深化公共数据开放、释放其要素价值、赋能数字经济与实体经济深度融合，是加快培育新质生产力、实现高质量发展的关键路径。构建“制度—技术—市场”整合性理论框架，系统揭示区域公共数据开放过程中的制度环境构建、技术创新赋能和数据要素市场塑造功能及其驱动数实融合的内在机理。基于2010—2023年中国285个地级市面板数据，利用专利共分类法构建区域数实融合水平测度指标，运用双重机器学习模型进行实证检验。研究发现：①公共数据开放能显著促进城市数实融合水平提升，并通过改善区域制度环境、提升数字技术创新和数据要素市场化水平间接驱动数实融合。②进一步分析发现，这种驱动效应存在显著异质性，在公共数据开放质量高的城市以及高活力与低活力城市显著，而在公共数据开放质量较低及中等活力城市不显著。③空间效应分析表明，公共数据开放有助于缩小省内中心城市与外围城市的数实融合差距，在“胡焕庸线”东侧，中心地区对邻接城市表现出显著虹吸效应，在“胡焕庸线”西侧则没有显著影响。据此，提出深化公共数据开放、促进数实融合发展的政策建议。

企业数字化转型已成为缓解供应链依赖、提升产业链韧性的关键途径。全球供应链不确定性持续加剧背景下，如何有效缓解企业“供应链依赖症”，构建更具韧性和协同能力的供应链体系，成为亟待解决的重要课题。基于2011—2023年沪深A股上市企业数据，考察焦点企业数字化转型对供应链上下游企业依赖的溢出效应及作用机制。结果表明，焦点企业数字化转型显著加剧上游供应商对供应链的依赖，同时有效缓解下游客户对供应链的依赖，呈现出焦点企业对上下游企业供应链依赖的非对称影响。机制分析发现，焦点企业依据其在供应链中所处的供需位置差异，通过数字化转型重塑企业间权力结构，进而引致上下游企业非对称的依赖行为。异质性检验结果表明，焦点企业数字化转型会加剧上游供应商对采购环节的依赖，同时缓解下游客户对销售环节的依赖。当焦点企业与上游供应商数字化协同水平较低且地理距离较近时，上游依赖显著增强；当焦点企业与下游客户数字化协同水平较高且位于经济发达地区时，下游依赖显著减弱。值得注意的是，数字化转型对上游依赖的强化主要发生于非制造业，对下游依赖的抑制则集中于制造业。研究为企业通过数字化转型优化供需关系、提升产业链供应链韧性提供了理论依据与经验证据。

“达尔文死海”作为科技成果商业化进程中的系统性瓶颈，其跨越过程高度依赖耐心资本与科技创新深度耦合及动态适配。首先，在梳理既有文献的基础上，对耐心资本的核心内涵、多维特征及其在消解传统风险投资短视主义方面的独特价值进行理论界定与分析；其次，构建资本属性—技术周期、风险收益—创新不确定性、要素协同—创新生态、制度支撑—宏观环境四维动态适配机制，深入剖析耐心资本驱动科技创新跨越“达尔文死海”的内在机制与价值创造逻辑；最后，立足理论推演与中国实践，从顶层制度创新、多层次市场培育、精准政策优化、创新生态重构与高水平国际协同5个维度，提出相关实现路径。研究旨在为突破当前面临的核心技术“卡脖子”难题，完善科技金融制度体系提供理论依据与实践参考。

为提升专利申请在早期阶段的创新性识别与评估效率，服务于科研管理与技术管理决策支持需求，构建了一种基于审查员引用驱动的多任务学习模型。该模型将审查员引用类型视为制度化专家评审信号，融合SciBERT语义嵌入与关系图卷积神经网络，对专利文本语义与引文结构进行联合建模，并引入基于引文类型的动态调整策略，以揭示不同审查信号对创新性判断的差异化影响。实验结果表明，该模型在引文关系链路预测与创新性预测两项任务上均显著优于BERT和DeepSeek-R1等基线模型，在专利正式授权前能够有效识别潜在的高创新技术。对欧洲专利局生物医药领域审查数据进行实证分析，发现融合引文结构与语义特征的多任务模型不仅能提升早期创新识别的准确性，也能增强预测结果与审查逻辑之间的一致性与可解释性。该方法为专利布局、技术筛选与创新资源配置提供了辅助支持，可进一步拓展至多语言、多法域专利数据，以检验其在不同审查制度下的稳健性。

人工智能应用存在潜在社会风险，对其深刻认识和治理成为广泛关注的议题。尽管学界开展了积极探索，但由于人工智能这一新兴技术领域的特殊性，其系统研究存在如下困境：本体论层面对风险的认识存在割裂，认识论层面学科壁垒较高且存在范式冲突，方法论层面定性与定量分析难以整合。基于知识社会学与技术哲学双重视角，整合社会技术系统理论，构建关系论风险认识范式，揭示人工智能应用社会风险本质上是“具身—嵌入—情境”三维度动态耦合的结果，提出人工智能应用优化逻辑，为破解学科割裂与治理失灵提供理论工具。结合智能驾驶领域典型案例，从本体论重构、认识论转型、方法论创新、制度重建等方面对其作进一步阐释和说明。据此，提出应打破学科壁垒，开展跨学科、跨部门、跨领域人工智能应用社会风险认识研究，建立跨学科知识生产共同体与人工智能应用社会风险治理研究网络，完善相关法律体系和行业标准，构建规范化的社会风险认识体系。

随着新技术快速迭代与应用场景不断涌现，跨界技术整合成为新兴产业企业实施开放式创新的重要手段。这一过程中，兼具数字素养与企业家特质的数字企业家发挥关键作用。结合高阶理论与组织学习理论，构建“数字企业家—关系学习—跨界技术整合”分析框架，基于216家企业数据进行实证分析发现：数字企业家正向影响跨界技术整合；关系学习的3个维度（信息共享、共同理解与特定关系记忆）在数字企业家与跨界技术整合间发挥显著中介作用。研究揭示了数字企业家赋能企业跨界技术整合的作用机制，将观察视角拓展至组织学习层面，为企业有效发挥数字企业家的作用、提升跨界技术整合效果提供了理论依据。

作为我国市场经济的重要主体，家族企业在经济发展中占据重要地位，但独特的治理结构和传承模式使其面临诸多挑战。以家族企业代际传承前多个继承人为切入点，选取2004—2024年中国A股上市家族企业为样本，实证检验多个继承人对家族企业创新投入的影响。研究表明，多个继承人显著抑制家族企业创新投入，机制检验发现，这种影响主要通过加剧融资约束、降低企业风险承担意愿以及削弱管理层激励等路径实现。异质性分析显示，当行业竞争程度较低、儒家文化影响较强、高管家族成员占比较高、存在非法定继承人时，多个继承人对企业创新投入的抑制效应更为显著。进一步研究发现，在多个继承人代际传承后的家族企业中，传承后的继承人会提升创新投入水平与创新投入持续性，但抑制创新效率提升。研究厘清了“多个继承人”这一特殊治理情境对家族企业创新投入的影响机制，深化了对代际传承过程中治理冲突经济后果的理论理解，为优化家族企业治理结构提供经验证据。

加强团队学术合作是推进有组织科研的重要内容，而科研工作者自恋特质是影响团队学术合作行为的重要个体因素。基于31所知名高校650位高级职称科研人员数据，从个体与团队两个层面考察团队情境下科研工作者自恋特质对其学术合作行为的影响，并实证检验团队自恋水平差异所发挥的调节作用。研究表明，科研工作者自恋特质对学术合作行为具有显著正向影响，团队自恋水平差异对学术合作行为具有显著负向影响，并在科研工作者自恋特质与学术合作行为间发挥负向调节作用；科研工作者在团队中的相对自恋水平对其学术合作行为具有显著正向影响，且团队自恋氛围和团队规模发挥跨层调节作用。研究丰富了自恋特质理论，将自恋特质理论拓展至科研管理领域，为学术团队管理和科研工作者学术发展带来启示。

