A Dataset Assessing Satisfaction and Continuous Intention to Use Image-Generative Artificial Intelligence among Visual Arts Students: An Integrated UTAUT–ECM Perspective
Description
This dataset contains quantitative survey data collected from 278 undergraduate Visual Art students at Kwame Nkrumah University of Science and Technology (KNUST), Ghana, examining factors influencing students’ satisfaction and continuous usage of Image-Generative Artificial Intelligence (Image-GenAI) . The dataset includes respondents’ demographic characteristics (age, gender, academic level, department, and frequency of GenAI use) and 5-point Likert-scale measures adapted from established models, including UTAUT, Expectation–Confirmation Model (ECM).Key variables cover satisfaction, continuous intention to use, effort expectancy, performance expectancy, social influence, facilitating conditions, and expectation confirmation. The data are suitable for PLS-SEM analysis, technology acceptance studies, and research on AI adoption and creative cognition in visual art education.
Files
Steps to reproduce
Open the dataset file using Microsoft Excel or any compatible spreadsheet software. Review the demographic variables to understand the characteristics of the respondents. Examine the Likert-scale items measuring image-generative AI usage, satisfaction, and continuous intention to use. Import the dataset into statistical analysis software such as SmartPLS, SPSS, or similar tools. Conduct descriptive statistics to summarise the data. Apply PLS-SEM to analyse the relationships among the constructs as defined in the study model.
Institutions
- Kwame Nkrumah University of Science and Technology