Big data analytics in enhancing public sector auditing: drivers, benefits and the moderating role of auditor certification

Purpose This study examines the adoption of big data analytics (BDA) in public sector auditing through a modified technology acceptance model (TAM) framework, focusing on system quality, complexity, self-efficacy, ease of use and usefulness. It also analyzes the impact of BDA usage on audit performance and quality, with auditor certification assessed as a moderating factor. Design/methodology/approach This study employs a survey questionnaire method and involves government auditors at Indonesia’s Supreme Audit Agency as samples. The data are analyzed using partial least squares techniques. Findings The findings show that system quality positively influences both perceived ease of use and usefulness, while complexity has no significant effect. Self-efficacy enhances ease of use but not usefulness. Both perceived ease of use and usefulness significantly drive the intention to adopt BDA, which in turn leads to actual usage. BDA usage improves audit performance and quality; however, auditor certification only moderates the relationship between BDA usage and audit performance, not audit quality. Certification also does not moderate the effect of perceived ease of use or usefulness on adoption intention. Originality/value This study offers a significant contribution by extending the TAM framework to the context of public sector auditing in developing countries, a setting often overlooked in existing literature. Uniquely, it introduces auditor certification as a moderating variable, providing fresh insights into how professional credentials shape the relationship between BDA adoption and audit outcomes.