Semester of Graduation
Summer 2026
Degree Type
Dissertation
Degree Name
Business Administration
Department
Information Systems and Security
Committee Chair/First Advisor
Khawaja Saeed
Second Advisor
Pallab Sanyal
Third Advisor
Reza Vaezi
Abstract
The rapid growth of generative artificial intelligence (GenAI) has increased its use in productivity, content creation, and decision support; however, adoption depends on user trust, as GenAI performs tasks that are often associated with human judgment. Drawing on the Trust in Technology Model, this dissertation examines how structural assurance and situational normality influence Trust in GenAI, and how Trust in GenAI affects trusting intentions and users' willingness to depend on GenAI. Structural assurance and situational normality, captured through several factors, were modeled as antecedents of Trust in GenAI and, in turn, trusting intentions. The model was tested using survey-based experimental data from 246 participants with prior GenAI experience, analyzed via exploratory factor analysis, measurement invariance assessment, and partial least squares structural equation modeling. Trust in GenAI was shaped more strongly by capability-related and interactional features than by structural clarity alone. Intelligence, persistence, textual gestures, and textual emotions significantly predicted trust, while explainability, understandability, and responsiveness did not have significant direct effects. Trust in GenAI strongly predicted trusting intentions, confirming that willingness to rely on GenAI is closely tied to trust in the system. This dissertation contributes to trust research by contextualizing the Trust in Technology Model for text-based GenAI, showing that trust formation involves more than clarity or transparency. Users evaluate GenAI by whether it seems intelligent, maintains continuity, and communicates in socially meaningful ways. Designers should treat explainability and responsiveness as baseline requirements while prioritizing intelligence and social communication. This study advances understanding of trust and willingness to rely on GenAI.