Big data analytics-based auditing adoption in public sector: Indonesian evidence

This is the authors' abstract. We don't add key points for this paper.

This research investigates the key determinants that drive auditors’ intentions to adopt big data analytics (BDA) technology in audit practices in the public sector with a focus on the Indonesian Supreme Audit Agency (SAA). This research also examines the effect of adopting the BDA technique in auditing in improving auditor performance. This research adopts the Unified Theory of Acceptance and Use of Technology (UTAUT) framework, which integrates performance expectations, effort expectations, social influences, and facilitating conditions. This study was conducted through a survey of 126 government auditors in Indonesia. Data analysis uses the Structural Equation Model-Partial Least Square (SEM-PLS) method. Results show that performance expectancy, effort expectancy, and social influences positively influence auditors’ intention to adopt BDA in audit practice. However, facilitating conditions do not affect adoption intention. In addition, the intention to adopt BDA increases the actual usage of BDA and directly drives auditor performance. Thus, this research implies that BDA adoption can optimize auditor performance to support improvements in state financial governance.