This study aims to examine whether clients’ degree of digitalization and audit firms’ expertise in information technology (IT) influence audit quality (AQ).
Data of Chinese A-share firms listed on the primary board of the Shanghai and Shenzhen stock exchanges from 2011 to 2019 are taken as the sample. All the data are obtained from the China Stock Market and Accounting Research. Clients’ digitalization is determined using the keywords “AI technology,” “blockchain,” “cloud computing,” “big data technology” and “digital technology.” Auditor firm’s digital expertise is determined by the proportion of higher IT expertise. As the proxy for AQ, this study uses audit fees, given that its quantum reflects the effort auditors expend that in turn affects the AQ.
A fixed-effect regression model shows that clients with high digitalization attain AQ. This study also finds a significant and positive coefficient of audit fees, indicating that AQ is high in the same situation if an audit firm’s IT is mature and developed. Furthermore, results confirm the moderating effect of clients’ digitalization and auditors’ expertise and on AQ. Auditors’ expertise in IT mitigates the audit risk and increase AQ.
Findings can enhance AQ and corporate governance literature by clarifying how external audits must evolve through digitalization and incorporating newly developed digital tools such as big data, analytics, artificial intelligence and robotic process automation. This study also provides important insights regarding how the development of new digital tools allow the audit profession to perform as a corporate governance mechanism.
Data availability statement: The data collected in the paper are available from CSMAR.
Conflict of interest disclosure: On behalf of all authors, the corresponding author states that there is no conflict of interest.
Rahman, M.J. and Ziru, A. (2022), "Clients’ digitalization, audit firms’ digital expertise, and audit quality: evidence from China", International Journal of Accounting & Information Management, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/IJAIM-08-2022-0170
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