Graduation Year

2026

Document Type

Dissertation

Degree

Ph.D.

Degree Name

Doctor of Philosophy (Ph.D.)

Degree Granting Department

Information Systems and Decision Sciences

Major Professor

Alan Hevner, Ph.D.

Co-Major Professor

Donald Berndt, Ph.D.

Committee Member

Daniel Zantesdeschi, Ph.D.

Committee Member

Manish Agrawal, Ph.D.

Committee Member

Robert Hooker, Ph.D.

Keywords

Adaptivity, Decomposability, Malleability, Openness, Ethical Governance

Abstract

Over the last decade, new artificial intelligence models, frameworks, and applications have emerged that have radically changed the socio-technical landscape. These new technologies represent both great opportunities and great potential risks. Due to the recency of their emergence, there is little known about the longevity and long-term repercussions of artifacts powered by new artificial intelligence technology. In this dissertation, I evaluate A.I. and A.I.-based artifacts through the lens of the fitness-utility model, a tool developed by Gill and Hevner (2013) for the evaluation of an artifact's ability to survive and reproduce over long time horizons. In so doing, I also evaluate the efficacy of the fitness-utility model itself, identifying changes that are likely to enhance and augment the fitness-utility model's effectiveness as a tool for evaluating A.I.-based artifacts. This is accomplished through the use of the elaborated action design research (eADR) methodology. The dissertation comprises three eADR cycles. In the first cycle, I review literature related to design fitness to develop a framework informed by research. In the second cycle, I use A.I. to conduct analyses of several case studies of A.I.-based artifacts developed within large organizations, using these analyses to create an Augmented Fitness-Utility Model for the evaluation of A.I. artifacts. In the third cycle, I evaluate and refine this model using a focus group comprising practitioners who work directly with A.I. in their respective organizations. The model developed through this process represents a step forward in research on design fitness and artificial intelligence, promising to serve as a useful tool for both researchers and practitioners going forward.

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