Date of Award
12-2025
Document Type
Thesis
Degree Name
Master of Science (MS)
Department
Industrial-Organizational Psychology
Committee Chair/Advisor
Dr. Mary Anne Taylor
Committee Member
Dr. Patrick J. Rosopa
Committee Member
Dr. Jenna Van Fossen
Abstract
Artificial intelligence (AI) is a burgeoning technology whose development has many implications for organizations and employees. Using a modified version of the Unified Theory of Acceptance and Use of Technology (UTAUT) framework, this study tested performance expectancy (PE; the belief that AI will enhance personal job performance) and prior AI experience (EXP) as predictors of behavioral intention (BI) to use an AI coaching tool, Nadia. We expected both PE and EXP to be positively related to adoption intentions. Additionally, social influence (SI; respondent’s belief that other individuals think they should adopt AI) and facilitating conditions (FC; appropriate infrastructure and adequate technical support for AI) were modeled as moderates of PE and EXP. Finally, a third goal of this study was to examine the impact of employees’ age on AI adoption intention and the potential mediating effect of technology anxiety (ANX) on the relationship between respondent age and AI adoption. While age was expected to be negatively related to AI adoption intentions, this relationship was expected to be attenuated when the role of technology anxiety is controlled.
Full-time employees (N = 272) were recruited via Prolific, viewed a standardized Nadia demonstration and completed Time 1 measures (PE, EXP, SI, FC, ANX, and demographics); BI was assessed one week later. Confirmatory factor analysis was conducted to refine the measurement model, and structural equation modeling (SEM) was used to test hypothesized model. Results indicated the model had good fit, however only two relationships were significant. Performance expectancy positively predicted BI, while SI moderated this relationship. The full model with all moderators explained about 56 percent of the variance in BI, and a reduced model including only the significant moderation PE x SI showed a significant improvement over the baseline model without any moderators. These findings refine AI-specific extensions of the UTAUT by highlighting PE as a significant predictor and SI as a boundary condition rather than a direct predictor while also providing new insights for organizations looking to integrate AI technologies.
Recommended Citation
Wituk, Emma G., "Factors Influencing Employees’ AI Adoption Intentions In Occupational Settings" (2025). All Theses. 4634.
https://open.clemson.edu/all_theses/4634
Author ORCID Identifier
0009-0005-7610-7749