E-Audit, Artificial Intelligence, and Data Analytics in Internal Audit Quality: The Moderating Role of Auditor Competence

Description

Digital transformation is reshaping internal auditing, yet evidence on whether audit technologies translate into higher audit quality, and under which human conditions, remains inconsistent. This study examines the effects of e-audit, artificial intelligence (AI), and data analytics on internal audit quality and tests auditor competence as a moderator. Drawing on the Technology Acceptance Model and Task–Technology Fit theory, an explanatory survey was administered to 135 internal auditors in Indonesian ministries and agencies, state- and region-owned enterprises, local governments, and public accounting firms, and the data were analyzed using moderated regression analysis. The model explains 55.1% of the variance in audit quality (F = 22.265, p < 0.001). E-audit (β = 0.205, p = 0.004), AI (β = 0.168, p = 0.016), and auditor competence (β = 0.225, p = 0.040) positively affect audit quality, whereas data analytics has no significant direct effect (p = 0.578). Auditor competence does not moderate the e-audit relationship (p = 0.990), weakens the AI–quality relationship (β = −0.078, p = 0.001), and strengthens the data analytics–quality relationship (β = 0.092, p = 0.006). The results indicate that AI partly substitutes for auditor expertise, whereas data analytics complements it. Investment in audit technology should therefore be paired with competence development that preserves professional judgment.

Institutions

• Binus University

  Jakarta, Jakarta

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