Document Type
Article
Abstract
This study examines audit fee determinants in the U.S. retail sector using 664 firm-year observations from 83 publicly traded firms between 2016 and 2023. Applying a multi-method approach including Ordinary Least Squares (OLS) regression, Classification and Regression Trees (CART), and K-Means Clustering, the analysis evaluates how client characteristics, auditor attributes, and engagement-specific factors influence audit pricing, particularly during disruptions such as COVID-19 and the rise of e-commerce. OLS results show that client size, operational complexity, and auditor quality are the most consistent predictors of audit fees. Internal control weaknesses, corporate restructurings, governance activity, and client prominence also contribute significantly. Notably, the results reveal that the COVID-19 pandemic did not independently alter audit fees in the retail sector, suggesting that underlying firm-level financial conditions remained the primary drivers of audit pricing during those unprecedented times. CART highlights the conditional importance of non-audit fees, while clustering reveals two distinct firm profiles: larger, lower-risk firms with higher fees and smaller, riskier firms with lower fees. These findings underscore the multidimensional nature of audit pricing and demonstrate the value of combining econometric and data-driven techniques. The study offers practical insights for auditors, audit committees, and retail executives, emphasizing risk-based audit planning and the need for industry-specific modeling for the dynamic retail environment.
Recommended Citation
Kaufinger, G., and C. Neuenschwander. 2025. Audit Fee Determinants in U.S. Retail: A Multi-Method Analysis (2016–2023). The North American Accounting Studies 8 (1): 1-32.