数据要素是人工智能时代的新型生产要素,但鲜有研究系统解析如何加快推动数据跨境流动,释放数据要素乘数效应。针对数据跨境流动面临的场景稀缺和制度逻辑冲突等瓶颈问题,基于场景驱动创新和制度逻辑理论,构建“主体—场景—制度”三位一体理论框架,探讨场景如何驱动数据要素跨境流动。研究发现:制度逻辑差异是制约数据跨境流动和融通交易的核心因素,场景驱动的“需求牵引—数据增值—循环反哺”机制能够破解制度摩擦,加快数据跨境流动并促进高效交易,从而释放数据要素乘数效应。进一步结合医疗数据跨境流动典型案例,分析场景驱动数据跨境流动新机制在优化资源配置、促进国际合作方面发挥的关键作用。据此,提出培育跨境场景生态、完善复合监管体系、利用可信数据空间破解信任难题等政策建议,为我国加快数据要素流动、提升全球数字经济竞争力提供理论支撑与实践启示。

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