【刊出新闻】《外国经济与管理》2026年第8期目录
本期共 8 篇文章
从资源整合到价值创造：财务共享服务中心与企业数据资产化
摘要 · ABSTRACT
数据已成为重塑生产与竞争格局的关键要素，如何高效推动企业数据资产化成为亟待解决的重要议题。基于2007—2024年沪深A股上市公司数据，本文考察财务共享服务中心对企业数据资产化的影响。研究发现，财务共享服务中心显著促进企业数据资产化，且运行时间越长作用越明显。机制检验表明，财务共享服务中心通过提升数据治理水平、改善数据质量及提高数据流通效率，促进企业数据资产化。异质性分析发现，财务共享服务中心对数据资产化的促进作用在内部组织结构复杂、业务多元化程度较低，以及地区数据要素市场发展水平较高、知识产权保护较完善的样本中更为显著。进一步经济后果分析显示，财务共享服务中心通过提升数据资产化水平，显著提高企业全要素生产率。本文不仅拓展了数据资产化驱动因素的研究视角，也为企业优化组织架构与强化数据资产管理提供了实践启示。
关键词 · KEYWORDS
Abstract
As data have become a pivotal factor of production reshaping the landscape of production and competition, how to effectively promote corporate data assetization has emerged as a critical and urgent question. Leveraging data from Shanghai and Shenzhen A-share listed companies from 2007 to 2024, this paper examines the impact of financial shared service centers (FSSCs) on corporate data assetization. The empirical findings demonstrate that the establishment of FSSCs significantly promotes data assetization, with the effect strengthening as FSSCs operate for a longer period. Mechanism testing reveals that FSSCs effectively drive data assetization by strengthening data governance capabilities, improving data quality, and enhancing internal data flow efficiency. Heterogeneity analysis shows that this effect is more pronounced in firms with complex internal organizational structures and lower levels of business diversification, as well as in regions with more developed data factor markets and stronger intellectual property protection. Further analysis of economic consequences indicates that FSSCs significantly improve corporate total factor productivity by elevating the level of data assetization. This paper not only broadens the research perspective on the drivers of data assetization but also provides practical implications for firms seeking to optimize organizational structures and strengthen data asset management.
相得益彰：人智协同如何影响企业投资效率？
摘要 · ABSTRACT
数字经济背景下，人力资本与人工智能形成的协同互补机制成为企业优化投资决策、提升投资效率的重要支撑。基于此，本文以2018—2024年我国A股上市公司为研究样本，实证检验人智协同对企业投资效率的影响。研究发现，人智协同有助于提升企业投资效率，基于人工智能技术发展阶段差异的分析同样支持该结论。机制分析表明，人智协同主要通过提升企业信息获取与分析能力、抑制管理层非理性决策影响投资效率。基于“动机—机会—借口”视角的异质性分析表明，人智协同对企业投资效率的影响在管理层外部薪酬差距较大、机构投资者持股比例较高以及战略激进度较低的企业中更加显著；同时，基于企业资源基础的异质性分析表明，该影响在企业规模较大、融资约束较小的企业中更加显著。区分投资不足和投资过度的检验发现，人智协同能同时有效缓解投资不足、抑制投资过度。经济后果表明，人智协同对投资效率的影响最终有助于提升企业价值。本文为企业如何在数字时代构建人智协同机制来提高投资效率、实现价值创造提供了经验参考。
关键词 · KEYWORDS
Abstract
In the digital economy, collaboration between human capital and artificial intelligence (AI) provides a critical foundation for enterprises to optimize investment decisions and improve investment efficiency. Taking China’s A-share listed companies from 2018 to 2024 as the research sample, this paper empirically investigates the impact of human-AI collaboration on enterprise investment efficiency. The results show that human-AI collaboration enhances investment efficiency, and analysis based on the varying developmental stages of AI supports this conclusion. Mechanism testing indicates that human-AI collaboration enhances investment efficiency primarily by improving enterprise capabilities in acquiring and analyzing information and by curbing irrational managerial decisions. Heterogeneity analysis from the perspective of “motivation-opportunity-rationalization” shows that the positive effect of human-AI collaboration on investment efficiency is more pronounced in enterprises with larger managerial external pay gaps, higher institutional ownership, and lower strategic aggressiveness. Heterogeneity analysis based on enterprise resource endowments reveals that this effect is stronger among larger enterprises and those facing fewer financing constraints. The test that separates overinvestment from underinvestment shows that human-AI collaboration effectively alleviates both underinvestment and overinvestment. The economic consequences indicate that the impact of human-AI collaboration on investment efficiency ultimately contributes to an enhancement in corporate value. This paper provides practical insights for enterprises on how to build human-AI collaboration mechanisms in the digital economy to enhance investment efficiency and achieve value creation.
