Determinants of Junior College Students’ Satisfaction and Intentions to Adopt Artificial Intelligence in Chengdu, China
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Abstract
Purpose: This article aimed to research the critical factors impacting junior college students' satisfaction with and intention to use artificial intelligence in Chengdu, China. The conceptual framework presented cause-and-effect relationships between informational support, emotional support, perceived ease of use, perceived usefulness, satisfaction, attitude, and intention to use. Research design, data, and methodology: The researcher used a quantitative technique (n=500) to administer a questionnaire to junior art college students at the Sichuan University of Media and Communications in Chengdu, China. Non-probability sampling included judgmental sampling to select four art majors of Sichuan University of Media and Communications, quota sampling to define the sample size, and convenience sampling to collect data and distribute the questionnaires online and offline. The researcher carried out the data analysis, using structural equation modeling (SEM) and confirmatory factor analysis (CFA). Results: The results show that informational support, emotional support, perceived usefulness, and perceived ease of use have a significant effect on satisfaction, and satisfaction, as an intermediate variable, has a significant effect on intention to use. Also, the attitude has a significant effect on the intention to use. Conclusions: This study suggest that to increase the use of AI in higher education, continuous attention should be paid to the factors affecting student satisfaction and intention to use AI, and continuous feedback should be provided to optimize and adapt.
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