Date
10-14-2026
Department
School of Behavioral Sciences
Degree
Doctor of Philosophy in Psychology (PhD)
Chair
Jerry W. Green
Keywords
artificial intelligence, generative artificial intelligence, virtual assistant, job satisfaction, self-efficacy
Disciplines
Psychology
Recommended Citation
Vail, Dalton R., "Examining How the Frequency of Artificial Intelligence Use in the Workplace Relates to Employee Self-Efficacy and Job Satisfaction" (2026). Doctoral Dissertations and Projects. 8986.
https://digitalcommons.liberty.edu/doctoral/8986
Abstract
Artificial intelligence is a prominent topic in society. Its adoption in the healthcare sector has occurred rapidly. Nonetheless, much existing research focuses on clinical healthcare professionals, thereby overlooking those in non-clinical roles. Non-clinical roles make up a small portion of the healthcare industry, but they account for a large share of burnout in the field. This quantitative study aimed to explore the relationships among artificial intelligence, job satisfaction, and self-efficacy. It posited that these variables are interconnected concerning artificial intelligence in a non-clinical context. Furthermore, the study sought to determine whether job satisfaction and self-efficacy are influenced by how often participants use artificial intelligence. To empirically test these hypotheses, a total of 119 participants (61 users and 58 non-users) were recruited from rural hospitals. The participants completed a survey that addressed participant eligibility, sample demographics, artificial intelligence usage, job satisfaction, and self-efficacy. The survey data were analyzed with a Mann-Whitney U test and Spearman’s Correlation. The results found that non-clinical healthcare workers who did not use artificial intelligence reported higher job satisfaction. Furthermore, as participants reported more frequent artificial intelligence use, they reported decreased job satisfaction. However, there was no relationship between artificial intelligence use and self-efficacy. From a practical standpoint, these findings highlight the need for a clear reason for artificial intelligence implementation. From a theoretical standpoint, these findings contribute to a deeper understanding of artificial intelligence, job satisfaction, and self-efficacy.
