Investigating the Drivers of Attitudes and Behavioral Intentions Toward AI Chatbot Use Among Undergraduate Students in Guangdong, China
Keywords:
Attitude, Behavioral Intention, AI Chatbots, Undergraduate Students, Technology AcceptanceAbstract
Purpose: This study examines the key factors influencing undergraduate students’ attitudes and behavioral intentions toward AI chatbot use in Guangdong, China, using ChatGPT as a representative case. Research design, data and methodology: A quantitative approach was adopted by surveying 500 undergraduate students from the College of Mechanical and Electrical Engineering at Guangdong University of Petrochemical Technology. A non-probability sampling strategy was applied, including judgment sampling, quota sampling, and convenience sampling. A pilot test with 30 respondents ensured content validity and reliability. Data were analyzed using confirmatory factor analysis and structural equation modeling to assess reliability, validity, and model fit. Results: The findings indicate that effort expectancy, social influence, facilitating conditions, hedonic motivation, and performance expectancy significantly influence attitude. Among these factors, social influence shows the strongest effect (β = 0.375, p < 0.05). Attitude has the strongest direct effect on behavioral intention (β = 0.450, p < 0.05) and plays a central role linking user perceptions to behavioral intention. Conclusions: The results suggest that universities should integrate interactive AI-based learning activities, strengthen institutional support, and develop AI literacy programs. This study extends TAM and UTAUT by incorporating hedonic motivation and providing empirical evidence on the mediating role of attitude in AI chatbot adoption within a Chinese higher education context.
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