Audit data analytics of unregulated voluntary disclosures and auditing expectations gap

Michael, A; Dixon, R

Abstract

The study is concerned with the usefulness of using audit data analytics of unregulated voluntary disclosures in reducing the auditing expectations gap. It argues that the lack of credibility and assurance of the unstructured voluntary disclosures and other big data, will impact the level of public users’ expectations towards the quality of these unregulated voluntary disclosures. Therefore, we argue that non-financial, as well as financial; data require assurance by an independent auditor. Consequently, this would expand the auditors’ role and responsibilities which will lead to raising the degree of stakeholders’ satisfaction and approaching their expectations potentially reducing the auditing expectations gap. Auditors will need to rely more heavily on big data analytics and technological techniques to perform this new role efficiently and effectively. Therefore, we provide empirical evidence that the perceptions of auditors, bankers, investors and academics, support the use of audit data analytics when providing assurance of unregulated voluntary disclosures in reducing auditing expectations gap. To do so, we categorized unregulated voluntary into 8 different categories that auditing data analytics is required to capture from various sources and analyze in an informative and useful fashion.

Citation

Michael, A., & Dixon, R. (2019). Audit data analytics of unregulated voluntary disclosures and auditing expectations gap. International Journal of Disclosure and Governance, 16(4), 188-205. https://doi.org/10.1057/s41310-019-00065-x

• Journal Article Type: Article
• Acceptance Date: Sep 7, 2019
• Online Publication Date: Sep 6, 2019
• Publication Date: Dec 31, 2019
• Deposit Date: Sep 14, 2019
• Publicly Available Date: Sep 6, 2020
• Journal: International Journal of Disclosure and Governance
• Print ISSN: 1741-3591
• Electronic ISSN: 1746-6539
• Publisher: Palgrave Macmillan
• Peer Reviewed: Peer Reviewed
• Volume: 16
• Issue: 4
• Pages: 188-205
• DOI: https://doi.org/10.1057/s41310-019-00065-x
• Public URL: https://durham-repository.worktribe.com/output/1322002