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Intelligent manufacturing, auditor selection and audit quality

审计 业务 质量审核 选择(遗传算法) 会计 质量(理念) 运营管理 计算机科学 经济 人工智能 认识论 哲学
作者
Siqi Chen,Yang Jie
出处
期刊:Management Decision [Emerald (MCB UP)]
被引量:2
标识
DOI:10.1108/md-09-2023-1518
摘要

Purpose The purpose of this paper is to examine the influence of intelligent manufacturing on audit quality and its underlying mechanism as well as the variation in this influence across different types of organizations. Design/methodology/approach This research utilizes a difference-in-differences (DID) method to examine how enterprises that apply intelligent manufacturing choose auditors and impact their audit work. The study is based on 15,228 observations of Chinese-listed A-shares from 2011 to 2020. Findings (1) There is a strong correlation between intelligent manufacturing and audit quality. (2) This positive correlation is statistically significant only in state-owned enterprises (SOEs), those that have steady institutional investors and where the roles of the CEO and chairman are distinct. (3) Enterprises that have implemented intelligent manufacturing are more inclined to employ auditors who possess extensive industry expertise. The auditor's industry expertise plays a crucial role in ensuring audit quality. (4) The adoption of intelligent manufacturing also leads to higher audit fees and longer audit delay periods. Practical implications This paper validates the beneficial impact of intelligent manufacturing on improving corporate governance. In addition, it is recommended that managers prioritize the involvement of skilled auditors with specialized knowledge in the industry to ensure the high audit quality and the transparency of information in intelligent manufacturing enterprises. Originality/value This study builds upon previous research that has shown the importance of artificial intelligence in enhancing audit procedures. It contributes to the existing body of knowledge by examining how enterprise intelligent manufacturing systems (IMS) enhance audit quality. Additionally, this study provides valuable information on how to improve audit quality in the field of intelligent manufacturing by strategically selecting auditors based on resource dependency theory.
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