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Users' continuance intention towards an AI painting application: An extended expectation confirmation model
Xiaofan Yu1, Yi Yang2, Shuang Li1
1Postdoctoral Research Station, Central Academy of Fine Arts, Beijing, China.
Plos One
|May 15, 2024
Summary
This study explores user intention for AI painting apps, finding confirmation, satisfaction, and social influence are key drivers. Habit can reduce the impact of social influence on continued use of AI painting tools.
Area of Science:
- Human-Computer Interaction
- Artificial Intelligence Applications
- Digital Art Technologies
Background:
- Artificial Intelligence (AI) painting is a rapidly advancing technology.
- Understanding user adoption and continued use of AI painting applications is crucial for development.
Purpose of the Study:
- To empirically investigate users' continuance intention toward AI painting applications.
- To extend existing technology acceptance models (ECM, TAM, UTAUT) and Flow Theory for AI painting context.
Main Methods:
- A comprehensive research model integrating multiple theories was proposed.
- Data collected from 443 users with AI painting experience via questionnaires.
- Hypotheses tested using structural equation modeling.
Main Results:
- Confirmation significantly predicts satisfaction and social impact.
- Personal innovativeness influences confirmation.
- Satisfaction, flow experience, and social influence positively predict intention; social influence has the greatest impact.
- Perceived usefulness, enjoyment, and performance expectancy did not significantly impact intention.
- Habit negatively moderates the relationship between social influence and continued intention.
Conclusions:
- User confirmation, satisfaction, and social influence are critical for continued AI painting app usage.
- Personal innovativeness impacts initial confirmation.
- Habit can diminish the effect of social influence on sustained use.
- Findings provide insights for AI painting utilization and development strategies.
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