
Research on the quality characteristics of accounting information in the intelligent era
- 1 University of Shanghai for Science and Technology
* Author to whom correspondence should be addressed.
Abstract
In the context of the intelligent era, the rapid development of technologies such as big data, cloud computing, and artificial intelligence has raised new challenges and requirements for the quality of accounting information. The demands of accounting information users have presented diversified characteristics, including an increased need for future information, relevance, timeliness, non-financial information, and unstructured data. These changes have exerted a profound influence on the quality characteristics of accounting information, such as the potential impact of irrelevant information, new challenges to completeness, the double-edged sword effect of timeliness, and greater difficulty in regulation and reliability issues. To adapt to these changes, the quality characteristics of accounting information need to fulfill new requirements such as faithful representation, completeness, traceability, and measurement accuracy. To guarantee the quality of accounting information, this paper puts forward safeguard measures like the application of intelligent technologies for collaborative data collection, enhancement of the professional skills of accounting personnel, establishment of an accounting information disclosure and analysis center, and strengthening of external supervision.
Keywords
intelligent era, accounting information quality, accuracy
[1]. Zhang, G., & Yu, M. (2024). The impact of intelligent financial transformation on the comparability of accounting information. Accounting Monthly, 45(20), 69-74.
[2]. Qiao, P., & Li, S. (2022). Research on the path and mechanism of blockchain in improving accounting information quality. Finance & Accounting Communication(11), 15-19+117.
[3]. Tang, M. (2024). Evaluation and optimization of enterprise accounting information quality in the big data era - Review of the book "Research on the improvement of enterprise accounting information quality in the context of big data" by Economic Science Press. Price: Theory & Practice(04), 231.
[4]. Hu, Y. (2024). On accounting information quality in the digital age. Accounting Monthly, 45(05), 11-17.
[5]. Cheng, P., Zhu, Z., & Fu, Y. (2023). Research on intelligent financial reporting based on ChatGPT. Accounting Monthly, 44(16), 64-69.
Cite this article
Zhou,Z. (2025). Research on the quality characteristics of accounting information in the intelligent era. Journal of Applied Economics and Policy Studies,18(1),65-70.
Data availability
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Journal:Journal of Applied Economics and Policy Studies
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