“以数治税”能否揭露企业信用风险？——来自债券市场的证据
摘要 · ABSTRACT
在信息不对称条件下，传统税收征管模式使企业潜在信用风险得以延迟暴露。随着“以数治税”的推进，税收征管数字化通过强化信息透明度和数据穿透能力，以重塑企业信用风险在资本市场中的定价方式。本文以2021年12月启动的全面数字化电子发票试点（金税四期）作为制度冲击，基于2020年1月至2024年12月企业债券二级市场月度信用利差数据，采用双重差分方法检验税收征管数字化对信用风险显性化的影响。研究发现，“以数治税”显著推高了试点地区企业债券信用利差，表明企业既有信用风险在市场定价中加速显性化；该效应主要通过风险信号、风险兑现和风险联动三条机制实现，并在现金流状况较弱、内部控制水平较低以及避税动机和避税行为更为突出的企业中表现得更为显著。研究表明，税收征管数字化不仅提升了征管效能，也通过强化资本市场的风险识别功能，对金融稳定与治理现代化产生重要影响。
关键词 · KEYWORDS
Abstract
Under conditions of information asymmetry, traditional tax administration systems allow firms’ latent credit risks to remain partially concealed and delayed in market disclosure. With the advancement of “data-driven tax governance”, tax administration has strengthened information transparency and data penetration, potentially reshaping how corporate credit risks are priced in capital markets. Taking the nationwide pilot of fully digitalized electronic invoices (Golden Tax Phase IV), launched in December 2021, as an exogenous institutional shock, this paper uses monthly credit spread data from the secondary bond market spanning January 2020 to December 2024 and employs a DID design to examine whether tax administration digitalization accelerates the market revelation of corporate credit risks. The results show that “data-driven tax governance” significantly increases corporate bond credit spreads in pilot regions, indicating a heightened degree of credit risk revelation in market pricing. This effect operates primarily through three channels: risk signaling, risk realization, and risk linkage; and it is more pronounced among firms with weaker operating cash flows, less effective internal control systems, and stronger tax avoidance incentives and behaviors. The study suggests that tax administration digitalization not only improves tax enforcement efficiency but also enhances the risk-identification function of capital markets, with important implications for financial stability and the modernization of economic governance.
国家审计信息化与国企债务违约风险——基于金审三期工程的准自然实验
摘要 · ABSTRACT
国有企业债务违约风险治理关乎国家经济安全与金融稳定，以金审三期工程为核心的国家审计信息化建设是风险治理与防范的重要举措。本文以金审三期工程实施为背景，选取2013—2024年A股国有上市公司为样本，采用双重差分模型实证检验了国家审计信息化建设对国有企业债务违约风险的影响。研究发现，国家审计信息化建设显著抑制了国有企业债务违约风险。机制检验表明，国家审计信息化建设通过提升信息质量、加强公司治理、促进监督协同来抑制国有企业债务违约风险。异质性检验发现，该影响在东部和中部地区、未进行产融结合以及大数据应用水平高的国企中更为显著。进一步检验发现，国家审计信息化建设在动态演进改革过程中富有成效，而金审三期较金审二期对国有企业债务违约风险的影响更强，同时，金审三期对地方国企产生了直接的风险抑制作用，对央企也产生了显著影响。经济后果检验表明，国家审计信息化建设对国有企业的风险治理作用具有显著的行业与地区溢出效应，国家审计信息化建设通过缓解违约风险显著提升了国有企业的发展质量。本文的研究对深化国家审计数字化发展战略、防范化解系统性金融风险具有现实意义。
关键词 · KEYWORDS
Abstract
Managing default risk in state-owned enterprises (SOEs) is critical to national economic security and financial stability. The advancement of national audit informatization, centered on the Golden Audit Phase III Project, constitutes a key measure for risk prevention. Against the backdrop of Phase III’s implementation, this paper selects A-share listed SOEs from 2013 to 2024 and employs a DID model to examine the impact of this initiative on SOEs’ debt default risk. The findings show that the initiative significantly mitigates debt default risk in SOEs. Mechanism testing indicates risk mitigation through improved information quality, strengthened corporate governance, and enhanced supervisory coordination. Heterogeneity analysis shows that this effect is more pronounced in eastern and central regions, among SOEs without industry-finance integration, and among those with higher big-data application. Further analysis reveals that the national audit informatization initiative has been effective throughout the process of dynamic reforms, with Phase III demonstrating a stronger impact on SOEs’ debt default risk than Phase II. Additionally, Phase III exerts the most direct risk-mitigating effect on local SOEs while also significantly affecting centrally administered SOEs. Economic consequence testing reveals significant industry and regional spillover effects of national audit informatization in SOE risk governance. By reducing debt default risk, the initiative significantly improves SOEs’ development quality. This paper offers practical implications for deepening the national audit digital development strategy and for preventing systemic financial risks.
