  This study investigated how perceived workload, satisfaction, performance
expectancy, and risk-benefit perception influenced users' trust in Chat
Generative Pre-Trained Transformer (ChatGPT). We aimed to understand the
nuances of user engagement and provide insights to improve future design and
adoption strategies for similar technologies. A semi-structured, web-based
survey was conducted among adults in the United States who actively use ChatGPT
at least once a month. The survey was conducted from 22nd February 2023 through
24th March 2023. We used structural equation modeling to understand the
relationships among the constructs of perceived workload, satisfaction,
performance expectancy, risk-benefit, and trust. The analysis of 607 survey
responses revealed a significant negative relationship between perceived
workload and user satisfaction, a negative but insignificant relationship
between perceived workload and trust, and a positive relationship between user
satisfaction and trust. Trust was also found to increase with performance
expectancy. In contrast, the relationship between the benefit-to-risk ratio of
using ChatGPT and trust was insignificant. The findings underscore the
importance of ensuring user-friendly design and functionality in AI-based
applications to reduce workload and enhance user satisfaction, thereby
increasing user trust. Future research should further explore the relationship
between the benefit-to-risk ratio and trust in the context of AI chatbots.