数据在驱动大模型快速发展的同时也带来了严峻的治理挑战，面向大模型的数据治理需求日益迫切。分析责任式创新范式兴起与大模型数据治理面临的主要困境，阐释基于责任式创新范式的大模型数据治理内涵及特征。识别大模型数据治理中的数据质量、数据隐私和数据伦理三大核心议题，基于责任式创新范式将治理范畴拓展为技术、经济、道德及社会四大维度，在此基础上以治理议题与治理维度为纵横两轴，采用矩阵式思维方式，从理论上廓清大模型数据治理的12种实现进路，由此构建基于责任式创新范式的大模型数据治理分析框架。进一步对大模型数据质量、数据隐私、数据伦理实现路径进行深入探讨，并为每条路径提供具体操作指引。研究拓展了数据治理内涵和研究情景，为大模型数据治理提供适配的范式依据，助推大模型数据治理理论发展与效能提升，为大模型健康有序、安全可控发展提供启示。

研发是企业实现突破式创新的重要途径，民营高技术企业作为突破“卡脖子”技术的关键主体，其研发模式与情境要素的适配机制亟待破解。引入权变理论，利用2017-2023年民营高技术上市企业数据进行模糊集定性比较分析。研究发现：①研发模式并不是实现突破式创新的必要条件，民营高技术企业的研发模式需与内外部情境要素适配才能产生预期成效。②在研发模式与内外情境要素适配下，有技术深耕型自主创新、竞争驱动型开放式创新、家族治理型内研创新和制度赋能型双轨创新4种实现高水平突破式创新的组态路径。其中，在自主研发过程中企业知识基础是核心条件，而在合作研发中企业的市场竞争力是核心条件；当企业为家族治理模式时，其更倾向于通过自主研发实现突破式创新，而在良好的制度环境下，企业倾向通过自主研发和合作研发相结合的研发方式实现突破式创新；③导致民营高技术企业产生非高突破式创新与高突破式创新的组态并不呈现完全对称关系，无论采用哪种研发模式，缺乏知识基础和市场制度保障是导致非高突破式创新的主要原因。研究拓展了企业突破式创新前置因素的理论分析，为民营高技术企业实现突破式创新提供了启示。

突破传统行业与社会网络的同群关系界定框架，以2001—2024年A股上市公司技术并购事件为样本，基于创新关联形成的独特同群关系，探究企业技术并购同群效应及其作用机制与经济后果。研究发现，上市公司技术并购决策受创新关联同群企业行为的影响，其核心传导机制为竞争性模仿与信息获取性模仿，且企业筛选模仿对象的决策逻辑遵循逻辑模仿律与先内后外律；经济后果层面，技术并购同群效应与企业自发并购行为对创新绩效的影响呈现阶段性差异，短期内，技术并购同群效应主导创新绩效增长，长期看，企业自发技术并购成为驱动创新绩效持续提升的关键因素。研究拓展了同群效应理论在企业技术并购决策领域的应用边界，为企业优化技术并购战略提供了重要理论支撑与实践启示。

融通创新已成为突破产业链关键核心技术创新瓶颈的重要路径。作为产业链核心主体，链主企业不仅需要强化自身在融通创新中的治理职能，更需要构建支撑产业链关键核心技术创新的多维能力基础。然而，现有研究对链主企业融通创新治理的探讨不足，也未揭示其对关键核心技术创新能力的影响。引入产业链视角，将链主企业融通创新治理策略划分为链路融合治理与主体融合治理，揭示其关键核心技术创新能力包含持续高强度研发投入能力、共性需求驱动基础研究能力、产业数智化转型支撑能力和高影响力创新产出能力4个维度，并以此为基础，系统分析两类治理策略对关键核心技术创新能力的影响机制与边界条件。结果表明：融通创新治理下链路融合治理与主体融合治理均显著提升链主企业关键核心技术创新能力；政府治理情境对上述影响存在差异化调节效应，其中，政府创新补助削弱链路融合治理的促进效应，增强主体融合治理的促进效应；知识产权保护显著增强链路融合治理的作用，而对主体融合治理无显著影响。在链主企业关键核心技术创新能力提升过程中，两类融通创新治理策略分别发挥基于政府创新补助的单门槛效应和双门槛效应。研究对深化链主企业治理职能，加快提升我国企业关键核心技术创新能力具有重要意义，也为政府优化治理情境以实现与企业治理高效协同提供有益参考。

近年来，中国跨国企业国际化情境逐渐由来源国劣势转为来源国优势，但鲜有研究明确来源国优势情境下中国跨国企业面临的合法性压力，提出相应合法化路径与策略。以合法性压力判断为逻辑起点，通过多案例研究发现：来源国优势情境下，中国跨国企业面临的合法性压力有所降低，但不同细分情境下，合法性压力的表现及判断存在差异。根据合法性压力强度的高低，中国跨国企业可采用传统合法性压力克服路径及新情境下来源国优势利用路径。前者强调通过颠覆性改变既往国际化策略以获取合法性，后者则主张通过适应性利用既有来源国优势来获取合法性。其中，来源国优势利用路径包括合法性转换与合法性溢出两种方式。此外，根据合法性压力类型的不同，中国跨国企业可采用身份选择、标准范式、行业行动策略等方式获取规制、规范和认知合法性。围绕中国跨国企业国际化的新情境，提出合法性获取的新路径和合法化策略的新框架，可为中国跨国企业在国际市场中获取合法性提供参考。

耐心资本凭借跨周期投资优势与风险共担机制，为关键共性技术创新注入稳定动能。基于2010-2023年制造业A股上市公司数据，从稳定型股权和关系型债权两个维度，实证分析耐心资本对企业关键共性技术创新的影响及深层作用机制。结果表明，耐心资本有效促进企业关键共性技术创新，其中，稳定型股权的促进作用更为明显。进一步分析发现，内部控制质量和融资约束在耐心资本促进关键共性技术创新中起中介作用；市场竞争强度负向调节耐心资本与关键共性技术创新的关系。此外，耐心资本的促进效果在非国企、高技术企业中更显著。研究深化了关键共性技术创新前因变量的相关研究，丰富了耐心资本的理论认知，为系统揭示耐心资本缺乏的微观成因提供了机制证据，具有积极的理论和实践启示意义。

随着制造企业智能化转型的深入，如何高效采纳人工智能技术并协调其对劳动力就业的关系，成为企业及社会关注的议题。利用2023年148家A股上市制造企业数据，运用fsQCA和回归分析混合方法，探究组态视角下制造企业TOE（技术—组织—环境）配置模式驱动AI采纳强度的多元路径及其对劳动力需求规模的影响。研究发现：①技术与环境薄弱的组织双核驱动型、非高研发投入下政策推动的技术—组织协同创新型、技术投入与风险承担主导型3种TOE配置模式有助于提升企业AI采纳强度；②高强度AI采纳总体上会抑制企业劳动力需求规模，前两种TOE配置模式会抑制企业劳动力需求规模，第三种配置模式对劳动力需求规模的负向影响效应不显著。研究为制造企业科学选择人工智能采纳路径、优化劳动力管理与就业保障提供指导，同时为平衡人工智能技术应用与就业稳定提供政策参考。