企业人工智能价值对齐：研究进展与未来展望
摘要 · ABSTRACT
人工智能技术的迅速发展为企业管理带来了重要机遇，也提出了新的治理挑战。企业人工智能价值对齐作为降低价值偏离、伦理风险和安全风险的重要治理路径，受到越来越多的关注。相较而言，国内相关研究仍处于起步阶段。鉴于此，本研究对现有文献进行系统性梳理和归纳，阐明人工智能价值对齐的概念内涵、结构维度及其测量方式，构建人工智能价值对齐的实现机制，并对企业人工智能价值对齐的前因和效应进行梳理，形成整合性的理解框架，最后对该领域的未来研究方向进行展望。本研究有助于促进对人工智能价值对齐的深层次理解，并为国内企业人工智能价值对齐的实证研究和实践应用提供参考。
关键词 · KEYWORDS
Abstract
The rapid development of artificial intelligence (AI) technology has brought significant opportunities for enterprise management, while also posing new governance challenges. As a critical governance pathway for mitigating value deviations, ethical risks, and security risks, AI value alignment in enterprises has garnered increasing attention. In contrast, domestic research in this field is still in its infancy. In view of this, this paper systematically reviews and synthesizes existing literature, clarifies the conceptual connotation, structural dimensions, and measurement approaches of AI value alignment, constructs a mechanism for its realization, and sorts out the antecedents and consequences of AI value alignment in enterprises to form an integrated understanding framework. Finally, future research directions in this field are prospected. This paper contributes to a deeper understanding of AI value alignment and provides a reference for empirical research and practical applications of AI value alignment in enterprises in the domestic context.
数据知识产权保护与企业数据技术创新——来自生成式大语言模型的经验证据
摘要 · ABSTRACT
企业数据技术创新是实现数据要素价值转化的关键路径，然而，数据知识产权保护能否以及如何有效激励企业开展数据技术创新，尚缺乏系统的实证检验。本文以全国首批数据知识产权工作试点为准自然实验，利用2015—2024年A股上市公司数据，运用大语言模型识别企业的数据技术专利，构建双重差分模型考察数据知识产权保护对企业数据技术创新的影响。研究发现，数据知识产权工作试点显著促进了企业数据技术创新。机制检验表明，数据知识产权保护主要通过激励专用要素投入和促进知识溢出发挥作用。异质性分析显示，该促进效应在国有企业、数据原生企业以及数字基础设施较完善地区更为突出，在不同科创属性企业间未呈现显著差异。经济后果分析表明，数据知识产权保护通过促进企业数据技术创新，进而提升了企业全要素生产率。研究为理解数据知识产权保护的微观创新效应提供了新证据，对建设全国一体化数据市场、培育新质生产力与推动数字经济高质量发展具有重要政策启示。
关键词 · KEYWORDS
Abstract
Enterprise data technology innovation is a crucial process for realizing the value conversion of data elements. However, there is still a lack of systematic empirical research on how to effectively incentivize it through data intellectual property protection. Based on data from China’s A-share listed companies from 2015 to 2024, this paper innovatively employs a generative large language model to identify enterprise data technology patents. Using the first batch of national pilot projects for data intellectual property as a quasi-natural experiment, it constructs a DID model to systematically assess the impact of data intellectual property protection on enterprise data technology innovation. The results show that the data intellectual property pilot significantly promotes enterprise data technology innovation. Mechanism testing indicates that this effect is primarily realized through incentivizing specialized factor inputs and facilitating knowledge spillovers. Heterogeneity analysis reveals that the promoting effect is more pronounced in state-owned enterprises, data-native enterprises, and regions with better-developed digital infrastructure, while no significant difference is observed among enterprises with different technological innovation attributes. Further research demonstrates that by fostering enterprise data technology innovation, data intellectual property protection effectively enhances enterprise TFP. This paper provides new evidence for understanding the micro-level innovation effect of data intellectual property protection and offers important policy implications for building a unified national data market, cultivating new quality productive forces, and promoting high-quality development of the digital economy.
