Author ORCID Identifier

https://orcid.org/0000-0003-3080-4379

Semester

Spring

Date of Graduation

2026

Document Type

Dissertation

Degree Type

DBA

College

Chambers College of Business and Economics

Department

Management

Committee Chair

Ryan Angus

Committee Co-Chair

Jeffery Houghton

Committee Member

Jeffery Houghton

Committee Member

Hyeonsuh Lee

Committee Member

Richard Oxarart

Abstract

The purpose of this single-case qualitative project is to uncover how self-leadership’s behavioral and cognitive strategies influence perceptions of Artificial Intelligence (AI) and AI adoption. While existing literature acknowledges that there is an association between self-leadership, technology’s perceived usefulness and ease of use, as well as comfort with AI preparedness and its professional impact, there is limited research on how self-leadership shapes perceptions of AI and AI adoption in real-life contexts. To fill this gap in contemporary literature, this project analyzes qualitative semi-structured interviews with 25 healthcare professionals involved in the implementation of ‘AI Model 1’ at their mid-sized United States healthcare system. Interview data was analyzed inductively using Qualitative Content Analysis and followed grounded-theory coding best practices. Results indicated that participants high in self-leadership described more concerns about AI and expressed more benefits of augmented AI use, when compared to low self-leaders. Perceived ease of using AI also emerged as a mediator between self-leadership and AI use. Finally, this project’s most profound contribution to self-leadership and technology adoption theory is the Theory of Self-Leadership-AI Reciprocity (SLAIR), which explains that a reciprocal relationship exists where AI use strengthens the very psychological resource (i.e. self-leadership) that predicts its own adoption. This project extends our theoretical understanding by identifying self-leadership as a key antecedent driving perceptions of AI and AI adoption, while AI use, in turn, drives self-leadership development. In terms of practical implications, this project highlights the importance of implementing AI tools that enhance self-leadership, support autonomy, and facilitate augmented decision-making, rather than replacing humans with AI.

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