With the deep advancement of the digital economy, the digital transformation of industrial internet platforms under institutional constraints has exhibited structural stagnation. Pharmaceutical commercial platforms, owing to their high dependence on the external institutional environment, are confronted with more complex challenges. Multiple constraints from policies, regulations, industry standards, and social responsibilities have has heightened the complexity of resource integration and business expansion for pharmaceutical platforms while fundamentally transforming their value creation paradigms. Meanwhile, traditional value creation theories, which are rooted in the monopolization of resources and control over single processes, contradict the core nature of platforms (openness, integration, and convergence) and thus fail to guide or explain the evolution of pharmaceutical business platforms. Therefore, it has become an urgent key issue to thoroughly explore how pharmaceutical business platforms achieve value creation through phased open iteration under institutional constraints. This study employs a single-case research method, using Jiuzhoutong as a case to explore the process of open iteration of platform ecology, the evolution path of service strategies, and the mechanisms of value creation under service-dominant logic. The study adopts a grounded theory methodology, systematically conducting open coding to generate 22 constructs from 31 initial concepts. Axial coding was then performed to identify six core categories, including resource integration, service exchange, institutional arrangements, as well as resource, structural, and rule advantages. Selective coding was subsequently utilized to distill service strategy and value creation as the overarching phenomena. Analytical rigor was enhanced through continuous, "data-logic" cyclic iteration, multiple rounds of member checking with executive participants, cross-source comparative verification, and theoretical saturation testing. Findings reveal a three-stage open iteration path: bilateral embedding, network expansion, and boundary permeation. Jiuzhoutong's journey demonstrates this path. Its survival-focused bilateral embedding phase targeted underserved primary healthcare/OTC markets, bypassing monopolies via operational innovations: a "Quick Batch-Quick Distribution" model, precision logistics, and cost control, building a foundational physical network. Capitalizing on liberalization, the network expansion phase involved horizontal mergers creating a vast certified warehousing network and vertical integration into TCM cultivation and B2C/O2O e-commerce. Establishing key functional platforms marked its shift to a service provider. Facing tightening regulations and public health demands, the boundary permeation phase featured cross-industry alliances, "Pharma+Tech+Finance" integration, and solutions like its Prescription Outflow Platform. Crucially, this process exhibited dynamic adaptation, forging asynchronous adaptive advantages that sustained growth under constraint and drove service strategy evolution. Service strategy evolution, governed by co-creation imperatives, links iteration to value creation across three phases: service deepening, service radiation, and service coupling. Service deepening established operational excellence and trust through cost control, guaranteed service accessibility, and consistent rules, ensuring stability. Service radiation diffused proven logistics/cost models nationally, adding advanced resource configuration across the pharmaceutical chain and sophisticated data integration via the "FBBC" mechanism, alongside business process standardization and relationship coordination tools, enabling reliable scalability. Boundary permeation necessitated service coupling-integrating diverse service forms beyond provision, manifested in the "Three New and Two Transformations" strategy( which refers to new products, new retail, new medical services, digitalization, and real estate investment trusts) and embedded governance, creating holistic ecosystem solutions. Value creation advanced progressively: transactional value, relational value, and ecological value. Bilateral Embedding generated transactional value via scale economies. Building resource advantages, structural advantages, and rule advantages established the transactional base. Network Expansion cultivated relational value: enhanced resource advantages from deeper partnerships/data synergy; structural advantages empowering participants; solidified rule advantages as standards became industry benchmarks. Boundary Permeation unlocked Ecological Value-emergent synergy from cross-domain co-creation: resource advantages via cross-industry recombination; structural advantages through distributed, collaborative networks; rule advantages culminating in active industry rule-setting. This delivered significant implicit social value alongside economic returns, including improved pharmaceutical accessibility and reduced health disparities. This study proposes a "Platform Ecosystem Open Iteration-Service Strategy Evolution-Value Creation" framework that extends service-dominant logic by elucidating complex dynamics within institutionally constrained industrial platforms. It fills critical theoretical gaps concerning platform evolution under institutional pressure, the dynamic co-evolution of openness and service strategies, and the integrated co-creation of economic and social value. For platform operators in regulated sectors, the framework yields actionable insights on stage-specific strategizing, synergistic orchestration of resource-structure-rule configurations, and the dual imperative of efficiency and societal welfare.

Innovation is a crucial driver for enterprise development and maintaining competitive advantages. The possession of high-value and scarce heterogeneous resources is key for enterprises to sustain their competitive edge in ongoing market competition. Open innovation helps create value for enterprises by establishing cross-organizational innovation cooperation, acquiring and utilizing complementary resources. Dynamic capabilities emphasize the development and utilization of heterogeneous resources, enabling enterprises to build competitive advantages through the accumulation of core resources. There exists a coupling relationship between these two concepts, where the coordination and rational flow of resource elements help enterprises construct complementary advantages. The integrated circuit design industry, as a strategic, foundational, and leading sector supporting economic and social development, has become the cornerstone for driving innovation in emerging fields such as artificial intelligence, automotive electronics, and the Internet of Things. Do the mechanisms through which innovation openness and dynamic capabilities influence corporate innovation vary across different industrial supporting environments? How do open innovation and dynamic capabilities shape the innovation activities of integrated circuit design enterprises? In what ways do regional industrial configuration and industrial capacity affect the enhancement of corporate innovation capabilities? These questions warrant further in-depth investigation. Using panel data from integrated circuit design enterprises in the Yangtze River Delta region of China from 1997 to 2023, this study employs fixed-effects and system GMM models to estimate the impact mechanisms of innovation openness and dynamic capabilities on enterprise innovation capability, while also examining the moderating effect of industrial supporting capability. The research results indicate that there is an inverted U-shaped relationship between the breadth of enterprise innovation openness and innovation capability, as well as between dynamic capability and innovation capability. The depth of enterprise innovation openness has a positive impact on innovation capability. Currently, the breadth of innovation openness and dynamic capabilities of integrated circuit design enterprises in the Yangtze River Delta region fall significantly short of the optimal levels. For most enterprises in this sector, there is still a need to further expand the breadth of innovation openness to achieve optimal innovation capacity. In addition, industrial supporting capability suppresses the impact of innovation openness depth on innovation capability while promoting the influence of dynamic capabilities on innovation capability. When enterprises exhibit relatively low levels of innovation openness depth and dynamic capabilities, they can leverage the agglomeration effects brought by a high industrial supporting environment, such as cost-effective intermediate products and an abundant labor force, to achieve higher innovation capability. However, for enterprises with high innovation openness depth and dynamic capabilities, greater attention should be paid to the negative impacts of agglomeration effects, such as rising innovation costs and intensified competition. This study expands the theoretical boundaries of research on enterprise dynamic capabilities and innovation capability, providing a new analytical framework for how open innovation enhances the innovation capability of strategic industry enterprises in complex and dynamic environments. It reveals the intrinsic mechanism through which integrated circuit design enterprises improve innovation performance via resource synergy, thereby establishing a new theoretical foundation for future research. However, the research has several limitations: first, the sample is restricted to IC design enterprises in the Yangtze River Delta, limiting its representativeness of China's entire IC industry; second, temporal discontinuity in some indicator data may affect the reliability of empirical analyses on variable relationships; third, complex nonlinear relationships (e.g., between industrial supporting capability and innovation openness) remain underexplored, particularly the unobserved critical point at which innovation depth exhibits an inverted U-shaped relationship. Future research could explore the existence and boundary conditions of this critical point, systematically consider the representativeness and coverage of enterprise samples, and conduct large-sample empirical analyses to compare the mechanisms through which open innovation and dynamic capabilities influence enterprise innovation under different regional agglomeration characteristics and industrial policies. Furthermore, comparative studies on industrial supporting capability and its effects across different regions could be undertaken.

