中国上市公司数据资产入表行为及动机研究

Abstract

As the proportion of the global digital economy's output value continues to rise, the capitalization of data assets in corporate accounting has become a crucial issue for corporate value reconstruction and market resource allocation. In 2023, China issued the “Interim Provisions on the Accounting Treatment of Enterprise Data Resources”, clarifying the accounting standards for the capitalization of data assets, which marks a key step in the transformation of data elements from resources to assets. This paper uses Chinese listed companies in the first, second, and third quarters of 2024 as samples and employs the matching method to examine the driving mechanisms and heterogeneous characteristics of the capitalization of data assets. It constructs a theoretical analysis framework from a three - dimensional perspective that combines the accumulation of historical data resources, auditor prudence, and the moderating relationship between them. The capitalization of data assets is not only an inevitable choice for enterprises to comply with policies but also a strategic move for them to enhance their competitive advantages based on their data resource endowments.
Firstly, by comparing the differences in data asset disclosure from the first to the third quarter of 2024, this paper finds that the disclosure of data assets has gradually extended from on-balance sheet amounts to note source descriptions and off-balance sheet scenarios, and that the rate of companies with larger market capitalization has increased significantly, with the headline companies demonstrating a significant demonstration effect. China's listed companies pay more attention to data asset management, more than 90% of them have listed “data resources” in their financial statements, but the disclosure rate of the specific amount is still low, about two-thirds of the companies choose “intangible assets”, focusing on the long-term value of data precipitation, one-third of the companies choose “intangible assets”, and one-third of the companies choose “intangible assets”, focusing on the long-term value of data precipitation. About two-thirds of enterprises choose the “intangible assets” account, focusing on the long-term value precipitation of data, one-third of enterprises adopt the “development expenditure” account, and the manufacturing industry explores the innovative application of the “inventory” account. The above differences in the accounting treatment reflect the exploration of the path of realizing the value of the data elements in various industries, i.e., science and technology enterprises strengthen data The above differences in accounting treatment reflect the exploration of the value realization path of data elements in various industries, i.e., technology enterprises strengthen data assetization, traditional enterprises reconstruct valuation logic through data, and small and medium-sized enterprises make use of R&D investment to realize value transformation.
Secondly, the empirical results show that the driving mechanism of the capitalization of data assets exhibits complex characteristics, and its core driving force stems from the dynamic game between enterprise resource endowments and audit supervision. By combining core variables such as historical data resources, audit fees, and auditor prudence, this study systematically examines the driving mechanism and moderating effect of the capitalization of data assets. It is found that in terms of driving factors, historical data resources and auditor prudence are significantly positively correlated with the probability of data asset capitalization. At the same time, historical data resources and auditor prudence play a significant positive moderating role in the impact on the capitalization behavior, indicating that the combination of high - quality data foundation and strict audit ultimately reduces the uncertainty risk in the capitalization process and promotes enterprises to achieve data assetization more stably. Through robustness tests, the research conclusions are verified by replacing the core explanatory variables. Whether digital transformation or digital technology innovation is used to replace historical data resources, the significance direction and economic meaning of the main effect and moderating effect remain unchanged. The cross - sectional analysis results show that state - owned enterprises, enterprises with a high proportion of socially - trained executives, and high - tech enterprises focus more on historical data and auditor prudence; non - state - owned enterprises, enterprises with a low proportion of socially - trained executives, and non - high - tech enterprises are more affected by operating characteristics and traditional financial factors respectively. High finance constraint firms focus more on historical data resources and auditor prudence in driving data assets to the table, while low finance constraint firms rely more on factors such as changes in external auditors.
Third, this paper proposes the following policy recommendations: First, enterprises should prioritize data asset management and promote information disclosure transformation. Management should integrate data asset utilization and management into strategic planning, establish data asset management systems, and clarify data acquisition, storage, and application processes to enhance operational efficiency and market adaptability. Differentiated guidance mechanisms should be established based on ownership and industry characteristics. State-owned enterprises should focus on systematic integration and standardization of historical data, forming specialized task forces to develop unified data classification, storage, and sharing standards to address data silos. Non-state-owned enterprises should optimize audit resource allocation, reduce high audit costs through tax incentives or subsidies, and simplify compliance procedures. High-tech enterprises should formulate specialized technology transformation support policies, establish data asset innovation funds, and support technology R&D and pilot applications. Non-high-tech enterprises should strengthen data governance training to improve basic data processing capabilities. Second, the data asset ownership and valuation system should be improved to reduce audit risks. It is recommended to accelerate the establishment of a unified national standard for data asset ownership, valuation, and measurement, clarify the asset attributes and recognition conditions of data resources, and promote the participation of third-party professional institutions in data asset valuation to create a multi-tiered audit service market. A dynamic audit fee subsidy mechanism should be explored to provide phased financial support for SMEs or enterprises with large-scale data assets. Finally, the transparency of the audit service market should be enhanced by establishing a quality evaluation system for audit institutions, guiding enterprises to select cost-effective audit services. At the technological level, enterprises should be encouraged to adopt blockchain-based data certification and AI valuation models to improve the efficiency and accuracy of data asset valuation. These measures will effectively reduce audit risks and provide a solid institutional and technical foundation for data asset recognition.
As the market-oriented allocation of data elements deepens, data asset recognition will become a significant marker of enterprise digital transformation. In the future, policymakers need to further refine the data asset ownership and valuation system, promote the construction of cross-industry data sharing and trading platforms, and facilitate the efficient circulation and value transformation of data elements. Simultaneously, enterprises should continuously enhance their data asset management capabilities, explore diversified application scenarios for data assets, and fully leverage the critical role of data assets in improving competitiveness and market image. Through the dual drivers of policy guidance and enterprise practice, data asset recognition will inject new momentum into the high-quality development of China's economy.

Date of Award	20 May 2025
Original language	Chinese (Simplified)
Awarding Institution	China Europe International Business School
Supervisor	Lin Cheng (Supervisor) & Wei Yang (Supervisor)