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。

促进技术会聚，已成为撬动创新、引领经济增长的战略支点。基于技术会聚视角，采用纵向单案例研究方法，探究资源能力受限企业如何通过技术会聚实践实现突破创新，进而剖析技术会聚的动态演化机制。研究发现，企业技术会聚演化呈现三阶动态跃迁，各阶段异质性情境催生差异化会聚动因。受此驱动，企业采用“外生式整合—模块化合作—网络化共研”的会聚模式，呈现出“知识集聚—技术融合—产业联动”的阶段性会聚特征。由此，会聚能力实现“会聚知识管理能力—会聚技术集成能力—会聚产业网络能力”的阶梯式跃升，最终形成“单点吸收式—多维集成式—生态渗透式”的会聚趋势。研究进一步揭示，会聚模式的动态转化受技术情境约束、企业战略意图以及内生能力演进三维协同驱动，且存在边界条件。研究揭示了技术会聚的微观过程与演化逻辑，为企业在动态情境下实现创新突破提供路径参考。

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。

创新是驱动企业持续发展的核心动力，也是实现其经济转型升级的关键引擎。创新惰性严重制约企业创新活力释放与创新效能提升，揭示其诱发因素与形成路径具有重要理论价值与实践意义。基于COM-B理论视角，运用扎根理论和fsQCA方法对企业创新惰性诱发因素及形成路径进行分析与检验。研究结果发现：通过扎根理论分析识别出企业创新惰性诱发因素分别为认知能力局限、资源能力局限、外部机会缺失、内部机会缺失、心理惰性和心理抗拒六大特征变量。组态结果显示，单一创新惰性特征变量不构成企业创新惰性的必要条件。在企业创新惰性形成路径中，存在能力驱动型、机会驱动型和动机驱动型3类诱发创新惰性的构型，且结论通过稳健性检验。本研究揭示企业创新惰性诱发因素与形成路径，打开企业创新惰性形成的“黑箱”，为企业克服创新惰性提供参考。

有效推进突破性创新以破解关键领域核心技术瓶颈，是实现高水平科技自立自强的重要命题。当前，由人工智能引领的新一轮科技革命和产业变革正深刻重塑科研范式，为企业实现突破性创新提供了思维方法与实现路径。基于社会技术系统理论，以2020—2023年我国A股制造业上市公司为研究样本，采用K-Means聚类算法和CART决策树算法挖掘不同类型企业人工智能应用（探索型/利用型）、企业数字化转型程度、动态能力、高管数字化背景、政府数字化关注度对企业突破性创新绩效的组态效应，研究发现：①以社会技术系统特征为识别条件，将样本企业划分为滞缓型、先锋型和效率型3类，这3类企业在人工智能应用策略选择、社会子系统构成方面存在显著差异；②不同类型企业实现突破性创新绩效，依赖独特的社会技术系统要素组合。具体而言：滞缓型企业在高水平数字化转型的有力支撑下，需积极部署探索型人工智能应用；先锋型企业在数字化转型基础良好的条件下，需着力加强利用型人工智能应用；效率型企业在数字化转型基础相对薄弱情况下，需借助政策资源弥补技术短板，协同推进探索型与利用型人工智能应用。研究有助于拓展社会技术系统理论应用边界，可为政府部门制定人工智能产业政策提供参考，并为企业科学规划人工智能应用策略、优化突破性创新资源配置提供实践启示。

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。

数字技术革命浪潮下，人工智能应用成为推动企业价值链升级的重要引擎。基于2012—2024年中国A股上市公司年报的非结构化文本数据，采用ERNIE大语言模型构建企业人工智能应用指标，并结合“微笑曲线”理论探讨人工智能应用对企业价值链升级的影响。研究发现，人工智能应用能够显著推动企业价值链升级。机制检验表明，人工智能应用通过提升研发、生产、营销环节附加值牵引企业“微笑曲线”整体上移，从而推动企业价值链升级，其作用方式包括提升创新质量、赋能柔性生产和定制化营销。基于TOE框架的情境异质性分析表明，在高数据资产、高管理层激励以及市场化水平较高地区企业中，人工智能应用对企业价值链升级的促进作用更显著。拓展性分析表明，我国人工智能应用已超越“索洛悖论”，其生产率红利开始在微观层面显现。研究结论可丰富人工智能微观研究，为破解企业价值链低端锁定困局和推动经济转型升级提供经验证据。

