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Auditors’ skills and knowledge of data analytics and big data can influence their judgment at the audit planning stage. At this stage, the auditor will determine the level of audit risk and estimate how long the audit will take. This study aims to test whether big data and data analytics affect auditors’ judgment by adopting the cognitive fit theory.

This was an experimental study involving 109 accounting students as participants. The 2 × 2 factorial design between subjects in a laboratory setting was applied to test the hypothesis.

First, this study supports the proposed hypothesis that participants who are provided with visual analytics information will rate audit risk lower than text analytics. Second, participants who receive information on unstructured data types will assess audit risk (audit hours) higher (longer) than those receiving structured data types. In addition, those who receive information from visual analytics results have a higher level of reliance than those receiving text analytics.

This research has implications for external and internal auditors to improve their skills and knowledge of data analytics and big data to make better judgments, especially when the auditor is planning the audit.

Previous studies have examined the effect of data analytics (predictive vs anomaly) and big data (financial vs non-financial) on auditor judgment, whereas this study examined data analytics (visual vs text analytics) and big data (structured and unstructured), which were not tested in previous studies.

There is an increasing recognition in the public audit profession that the emergence of big data as well as the growing use of business analytics by audit clients has brought new opportunities and challenges. That is, should more complex business analytics beyond the customary analytical procedures be used in the engagement and if so, where? Which techniques appear to be most promising? This paper starts the process of addressing these questions by examining extant external audit research. 301 papers are identified that discuss some use of analytical procedures in the public audit engagement. These papers are then categorized by technique, engagement phase, and other attributes to facilitate understanding. This analysis of the literature is categorized into an External Audit Analytics (EAA) framework, the objective of which is to identify gaps, to provide motivation for new research, and to classify and outline the main topics addressed in this literature. Specifically, this synthesis organizes audit research, thereby offering guidelines regarding possible future research about approaches for more complex and data driven analytics in the engagement.

This study aims to explore the implementation of data analytics in the Big-Four accounting firms, including the extent to which a digital transformation is changing the work of financial auditors, why it is doing so and how these firms are managing the transformation process.

The authors conducted 23 interviews with 20 participants across four hierarchical levels from three of the Big-Four accounting firms in New Zealand.

The firms have entered the era of “smart audit systems”, in which auditors provide deep business insights that are communicated more effectively through data visualisation. The full potential, however, of data analytics depends not only on the transformation process within accounting firms but also on improvement in the quality of IT systems in client companies. The appointment of transformation managers, the recruitment of technology-savvy graduates and the provision of extensive training are helping to embed data analytics in the Big-Four firms. Accounting graduates in financial audit now need to show that they have the aptitude to become “citizen data scientists”.

The findings explain how data analytics is being embraced in the Big-Four auditing firms and underline the implications for those who work in them.

The findings challenge the “technological reluctance” thesis. In contrast, the authors observe a climate of positive attitudes towards new technology and accompanying actions in the Big-Four firms. The authors show how branches of the Big-Four firms operating distantly from their global headquarters, and with smaller economies of scale, are implementing the new technologies that characterise their global firms.

This research paper aims to investigate the effects of internal audit’s (IA) use of data analytics and the performance of consulting activities on perceived IA quality.

The authors conduct a 2 × 2 between-subjects experiment among upper and middle managers where the use of data analytics and the performance of consulting activities by internal auditors are manipulated.

Results highlight the importance of internal auditor use of data analytics and performance of consulting activities to improve perceived IA quality. First, managers perceive internal auditors as more competent when the auditors use data analytics. Second, managers perceive internal auditors’ recommendations as more relevant when the auditors perform consulting activities. Finally, managers perceive an improvement in the quality of relationships with internal auditors when auditors perform consulting activities, which is strengthened when internal auditors combine the use of data analytics and the performance of consulting activities.

From a theoretical perspective, this research builds on the IA quality framework by considering digitalization as a contextual factor. This research focused on the perceptions of one major stakeholder of the IA function: senior management. Future research should investigate the perceptions of other stakeholders and other contextual factors.

This research suggests that internal auditors should prioritize the development of the consulting role in their function and develop their digital expertise, especially expertise in data analytics, to improve perceived IA quality.

This research tests the impacts of the use of data analytics and the performance of consulting activities on perceived IA quality holistically, by testing Trotman and Duncan’s (2018) framework using an experiment.

The benefits of data analytics in the internal audit function (IAF) are clear; less is known about IAF adoption of analytics. The purpose of this study is to examine the factors driving IAF adoption of analytics.

The Common Body of Knowledge of Internal Auditing Database (IIA, 2015) provides auditor responses on key variables of analysis.

The results of this study indicate the most critical adoption factor is data-specific IT knowledge in the IAF. Critical thinking skills and business knowledge of chief audit executive (CAEs) also contribute to adoption. IAFs with fraud risk detection responsibly are more likely to adopt. IAFs in technologically advanced cultures are more likely to adopt analytics.

The results of this study document the critical factors driving adoption of audit analytics, benefitting both industry and research.

This paper aims to investigate how the internal audit function (IAF) modifies its activities and practices in relation to the digitalisation the organisation. This paper specifically examines the use of data analytics and the performance of consulting activities by internal auditors.

