Paper Number
ICIS2025-1392
Paper Type
Short
Abstract
The integration of artificial intelligence (AI) into auditing practices presents both opportunities and challenges for audit quality. Drawing on sociomateriality theory and DeAngelo's audit quality framework, this study investigates how AI capabilities become constitutively entangled with auditing practices to transform professional judgment and ultimately audit quality. Our research model conceptualizes this entanglement through novel constructs including augmented evidence and epistemic authority, which collectively reconfigure traditional audit pathways of detection capability and reporting integrity. Using a two-wave longitudinal survey design with 600 auditors across Chinese Big Four accounting firms, we employ lagged dependent variable models and propensity score matching to establish temporal precedence for causal inference while measuring the sociomaterial entanglement of AI and audit practices. This study contributes to the literature on technology in professional contexts by advancing a sociomaterial understanding of how AI transforms professional judgment in knowledge-intensive work, with implications for audit methodology development and professional standards.
Recommended Citation
Hou, Liyuan; Zhou, Yang; LI, Huifang; and Zeng, Jicheng, "AI-Enabled Audit Processes: A Sociomaterial Investigation of Professional Judgment" (2025). ICIS 2025 Proceedings. 5.
https://aisel.aisnet.org/icis2025/is_transformwork/is_transformwork/5
AI-Enabled Audit Processes: A Sociomaterial Investigation of Professional Judgment
The integration of artificial intelligence (AI) into auditing practices presents both opportunities and challenges for audit quality. Drawing on sociomateriality theory and DeAngelo's audit quality framework, this study investigates how AI capabilities become constitutively entangled with auditing practices to transform professional judgment and ultimately audit quality. Our research model conceptualizes this entanglement through novel constructs including augmented evidence and epistemic authority, which collectively reconfigure traditional audit pathways of detection capability and reporting integrity. Using a two-wave longitudinal survey design with 600 auditors across Chinese Big Four accounting firms, we employ lagged dependent variable models and propensity score matching to establish temporal precedence for causal inference while measuring the sociomaterial entanglement of AI and audit practices. This study contributes to the literature on technology in professional contexts by advancing a sociomaterial understanding of how AI transforms professional judgment in knowledge-intensive work, with implications for audit methodology development and professional standards.
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03-Transformation