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。

在数字经济浪潮中，数据资产作为企业转型的战略引擎，数据资产信息披露对价值创造的驱动机制备受关注。以2008—2023年我国A股上市公司为研究样本，实证检验数据资产信息披露对企业价值创造的作用路径。研究发现：① 数据资产信息披露能显著促进企业价值创造；② 机制分析表明，数据资产信息披露通过提升企业盈余水平和降低企业权益资本成本两条路径实现价值创造；③ 异质性分析揭示，数据资产信息披露对企业价值创造的促进作用在行业竞争激烈、地区数据市场化水平较高及内部治理完善的企业中更显著。研究揭示了数据资产信息披露在数字经济中的关键作用，为企业优化披露策略、提升市场竞争力提供了参考，为推动数据要素市场化配置提供了理论依据与实证支持。

绿色数字基础设施为产业升级提供绿色数字支持，促进现代化产业体系向智能化、绿色化迈进。基于2006—2023年中国277个地级市面板数据，利用多期双重差分法实证检验绿色数据中心对现代化产业体系的影响效应及作用路径。研究发现：① 绿色数字基础设施能够助力现代化产业体系发展，该结论经过一系列稳健性检验后依然成立。② 从供给侧看，绿色数字基础设施通过加快绿色技术创新与共享，促进产业融合，吸引人才集聚赋能现代化产业体系；从需求侧看，通过提升数字消费、绿色消费水平，加快消费结构升级，进而推动现代化产业体系发展。③ 异质性分析表明，绿色数字基础设施对现代化产业体系的影响因数字关注度、算力水平、数据水平不同而存在差异性。同时，绿色数字基础设施能够缩小不同区域之间现代化产业体系的相对发展差距，具有显著的区域协调效应。④ 协同效应分析表明，绿色数字基础设施与融合基础设施、创新基础设施的政策协同能够推动现代化产业体系发展。进一步，绿色数字基础设施有助于实现经济与环境双重红利，协同推进降碳、减污、扩绿、增长。研究丰富了绿色数字基础设施与现代化产业体系关系的理论框架，为政府统筹推进新型基础设施建设、因地制宜制定产业政策提供了实证依据与参考。

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。

专利开源作为专利持有者以零使用费许可不特定主体实施专利的模式，正成为驱动科技创新开放协同的重要机制。其激励创新的底层逻辑在于：开源意识激发技术创新自觉，开源模式破除专利丛林与反公地悲剧等制度性障碍，开源需求重塑创新资源市场配置路径。然而，当前专利开源面临管理秩序有待规范、技术自由流转障碍、生态信任基础薄弱等现实困境。为保障其长效运行，需构建市场、政府与自治组织协同治理的复合型管理模式，即通过建立核心技术双向回馈机制、设定共享型专利池准入规则、完善专利权领域反垄断制度、确立承诺禁反言原则并启动开源前审核程序等系统性优化措施，推动专利开源从自发探索走向规范治理，为开源生态建设与技术创新竞争提供制度支撑。

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。

随着人工智能大模型技术从闭源向开源跃迁，以DeepSeek、LLaMA等为代表的开源大模型凭借高度开放性、透明度及可定制性等特征展现出重要的应用价值。然而，技术跃迁在推动开源模型发展的同时，亦导致模型安全风险加剧和知识产权侵权风险凸显，偏离预期用途的风险呈现蔓延之势。而现有治理体系沿袭传统“命令—控制”式外部监管模式，难以应对风险的动态演化，无法在风险控制和创新激励之间实现平衡。对此，开源大模型治理范式需从外部监管转向嵌入式治理，将治理机制深度融入开源生态内在发展逻辑，充分激活其自治潜能。通过构建“开源生态自治—政府监管”递进治理架构，以开源生态自治网络为载体引入政府力量以实现共同治理，同步嵌入面向开源生态的制度规范与技术工具，最终形成契合开源特性的治理体系。研究可为我国开源人工智能安全治理提供理论参考，有效回应现实治理需求，对于推动开源生态有序发展具有启示意义。