In an era marked by escalating geopolitical tensions, recurrent global disruptions, and rapid technological trans formation, strengthening industrial chain resilience is a strategic imperative. It is crucial not only for advancing high-quali ty economic development and building a modern industrial system, but also for adapting to the reconfiguration of global supply chains and safeguarding national security. China has established a large-scale, competitive, and relatively independ ent modern industrial system, with industrial chains generally stable and resilience gradually improving. However, amid the dual pressures of domestic transformation and external uncertainties, structural vulnerabilities remain, particularly in foundational capabilities and critical technological bottlenecks in sectors including semiconductors, advanced materials, and core software. These weaknesses increasingly expose the fragility of key industrial chains, making resilience enhance ment an urgent strategic priority. The academic community has explored pathways to enhance industrial chain resilience from various perspectives, pri marily focusing on industrial agglomeration, the digital economy and finance, digital-real economy integration, innovation driven policies, and the chain leader system. However, research on the impact of artificial intelligence(AI) on industrial chain resilience remains relatively under-explored and requires further deepening. This gap mainly manifests in two as pects: First, the mechanism pathways through which AI drives resilience need further refinement. While existing studies identify the mediating role of technological innovation, they predominantly emphasize supply-side transmission mecha nisms and largely neglect the mediating role of demand-side factors. Second, AI’s application and development often facil itate cross-regional flows of production factors, potentially generating spatial spillover effects that transcend geographical boundaries, yet such effects are frequently overlooked in current research. On the basis of this theoretical foundation, this study employs panel data from 30 Chinese provincial-level administra tive regions(excluding Hong Kong, Macao, Taiwan, and Xizang) from 2011 to 2023. It takes industrial chain resilience as the dependent variable which is measured through a five-dimensional indicator system(foundation, resistance, recovery, sustainability, leadership) using the entropy method; and the study selects artificial intelligence as the core independent variable that assessed via input-output indicators(infrastructure, capital, talent, industrial application, economic output) . Technological innovation and residential consumption serve as mediating variables, while five indicators including the de gree of openness to the outside world,government participation, population density, environmental regulation intensity, and employment density are employed as control variables. It constructs mediating effect models and a spatial Durbin mod el to examine how artificial intelligence affects local industrial chain resilience through dual supply-demand mechanisms while investigating its spatial spillover effects on neighboring regions. Mediation analysis employs both two-step and three step methods with bootstrap tests for robustness, while spatial analysis uses bidirectional fixed effects and multiple weight matrices, revealing that indirect effects are smaller than direct effects. The main findings are follows: First, AI enhances industrial chain resilience through its data-driven nature, systemic intelligence, and pervasive penetration, strengthening foundational stability, resistance, recovery capacity, sustainability, and leadership across dimensions. Second, as resilience is influenced by both supply and demand where technological pro gress and consumption play crucial roles, AI improves resilience by promoting technological innovation and stimulating household consumption. Third, AI demonstrates significant spatial spillover effects in enhancing industrial chain resili ence, with positive spillovers dominating and direct local effects exceeding indirect cross-regional impacts. This paper makes two primary contributions. Theoretically, it advances the literature by integrating supply-side (technological innovation) and demand-side(household consumption) mechanisms into a unified analytical framework, ad dressing the prevailing bias toward supply-side explanations and offering a more comprehensive understanding of AI’s role in shaping industrial resilience. Methodologically, it enriches the spatial dimension of industrial resilience research by ex amining cross-regional spillovers, revealing geographical interdependence and broadening theoretical implications of AI in regional economic dynamics. In summary, these findings contribute to a more holistic, spatially informed understanding of how artificial intelligence influences industrial chain resilience in a complex, interconnected economy.

The deep integration of artificial intelligence (AI) with the real economy is critically important for cultivating endogenous innovation capabilities within Chinese enterprises, which is a prerequisite for achieving China's national goal of high-level self-reliance and strength in science and technology. This study provides a rigorous empirical evaluation of China's National Artificial Intelligence Innovation and Development Pilot Zone, a major industrial policy initiative. It specifically investigates the policy's dual impact on corporate innovation, meticulously examining its efficacy in promoting substantive technological advancements while also exploring its potential to inadvertently encourage a strategic "innovation bubble". The analysis uncovers the underlying causal mechanisms, heterogeneous responses across different types of firms, and any unintended negative consequences. The findings are intended to offer a comprehensive evidence base for policymakers to refine AI industrial strategies and reduce associated risks. To achieve these objectives, this study employs a robust quasi-experimental research design, leveraging the phased, quasi-random rollout of the pilot zones across China from 2007 to 2023 as a series of exogenous policy shocks. This temporal and geographical variation allows for a credible identification strategy. The policy data is meticulously combined with a detailed longitudinal micro-dataset covering all A-share listed companies in China over the same period. The constructed dataset is highly comprehensive, integrating firm-level financial statements, patent application and grant records from the State Intellectual Property Office, R&D expenditure disclosures, and key regional indicators such as ICT infrastructure and university density. The primary econometric methodology is a multi-period difference-in-differences model, which effectively isolates the causal impact of the pilot zone policy by comparing the evolution of innovation outcomes between treated firms and a carefully selected control group of firms before and after the policy's implementation. To ensure the robustness and credibility of the causal inferences, the study employs a battery of stringent tests. These include parallel pre-trend analyses, placebo tests using fictitious policy announcement dates, and alternative variable constructions to mitigate measurement error concerns. The empirical findings yield several nuanced and critical insights. First, the analysis confirms that the pilot zone policy has led to a statistically significant and economically meaningful increase in the output of high-quality AI-related patents among affected enterprises. This result validates the policy's primary objective of stimulating corporate engagement in cutting-edge AI research and development. Second, the analysis reveals significant heterogeneity in the policy's effects. The innovation-enhancing impact is markedly more substantial for enterprises that initially had lower levels of digital transformation, weaker pre-existing technological endowments, and those located in urban clusters with less developed intelligent infrastructure. This pattern suggests that the policy functions effectively as a form of compensatory support, providing a crucial "catch-up" mechanism for less-advanced firms and helping to reduce regional innovation disparities. A detailed mediation analysis further elucidates the dual channels through which the policy operates: it directly stimulates an increase in internal R&D investment by mitigating financial constraints and signaling strategic importance, and it indirectly fosters deeper and more formalized collaborative ties between industry, universities, and research institutions. This latter channel facilitates critical knowledge spillovers, access to specialized talent, and the shared use of advanced research infrastructure. Furthermore, while the policy demonstrates clear success in boosting measured innovation, a more granular investigation uncovers the emergence of an "innovation bubble" in certain contexts. This phenomenon manifests as strategic corporate behaviors aimed at signaling compliance with policy goals rather than pursuing genuine technological breakthroughs. It includes both "execution-side" bubbles, where firms invest in superficial AI projects disconnected from their core business to secure subsidies, and "acquisition-side" bubbles, characterized by a focus on patent quantity over quality. This critical finding highlights the complex interplay between strong policy incentives and corporate governance, underscoring the need for sophisticated policy design that rewards substantive innovation rather than mere metric inflation. The study concludes by discussing the broader implications of these findings for innovation policy theory and practice, offering concrete recommendations for designing more effective and resilient AI innovation ecosystems that can truly empower the real economy.

Against the backdrop of China's shift toward high-quality development and increasing emphasis on supply chain security, understanding how firms' information disclosure behaviors influence supply chain resilience has become a critical research issue. While prior literature has primarily focused on technological, financial, or structural determinants of supply chain resilience, less attention has been paid to how distorted innovation disclosures-particularly the misalignment between innovation narratives and actual innovation effort-affect firms' ability to withstand and recover from disruptions. This study introduces the concept of innovation narrative-action gap into the supply chain resilience literature and examines its impact, mechanisms, and heterogeneity using data from Chinese A-share listed firms from 2012 to 2023. Drawing on transaction cost theory, the study posits that innovation narrative-action gaps characterize a form of information distortion that undermines inter-firm trust and increases coordination frictions within supply chain networks. When firms exaggerate innovation progress or capabilities in their public disclosures, upstream and downstream partners may form misaligned expectations about the firm's production capacity, technological reliability, or fulfillment capability. Once these expectations fail to materialize, the resulting trust erosion triggers additional monitoring, renegotiation, and contractual safeguards-raising supply-demand coordination costs and reducing collaborative flexibility. Internally, such misaligned narratives may also distort goal alignment, increase administrative burdens, and generate resource misallocation, thereby weakening firms' operational agility under disruption. On this basis, the study develops three hypotheses predicting a negative effect of innovation narrative-action gaps on supply chain resilience and two mediating pathways through supply-demand coordination costs and internal coordination costs. To empirically test these predictions, the study constructs a firm-level measure of innovation narrative-action gap using standardized residuals from regressing textual innovation disclosure intensity on future patenting outcomes. Supply chain resilience is measured through an entropy-based composite index that integrates supply chain resilience and recovery capability. The results reveal that innovation narrative-action gaps significantly undermine firms' supply chain resilience, and this effect remains robust to alternative variable measurements and a battery of robustness checks, including entropy balancing and instrumental variable approaches. Mechanism analyses indicate that supply-demand coordination costs and internal coordination costs serve as significant mediating channels through which innovation narrative-action gaps impair supply chain resilience. Specifically, firms with larger gaps face greater supply-demand coordination costs as well as higher internal coordination costs due to increased managerial friction and organizational misalignment. Further heterogeneity analyses show that this negative effect is significantly more pronounced among high-tech firms, and among firms with higher customer concentration and supplier concentration. These findings highlight that innovation narrative-action gaps not only impair internal operational functioning but also propagate across organizational boundaries, weakening the overall resilience of supply chain ecosystems. The study offers several implications. Firms should enhance the accuracy and verifiability of innovation disclosures and avoid excessive narrative embellishment that may jeopardize long-term collaborative relationships. Policymakers may consider strengthening guidelines and consistency checks for non-financial innovation disclosure to reduce information asymmetry risks. Supply chain partners should incorporate disclosure-execution consistency into their risk assessment frameworks to better anticipate coordination frictions. Overall, this research expands the understanding of how micro-level information distortion behaviors influence supply chain resilience and provides actionable insights for improving both innovation governance and supply chain risk management.

