<![CDATA[The increasing adoption of Big Data technologies has significantly reshaped auditing practices; however, existing scholarship on Big Data auditing remains fragmented and lacks an integrated conceptual perspective. This study aims to systematically examine how Big Data influences audit practices and audit quality across technological, institutional, and epistemic dimensions. Using a Systematic Literature Review (SLR) based on the PRISMA protocol, this study synthesizes evidence from 30 peer-reviewed journal articles indexed in Scopus and Web of Science published between 2015 and 2025. The analysis identifies three dominant research clusters: (1) technological capability, reflecting the development of analytics-driven, continuous, and predictive auditing tools; (2) institutional readiness, highlighting regulatory gaps, organizational resistance, and skill asymmetry; and (3) epistemic transformation, concerning changes in professional judgment, algorithmic transparency, and accountability structures. The findings reveal a progressive datafication of auditing, characterized by a shift from traditional ex-post verification toward real-time and predictive assurance. Although Big Data improves audit efficiency, analytical scope, and risk detection capability, its implementation remains constrained by governance uncertainty and uneven organizational capabilities. To synthesize these insights, this study proposes the Big Data Auditing Framework (BDAF), which conceptualizes audit transformation as the dynamic interaction between Technological Infrastructure, Institutional Adaptation, and Epistemic Governance, moderated by Ethical and Regulatory Oversight. This framework contributes to the literature by offering an integrative perspective on how technological and institutional factors jointly shape the evolution of data-driven auditing and provides practical implications for regulators, educators, and audit firms in strengthening technological capacity, institutional preparedness, and ethical governance in digital audit environments.]]>
