Artificial intelligence (AI) and predictive modeling are tools used to diagnose a disease, determine how much a home is worth, estimate insurance risks, and detect fraud. AI and predictive modeling are so ubiquitous that they can be why one gets spam and why spam is automatically deleted. Information science integrates interdisciplinary elements of data-driven, behavioral, design, interpretive, and analytical research methodologies to design and understand interactions between digital media, information systems, and humans. This research focuses on the interaction between humans, AI, and predictive models. This research proposes a theoretical framework and a conceptual research model to understand the antecedents to reliance on AI and predictive modeling. The dissertation follows a traditional format that includes three studies. Study 1 employed a deductive quantitative research approach as a survey to model the relationship between trust in science and reliance on formal news sources. Study 2 employed a deductive quantitative research approach as a survey to understand the impact of framing questions and consider an alternative method of measuring society's reliance on science using predictive models. Study 3 employed a deductive quantitative research approach in the form of a survey to posit a new model based on the first two studies. This study benefited from a Toulouse Graduate School grant to fund research using the crowdsourcing platform https://lucidtheorem.com/ to generate a stratified sample of the U.S. population.

Enterprises are growing in complexity due to numerous interactions within and outside the enterprise. Enterprise modeling addresses this issue of complexity by helping to structure it. A review of the literature indicates several issues in the field of enterprise modeling need to be addressed. First, the terms related to enterprise modeling have numerous definitions, each one focusing on different aspects. These definitions are analyzed and a comprehensive definition is provided. Next, enterprise modeling methodologies and enterprise modeling frameworks in the literature focus on different views when modeling an enterprise, thus making it difficult for an enterprise to choose the framework that best fits their needs. In order to resolve this, an enterprise modeling framework is designed that attempts to incorporate all of the views of an enterprise. This framework is then extended, by taking into account various models and functionalities provided in enterprise modeling software packages.

The goal of this project is to advance the understanding of tendon disease and injury through the design and validation of a new model of tendon injury. Controlled, mechanically-induced traction injury was created in rat Achilles’ tendons with a tensioning apparatus through a minimally invasive surgical approach. Tendons were tensioned to subailure loads. Assessments of damage to tendons included gross morphology, evaluation of biomechanical of load strain curves, histopathology under light microscopy (H&E), collagen fibril structure and disruption under scanning electron microscopy, and immunohistochemical study of MMP-13 expression under confocal laser microscopy. Tendon injuries were found to be more severe with respect to loads placed upon them. Differences between treatment groups were identified subjectively in microscopic appearance (H&E), in collagen fibril organization (SEM), and in MMP-13 expression. However, analysis of the numerical data did not provide definitive evidence that this model for acute tendon injury is consistent and repeatable.

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