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论文编号：	14741
作者编号：	1120180933
上传时间：	2024/6/7 15:25:44
中文题目：	数据分析、相关审计准则与审计师审计调整决策
英文题目：	Data Analytics, Related Auditing Standards and Auditor’s Audit Adjustment Decisions
指导老师：	张继勋
中文关键字：	数据分析； 审计准则明确性； 审计调整决策
英文关键字：	Data Analytics; Clarity of Auditing Standards; Audit Adjustment Decisions
中文摘要：	近年来，随着现代信息技术的迅速发展，大数据等新兴科技正在深刻地改变 着企业的经营环境和审计师的信息环境，传统的审计方法可能难以适应这一变 化，因此审计师开始探索并应用创新的数据分析。数据分析拥有挖掘并分析大量 数据的能力，能够通过计算机模型对未来可能发生的经济活动及结果进行预测， 帮助审计师生成高质量的会计估计并提供审计调整建议。实践中审计调整建议 的来源存在差异，有些来源于数据分析，有些来源于传统的人类估值专家。虽然 数据分析具备提高审计质量和审计效率的潜力，但是目前数据分析的使用尚不 普遍。部分原因在于现行审计准则缺少对数据分析的明确许可。 针对这一问题， 国际审计准则制定机构正在考虑对准则进行修改， 并在征求意见稿中明确许可 了对数据分析的使用。 本文以心理学中的算法厌恶、罪责控制模型以及问责理论 为基础，采用实验研究方法，分析并检验了数据分析的使用、审计准则有无明确 相关规定对审计师审计调整决策的影响。 本文共分为六章，具体内容如下：第一章引言， 基于研究背景提出研究问题、 明确研究意义并说明研究框架、方法和创新；第二章回顾并梳理审计中使用数据 分析的相关文献；第三章以心理学中的算法厌恶、罪责控制模型以及问责理论为 基础，分析数据分析的使用、审计准则有无明确相关规定对审计师审计调整决策 的影响，并提出研究假设；第四章描述实验设计和过程， 介绍自变量的操控方法 以及因变量和中介变量的测量方法；第五章根据实验结果进行随机化检验、假设 检验和附加分析；第六章总结研究结论， 指出局限性和未来研究方向。 本文主要的研究结论和贡献包括： 1. 理论分析认为， 根据心理学中的算法厌恶现象， 数据分析的使用会影响 审计师的审计调整决策。 根据算法厌恶、罪责控制模型和问责理论， 数据分析的 使用和审计准则有无明确相关规定会共同影响审计师的审计调整决策。 在审计 准则未明确许可使用数据分析的情况下， 数据分析的使用会影响审计师向监管 机构证明合理性的容易程度，从而影响审计调整决策； 在审计准则明确许可使用 数据分析的情况下，审计调整建议来源于数据分析或人类估值专家不会影响审 计调整决策。摘要 II 2. 实验结果表明，数据分析的使用会影响审计师的审计调整决策，具体来 说，相比于审计调整建议来源于人类估值专家，当来源于数据分析时， 审计师提 出的审计调整金额更低。 以往涉及审计师使用数据分析的文献大多采用访谈等 定性研究的方法，本文从定量研究的层面补充了审计师使用数据分析经济后果 的相关文献。 3. 实验结果表明，数据分析的使用和审计准则有无明确相关规定会共同影 响审计师的审计调整决策，具体来说： 在审计准则未明确许可使用数据分析的情 况下，相比于审计调整建议来源于人类估值专家，当来源于数据分析时，审计师 提出的审计调整金额更低； 在审计准则明确许可使用数据分析的情况下，不论审 计调整建议来源于数据分析或人类估值专家，审计师提出的审计调整金额的差 异比较小。 最新发布的国际审计准则征求意见稿明确许可了数据分析在审计中 的应用，但已有研究尚未对此进行关注，本文能够为监管机构和准则制定者提供 有关审计准则修订的先验性证据。 4. 中介效应分析的结果表明， 在审计准则未明确许可使用数据分析的情况 下， 审计调整建议的来源对审计师审计调整决策的影响， 被感知的执行高质量审 计的意图所中介； 审计调整建议的来源对审计师审计调整决策的影响， 被审计师 向监管机构证明合理性的容易程度所中介。
英文摘要：	In recent years, with the rapid development of modern information technology, emerging technologies such as big data are profoundly changing the business environment of enterprises and the information environment of auditors. Traditional audit methods may be difficult to adapt to this change, so auditors have begun to explore and apply innovative data analytics. Data analytics has the ability to mine and analyze large amounts of data, and can use computer models to predict possible future economic activities and related results, helping auditors generate high-quality accounting estimates and audit adjustment advice. In audit practice, sources of audit adjustment advice are different. Some advice comes from data analytics, and some comes from traditional human valuation specialists. Although data analytics has the potential to improve audit quality and audit efficiency, the use of audit data analytics is not yet common. This is partly because current auditing standards lack explicit approval for the use of data analytics. In response to this problem, international auditing standard-setting bodies are considering revising auditing standards, and the use of data analytics is explicitly approved in the exposure draft of revised auditing standards. This dissertation is based on the algorithm aversion, culpable control model and accountability theory in psychology, and adopts experimental research methods to analyze and test the impact of data analytics usage and whether auditing standards explicitly approve it on auditors' audit