司法效能提升与企业创新——来自民事诉讼程序繁简分流改革的证据
摘要 · ABSTRACT
繁简分流改革是推进全面依法治国、促进国家治理体系和治理能力现代化的重要举措。本文以民事诉讼程序繁简分流改革试点工作为准自然实验，考察繁简分流改革对企业创新的影响及其作用机制。研究发现，相较于非试点地区企业，改革实施后试点地区企业的专利申请数平均增加约13.72%。机制检验发现，上述影响主要通过成本节约效应、供应链依赖缓解效应、市场预期效应得以实现。异质性分析发现，该促进效应在数据资产化水平较高、商业信用水平较低以及所处行业竞争程度较高的企业中更强，繁简分流改革对企业创新的影响更强。进一步分析发现，繁简分流改革提高了企业突破性创新能力和创新效率。本文拓展了繁简分流改革经济后果的研究，从司法效能提升角度为推动创新驱动发展战略提供了经验证据和政策启示。
关键词 · KEYWORDS
Abstract
The reform of the division between complex and simple cases is a key measure for advancing the rule of law and promoting the modernization of the national governance system and governance capacity. This paper uses the pilot reform of the division between complex and simple cases as a natural experiment to examine the impact of the reform on corporate innovation and its underlying mechanisms. The results show that, compared with firms in non-pilot regions, firms in pilot regions experience an approximately 13.72% increase in patent applications following the reform. Mechanism testing reveals that this effect is primarily achieved through the effects of cost saving, supply chain dependency relief, and market expectation. Heterogeneity analysis shows that the effect of the reform on corporate innovation is stronger for firms with a higher level of data assetization, those with a lower level of trade credit, and those operating in industries with more intense market competition. Further analysis shows that the reform enhances the breakthrough innovation capability and efficiency of firms. This paper extends the research on the economic consequences of the reform and provides intellectual support and policy insights for promoting innovation-driven development strategies from the perspective of enhancing judicial efficiency.
最优区分视角下新创企业商业模式创新的动态演化——基于理想汽车的案例研究
摘要 · ABSTRACT
在新兴产业快速演进和制度环境持续变化的背景下，新创企业商业模式创新既要回应制度规范和市场认知的一致性要求，也需通过创新活动塑造差异性竞争优势。然而，对于新创企业如何在一致性与差异性张力约束下推动商业模式创新动态演化，现有研究仍缺乏系统解释。基于最优区分理论，本文选取理想汽车开展纵向单案例研究，探讨新创企业商业模式创新的动态演化机制。研究发现，新创企业商业模式创新并非创新活动的简单累积，而是随着产业情境挑战的变化，在二元合法性与双元创新的协奏下实现阶段性跃迁。具体而言，起步期，企业面临结构性挑战，通过构建型二元合法性与组合式双元创新形成门槛协奏，实现适应型商业模式创新；扩张期，企业面临认知性挑战，通过修复型二元合法性与平衡式双元创新形成补偿协奏，实现聚焦型商业模式创新；转型期，企业面临系统性挑战，通过重构型二元合法性与协同式双元创新形成整合协奏，实现复杂型商业模式创新。本文揭示了最优区分由门槛协奏、补偿协奏到整合协奏的动态实现过程，也为理解二元合法性、双元创新与新创企业商业模式创新之间的联动关系提供了新的解释框架。
关键词 · KEYWORDS
Abstract
Against the backdrop of rapid industrial evolution and institutional change in emerging industries, business model innovation in new ventures must address conformity requirements from institutional norms and market cognition while building distinctive competitive advantages through innovation. However, there has been insufficient explanation on how new ventures promote the dynamic evolution of business model innovation under conformity–distinctiveness tensions. Drawing on the optimal distinctiveness theory, this paper conducts a longitudinal single-case study of Li Auto to examine the dynamic evolution mechanism of business model innovation in new ventures. The results show that business model innovation is not a simple accumulation of innovation activities, but a staged transition achieved through the orchestration of dual legitimacy and ambidextrous innovation as industrial contextual challenges change. Specifically, in the start-up stage, structural challenges lead to threshold orchestration between constructive dual legitimacy and combined ambidextrous innovation, resulting in adaptive business model innovation; in the expansion stage, cognitive challenges lead to compensatory orchestration between restorative dual legitimacy and balanced ambidextrous innovation, resulting in focused business model innovation; in the transformation stage, systemic challenges lead to integrative orchestration between reconstructive dual legitimacy and synergistic ambidextrous innovation, resulting in complex business model innovation. This paper reveals the dynamic realization of optimal distinctiveness from threshold orchestration to compensatory and integrative orchestration, and offers a new explanation of the linkage among dual legitimacy, ambidextrous innovation, and business model innovation in new ventures.