Value co-creation in an innovation ecosystem refers to the collaborative and open interactions between complementors and the core firms, involving resource sharing and joint efforts to achieve both individual value capture and collective value objectives. Achieving value co-creation between core firms and complementors in innovation ecosystems represents both a frontier research topic and a critical practical challenge for the healthy development of such ecosystems. However, the mechanisms through which core firms' relational norms influence value co-creation in innovation ecosystems, as well as the boundary conditions under which this occurs, remain under-researched. Therefore, drawing on relational exchange theory and resource dependence theory, this study explores how core firms' relational norms affect value co-creation in innovation ecosystems. Core firms' relational norms are defined as the behavioral expectations established by the core firms based on mutual benefit-sharing with complementors. These norms emerge through shared social norms and values, manifesting as specific behavioral rules and standards. Key dimensions of these relational norms include information exchange, solidarity, and flexibility. Complementors' innovation resource interaction refers to complementors' behaviors involving recombining and reutilizing core firms' resources. The dependence of complementors on core firms is categorized into joint dependence and asymmetric dependence. Joint dependence refers to the mutual need for each other's strategic resources, while asymmetric dependence refers to the unequal degree of resource dependence between complementors and core firms. This study constructs a conceptual framework with core firms' relational norms as the independent variable, value co-creation in innovation ecosystems as the dependent variable, complementors' innovation resource interaction as the mediator, and complementors' dependence on core firms as the moderator. The measurement scales for these variables are mature and widely validated. This study collected data through a questionnaire survey of sample companies selected from four industries(i.e., home appliances, industrial internet, information technology, and new energy vehicles) in the Yangtze River Delta region. The screening criteria ensured that all sample companies were complementors within core firms' innovation ecosystems. The survey was conducted from July to August 2023, yielding 318 valid responses. A series of hierarchical regression analyses was employed to test the hypotheses. The results reveal that (1) core firms' relational norms positively influence value co-creation in innovation ecosystems; (2) complementors' innovation resource interaction mediates the relationship between core firms' relational norms and value co-creation in innovation ecosystems; (3) joint dependence strengthens the positive relationship between core firms' relational norms and complementors' innovation resource interaction, while asymmetric dependence weakens this positive relationship. Furthermore, joint dependence enhances the indirect effect of core firms' relational norms on value co-creation in innovation ecosystems through complementors' innovation resource interaction, whereas asymmetric dependence attenuates this indirect effect. This study makes theoretical contributions in the following three aspects: First, it demonstrates that relational norms established by core firms exert a significant positive influence on value co-creation in innovation ecosystems. This finding extends prior research on the antecedents of value co-creation and the consequences of relational norms by elevating the analytical focus to the innovation ecosystem level. Second, the results indicate that complementors' innovation resource interaction mediates the relationship between core firms' relational norms and value co-creation in innovation ecosystems. This insight not only enriches understanding of the underlying mechanisms through which relational norms affect value co-creation in innovation ecosystems but also provides a conceptual foundation for exploring pathways linking other variables to value co-creation in such ecosystems. Third, the study reveals that the nature of complementors' dependence on core firms, specifically, joint versus asymmetric dependence, moderates the indirect effect of core firms' relational norms on value co-creation via complementors' innovation resource interaction. This finding provides a more nuanced understanding of the boundary conditions shaping the efficacy of relational norms, advances research on the interplay between relational norms and interorganizational dependence structures, and responds to scholarly calls for greater attention to how different forms of dependence differentially influence organizational outcomes. Moreover, it offers empirical support for the integrative application of relational exchange theory and resource dependence theory in explaining complex interorganizational dynamics.

Amid stringent environmental regulations and rising survival pressures, firms have developed a heightened awareness of environmental sustainability. However, despite this growing recognition, green initiatives often fail to deliver short-term performance gains because they require high upfront investment, yield slow returns, and involve persistent information asymmetry, thus making it difficult for firms to convert such efforts into measurable outcomes within a limited time frame. Meanwhile, although digital technologies are recognized as catalysts for green innovation and low-carbon transition, they also entail substantial energy consumption and pose a risk of carbon rebound effects. This dual nature implies that pursuing either greening or digitalization alone cannot simultaneously achieve economic performance and environmental governance goals. Against this backdrop, digital-green integration (DGI), which represents the coordinated advancement of digital and green transformation pathways, has emerged as an important strategic route for manufacturing firms. Existing research on DGI has predominantly emphasized internal governance mechanisms while paying insufficient attention to the contextual influences of external stakeholders such as markets, governments, and the public. Empirical evidence at the micro level regarding the comprehensive impact of DGI on the sustainable development performance (SDP) of manufacturing firms is also limited. Consequently, the mechanisms through which DGI contributes to SDP and the boundary conditions that amplify or constrain its effects remain inadequately understood. These research gaps call for a more comprehensive analytical framework that integrates internal mechanisms with external stakeholder pressures. To address these issues, this study draws on strategic synergy theory and stakeholder theory to examine how DGI affects the SDP of manufacturing firms and how market-oriented, governmental, and societal stakeholders shape this relationship. Specifically, the study incorporates multiple forms of external stakeholder influence including supply chain pressure, competitive pressure, government regulatory pressure, R&D subsidy incentives, and public attention into the analysis. Using panel data from Shanghai and Shenzhen A-share listed manufacturing firms between 2009 and 2023, the study employs two-way fixed effects models and moderation analyses to investigate the effects of DGI on financial and ESG performance and to evaluate the moderating roles of these external pressures. The findings demonstrate three key insights. First, DGI improves both financial performance and ESG performance, thereby enhancing overall SDP, and these results remain robust across multiple robustness tests. Second, different external stakeholders play heterogeneous moderating roles. Competitive pressure and public attention strengthen the positive effect of DGI on SDP, suggesting that firms respond more proactively to DGI initiatives when competitive intensity and public visibility are high. In contrast, R&D subsidy incentives weaken this relationship, indicating that subsidies may induce opportunistic behavior or reduce firms’ incentives to invest in substantive capability building. Meanwhile, supply chain pressure and government regulation reinforce the impact of DGI on ESG performance but do not significantly alter its relationship with financial performance. Third, heterogeneity analysis reveals that the effects of DGI vary markedly across managerial expertise, ownership type, industry pollution intensity, and levels of environmental uncertainty. The positive effect of DGI on financial performance is stronger in firms whose senior managers possess digital or environmental expertise, in state-owned enterprises, and in firms operating under low environmental uncertainty. For ESG performance, the positive effect is more pronounced in non-state-owned enterprises and in firms facing lower environmental uncertainty. Additionally, DGI enhances financial performance in non-polluting industries but dampens it in heavily polluting ones, likely due to higher compliance costs and stricter environmental constraints in these sectors. The implications emerge from these findings. Governments should refine institutional frameworks, strengthen the supervision of subsidy implementation, and establish more effective regulatory mechanisms to encourage firms to advance DGI in a substantive and efficient manner. Manufacturing firms should develop adaptive DGI strategies that align with external stakeholder expectations and their internal resource endowments, transform external pressures such as supply chain demands, competitive dynamics, regulatory oversight, and public scrutiny into sustainable competitive advantages, strengthen managerial capabilities in ESG stewardship and digital transformation, and tailor DGI pathways to their organizational characteristics and evolving market conditions.

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.

