Corporate investments in artificial intelligence and audit costs: does audit quality matter?
Mohsen Anwar Abdelghaffar Saleh et al.
What the paper says
Purpose This study aims to investigate the influence of clients’ investments in artificial intelligence (CINV_AI) on audit costs within the Chinese context. Furthermore, this study moderates the role of audit quality on the relationship between corporate investments in AI and audit costs. Design/methodology/approach To test the hypotheses, this study uses an ordinary least squares regression using a final sample of 26,654 firm-year observations spanning the period 2016–2023. To mitigate potential endogeneity concerns, the researchers adopted the instrumental variable technique, specifically the two-stage least squares method. Findings This study reveals that corporate investments in AI has a statistically significant positive effect on audit costs, suggesting that clients with high investments in AI-increasing operational complexity and risk, increasing audit effort, improving audit efficiency and ultimately incurring higher audit costs. Furthermore, the results indicate that audit quality positively and significantly reinforces the link between corporate investments in AI and audit costs. Finally, the robustness tests support the main findings and confirm their validity. Practical implications This paper provides valuable insights for corporate managers, investors and auditors. For managers and investors, it emphasizes that AI implementation constitutes a substantial investment, encompassing considerable direct expenditures on assets and technology, as well as indirect costs such as increasing audit costs. For auditors, it emphasizes that these AI investments necessitate more audit effort and team members with specific IT expertise. Originality/value The results provide new evidence contributing to the recent inconclusive literature that investigates the impact of client IT capabilities (AI) on audit costs. To the best of the authors’ knowledge, this is the first study to investigate the moderating role of audit quality in the relationship between corporate AI investment and audit costs.
1 citation
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.16 × 0.4 = 0.06 |
| M · momentum | 0.53 × 0.15 = 0.08 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.