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.

中文翻译：

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不受管制的自愿披露的审计数据分析和审计期望差距

该研究关注使用不受监管的自愿披露的审计数据分析在减少审计期望差距方面的有用性。它认为缺乏对非结构化自愿披露和其他大数据的信誉和保证，将影响公众用户对这些不受监管的自愿披露的质量的期望水平。因此，我们认为非金融以及金融；数据需要独立审核员的保证。因此，这将扩大审计师的作用和职责，从而提高利益相关者的满意度，并达到他们的期望，从而可能缩小审计期望的差距。审计师将需要更加依赖大数据分析和技术手段来有效地履行这一新职责。因此，我们提供了经验证据，证明审计师，银行家，投资者和学者的看法在保证不受监管的自愿披露以减少审计期望差距时支持使用审计数据分析。为此，我们将不受监管的自愿者分为8个不同类别，需要审计数据分析才能从各种来源中获取信息并以信息性和有用的方式进行分析。