Tang Yungang, Liu Yaoqian, Liu Daxin
School of Economics and Management, Quanzhou University of Information Engineering, Quanzhou, Fujian, China.
Faculty of Humanities, Arts and Social Sciences, University of Exeter, Exeter, United Kingdom.
PLoS One. 2025 Aug 18;20(8):e0328926. doi: 10.1371/journal.pone.0328926. eCollection 2025.
In the digital economy era, the significance of data assets has increasingly become evident, particularly against the backdrop of the rapid development of Generative Artificial Intelligence. This paper constructed a data asset valuation model based on Generative AI, aimed at dynamically assessing the commercial value of data assets. The model integrates data feature extraction, value generation algorithms, and market adaptability evaluations to address the shortcomings of traditional valuation methods in dynamic market environments. The validity and applicability of the model were verified through an empirical analysis of data from Chinese A-share listed companies from 2015 to 2023. The results indicated that the integrated model exhibited a significant advantage over individual models in accuracy and stability, especially in data-intensive industries such as information technology and financial services. This research provided new perspectives and methodologies for enterprises in digital transformation and data asset management, thereby promoting the sustainable development of the data economy.
在数字经济时代,数据资产的重要性日益凸显,尤其是在生成式人工智能快速发展的背景下。本文构建了一个基于生成式人工智能的数据资产估值模型,旨在动态评估数据资产的商业价值。该模型整合了数据特征提取、价值生成算法和市场适应性评估,以弥补传统估值方法在动态市场环境中的不足。通过对2015年至2023年中国A股上市公司数据的实证分析,验证了该模型的有效性和适用性。结果表明,该集成模型在准确性和稳定性方面比单个模型具有显著优势,特别是在信息技术和金融服务等数据密集型行业。本研究为企业数字化转型和数据资产管理提供了新的视角和方法,从而推动了数据经济的可持续发展。