adjustment decisions. This dissertation is divided into six chapters, with the specific contents as follows: Chapter 1 is an introduction, which proposes research questions and implications based on research background, builds research framework, and explains innovations; Chapter 2 systematically reviews and comments on the literature related to data analytics usage in auditing; Chapter 3 draws on algorithm aversion, culpable control model and accountability theory in psychology to analyze and develop hypotheses about the effect of data analytics usage and whether auditing standards explicitly approve it on auditor's audit adjustment decisions; Chapter 4 describes experimental design, participants and process, clarifies manipulation of independent variables, and measurement of dependent and mediating variables; Chapter 5 analyzes experiment data, tests hypotheses, and conducts additional analysis; Chapter 6 concludes, and talks about research limitations and future research questions. The main research conclusions and contributions of this dissertation include: 1. Theoretical analysis believes that according to algorithm aversion in psychology, the use of data analytics will affect auditors’ audit adjustment decisions; and that according to algorithm aversion, culpable control model and accountability theory, data analytics usage and auditing standards clarity will jointly affect audit adjustment decisions. 2. Experiment results show that the use of data analytics will affect auditors' audit adjustment decisions. Specifically, compared with audit adjustment advice derived from human valuation specialists, auditors will propose smaller audit adjustment when derived from data analytics. Previous literature on auditors' use of data analysis are mainly qualitative research, such as interviews, and this dissertation contributes to it by examining economic consequences of audit data analytics with quantitative research method. 3. Experiment results show that data analytics usage and whether auditing standards explicitly approve it will jointly affect auditors' audit adjustment decisions. Specifically, when auditing standards do not explicitly approve data analytics usage, auditors will propose smaller audit adjustment when audit adjustment advice comes from data analytics than human valuation specialists; when auditing standards explicitly approve data analytics usage, the difference between proposed audit adjustment is relatively small, regardless of the source of audit adjustment advice, i.e., data analytics or human valuation specialists. The latest exposure draft of revised auditing standards explicitly approves the use of data analytics in auditing, but existing literature paid no attention to it. This dissertation provides priori evidence on recent auditing standards revision for regulators and standard setters. 4. The results of mediation effect analysis show that, when auditing standards do not explicitly approve data analytics usage, the impact of source of audit adjustment advice on auditors' audit adjustment decisions is mediated by the intention to conduct high-quality audits; the impact of source of audit adjustment advice on auditors' audit adjustment decisions is mediated by the ease with which auditors can justify to regulators.
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