Michael, A. and Dixon, R. (2019) 'Audit data analytics of unregulated voluntary disclosures and auditing expectations gap.', International journal of disclosure and governance., 16 (4). pp. 188-205.
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.
|Full text:||Publisher-imposed embargo until 06 September 2020. |
(AM) Accepted Manuscript
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|Publisher Web site:||https://doi.org/10.1057/s41310-019-00065-x|
|Publisher statement:||This is a post-peer-review pre-copyedit version of an article published in International journal of disclosure and governance. The definitive publisher-authenticated version 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 is available online at: https://doi.org/10.1057/s41310-019-00065-x|
|Date accepted:||07 September 2019|
|Date deposited:||25 September 2019|
|Date of first online publication:||06 September 2019|
|Date first made open access:||06 September 2020|
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