Keywords

Artificial intelligence; uncertainty; signal-environment fit; advice utilization; management accounting.

Subject Categories

Accounting | Business Administration, Management, and Operations | Management Information Systems

Abstract

This dissertation examines how the fit between the characteristics of artificial intelligence systems and the uncertainty environment shapes managerial reliance on AI advice. Organizations increasingly deploy different AI systems, such as predictive and generative, to support management accounting decisions. However, the accounting literature has mainly treated AI as a single undifferentiated entity. This dissertation addresses this gap through two related studies. In the first study, drawing on signaling theory and ecological rationality, I develop a signal-environment fit framework proposing that the reliance on a certain AI system depends on the match between system characteristics (input, process, output) and uncertainty characteristics. In addition, based on this framework, I develop a set of research questions to guide future inquiry. In the second study, I test some of the framework predictions in a demand forecasting setting. I use a 2x2 between-subjects experiment that manipulates the AI type (predictive versus generative) and uncertainty reducibility (low versus high), examining how the structure of the uncertainty environment and its fit with the AI system's process characteristics affect advice utilization. Results indicate that advice utilization increases when uncertainty is more reducible and provide the initial evidence that the effect of AI system type on advice utilization may depend on uncertainty reducibility. Together, the two studies contribute a theoretical foundation for understanding AI advice utilization as a fit phenomenon and provide initial experimental evidence that managers are sensitive to the structure of their decision environment when relying on AI advice.

Completion Date

2026

Semester

Summer

Committee Chair

Libby, Theresa

Degree

Doctor of Philosophy (Ph.D.)

College

Barry S. Miller College of Business

Department

Kenneth G. Dixon School of Accounting

Format

PDF

Document Type

Dissertation

Language

English

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