Identifying XBRL's data and information quality dimensions using text mining and topic analysis

Perdana, Arif, Robb, Alastair and Rohde, Fiona (2013). Identifying XBRL's data and information quality dimensions using text mining and topic analysis. In: 29 WCARS: 29th World Continuous Auditing and Reporting Symposium 2013. 29 WCARS: 29th World Continuous Auditing and Reporting Symposium 2013, Brisbane, Australia, (1-10). 21–22 November 2013.

Attached Files (Some files may be inaccessible until you login with your UQ eSpace credentials)
Name Description MIMEType Size Downloads
Author Perdana, Arif
Robb, Alastair
Rohde, Fiona
Title of paper Identifying XBRL's data and information quality dimensions using text mining and topic analysis
Conference name 29 WCARS: 29th World Continuous Auditing and Reporting Symposium 2013
Conference location Brisbane, Australia
Conference dates 21–22 November 2013
Proceedings title 29 WCARS: 29th World Continuous Auditing and Reporting Symposium 2013
Place of Publication Newark, NJ, USA
Publisher Rutgers University
Publication Year 2013
Sub-type Fully published paper
Open Access Status
Start page 1
End page 10
Total pages 10
Collection year 2014
Language eng
Formatted Abstract/Summary
The expectations for improvements to Data and Information Quality (DIQ) provided by XBRL are high, however, the applicable DIQ dimensions of XBRL remain unclear. To help gain insights into the relevant DIQ dimensions we explore professional perspectives relative to XBRL’s DIQ. We used professional discussions in social media, particularly in LinkedIn groups, to help obtain such perspectives. Prior research in XBRL has derived the DIQ dimensions largely from information systems (IS) and accounting literature. The IS and accounting research, however, only evaluated a limited number of XBRL’s DIQ dimensions (e.g., ease of understanding, value added, and relevancy, reliability, understandability, timeliness and comparability). The IS and accounting research fields, however, have yet to formulate a framework that assesses XBRL’s DIQ. This paper explores the discussion taking place on LinkedIn to seek insights into which DIQ dimensions most interest XBRL users. Text mining and topic analysis using sample data from the three largest LinkedIn XBRL groups were conducted to uncover the most relevant DIQ dimensions of XBRL. The findings of this study are expected to help direct future research into the DIQ dimensions of XBRL that should be empirically investigated.
Keyword eXtensible business reporting language
XBRL
Social media
Topic analysis
Text mining
LinkedIn
Q-Index Code E1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Conference Paper
Collections: Official 2014 Collection
UQ Business School Publications
 
Versions
Version Filter Type
Citation counts: Google Scholar Search Google Scholar
Created: Sat, 01 Feb 2014, 17:40:25 EST by Arif Perdana on behalf of UQ Business School