Tian Xu

Dr. Tian Xu


Research Focus & Metrics

Academic Career Length

6Years (2021-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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10 June 2021 in Contemporary Accounting Research
Verified
Journal Article
Closed access
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MMMichael Mayberry
Michael Mayberry
HPHyun Jong Park
Hyun Jong Park
TXTian Xu
Tian Xu

Verified
Journal Article
Open access
PDF file
David S KerrDavid S Kerr
David S Kerr
KSKatherine Taken Smith
Katherine Taken Smith
LSLawrence Murphy Smith
Lawrence Murphy Smith
TXTian Xu
Tian Xu
Past and current advances in artificial intelligence (AI) have resulted in a significant impact on business and accounting. Over time, AI has slowly transformed from the 1950s to today, from rule-based systems, also known as expert systems, to the deep learning architectures and sophisticated neural networks of modern generative AI. Early AI accounting applications of expert systems included a GAAP-based expert system to assess the appropriate accounting treatment for business combinations and an expert system to determine the proper type of audit report to issue. Recent accounting expert systems have been developed for document analysis, fraud detection, evaluating credit risk, and corporate default forecasting. The purpose of this study is to examine key events in the history of AI, current applications, and potential future effects pertaining to management accounting and society overall. In addition, the relationship of AI with economic and social factors will be evaluated. The study’s findings will be of interest to management accountants, businesspersons, academic researchers, and others who are concerned with artificial intelligence and its impact on management accounting and society overall.

24 February 2026 in Risks
Verified
Journal Article
Open access
PDF file
DJDonald Ray Deis Jr.
Donald Ray Deis Jr.
JRJ. Kenneth Reynolds
J. Kenneth Reynolds
CWChristopher Wertheim
Christopher Wertheim
TXTian Xu
Tian Xu
DZDaqun Zhang
Daqun Zhang
Corporate management and their auditors are required to evaluate whether there is a risk that the company’s ability to continue as a going concern is impaired. For fixed asset-intensive firms, however, regulatory inspections consistently identify problems with auditors’ testing of property, plant, and equipment (PPE), raising doubts about whether auditors understand the risks associated with these assets. This paper examines whether auditors incorporate the risks associated with PPE into their going concern evaluation and the accuracy of that evaluation. Using probit regression on financial and auditing data of U.S. public firms contained in S&P Global Compustat North America, Audit Analytics, and the Center for Research in Security Prices (CRSP) from 2000 to 2019, this paper examines the effects of PPE intensity, impairment, and age on the likelihood that an auditor issues a going concern modification. We test the accuracy of the auditor’s going concern evaluation by comparing it to the client’s subsequent viability or bankruptcy. Our results find that PPE intensity and PPE impairments are positively associated with the likelihood of an auditor issuing a going concern modification, indicating that auditors view PPE as contributing to substantial doubt about the entity’s ability to continue as a going concern. We do not find a significant association between PPE age and going concern modification. Additionally, the going concern evaluation is more accurate for firms with higher PPE intensity. These findings imply that auditors appropriately consider PPE assets in their going concern evaluations.