企业数字化转型是否影响审计意见类型?

IF 1.5 4区 经济学 Q2 ECONOMICS Economics & Politics Pub Date : 2024-04-09 DOI:10.1111/ecpo.12284
XiaoHong Dong, YinWei Jiang
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引用次数: 0

摘要

本研究利用机器学习技术进行文本分析,探讨了企业数字化转型对2011年至2020年中国A股上市公司审计意见的影响。研究结果表明,企业数字化转型有可能通过提高内部控制质量和信息透明度来加强标准无保留审计意见。此外,研究还发现市场化水平、产权性质和行业分类等因素对这一关系的形成至关重要。这些研究成果不仅拓展了审计意见研究的理论视野,也为审计师优化审计意见的有效性提供了宝贵的见解和建议。
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Does enterprise digital transformation affect audit opinion type?—Based on business evidence of Chinese listed companies

Utilizing text analysis through machine learning techniques, this study investigates the influence of enterprise digital transformation on audit opinions within China's A-share listed companies spanning from 2011 to 2020. The findings underscore that enterprise digital transformation has the potential to bolster standard unqualified audit opinions through the enhancement of internal control quality and information transparency. Additionally, factors such as marketization level, property rights nature, and industry classification are found to be pivotal in shaping this relationship. These research outcomes not only extend the theoretical horizons of audit opinion studies but also offer valuable insights and recommendations for auditors to optimize the efficacy of audit opinions.

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来源期刊
CiteScore
2.40
自引率
10.00%
发文量
32
期刊介绍: Economics & Politics focuses on analytical political economy, broadly defined as the study of economic and political phenomena and policy in models that include political processes, institutions and markets. The journal is the source for innovative theoretical and empirical work on the intersection of politics and economics, at both domestic and international levels, and aims to promote new approaches on how these forces interact to affect political outcomes and policy choices, economic performance and societal welfare. Economics & Politics is a vital source of information for economists, academics and students, providing: - Analytical political economics - International scholarship - Accessible & thought-provoking articles - Creative inter-disciplinary analysis
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