Electronic Theses and Dissertations Archive

Date

2026

Document Type

Dissertation

Degree Name

Doctor of Philosophy

Department

Accounting

Committee Chair

Zabihollah Rezaee

Committee Member

Joanna Golden

Committee Member

Joseph Zhang

Committee Member

Steve Lin

Abstract

My dissertation examines how investments in artificial intelligence (AI) and information technology (IT) reshape organizational governance, transparency, and assurance outcomes. As firms increasingly integrate AI-enabled systems into operational, reporting, and monitoring processes, important questions arise regarding whether these technologies strengthen or undermine corporate accountability. Across three essays, I provide evidence on the governance implications of AI and IT investment from three complementary perspectives: corporate compliance, climate disclosure and performance, and audit effectiveness. Essay 1 investigates whether AI investment enhances or undermines corporate compliance. Leveraging a novel dataset that combines firm-level AI-skilled labor measures with federal and state enforcement penalties from 2010 to 2024, the study examines AI’s dual role as both a monitoring technology and a performance-pressure amplifier. The findings reveal a robust positive association between AI investment and corporate misconduct—measured by the number of violations and total penalties—supporting the compliance-undermining hypothesis. However, a difference-in-differences design exploiting ASC 350-40 shows that AI-investing firms experience significant reductions in violations and penalties following the policy change. Mechanism analyses indicate that these effects are more pronounced among firms facing greater performance pressure, longer investment horizons, and reductions in workplace-related expenditures. Essay 2 shifts the focus to environmental governance by examining how AI capability influences firms’ climate disclosure and emissions management. Using a firm-year dataset combining AI capability measures with CDP climate disclosure data, the study documents that AI capability is positively associated with voluntary disclosure participation. Beyond the decision to disclose, AI capability is linked to higher-quality climate disclosures and stronger carbon emissions management performance. Multiple identification strategies—including matched samples and instrumental variable approaches—support a causal interpretation. Cross-sectional evidence further shows that the effects are stronger among firms with higher institutional ownership, more effective internal control systems, and greater exposure to standardized sustainability disclosure frameworks. Overall, this essay positions AI capability as a critical organizational infrastructure supporting climate transparency and environmental accountability. Essay 3 examines the assurance dimension by studying the implications of IT personnel investment for audit effectiveness. Drawing on the resource-based view and human capital theory, the essay argues that specialized IT expertise enhances auditors’ ability to process complex data environments. Empirical results indicate that greater investment in IT personnel is associated with higher audit quality, reflected in lower discretionary accruals and fewer financial restatements. These effects are more pronounced in high-growth audit offices where resource constraints are more acute and strengthen following the STEM OPT extension, which expands the supply of IT talent. Additional analyses show that higher compensation for IT professionals further incentivizes audit quality improvements. The findings are robust to multiple alternative specifications and endogeneity controls.

Comments

Data is provided by the student.”

Library Comment

Dissertation or thesis originally submitted to ProQuest/Clarivate.

Notes

Embargoed until 01-06-2029

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Available for download on Saturday, January 06, 2029

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