Academic Journal
Audit data analytics of unregulated voluntary disclosures and auditing expectations gap.
| Τίτλος: | Audit data analytics of unregulated voluntary disclosures and auditing expectations gap. |
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| Συγγραφείς: | Michael, Amir, Dixon, Rob |
| Πηγή: | International Journal of Disclosure & Governance; Dec2019, Vol. 16 Issue 4, p188-205, 18p |
| Θεματικοί όροι: | Big data, Auditors, Disclosure |
| Περίληψη: | 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 eight different categories that auditing data analytics is required to capture from various sources and analyse in an informative and useful fashion. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Disclosure & Governance is the property of Palgrave Macmillan Ltd. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Complementary Index |
| FullText | Links: – Type: other Text: Availability: 0 CustomLinks: – Url: https://dx.doi.org/doi:10.1057/s41310-019-00065-x Name: EDS - Springer Nature Journals (s7799221) Category: fullText Text: View record at Springer |
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| Items | – Name: Title Label: Title Group: Ti Data: Audit data analytics of unregulated voluntary disclosures and auditing expectations gap. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Michael%2C+Amir%22">Michael, Amir</searchLink><br /><searchLink fieldCode="AR" term="%22Dixon%2C+Rob%22">Dixon, Rob</searchLink> – Name: TitleSource Label: Source Group: Src Data: International Journal of Disclosure & Governance; Dec2019, Vol. 16 Issue 4, p188-205, 18p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Big+data%22">Big data</searchLink><br /><searchLink fieldCode="DE" term="%22Auditors%22">Auditors</searchLink><br /><searchLink fieldCode="DE" term="%22Disclosure%22">Disclosure</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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 eight different categories that auditing data analytics is required to capture from various sources and analyse in an informative and useful fashion. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of International Journal of Disclosure & Governance is the property of Palgrave Macmillan Ltd. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1057/s41310-019-00065-x Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 18 StartPage: 188 Subjects: – SubjectFull: Big data Type: general – SubjectFull: Auditors Type: general – SubjectFull: Disclosure Type: general Titles: – TitleFull: Audit data analytics of unregulated voluntary disclosures and auditing expectations gap. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Michael, Amir – PersonEntity: Name: NameFull: Dixon, Rob IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: Dec2019 Type: published Y: 2019 Identifiers: – Type: issn-print Value: 17413591 Numbering: – Type: volume Value: 16 – Type: issue Value: 4 Titles: – TitleFull: International Journal of Disclosure & Governance Type: main |
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