; efficiency improvement is mainly driven by organizational agility and financial resilience under competitive pressure. The analysis of non-high production efficiency configurations further corroborates these findings by identifying two constraints: strategic capability imbalance and resource poverty. This study makes three contributions. First, it shifts the focus from large enterprises to SMEs, enriching contextual understanding of IIP-empowered transformation. Second, it reveals the equifinal, configurational nature of IIP empowerment, demonstrating that no universally optimal pathway exists. Third, it highlights the active role of user firms, moving beyond the technology-centric view emphasizing platform functions alone. The findings also offer practical implications. Policymakers should shift from encouraging platform access to supporting effective platform use through differentiated policies, including data governance, model sharing, service vouchers, cloud credits, targeted subsidies, and talent incentives. SME managers should adopt a configurational mindset, identify their resource endowments and external conditions, and strategically combine internal capabilities with platform resources and environmental opportunities.

Innovation is a central driver of high-quality economic development and a key source of firms' long-term competitive advantage. Yet corporate innovation is typically characterized by high uncertainty, long investment cycles, weak collateralizability, and severe information asymmetry, which make innovative firms highly dependent on stable external financing. In China's bank-dominated financial system, traditional credit allocation has long faced problems such as fragmented information, repeated credit granting, and excessive lending among multiple banks. These problems may distort financial resource allocation and weaken the ability of credit markets to support long-term innovation. To address these issues, the China Banking and Insurance Regulatory Commission (CBIRC) introduced a pilot joint credit system in 2018, featuring a lead-bank mechanism, joint credit committees, unified credit limits, and financing ledgers. Using Chinese A-share listed firms from 2011 to 2024, this paper treats the implementation of the Joint Credit Granting System as a quasi-natural experiment and employs a propensity score matching difference-in-differences approach to examine its causal effect on corporate innovation. Innovation input is measured by R&D intensity, while innovation output is measured by patent applications. The results show that the Joint Credit Granting System significantly increases both R&D investment and patent output. In terms of economic magnitude, the policy raises R&D intensity by approximately 4.1% relative to the sample mean and increases expected patent output by about 14.0%. These findings remain robust after parallel trend tests, placebo tests based on 500 random assignments of pseudo-treated firms, alternative matching methods, alternative measures of key variables, different estimation models, lagged-control regressions, and additional controls for industry-specific trends and regional pilot effects. The evidence suggests that the innovation-promoting effect is unlikely to be driven by sample selection bias, omitted variables, model specification, or unobservable time trends. Mechanism analyses reveal three complementary channels. First, the system generates an information-sharing effect. By coordinating information among creditor banks, it reduces interbank information fragmentation, lowers information search costs, alleviates financing constraints, and improves firms' external information environment, thereby creating more stable credit expectations for long-term R&D projects. Second, it produces an innovation governance effect. Joint credit committees strengthen coordinated creditor monitoring, reduce managerial opportunism and agency conflicts, and restrain the inefficient use or diversion of innovation resources, thereby improving the allocation efficiency of financial resources. Third, it creates a collaborative innovation effect. Through reputational endorsement by multiple financial institutions, the system attracts governments, universities, research institutes, suppliers, and other external participants into firms' innovation networks, promoting joint patent applications and industry-university-research collaboration. Heterogeneity analyses show that the innovation-enhancing effect is stronger among manufacturing firms and firms located in regions with stronger intellectual property protection. Economic consequence tests further reveal that innovation induced by the system significantly improves firms' basic earnings per share. This paper contributes to the literature in several ways. First, it shifts the analysis of finance and corporate innovation from single bank-firm relationships to multi-bank coordinated credit governance, enriching financial intermediation theory from a collective governance perspective. Second, it expands research on the economic consequences of the Joint Credit Granting System by showing that this reform is not only a debt-discipline or risk-prevention mechanism, but also an innovation-enhancing institutional arrangement. Third, it opens the black box of how financial institutional reform affects firms' innovation decisions by identifying information sharing, governance monitoring, and collaborative innovation as interrelated mechanisms. Fourth, by linking joint credit granting, corporate innovation, and earnings performance, this paper provides micro-level evidence that supply-side financial reform can generate real economic value when credit governance is aligned with firms' long-term technological upgrading. These findings deepen our understanding of the institutional foundations of innovation financing and the role of coordinated credit governance in improving financial resource allocation.

