Generative AI-enabled intelligent auditing: an organizational adaptation mechanism study based on dynamic capability theory
Authors’ abstract
This study investigates how audit organizations leverage generative artificial intelligence technologies to enhance auditing capabilities through organizational adaptation mechanisms, examining the role of dynamic capabilities in facilitating successful AI adoption and performance improvements. A quantitative cross-sectional survey collected data from 312 audit professionals across diverse organizational contexts. Structural equation modeling examined relationships between dynamic capabilities, generative AI adoption, organizational adaptation mechanisms, and auditing performance with comprehensive measurement validation. Dynamic capabilities significantly influence generative AI adoption (β = 0.453, p < 0.001), which drives organizational adaptation mechanisms (β = 0.312, p < 0.001) that enhance auditing performance (β = 0.378, p < 0.001). Organizational adaptation mechanisms mediate 41.4% of the capability-performance relationship. The model explains 28.3% variance in AI adoption, 35.7% in adaptation mechanisms, and 31.2% in auditing performance. Audit organizations should prioritize developing sensing, seizing, and reconfiguring capabilities before AI investments, requiring comprehensive change management addressing structural, processual, and cultural dimensions simultaneously. AI-driven competitive advantages emerge through organizational transformation processes, with dynamic capabilities as antecedents and adaptation mechanisms as mediating processes.
Paper
- Authors
- W. Deng, O. Rashdi Syed, X. Xu, H. Sang, J. Wang
- Publication date
- 2025
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- Source license
- Creative Commons Attribution 4.0 International
- Recording license
- CC-BY-4.0
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