Internal Audit Competency, Audit Function Maturity, and ESG Assurance Engagement
Evidence From a Machine Learning Approach
Bibliographic Data
The increasing demand for Environmental, Social, and Governance (ESG) assurance has intensified scrutiny on internal auditors' roles in corporate sustainability efforts. Despite this, limited research examines how Internal Audit Competency (IAC) and Internal Audit Maturity (IAM) influence internal auditors' involvement in ESG assurance. Drawing on the Common Body of Knowledge in internal auditing dataset, this study employs a machine learning approach to assess the predictive power of IAC and IAM in shaping internal auditors' ESG assurance engagement. Our findings reveal that IAC is the most influential determinant, significantly outperforming other factors, including Chief Audit Executive (CAE) education and departmental maturity. Notably, internal audit maturity, often considered a cornerstone of audit effectiveness, exhibits a weaker‐than‐expected influence, suggesting that individual auditors' expertise outweighs structural factors in driving ESG assurance participation. Predictive performance is evaluated using a pre‐specified 80/20 train–test split (SPLIT indicator); the model explains 50.971% of the variance in the held‐out test sample (test R 2 = 0.50971; RMSE = 2.89164; MAE = 2.20016). These insights carry critical implications for organisations, regulators, and professional bodies, highlighting the need to refine competency development frameworks and reconsider the emphasis on structural maturity in internal audit functions. By bridging the gap between internal audit effectiveness and ESG assurance, this study provides a novel perspective on the intersection of corporate governance, assurance quality, and sustainable business practices
Audit · Audit plan · Control environment · Corporate governance · Information technology audit · Internal audit · Joint audit · Agricultural and Financial Auditing · Auditing, Earnings Management, Governance · Impact of AI and Big Data on Business and Society
Statistical Modeling
Representation Learning
Estimation of Causal Effects using Propensity Score Weighting
Reducing the Dimensionality of Data with Neural Networks
Improving propensity score weighting using machine learning
Propensity Score Estimation With Boosted Regression for Evaluating Causal Effects in Observational Studies.
The central role of the propensity score in observational studies for causal effects
A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity
An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies
Big Data
Machine Learning
| Unique citing works | 1 |
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| Citations per year | 1 |
| Citation span | 2026 - 2026 (1) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 1 |