Artificial intelligence(AI) has become a strategic technology shaping industrial transformation, national competitiveness, and global governance. However, rapid AI-related patent growth does not necessarily reflect improved innovation capability. Corporate AI innovation differs from conventional innovation in its strong dependence on high-quality data, interdisciplinary scientific and technological talent, computing capacity, and application scenarios. This study examines whether the policy for designating National Technological Innovation Demonstration Enterprises(NTIDEs) can promote both the quantity and quality of corporate AI innovation and generate external spillovers beyond designated firms. Using an unbalanced panel of Chinese A-share listed companies on the Shanghai and Shenzhen stock exchanges from 2007 to 2024, the study treats NTIDE designation as a quasi-natural experiment and estimates its effects with a staggered difference-in-differences model with firm and year fixed effects. AI innovation quantity is measured by the logarithm of AI-related invention patent applications identified through patent-abstract keyword matching, and quality by patent knowledge breadth. The analysis further examines two mechanisms of data utilization and talent concentration, and evaluates spillovers across industries, supply chains, and geographic space. Multiple identification and robustness procedures are employed, including instrumental-variable estimation, propensity-score-matching difference-in-differences, the Heckman two-stage method, parallel-trend and placebo tests, alternative measures, exclusion of potentially confounding AI pilot policies, and estimators robust to heterogeneous treatment effects. The results show that NTIDE designation significantly increases both the quantity and quality of corporate AI innovation, producing a clear dual improvement in innovation scale and technological quality. The findings remain stable across alternative specifications and endogeneity tests, indicating that the policy stimulates more than short-term patent expansion. Mechanism analysis shows that designation improves firms' data utilization and strengthens their capacity to attract and concentrate scientific and technological talent. The policy therefore operates through the factor foundations critical to AI innovation rather than merely through general R&D increases or symbolic recognition. The spillover analysis reveals substantial differences across transmission channels. At the industry level, the presence of a designated enterprise has no significant effect on peer firms' AI patent counts but significantly improves their innovation quality, suggesting that industry spillovers operate through technological demonstration, organizational learning, and competitive pressure rather than simple imitation or patent-volume expansion. At the supply-chain level, spillovers are strongly directional: when a designated enterprise acts as a supplier, its AI-enabled products and solutions significantly promote both the quantity and quality of AI innovation among customer firms, whereas no significant spillover to suppliers is observed when it acts as a customer. AI innovation therefore diffuses from the technology-supply side to the application side. At the regional level, geographic proximity alone does not produce significant spillovers, implying that AI knowledge diffusion depends more on substantive technological and economic linkages than on physical distance. Further analysis identifies important boundary conditions. The positive effects are concentrated among firms with stronger prior AI investment foundations and in high-technology industries, highlighting the importance of absorptive capacity and industrial compatibility. The effects are also more pronounced in regions with relatively weak computing infrastructure, suggesting that designation can partially compensate for local constraints through policy attention and resource access. In addition, the policy significantly enhances the continuity of corporate AI innovation, demonstrating sustained rather than one-off effects. This study contributes to the literature in three respects. First, it evaluates an innovation-oriented designation policy across AI innovation quantity, quality, and continuity. Second, it identifies data utilization and talent concentration as AI-specific mechanisms. Third, it clarifies the networked and directional nature of policy spillovers while demonstrating the limitations of geographically based diffusion. The findings suggest that future policy design should strengthen dynamic evaluation, emphasize innovation quality and sustained output, support enterprise data governance and high-level technical talent, and amplify demonstration effects through industry platforms and supply-chain collaboration.