Classifying internal audit quality using textual analysis: the case of auditor selection

Abstract

Purpose

This paper aims to assess internal audit quality (IAQ) by using automated textual analysis of disclosures of internal audit mechanisms in annual reports.

Design/methodology/approach

This paper uses seven text mining techniques to construct classification models that predict whether companies listed on the Athens Stock Exchange are audited by a Big 4 firm, an auditor selection that prior research finds is associated with higher IAQ. The classification accuracy of the models is compared to predictions based on financial indicators.

Findings

The results show that classification models developed using text analysis can be a promising alternative proxy in assessing IAQ. Terms, N-Grams and financial indicators of a company, as they are presented in the annual reports, can provide information on the IAQ.

Practical implications

This study offers a novel approach to assessing the IAQ by applying textual analysis techniques. These findings are important for those who oversee internal audit activities, assess internal audit performance or want to improve or evaluate internal audit systems, such as managers or audit committees. Practitioners, regulators and investors may also extract useful information on internal audit and internal auditors by using textual analysis. The insights are also relevant for external auditors who are required to consider various aspects of corporate governance, including IAQ.

Originality/value

IAQ has been the subject of thorough examination. However, this study is the first attempt, to the authors’ knowledge, to introduce an innovative text mining approach utilizing unstructured textual disclosure from annual reports to develop a proxy for IAQ. It contributes to the internal audit field literature by further exploring concerns relevant to IAQ.

Citation

Boskou, G., Kirkos, E. and Spathis, C. (2019), "Classifying internal audit quality using textual analysis: the case of auditor selection", Managerial Auditing Journal, Vol. 34 No. 8, pp. 924-950. https://doi.org/10.1108/MAJ-01-2018-1785