In the era of the digital-intelligence paradigm, the deep integration of data, algorithms, and computing power is propelling human society into a new stage of development. As a strategic frontier technology reshaping the global economic landscape, artificial intelligence (AI) is driving a new round of scientific and industrial revolutions. In response to this trend, China has placed technological innovation at the core of its national development strategy, focusing on cultivating future industries represented by AI to foster new quality productive forces and achieve high-quality growth. As core actors of these future industries, AI enterprises are characterized by cross-disciplinary integration, rapid iteration, and collective innovation. However, beneath this technological dynamism lies a persistent challenge: many enterprises demonstrate strong technological exploration capacity but struggle to convert scientific achievements into sustainable market value. This structural disconnection between technology exploration and commercial transformation highlights the need to establish an efficient linkage across the entire innovation chain of knowledge-technology-market. Compared with traditional factor-driven industries, AI enterprises rely more heavily on intelligent factors that are fluid, self-evolving, and highly uncertain. These characteristics make innovation activities more complex and riskier, and render innovation performance increasingly dependent on entrepreneurs' capacity to judge technological trajectories and identify market opportunities. In this context, entrepreneurial spirit is no longer merely an individual trait but serves as a vital coordinating mechanism that bridges strategic vision and technological execution, directly influencing the efficiency of innovation resource allocation and innovation output. Nevertheless, existing research has primarily focused on external resources or organizational structures, paying insufficient attention to the internal cognitive and behavioral mechanisms through which entrepreneurial spirit shapes innovation performance. Particularly within the dynamic and complex technological environment of AI enterprises, the underlying mechanism remains insufficiently studied and theoretically underdeveloped. To better understand this issue, from a science-entrepreneurship ambidextrous perspective, this study adopts the innovation value chain theory as its analytical framework and constructs a two-stage model comprising knowledge R&D and technology valorization. The research sample consists of AI enterprises listed on China's Science and Technology Innovation Board (STAR Market), which represent the frontier of the nation's future industries. First, an LDA topic model is employed to identify four dimensions of entrepreneurial spirit—exploration, responsibility, resilience, and integration—which are further categorized into two subsystems: scientific-attribute spirit and entrepreneurial-attribute spirit. These dimensions are quantitatively measured through text analysis combined with a Word2Vec model. Second, a two-stage network Data Envelopment Analysis (DEA) model is used to evaluate the efficiency of each stage in the innovation value chain and the overall performance formed by their coupling. Finally, multiple regression analysis is conducted to test the effects of entrepreneurial spirit on stage-specific innovation efficiency and overall innovation performance. The results reveal three main findings: (1) Scientific-attribute spirit primarily influences the knowledge R&D stage, where exploration spirit significantly enhances R&D efficiency, and responsibility spirit exhibits an inverted "U-shaped" pattern of "first inhibiting, then promoting"; (2) Entrepreneurial-attribute spirit mainly affects the technology valorization stage, where resilience spirit positively promotes commercialization efficiency, while integration spirit demonstrates an inverted-U effect; (3) The synergy between scientific and entrepreneurial spirits significantly enhances the overall performance of the innovation value chain, with the effect being stronger among entrepreneurs possessing research backgrounds. Drawing on the innovation value chain perspective, and grounded in a science-entrepreneurship ambidextrous framework, this study systematically explores the mechanism through which entrepreneurial spirit drives innovation in AI enterprises. The framework reveals both the stage-specific and synergistic effects of multidimensional entrepreneurial spirit, and clarifies the mechanisms and pathways through which knowledge R&D and technology transformation achieve coordination. The findings refine the process perspective of the innovation value chain, expand the theoretical boundary of entrepreneurial spirit in hard-technology contexts, and provide valuable insights for promoting the sustainable innovation of China's future industries.

Amid intensifying global technological competition and the growing demand for high-level technological self-reliance,breakthroughs in key core technologies（KCTs） have become vital to national security,industrial competitiveness,and sustainable development.KCTs combine the public-good attributes of basic research with the strategic importance of defense and industrial lifelines.Yet their innovation process is constrained by high investment,long cycles,and high uncertainty,leading to a persistent gap between high social and low private returns.This market failure discourages continuous R＆D,making enterprise-led collaborative research and development（R＆D） a key strategy to overcome technological bottlenecks and achieve breakthroughs.Enterprises,driven by market competition,are rational actors in KCT breakthroughs but face dual constraints :prohibitive capital demands from long-cycle R＆D,and talent/knowledge gaps from interdisciplinary complexity.These internal limitations,compounded by KCTs’ inherent market failure（high social returns vs.low private returns）,necessitate collaborative R＆D with external partn ers to access resources and overcome bottlenecks.Current literature spans two domains: KCT breakthrough studies emphasize exogenous drivers like policy and infrastructure but lack multi-level theoretical frameworks for enterprise-centered incentives; collaborative R＆D research focuses on micro-level outcomes and regional innovation, overlooking how multi-agent synergies can address KCT-specific chall enges under complex knowledge architectures.This study employs panel data of A-share listed firms in Shanghai and Shenzhen from 2013 to 2023 to examine the impact of collaborative R＆D on KCT breakthroughs. Patent data were obtained from the State Intellectual Property Office of China, and firm-level financial data from the CSMAR database. Abnormal samples（ST and *ST firms） were excluded, and all continuous variables were winsorized at the 1st and 99th percentiles. The empirical framework includes baseline regressions, instrumental variable estimation, robustness checks, and mechanism and heterogeneity analyses. The design integrates theoretical reasoning and econometric testing to reveal how collaborative R＆D models contribute to technolo gical breakthroughs and through which channels they operate.The results show that enterprise collaborative R＆D significantly promotes KCT breakthroughs, and this conclusion remains robust after addressing endogeneity. Collaborative R＆D drives breakthroughs through three mechanisms: easing financial constraints, strengthening talent advantages, and broadening knowledge bases. The effect is heterogeneous—stronger among firms with higher risk tolerance, those in regions with lower innovation resource misallocation, and those operating under stronger intellectual property protection. Both inter-firm and industry–university–research collaborations enhance KCT breakthroughs, while bilateral cooperation proves more efficient than multilateral partnerships.The first key recommendation is to establish a dedicated policy support system by expanding the scope and intensity of policy support for major, frontier, and interdisciplinary fields, integrating universities and research institutes into collaborative networks via co-constructed platforms to strengthen industry-university-research-application synergy, and guiding joint efforts toward industrial and technological bottlenecks through government-organized expert assessments. Second, since collaborative R＆D remedies capital shortfalls critical to KCT progress, central and local governments should increase fiscal support through special funds, R＆D subsidies and tax incentives, and establish robust evaluation mechanisms to adjust policies timely. Third, relevant parties should support the construction of physical and online exchange platforms with funding and venues, and promote deep cooperation between enterprises and universities/research institutes to improve two-way talent exchange and knowledge sharing. Fourth, given that bilateral collaborative innovation drives KCT breakthroughs more significantly than multi-party cooperation and serves as the core of the current innovation system, stakeholders should construct a "bilateral-led, multi-party-coordinated" pattern, prioritize bilateral partnerships through policy guidance and incentives to boost efficiency, and develop multi-party R＆D alliances and industrial innovation communities based on mature bilateral cooperation, with supporting coordinating institutions, governance norms, and bene fit-balancing mechanisms.This study makes several contributions.Theoretically,it identifies enterprise collaborative R＆D as an internal and controllable driver of KCT breakthroughs,extending research on innovation in strategic technologies.Empirically,it clarifies how collaboration alleviates financial,human,and knowledge constraints within a unified analytical framework.Methodologically,it innovates by using the GPT API to annotate large-scale patent data and develop a refined classification of collaborative R＆D types,improving measurement precision.Practically,the findings suggest strengthening policy support for enterprise collaborative R＆D,expanding financial incentives,and fostering talent and knowledge exchange platforms.