This paper is based on a survey conducted with 82 chief audit executives based in the USA and members of the institute of internal auditors.

Results indicate a positive relation between the organisation’s level of digitalisation and the use of data analytics by internal auditors during their missions. Results also indicate that the organisation’s level of digitalisation has an indirect effect on the proportion of the internal audit planning dedicated to consulting activities. Specifically, the use of data analytics mediates the relationship between the organisation’s level of digitalisation and the proportion of the internal audit planning dedicated to consulting activities.

This research was conducted amongst internal auditors based in the US Future research could investigate the insights of other internal audit stakeholders and investigate different legal contexts.

Results show that digitalisation increases the use of data analytics by internal auditors and the performance of consulting activities. The results, therefore, highlight the importance of these two aspects for the IAF to continue to bring value to organisations.

This research provides more insights on internal audit working practices. The digitalisation of the organisation leads the IAF to use more data analytics and perform more consulting activities.

The role of big data and data analytics in the audit engagement process is evident. Notwithstanding, understanding how big data influences cognitive processes and, consequently, on the auditors’ judgment decision-making process is limited. The purpose of this paper is to present a conceptual framework on the cognitive process that may influence auditors’ judgment decision-making in the big data environment. The proposed framework predicts the relationships among data visualization integration, data processing modes, task complexity and auditors’ judgment decision-making.

The methodology to accomplish the conceptual framework is based on a thorough literature review that consists of theoretical discussions and comparative studies of other authors’ works and thinking. It also involves summarizing and interpreting previous contributions subjectively and narratively and extending the work in some fashion. Based on this approach, this paper formulates four propositions about data visualization integration, data processing modes, task complexity and auditors’ judgment decision-making. The proposed framework was built from cognitive theory addressing how auditors process data into useful information to make judgment decision-making.

The proposed framework expects that the cognitive process of data visualization integration and intuitive data processing mode will improve auditors’ judgment decision-making. This paper also contends that task complexity may influence the cognitive process of data visualization integration and processing modes because of the voluminous nature of data and the complexity of business processes. Hence, it is also expected that the relationships between data visualization integration and audit judgment decision-making and between processing mode and audit judgment decision-making will be moderated by task complexity.

There is a dearth of studies examining how big data and big data analytics affect auditors’ cognitive processes in making decisions. This paper will help researchers and auditors understand the behavioral consequences of data visualization integration and data processing mode in making judgment decision-making, given a certain level of task complexity.

With the advent of big data and the evolution of innovative audit procedures, the constructed framework can be used as a theoretical foundation for future empirical studies concerning auditors’ judgment decision-making. It highlights the potential of big data to transform the nature and practice of accounting and auditing.

This paper aims to explore organisational intentions to use Big Data and Big Data Analytics (BDA) in external auditing. This study conceptualises different contingent motivating factors based on prior literature and the views of auditors, business clients and regulators regarding the external auditing practices and BDA.

Using the contingency theory approach, a literature review and 21 in-depth interviews with three different types of respondents, the authors explore factors motivating the use of BDA in external auditing.

The study presents a few key findings regarding the use of BD and BDA in external auditing. By disclosing a comprehensive view of current practices, the authors identify two groups of motivating factors (company-related and institutional) and the circumstances in which to use BDA, which will lead to the desired outcomes of audit companies. In addition, the authors emphasise the relationship of audit companies, business clients and regulators. The research indicates a trend whereby external auditors are likely to focus on the procedures not only to satisfy regulatory requirements but also to provide more value for business clients; hence, BDA may be one of the solutions.

The conclusions of this study are based on interview data collected from 21 participants. There is a limited number of large companies in Lithuania that are open to co-operation. Future studies may investigate the issues addressed in this study further by using different research sites and a broader range of data.

Current practices and outcomes of using BD and BDA by different types of respondents differ significantly. The authors wish to emphasise the need for audit companies to implement a BD-driven approach and to customise their audit strategy to gain long-term efficiency. Furthermore, the most challenging factors for using BDA emerged, namely, long-term audit agreements and the business clients’ sizes, structures and information systems.

The original contribution of this study lies in the empirical investigation of the comprehensive state-of-the-art of BDA usage and motivating factors in external auditing. Moreover, the study examines the phenomenon of BD as one of the most recent and praised developments in the external auditing context. Finally, a contingency-based theoretical framework has been proposed. In addition, the research also makes a methodological contribution by using the approach of constructivist grounded theory for the analysis of qualitative data.

The main objective of this study is to examine the impact of audit data analytics (ADA) on audit quality (AQ) and audit review continuity (ARC).

Using 452 CPAs in Thailand as samples, mail questionnaires were used and sent to collect the data. Descriptive analysis, correlation matrix and path analysis were used to analyze the data.

The results of this study indicated that audit data analytics had a positive impact on AQ and ARC. Cybersecurity, used as a moderator in this study, was found to be the interaction between ADA, AQ and review continuity.

Auditors and audit firms can consider using big data in their data analytics to improve AQ and ARC.

Resource advantage theory has been used in this study to explain the impact of ADA on AQ and ARC in Thailand.