The use of big data and analytics in external auditing: Does audit firm size matter? Evidence from a developing country

Abstract

Purpose: The aims of this research are to investigate the reasons for adopting big data (BD) and big data analytics (BDA), determine their extent of usage, and identify potential obstacles to their adoption in a developing country, Egypt.

Motivation: Prior literature criticized the audit profession for the slow adoption of BDA, and little is known about the adoption of BD and BDA in developing countries. The reluctance to incorporate BD and BDA into auditing can be attributed to their potential obstacles. In addition, prior studies focused on the Big-4 audit firms in developed countries with little known about adopting BD and BDA in local audit firms and developing countries.

Design/methodology/approach:To achieve the objectives of this study, 16 audit practitioners with various positions, specializations, and experience levels were interviewed. The 16 participants belong to audit firms of different sizes: international audit firms, local audit firms, and a governmental auditing agency. Thematic analysis was employed through using the MAXQDA software package to analyze the data.

Main findings: The findings revealed that the reasons for using BD and BDA go beyond improving audit efficiency and effectiveness and satisfying clients. All audit firms collect and analyze large volumes of traditional accounting data. However, the Big-4 firms manage and analyze non-financial data and new data items as complementary audit evidence. Also, it was found that the type of audit firm affects the use of these technologies, with international firms being superior to other firms. The Accountability State Authority lags behind other audit firms in adopting BD and BDA. Furthermore, it was found that some obstacles to adopting BD and BDA arise due to the specific characteristics of the Egyptian context, while others are universal.

Practical implications/Managerial impact: Determining the reasons for and obstacles to adopting BD BDA is useful for audit firms and regulators to remove these obstacles and encourage using such new audit technologies. The findings might help developers of BDA software packages to enhance their packages to meet auditor requirements. Moreover, academic scholars can benefit from the findings of this study by gaining an understanding of the main differences between developed and developing countries in relation to adopting BD and BDA.

Novelty/Contribution: This study was conducted in Egypt, a developing country with a an underdeveloped audit environment. Therefore, this study enriches the relevant literature by providing information about BD and BDA in an unexplored developing environment. Besides, it delves deeper into the reasons and obstacles to using BD and BDA in external financial auditing in a developing country. Also, it adds to the literature by identifying the most common BDA software packages and techniques that auditors use in a developing country. Moreover, it considers audit practitioners’ viewpoints on audit firms of various sizes.

Keywords:

• adoption of BDA
• audit firm size
• reasons for BDA adoption
• external financial auditing
• obstacles to BDA adoption
• qualitative research

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1 The exception to this was Participant 16, who was interviewed on 18 January 2023.

2 More details about the ASA strategy are available at https://asa.gov.eg/Page.aspx?id=5_1284.

3 More details are available at http://www.mpbs.gov.eg/Arabic/AboutMinistry/Pages/MissionAndVision.aspx.

4 The authors made this participant anonymous because his/her quotation could reveal his/her identity and the audit firm in which he/she works.

5 When participants use the term comparative analysis, they mean setting expectations at the beginning of the audit, comparing these expectations to actual numbers, and determining variances as indicators of potential red flags.

6 Citizen data scientists are not advanced experts in data science but have sufficient skills and knowledge to execute simple and moderately sophisticated analytical tasks that would have previously required more expertise. Citizen data scientists do not typically have coding skills but can use no- or low-coding analytics tools.