数据织网：供应链网络中数据资产的溢出效应

王欣兰¹　许安迪¹　宋晓悦²

（1.山东工商学院；2.西安交通大学）

内容提要：在供应链网络中，数据资产共享作为数据要素市场建设的核心，是推动供应链多维协同、优化资源配置和提升运营效率的关键所在。本文以2009—2024年沪深A股上市公司为研究样本，实证考察供应链网络中企业数据资产的溢出效应。研究结果表明，目标企业数据资产能够沿供应链向上下游溢出，促进其供应商（客户）企业配置数据资产，即供应链网络企业间数据资产具有溢出效应；机制分析发现，该溢出效应主要通过合作学习效应与动态竞争效应双重机制予以实现；进一步研究发现，资产化的数据资产溢出效应最强，在数据资产政策支持环境较好、供应链网络位置中心性较高的情况下，数据资产溢出效应的作用机制发挥更优。本文将数据资产研究拓展至供应链联动领域，深化了数据资产在供应链管理中的理论应用，丰富了数据资产溢出效应的实证研究，为推动供应链数字化和供应链高效协同发展提供了理论支持和实践证据。

关键词：数据资产；供应链网络；合作学习；动态竞争；溢出效应

作者简介：王欣兰，山东工商学院会计学院教授，烟台，264005；许安迪，山东工商学院会计学院硕士研究生，通信作者；宋晓悦，西安交通大学管理学院博士生，西安，710049。

基金项目：教育部人文社会科学研究规划基金项目“数据资产‘效率—风险’双元溢出效应下的供应链竞合机制与网络韧性研究”（25YJA630086）；山东省社会科学规划研究项目“高质量发展视阈下山东营商环境优化综合测度、驱动机制及助推政策研究”（24CJJJ23）

引用格式：王欣兰,许安迪,宋晓悦.数据织网:供应链网络中数据资产的溢出效应[J].经济与管理研究,2025,46(12):57-73.

Data Weaving: Spillover Effects of Data Assets in Supply Chain Networks

WANG Xinlan¹, XU Andi¹, SONG Xiaoyue²

(1. Shandong Technology and Business University, Yantai 264005;

2. Xi’an Jiaotong University, Xi’an 710049)

Abstract: In supply chain networks, data asset sharing has become a crucial driver of multidimensional collaboration, resource optimization, and operational efficiency gains. With the increasing centrality of data as a production factor, understanding how data assets diffuse across firm boundaries is critical for both theoretical exploration and managerial practice. This paper investigates the spillover effects of corporate data assets in supply chain networks, with particular emphasis on how data sharing advances the digital transformation and efficiency gains of interconnected enterprises.

Drawing on a panel dataset of A-share listed companies in Shanghai and Shenzhen from 2009 to 2024, the paper employs a rigorous empirical design. Specifically, Python-based machine learning techniques and the Word2Vec neural network model are used to conduct large-scale text mining of firms’ annual reports. Then, a novel data asset index is constructed, which allows for a fine-grained measurement of corporate data assets and their deployment over time. Using this indicator, the paper examines how focal firms’ data assets exert spillover effects on their supply chain partners, influencing the allocation of data assets by both suppliers and customers. The empirical findings indicate that data assets held by focal firms diffuse along supply chains, encouraging upstream and downstream enterprises to increase their own data asset investment and utilization. Mechanism analysis reveals that the observed spillover effects are primarily realized through two pathways: cooperative learning and dynamic competition. Furthermore, the paper provides new insights into the evolutionary nature of data asset spillovers. The strength of spillover effects is found to vary across stages of data asset development and to intensify as firms progress from resourceization to productization and ultimately to assetization. This dynamic trajectory underscores the importance of recognizing the maturity of data assets when assessing their value and influence. The analysis also highlights important contextual moderators: spillovers are stronger in environments with greater policy support for data assets, as well as in network structures where firms occupy central positions within supply chains.

The findings underscore the role of data assets as a novel and strategic production factor. Through their circulation and sharing, data assets facilitate information exchange, promote resource integration, and generate synergistic benefits across the supply chain. By extending data asset research into the domain of supply chain linkages, this paper not only enriches the empirical literature on spillover effects but also advances the theoretical application of data assets in supply chain management. The results provide both theoretical contributions and practical implications, offering evidence-based recommendations for policymakers and managers seeking to foster digitalization, strengthen supply chain resilience, and promote high-quality collaborative development among interconnected enterprises.

Keywords: data assets; supply chain network; cooperative learning; dynamic competition; spillover effect

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