Resilience constitutes a core strategic capability that enables enterprises to withstand risks and achieve sustainable development. Whether executive equity incentives can enhance resilience serves as a key indicator of their effectiveness. Using data from Chinese A-share listed companies between 2007 and 2023, this study investigates the impact of executive risk-taking incentives（proxied by Vega） on corporate resilience and its underlying mechanisms. The results demonstrate that such incentives significantly strengthen both risk resistance and recovery capacity, primarily through the channels of strategic resource reallocation and digital-physical integration. Further analysis indicates that environmental uncertainty, complexity, and financial distress positively moderate this relationship. Specifically, risk-taking incentives more markedly improve risk resistance in firms characterized by low long-term debt, small scale, or digital industrialization, whereas they more effectively enhance recovery capabilities in firms with high long-term debt, larger scale, or digital business models. By integrating dynamic capability theory with internal and external crisis perspectives, this study elucidates how executive risk-taking incentives strengthen corporate resilience through enhanced dynamic capabilities, offering impo rtant insights for improving crisis response and corporate governance systems.This study makes the following theoretical contributions:（1） It extends the research on executive risk-taking incentives from conventional contexts of strategic decision-making and financial compliance to resilience cultivation during crises, providing a novel perspective on how governance incentives facilitate dual resilience-building in crisis situations.（2） It enriches dynamic capability theory by not only extending its core concept of resource reconfiguration from a strategic resource-adjustment perspective but also innovatively incorporating digital-physical integration as a responsiveness variable in digital contexts. The study systematically reveals the critical pathway through which governance incentives are transformed into resilience output via capability iteration, thereby bridging theoretical gaps and offering a systematic theoretical explanation for resilience development.（3） While existing literature predominantly relies on static or single-event crisis perspectives, this study integrates three recurrent crisis scenarios into a unified analytical framework. It validates the applicability of principal-agent theory in dynamic environments and provides a theoretical basis for designing differentiated corp orate governance mechanisms.In light of the study’s conclusions, this study offers several managerial implications. Firms should optimize equity incentive contracts to dynamically align executives’ wealth with stock return volatility, thereby enhancing their risk-taking willingness. For instance, incorporating crisis-linked vesting conditions or flexible incentive terms can direct managerial focus toward strategic resource reallocation and digital-physical integration. This approach strengthens dynamic capabilities and transforms external pressures into internal resilience drivers. Moreover, firms should refine resource allocation by establishing thresholds for financial investments and R＆D ratios, thereby channeling resources toward core operations and innovation. Enterprises are also encouraged to advance digital-physical integration: physical firms should adopt digital tools to improve forecasting and risk response, while digital firms ought to develop solutions based on actual industrial needs, enabling risk diversification and value co-creation. Finally, differentiated equity incentives tailored to firm-specific characteristics—such as debt structure, scale, and industry—should be implemented. For example, financially distressed firms may introduce pressure-responsive options to facilitate technological restructuring and light-asset transformation, while digital industrialization enterprises should adopt performance metrics based on patent quality and technological iteration speed, supported by deferred technology dividend payouts. Such customization ensures that incentive mechanisms align with each firm’s stage of dynamic capability development.Building on its findings and limitations, several promising research directions emerge. Future studies could examine the effects of such incentives across diverse institutional and cultural contexts to assess the cross-context generalizability of the findings. Furthermore, introducing more varied internal and external contextual variables would deepen understanding of the boundary conditions influencing incentive effectiveness. Additionally, exploring the interplay between risk-taking incentives and other governance mechanisms, such as board oversight and top management team structure, could contribute to developing a more systematic governance-resilience integrated model. Finally, adopting a micro-level cognitive and behavioral perspective to analyze the psychological processes underlying executive decisions may further illuminate the intrin-sic behavioral mechanisms in the pathway to resilience formation.

Currently, China’s economy has entered a new normal after a period of rapid development, with market demand gradually becoming saturated and the limitations of exploitative innovation increasingly evident. The transition from exploitative innovation to exploratory innovation, known as an innovation leap, is considered a crucial strategy for enterprises to escape from existing innovation routines, identify future technological trajectories, and seize R＆D opportunities. If enterprises lack the capability to achieve an innovation leap from exploitative innovation to exploratory innovation, they risk falling into the “exploitative innovation trap” due to their inability to cross the innovation gap in shifting innovation models. Consequently, enterprises urgently need to upgrade and transform their ambidextrous innovation models to break free from the trap. Particularly in the current context of restrictions on key core technologies, enhancing innovation openness has become a critical pathway for enterprises to access external resources and achieve innovation leap. However, previous research has rarely examined the antecedent mechanisms of innovation leap, and there is a lack of exploration into innovation leap in the digital context. To address these research gaps, this study analyzes how innovation openness influences enterprise innovation based on innovation recombination theory.This study constructs a pathway for achieving enterprise innovation leap based on innovation recombination theory. Using a panel data set of 330 Chinese A-share listed companies spanning the period 2007 to 2024, it empirically examines how innovation recombination differences, manifested in knowledge flexibility and knowledge complexity triggered by the breadth and depth of innovation openness, affect enterprises’ innovation leap, as well as the moderating role of enterprise digitalization level in the above relationship.The study results indicate that the innovation openness breadth positively facilitates the innovation leap from exploitative innovation to exploratory innovation by enhancing knowledge flexibility, while the innovation openness depth exhibits an inverted U-shaped effect on the innovation leap due to increased knowledge complexity. Furthermore, enterprises’ digitalization level positively moderates the relationship between innovation openness breadth and innovation leap, and further strengthens the inverted U-shaped relationship between innovation openness depth and innovation leap.The theoretical contributions of this paper are presented as follows. First, departing from previous studies which have narrowly focused on the consequences of enterprise innovation leap, this paper shifts the focus to the influence mechanism of innovation openness on innovation leap, expanding the knowledge-based antecedents of innovation leap, answering the question of “how innovation leap occurs”. Second, based on innovation recombination theory, this paper distinguishes between the breadth and depth of innovation openness and thoroughly investigates their differential effects on the innovation leap through the distinct recombination mechanisms of knowledge flexibility and knowledge complexity. Thereby, it not only provides a novel theoretical perspective for research on innovation leap but also extends the application boundaries of innovation recombination theory. Finally, this paper introduces the moderating role of enterprise digitalization level in the impact of innovation openness on innovation leap. It delves into the mechanism and impact of digitalization level on changes in enterprises’ ambidextrous innovation strategy decisions. The findings not only broaden the research scope of enterprise digitalization but also enrich and deepen the theoretical understanding of ambidextrous innovation strategic management in a digital context.To foster a virtuous cycle between exploitative innovation and exploratory innovation, enterprises should adopt open innovation strategies while balancing the breadth and depth of openness. Expanding openness breadth requires actively collaborating with suppliers, universities, research institutions, and other external partners to integrate diverse knowledge resources, which fuels sustained innovative breakthroughs. For openness depth, enterprises need moderately close, longterm stable partnerships with collaborators to facilitate effective knowledge sharing and conversion. They should avoid over-reliance on narrow knowledge domains to maintain an optimal level. Additionally, advancing digital transformation is critical. Enterprises can leverage cloud computing, big data, and AI to build a flexible, efficient innovation ecosystem. When enterprises strengthen their capabilities in data analysis and opportunity identification, they can achieve innovation leaps at lower transition costs and enhance the scientific rigor and adaptability of their innovation strategy decisions.

For a long time, the catch-up development strategy, characterized by the suppression of the prices of factors such as labor and capital, has promoted the rapid growth of China’s economy, but it has also led to the factor market distortions, generating problems such as uneven spatial distribution of factors, poor mobility and price distortions, which in turn triggered the factor allocation distortion. Some studies have shown that when there is no factor allocation distortion, factor allocation optimization can improve China’s economic efficiency by 88.12% on average. At the same time, the digital economy, as an innovative economic form, is digitally penetrating and transforming the real industry, and this process of digital-real integration provides new opportunities for improving factor allocation distortion. Digital-real integration not only promotes the combination of data elements and traditional factors of production, broadens the boundaries of the factor market, promotes cross-domain flows and efficient allocation of various types of factors, but also promotes intelligent producti on and refined management, effectively reducing factor redundancy and mismatch.A large amount of existing literature has explored the impact of the digital economy on factor allocation distortion, but less attention has been paid to the effect of the emerging digital context of digital-real integration on factor allocation distortion. Although individual scholars have explored the relationship between digital-real convergence and factor market distortion, the scope of their research is limited to the provincial level, and they have not conducted in-depth discussions on the mechanisms at play. On the other hand, existing research on the economic effects of digital-real integration mainly focuses on green innovation, Chinese-style modernization, total factor productivity and new quality productivity, etc., yet it rarely specifically explores the impact of digital-real integration on factor allocation distortion, which is profoundly affecting factor flows, factor transactions and factor allocation efficiency, and will inevitably have a far-reaching impact on factor allocation distortion. Thus, this paper utilizes the panel data of 284 cities in China from 2011 to 2023 to deeply explore the impact of digital-real integration on factor allocation distortion and its functioning mechanism from both theoretical and empi rical levels.This paper mainly obtains the following research conclusions: First, the digital-real integration significantly mitigates the factor allocation distortion, which still holds after the endogeneity and robustness test, and when the factor market is subdivided into the labor market and the capital market, the improvement effect still exists significantly. Second, the mechanism test shows that the cost adjustment effect, market integration effect, and technological innovation effect are important mechanisms by which digital-real integration improves factor allocation distortion. Specifically, digital-real integration improves factor allocation distortions by raising the cost of labor use and lowering the opportunity cost of capital, promoting the integration of the labor market and capital market, and enhancing technological innovation. Third, the improvement effect of digital-real integration on factor allocation distortion is more significant in the state of under-allocation of labor and capital at the same time, service-oriented cities, cities with high network size, large cities, eastern cities, and coast al cities.The possible marginal contributions of this paper are as follows: First, aiming at the limitation of the lack of digitalreal integration perspective in the existing research, this paper tries to include the digital-real integration and factor allocation distortion into the same analytical framework, to explore the relationship between the two in depth and provide a new research perspective for the issue of digital-real integration and factor allocation distortion. Second, this paper systematically analyzes the mechanism of the impact of digital-real integration on factor allocation distortion from the economic perspectives of cost adjustment, market integration and technological innovation, which helps to deepen the understanding of the relationship between the two and enrich the theoretical framework in the field of digital-real integration and factor allocation distortion, and provide theoretical support for the subsequent research. Third, this paper further explores the differential impact of digital-real integration on factor allocation distortion under different factor allocation states, industrial bases, network effects, city sizes, and geographic locations, and these differentiated analyses provide empirical evidence for more effectively correcting factor allocation distortion and enhancing the level of digital-real integration.

