Data Analytics Adoption and Audit Evidence Quality Enhancement

Abstract

This research investigates the transformative impact of data analytics adoption on
the quality of audit evidence, proposing a novel framework that integrates cognitive
science principles with traditional audit methodologies. While prior literature has examined data analytics in auditing from a technological efficiency perspective, this study
uniquely conceptualizes audit evidence quality through the lens of cognitive load theory
and pattern recognition fidelity. We argue that conventional audit approaches, even
when supplemented with basic analytical tools, often overwhelm auditor cognition with
unstructured data, thereby diminishing evidence quality through confirmation bias and
pattern neglect. Our methodology develops and tests the Cognitive-Audit Analytics
Integration (CAAI) framework, which restructures the audit evidence collection process around human cognitive architecture. Through a controlled experiment with 142
audit professionals and a field study analyzing 78 audit engagements, we demonstrate
that the CAAI framework significantly enhances evidence quality across three novel dimensions: inferential robustness, causal transparency, and predictive validity. Results
indicate a 37