Understanding AI tool engagement: A study of ChatGPT usage and word-of-mouth among university students and office workers

Abstract

This study aims to explore the determinants of user behaviors toward an artificial intelligence (AI) tool, ChatGPT, focusing on university students and office workers. In this study, we present a comprehensive model to understand user engagement with AI tools, specifically focusing on ChatGPT. The model is grounded on four primary stages, each containing distinct variables: 1) fundamental (comprising perceived intelligence and system quality), 2) knowledge and service (covering knowledge acquisition, application, personalization, and trust), 3) gain with user tendency (encompassing utilitarian benefits, individual impact, satisfaction, and personal innovativeness), and 4) behavior (including behavioral intention, continued usage, and Word-of-Mouth (WOM)). A total of 13 variables have been examined. A survey was conducted on a sample of 645 university students and office workers, and the collected data were analyzed using partial least squares structural equation modeling (PLS-SEM). The results reveal significant associations between perceived intelligence and knowledge management, and personalization. System quality also significantly impacts knowledge management and personalization. Knowledge acquisition and application were found to significantly affect utilitarian benefits and individual impact, but not satisfaction. Personalization significantly influenced utilitarian benefits, individual impact, and satisfaction. Trust significantly impacts behavioral intention. Utilitarian benefits and individual impact had a positive effect on satisfaction, behavioral intention, and WOM. Personal innovativeness was significantly associated with behavioral intention. Behavioral intention significantly affected usage and WOM, while usage did not significantly associate with WOM. Among control variables, only age affects behavioral intention. This study also confirmed the indirect effects and conducted a multi-group analysis (MGA) between students and workers. MGA results show that there are significant differences in three relationships (personalization-satisfaction, utilitarian benefits-WOM, and behavioral intention-WOM) between students and workers. This research extends the understanding of AI tool usage and provides theoretical and practical insights for researchers, practitioners, and policymakers in AI and related fields.