Predicting University Students’ Satisfaction and Continuance Intentions to Use AI-Powered Chatbots in Chengdu, China - Lu, Xingwei (Scholar: Human Sciences, 2025)
Purpose: This study aims to investigate the determinants of university students’ satisfaction and continuance intentions toward AI-powered chatbots in Chengdu, China. The conceptual framework was adapted from previous studies, which proposed a significant relationship among problem-solving, user interface, perceived usefulness, perceived ease of use, trust, satisfaction, and continuance intention. Research design, data, and methodology: The researcher used a quantitative method (n=500) to distribute questionnaires to undergraduate students. The researcher applied probability and non-probability sampling, including purposive, stratified random, and convenience sampling. The research applied the Structural Equation Model (SEM) and Confirmatory Factor Analysis (CFA) for the data analysis, including model fit, reliability, and validity of the constructs. Results: The results explained that problem-solving, user interface, perceived usefulness, and perceived ease of use support have a significant influence on satisfaction. Perceived Usefulness support showed the strongest influence on satisfaction, followed by user interface, perceived ease of use, and problem-solving. Satisfaction and trust have a significant influence on continuance intention. Satisfaction has the strongest influence on continuance intention, followed by trust. Conclusions: Six hypotheses were proven to fulfill research objectives. By measuring and evaluating user satisfaction, companies providing AI-powered chatbots can understand users’ needs and expectations and their feelings about the user experience.