Posted: May 11, 2023
The evolution of audit methodologies has increasingly emphasized risk-based approaches as fundamental to effective corporate governance and financial oversight. Traditional audit planning has undergone significant transformation with the adoption of risk-based frameworks, yet the empirical evaluation of these methodologies remains constrained by conventional metrics that fail to capture the computational and organizational complexities inherent in large corporations. This research addresses this gap by introducing a novel evaluation framework that integrates computational complexity theory with organizational network analysis to assess risk-based audit planning effectiveness. Risk-based audit planning represents a paradigm shift from compliance-focused approaches to methodologies that prioritize areas of highest risk. While this theoretical foundation is well-established in audit literature, the practical implementation and evaluation of risk-based approaches have remained largely qualitative and anecdotal. The absence of robust quantitative frameworks for assessing audit planning effectiveness has limited the ability of organizations to optimize their audit functions and allocate resources efficiently. This study addresses three fundamental research questions that have received limited attention in existing literature. First, how can computational complexity metrics be applied to evaluate the efficiency of risk-based audit planning algorithms? Second, what organizational
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