With the rise of service-dominant logic and service development paradigms, service-oriented transformation has become a key way for traditional manufacturing firms to build unique competitive advantages. How to improve servitization performance of manufacturing firms to achieve successful service-oriented transformation is a hot topic in current research. Recent studies show that achieving new competitive advantages through such transformation requires manufacturers to update and reshape resources and capabilities while optimizing existing production and manufacturing systems. As two core components of the manufacturing system, product R&D and production processes/integrated innovation are the key to optimizing the system and enhancing servitization performance. Thus, how should manufacturers build this integrated innovation to achieve better performance? Existing literature remains controversial on the impact of product-process integrated innovation on firms' performance. Specifically, for manufacturers' servitization transformation, the mechanisms through which this innovation affects servitization performance have not been deeply explored. Additionally, manufacturing system effectiveness depends on production scale and market conditions. How do these two factors moderate the impact of product-process integrated innovation on servitization performance? This is another important issue requiring attention. To address the above research gaps, this paper, by adopting a panel dataset of 357 listed Chinese manufacturing firms over the period 2010–2023 and applying a multivariate regression model, studies the impact of product-process integrated innovation on manufacturing firms' servitization performance, and the moderating effect of production scale and multi-market contact on the relationship between them. It finds that moderate product-process integrated innovation exerts a positive effect, significantly improving manufacturing firms' servitization performance. However, when product-process integrated innovation exceeds a threshold, the negative effect begins to emerge, which has a negative impact on manufacturing firms' servitization performance. Both production scale and multi-market contact strengthen the inverted U-shaped relationship between product-process integrated innovation and manufacturing firms' servitization performance. As production scale and multi-market contact levels increase, moderate product-process integrated innovation becomes more effective in enhancing manufacturing firms' servitization performance, while excessive innovation further exacerbates its negative impact. Meanwhile, knowledge coupling, as a mediating mechanism, exhibits significantly heterogeneous effects across different levels of production scale and multi-market contact. When production scale and multi-market contact are at an optimal level, moderate product-process integrated innovation, mediated by knowledge coupling, enhances manufacturing firms' servitization performance. When production scale and multi-market contact reach a high level, excessive product-process integrated innovation, mediated by knowledge coupling, inhibits manufacturing firms' servitization performance. The management implications of this paper are as follows: Manufacturing firms should leverage product-process integrated innovation for servitization, avoid blindly escalating innovation efforts and pursue a balanced approach. For example, by establishing cross-functional project teams, they can moderately advance product-process integrated innovation, enabling design and production personnel to collaborate in product development. This fosters tight coordination between design and production phases, ensuring both the feasibility and cost-effectiveness of designs in manufacturing, thereby accumulating collaborative advantages for servitization. Simultaneously, it is critical to guard against excessive integration to prevent negative effects such as process rigidity and technological lock-in, which could inhibit firms' servitization performance. Additionally, during servitization, manufacturing firms should scientifically plan production scales and multi-market contact based on their level of product-process integration.

Against the backdrop of the accelerated formation of global innovation strategic networks, enterprises increasingly find it difficult to pursue “go-it-alone” innovation. As a vital component of open innovation and an advanced stage of internationalization, R＆D internationalization is crucial for enterprises in emerging economies such as China to enhance innovation performance and international competitiveness. However, when constructing overseas R＆D networks, these enterprises often face the dual challenges of substantial information gaps and high entry barriers, which in turn increase hostcountry organizational adaptation costs, reduce the efficiency of cross-border resource allocation, and lead to redundant R＆D investment. At the same time, digital transformation has become a core strategic initiative for enterprises to capture market opportunities and respond to environmental challenges. This raises several key questions: Can digital transformation help Chinese enterprises leverage digital capabilities to serve as a “springboard” for R＆D internationalization? What heterogeneity characterizes its effects? How can the theoretical mechanism between the two be explained? What roles do host-country information accessibility and market entry level play in this process?Drawing on the new OLI theoretical paradigm, this study employs data on Chinese listed firms and their overseas investments from 2007 to 2023, and applies a fixed-effects panel model to empirically examine the impact of digital transformation on the level of R＆D internationalization. Further, it investigates heterogeneous effects based on ownership（stateowned vs. non-state-owned） and executives’ overseas backgrounds. In addition, mechanism tests are conducted to explore whether digital transformation enhances enterprises’ ability to exert the new ownership–location–internalization（OLI）advantages of open resources, linkage, and integration by improving host-country information accessibility and market entry level, thereby overcoming the inherent barriers to R＆D internationalization. From a theoretical perspective, the new OLI framework provides an important analytical basis and reference for outward international investment in the digital era. Digital transformation strengthens these three advantages, shrinking information gaps and entry barriers, cutting risk/cost and boosting R＆D efficiency so firms expand and refine global R＆D networks. Moreover, host-country information accessibility and market entry level mediate this process: digital tools reduce information asymmetry and allow real-time monitoring of regulatory change, lowering the liability of foreignness and sustaining R＆D internationalization.Empirical results demonstrate that digital transformation significantly enhances the level of R＆D internationalization, and this conclusion remains robust across a series of tests. Heterogeneity analysis further shows that the effect is more pronounced in state-owned enterprises and in firms with executives holding overseas backgrounds. Mechanism testing additionally confirms that digital transformation effectively improves host-country information accessibility and market entry level, which enables enterprises to realize the new OLI advantages, thereby alleviating information asymmetry and entrybarrier problems and ultimately advancing their R＆D internationalization.Building on these findings, this paper proposes several policy recommendations. First, enterprises should accelerate digital transformation, strengthen their capability to embed in global R＆D networks, deploy core digital technologies, and digitally reengineer R＆D processes to enhance real-time insight into host-country markets, technologies, and talent information. Second, differentiated guidance should be implemented: state-owned enterprises should be encouraged to increase digital investment and closely link it to overseas R＆D performance, while small and medium-sized enterprises should receive targeted "diagnosis–assistance" support to address specific challenges such as information asymmetry and shortages of international talent. Policy support should further incorporate the international experience and digital literacy of executive teams into its design.Third, host-country market access and information service guarantees should be reinforced, including building an authoritative and dynamic host-country R＆D environment information database, promoting mutual recognition and institutional alignment in areas such as cross-border data flows and market entry, strengthening cooperation on intellectual property protection, and simplifying procedures for establishing overseas R＆D institutions. In addition, building industry-or region-level digital R＆D collaboration platforms can foster the sharing of innovation resources, reduce information asymmetry, and thus create a global innovation cooperation ecosystem that advances collaborative R＆D.