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。

在“双碳”目标引领城市低碳转型和数字经济崛起的时代背景下，探究数字经济、城市空间结构与碳排放关系具有重要意义。以成渝城市群为例，运用时间固定效应模型、空间杜宾模型和门槛模型，对数字经济、城市空间结构与碳排放三者关系及作用机理进行实证分析。结果表明：①数字经济对碳排放的影响呈先增后降的倒“U”型曲线，当超过一定阈值后，数字经济的碳减排效应显著；②数字经济对碳排放的影响存在空间溢出效应，在空间邻接矩阵下本地数字经济发展增加了邻近区域碳排放，而在经济地理嵌套矩阵下本地数字经济发展会抑制邻近区域碳排放；③城市空间结构对数字经济的碳减排效应具有门槛效应，当城市空间结构低于一定门槛值时数字经济与碳排放关系为正，表明在分散的多中心城市空间下数字经济导致碳增排，而当城市空间结构超过门槛值时数字经济与碳排放关系为负，表明在紧凑的多中心城市空间结构下数字经济发展促使碳减排。基于实证结果，提出控制数字产业碳排放、引导邻近城市承接绿色产业转移、合理布局城市空间结构等举措，以协同推动成渝城市群实现数字经济碳减排效应。

Artificial intelligence has been deeply embedded in the network of social operations, serving as the underlying architecture and infrastructure of the digital society. Academia has conducted in-depth typological analyses of data risks in generative artificial intelligence from various perspectives and methodologies, including the life cycle perspective, rights theory perspective, and data security perspective, and has proposed corresponding measures to address these data risks. However, a common limitation of the aforementioned life cycle perspective, rights theory perspective, and data security perspective lies in the disconnection between theoretical logic and practical dilemmas, specifically, they fail to fully recognize the unique characteristics of data risks in generative AI and systematically examine the complex feedback mechanism between training data and generated content. Consequently, these perspectives are trapped in an internal perspective of data risk governance, neglecting the spillover effects, transmissibility, and interconnection inherent to generative AI data risks themselves. Overall, academic analyses of generative AI data risks have long been confined to the cognitive limitation of "centering on governance objects"—that is, overemphasizing the attribute characteristics of "data" and the morphological manifestations of "data risks" while ignoring the core attributes and process mechanisms of "governance" itself. This has rendered existing analytical approaches and models unable to respond to the complexity of governance practices, ultimately reducing governance schemes to mere "paper compliance" that cannot be effectively implemented. Therefore, it is necessary to take governance theory as the starting point, analyze the practical dilemmas in generative artificial intelligence data risk governance, and propose targeted optimization schemes for such governance. Professor Gerry Stoker has further refined a "governance" theory that can provide an organizational framework. "Governance as theory" emphasizes four key aspects: the clarity of governance goals, the synergy of governance subjects, the completeness of governance basis, and the flexibility of governance means. The governance dilemmas in generative AI data risks can thus be categorized into four dimensions: ambiguity of governance goals, fragmentation of governance subjects, absence of governance basis, and rigidity of governance means. Accordingly, the governance dilemmas in generative artificial intelligence data risks can thus be categorized into four major types: the dilemma of misaligned governance goals, which includes difficulties in balancing security and development, reconciling national and corporate goals, and aligning short-term and long-term objectives; the dilemma of fragmented governance subjects, which involves complex games among public authorities, the binary opposition between the state and enterprises, and international competition for rule-making power; the absence of governance basis, which covers the lack of laws and regulations, industry standards, and ethical norms; and the rigidity of governance means, which includes the path dependence on traditional administrative tools, institutional obstacles to technological empowerment, and the breakdown of synergy among multiple tools. In response, system synergy, dynamic adaptation, risk communication, and multi-stakeholder co-governance shall serve as core concepts. Goal calibration is to be achieved through a risk-classified dynamic balance mechanism, a synergistic coupling mechanism of government-enterprise interests, and a temporal cohesive mechanism for short-term and long-term goals; subject synergy is to be realized through a collaborative linkage mechanism among public authorities, a co-governance operation mechanism for government-enterprise symbiosis, and a collaborative mechanism for international rule-making; the governance basis is to be improved through a hierarchical legislative legal guarantee mechanism, a multi-stakeholder co-governance standard-setting mechanism, and an institutionally embedded ethical constraint mechanism; and governance means are to be innovated through a precision-oriented reform mechanism for administrative tools, an institutional activation mechanism for technological tools, and a synergistic coupling mechanism for multiple tools. Furthermore, through in-depth coupling of governance goals, subjects, foundations, and means, as well as systematic reconstruction of interrelated mechanisms, continuous innovation in the generative artificial intelligence data risk governance system and a qualitative leap in governance mechanisms can be ultimately promoted, laying a solid foundation for the healthy and sustainable development of generative artificial intelligence.

