Liang Lijun, Dai Tongxin, Cui Litong, Song Meng
School of Business, Beijing Information Science & Technology University, No. 55, Taihang Road, Changping District, Beijing, 102206, China.
School of Management, Hebei University, No. 2666, Qiyi East Road, Bailou Town, Lianchi District, Baoding City, 071002, Hebei Province, China.
Sci Rep. 2025 Aug 25;15(1):31222. doi: 10.1038/s41598-025-16444-0.
In the global wave of digital transformation, the extent to which digital audit talent mitigates detection risk remains understudied. This study investigates whether and how the adequacy of digital audit talent reduces the level of detection risk through the degree of audit digitization. The empirical analysis draws on data from the Chinese Institute of Certified Public Accountants ([Formula: see text]) (2020-2022). The results reveal that: (1) Adequacy of digital audit talent significantly reduces detection risk; (2) Digital audit talent enhances audit digitization, whereas digitization itself increases detection risk; (3) The relationship between the adequacy of digital audit talent and the level of detection risk exhibits significant heterogeneity across audit institutions of different sizes. This study expands the Resource-Based View by validating the transmission paths of talent, technology, and risk, and proposes a tiered policy framework to address digitization risks. Limitations include regional data scope, urging future research on cross-country comparisons and generative AI's impact.
在全球数字转型浪潮中,数字审计人才降低检查风险的程度仍未得到充分研究。本研究探讨数字审计人才的充足性是否以及如何通过审计数字化程度来降低检查风险水平。实证分析采用了中国注册会计师协会([公式:见正文])(2020 - 2022年)的数据。结果表明:(1)数字审计人才的充足性显著降低检查风险;(2)数字审计人才提升审计数字化水平,而数字化本身会增加检查风险;(3)数字审计人才的充足性与检查风险水平之间的关系在不同规模的审计机构中表现出显著的异质性。本研究通过验证人才、技术和风险的传导路径扩展了资源基础观,并提出了一个分层政策框架来应对数字化风险。局限性包括区域数据范围,促使未来开展跨国比较研究以及关于生成式人工智能影响的研究。