Dissertation/Thesis Abstract

Quantitative Influences of Trust and Unified Use and Acceptance Factors on AI Adoption in Healthcare
by Carbone, Sergio C., Ph.D., Capella University, 2020, 141; 28094570
Abstract (Summary)

The research topic for this study was the adoption of artificial intelligence technology in healthcare. Healthcare is at a critical turning point because of the growing costs of detection and treatment, and society needs the potential benefits of artificial intelligence healthcare technologies. The factors that affect artificial intelligence technology adoption in healthcare are not known. Society needs to identify issues blocking the adoption of artificial intelligence healthcare technologies to promote adoption. The purpose of this research was to examine what factors affect artificial intelligence technology adoption in healthcare and close this gap. This study asked the following question: To what extent, if any, do unified use and acceptance factors (performance expectancy, effort expectancy, social influence, innovativeness, perceived risk, and trust in system) influence the level of behavioral intention to adopt artificial intelligence technology among U.S. healthcare IT professionals? This study was a quantitative nonexperimental correlational cross-sectional survey study that used an anonymous online survey. This research study sampled from the population of 803,090 U.S. healthcare IT professionals and collected a total sample of N = 215. This study conducted a hierarchical linear regression analysis. The results of the hierarchical multiple regression analysis indicate that performance expectancy, social influence, innovativeness, and trust in system influenced the level of behavioral intention to adopt artificial intelligence technology among U.S. healthcare IT professionals. These findings indicate that trust had the strongest influence on artificial intelligence technology adoption among U.S. healthcare IT professionals. However, effort expectancy performed inconsistently in the model, and perceived risk did not contribute.

Indexing (document details)
Advisor: McKibbin, William J.
Commitee: Lentz, Cheryl, Witteman, Pamelyn
School: Capella University
Department: School of Business and Technology
School Location: United States -- Minnesota
Source: DAI-B 82/3(E), Dissertation Abstracts International
Source Type: DISSERTATION
Subjects: Information Technology, Artificial intelligence, Health care management
Keywords: Artificial intelligence, Healthcare, Technology adoption
Publication Number: 28094570
ISBN: 9798672189642
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