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.

Against the backdrop of China's "dual carbon" strategy, the rapid proliferation of green patents has become an important symbol of firms' environmental responsibility efforts. However, owing to regulatory pressures or the desire to project a positive environmental image, a growing number of firms tend to pursue the quantity rather than the quality of green patents. This has led to the emergence of "green patent bubbles", characterized by excessive patent filings with limited technological substance or environmental value. Such behavior not only distorts the market's assessment of firms' true innovation capability but also undermines public trust. Therefore, identifying green patent bubbles and effectively restraining firms' strategic innovation behavior are crucial for promoting their long-term sustainable development. In this context, the establishment of the Investor Interaction Platform (IIP) by the Shanghai and Shenzhen Stock Exchanges provides a new institutional channel for information exchange and corporate governance. Unlike traditional one-way disclosure mechanisms, the platform facilitates direct, transparent, and real-time communication between investors and listed firms. Through online question-and-answer interactions, investors can express concerns, request clarifications, and obtain informal yet highly valuable information. Firms, in turn, are required to respond promptly and appropriately, with the quality and timeliness of their responses included in disclosure evaluations. However, existing literature mostly focuses on one-way indicators, such as the number of questions asked on the platform, which not only ignores the bidirectional nature of information exchange and the quality of interaction but also fails to reveal the intrinsic connection between two-way information interaction mechanisms and corporate green patent bubbles. To address this gap, this study draws on data from Chinese A-share listed companies between 2010 and 2023, and employs two-way interaction data from the Investor Interaction Platform to measure information exchange behaviors in terms of interaction quantity and interaction quality, and systematically examines how such bidirectional information interaction between investors and firms affects corporate green patent bubbles. The findings show that two-way information exchange significantly curbs corporate green patent bubbles, with interaction quality exhibiting a more pronounced inhibitory effect compared to interaction quantity. The mechanism analysis indicates that two-way information exchange operates through two pathways: on the one hand, it effectively alleviates information asymmetry between firms and investors; on the other hand, it significantly enhances the internal governance level of firms. Both of these pathways effectively curb the formation of green patent bubbles. Heterogeneity analysis shows that the inhibitory effect of two-way information exchange on green patent bubbles is more pronounced in firms with a higher proportion of institutional investor ownership, higher media attention, and executives with stronger environmental backgrounds. Moreover, the analysis of economic consequences suggests that two-way information exchange can lead to a sustained improvement in firms' substantive green innovation capabilities by suppressing green patent bubbles. This study makes three key theoretical contributions. First, it broadens corporate green innovation research by focusing on green innovation bubbles. By quantifying bubble severity and integrating the institutional context of official investor interaction platforms, it systematically analyzes how bidirectional information exchange governs such bubbles. This addresses the "quantity-over-quality" issue in corporate green innovation practices, offering new theoretical and empirical insights into the formation and governance of quality-deviant green innovation. Second, it enriches literature on interaction platforms' economic impacts by centering on bidirectional information interaction. Through a dual-dimensional index system (interaction quantity and quality), it comprehensively characterizes investor-firm information exchange, exploring its mechanisms via "information effect" and "governance effect" pathways. This reveals platforms' critical role in driving firms from formalistic to substantive green innovation. Finally, it provides policy and governance guidance for multi-stakeholder groups to use interaction platforms in promoting firms' shift from strategic to substantive green innovation. The findings support governments in optimizing information disclosure and platform regulation, guide firms in enhancing response quality and standardizing green innovation, and help investors identify bubbles and avoid greenwashing risks.

