Does XBRL help improve data processing efficiency?

Yanchao Rao and Ken Huijin Guo

International Journal of Accounting & Information Management, 2021, vol. 30, issue 1, 47-60

Abstract: Purpose - The US Securities and Exchange Commission (SEC) requires public companies to file structured data in eXtensible Business Reporting Language (XBRL). One of the key arguments behind the XBRL mandate is that the technical standard can help improve processing efficiency for data aggregators. This paper aims to empirically test the data processing efficiency hypothesis. Design/methodology/approach - To test the data processing efficiency hypothesis, the authors adopt a two-sample research design by using data from Compustat: a pooled sample (N = 61,898) and a quasi-experimental sample (N = 564). The authors measure data processing efficiency as the time lag between the dates of 10-K filings on the SEC’s EDGAR system and the dates of related data finalized in the Compustat database. Findings - The statistical results show that after controlling for potential effects of firm size, age, fiscal year and industry, XBRL has a non-significant impact on data efficiency. It suggests that the data processing efficiency benefit may have been overestimated. Originality/value - This study provides some timely empirical evidence to the debate as to whether XBRL can improve data processing efficiency. The non-significant results suggest that it may be necessary to revisit the mandate of XBRL reporting in the USA and many other countries.

Keywords: XBRL; Data aggregation; Data processing efficiency (search for similar items in EconPapers)
Date: 2021
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.emerald.com/insight/content/doi/10.110 ... d&utm_campaign=repec (text/html)
https://www.emerald.com/insight/content/doi/10.110 ... d&utm_campaign=repec (application/pdf)
Access to full text is restricted to subscribers

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eme:ijaimp:ijaim-07-2021-0155

DOI: 10.1108/IJAIM-07-2021-0155

Access Statistics for this article

International Journal of Accounting & Information Management is currently edited by Dr Xin (Robert) Luo and Professor Han Donker

More articles in International Journal of Accounting & Information Management from Emerald Group Publishing Limited
Bibliographic data for series maintained by Emerald Support ().