corporate artificial intelligence activity and accounting information comparability: evidence from CEO generational differences

Accounting information comparability reflects not only common accounting standards but also the information-production process through which firms translate economic events into accounting outcomes. Using 12,355 firm-year observations for nonfinancial Chinese A-share listed firms from 2012 to 2022, we examine the relation between corporate AI activity and accounting information comparability. Our primary explanatory variable is the one-year-lagged standardized measure of ln(1 + AI patent applications)identified from patent classification codes, which captures firms’ verifiable AI technological capability. Firms with stronger AI capability exhibit greater accounting information comparability. Mechanism tests show that earlier AI activity is followed by stronger internal control and lower earnings management, both of which are associated with higher comparability. CEO cohort further shapes the organizational conversion of technological capability: the AI effect is stronger in firms led by CEOs born in 1970 or later. To assess the construct validity of the patent-based measure, we triangulate evidence from patents related to accounting information production, AI-related software and hardware investment, and AI signals in MD&A and annual reports. The relation between AI capability and comparability is stronger when firms make more explicit AI-related resource commitments and exhibit greater organizational attention to AI. The stronger effects among firms with greater analyst attention and higher internal coordination demands further point to the roles of external demand for comparable information and internal information-processing needs. The main findings are robust to alternative measures, double machine learning, a policy-exposure instrumental-variable design, and entropy balancing. Overall, the evidence shows how AI technological capability can translate into more comparable accounting information through information production, organizational embedding, and managerial influence.

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Publication Details

Journal
Scientific Reports
Published
2026-09-29
DOI
https://doi.org/10.1038/s41598-026-69633-w
Primary Topic
Auditing, Earnings Management, Governance
Type
article
Field-Weighted Citation Impact
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corporate artificial intelligence activity and accounting information comparability: evidence from CEO generational differences

Chunli Wang, Qianchao Li, Linrong Wu, Juping Li
Scientific Reports
Auditing, Earnings Management, Governance
article

corporate artificial intelligence activity and accounting information comparability: evidence from CEO generational differences

Chunli Wang, Qianchao Li, Linrong Wu, Juping Li
article en

Abstract

Accounting information comparability reflects not only common accounting standards but also the information-production process through which firms translate economic events into accounting outcomes. Using 12,355 firm-year observations for nonfinancial Chinese A-share listed firms from 2012 to 2022, we examine the relation between corporate AI activity and accounting information comparability. Our primary explanatory variable is the one-year-lagged standardized measure of ln(1 + AI patent applications)identified from patent classification codes, which captures firms’ verifiable AI technological capability. Firms with stronger AI capability exhibit greater accounting information comparability. Mechanism tests show that earlier AI activity is followed by stronger internal control and lower earnings management, both of which are associated with higher comparability. CEO cohort further shapes the organizational conversion of technological capability: the AI effect is stronger in firms led by CEOs born in 1970 or later. To assess the construct validity of the patent-based measure, we triangulate evidence from patents related to accounting information production, AI-related software and hardware investment, and AI signals in MD&A and annual reports. The relation between AI capability and comparability is stronger when firms make more explicit AI-related resource commitments and exhibit greater organizational attention to AI. The stronger effects among firms with greater analyst attention and higher internal coordination demands further point to the roles of external demand for comparable information and internal information-processing needs. The main findings are robust to alternative measures, double machine learning, a policy-exposure instrumental-variable design, and entropy balancing. Overall, the evidence shows how AI technological capability can translate into more comparable accounting information through information production, organizational embedding, and managerial influence.

Scientific Reports
Guangxi Normal University (CN), Guilin University
Decent work and economic growth
Openalex Percentile: Top 4%
Auditing, Earnings Management, Governance
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