China has long adhered to a single-track administrative model for patent confirmation of rights, and the reform of this model has always been a hot topic in academic circles. Various scholars have proposed four typical reform paths. Existing research also points out the 'problems' in China's current single-track administrative model for patent validation, primarily the cumbersome procedures and their negative impact on the efficiency of infringement litigation, which serve as the impetus for reform. The aforementioned problems all point to the law and economics issues of litigation benefits and efficiency. From the perspective of law and economics, the Coase Theorem, which is standardized, considers the comparison of plans before and after the reform. The plans after the reform should have lower costs and higher benefits compared to those before the reform. From a law and economics perspective, the normative interpretation of the Coase Theorem provides a benchmark for evaluating pre- and post-reform institutional arrangements. Specifically, the post-reform regime should yield higher net benefits (i. e. , lower costs relative to benefits) than the pre reform one. By comparing the paths before and after the reform, it is found that the plans after the reform do not reduce the costs of trial or improve efficiency; instead, they may even lead to increased costs and decreased efficiency. Through the analysis of the costs of reform and the benefits after the reform, it is evident that either the cost of reform is extremely high and immeasurable, making it impossible to compare with the benefits after the reform, or the benefits after the reform are insufficient to compensate for the costs of reform. In long-term practice, China's administrative and judicial organs have closely cooperated to effectively achieve accurate and efficient trials of patent validity. They have continuously explored the matching and connection with patent infringement procedures, forming valuable Chinese experience and establishing a "single-track administrative path for patent confirmation of rights with Chinese characteristics" in practice. Its supporting mechanisms enable effective procedural coordination, and the existing three-tier process, alongside efficiency-enhancing measures, already meets current needs. The establishment and evolution of this model are aimed at reducing social costs and improving the efficiency of enterprises or judicial administration, and there is no necessity to alter the scope of review authority or reduce the number of trial levels. Therefore, this model which is grounded in China’s national conditions and shaped by its practical experience should be preserved and refined incrementally to address existing shortcomings. The optimization directions for China's patent rights confirmation model should include three aspects: First, after accepting patent infringement litigation cases, the People's Courts should file requests for prioritized examination of patent invalidation with the China National Intellectual Property Administration (CNIPA) for patents with uncertain stability. Regarding such requests initiated by the People's Courts, if CNIPA has already accepted an invalidation request for the patent involved in the case, it should apply the prioritized examination procedure for the review; If the CNIPA has not yet accepted an invalidation request regarding the patent in question, it shall first place the case on record. Upon subsequently accepting an invalidation request filed by a relevant party, the CNIPA shall directly apply the prioritized examination procedure to the review. Second, the Patent Examination Guidelines should clearly specify the time limits for the ordinary procedure of patent invalidation, the prioritized examination procedure, and the retrial procedure after a court revokes an invalidation decision, and the time limit for the prioritized examination of patent invalidation should be set at four months. Third, in the review decisions of patent invalidation declarations, a separate section should be dedicated to a detailed description of the interpretation of patent claims maintained as valid after review. Particularly for claims where the patentee has narrowed the scope using the specification, the narrowed scope should be explicitly recorded.

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.