Survey Data on AI Face-Swapping User Behavior in China

351 active users of FacePlay in China were surveyed to examine their continuance intention toward AI face-swapping technology. The data was collected and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) by Yanqing Xia. The study investigates push effects (fear), pull effects (temptation), and mooring factors like digital literacy and self-efficacy.

Use Cases

• Modeling technology continuance intention based on push-pull-mooring framework
• Analyzing the moderating role of digital literacy on user behavior
• Studying the comparative impact of fear versus temptation on technology use
• Investigating self-efficacy as a factor in AI technology adoption

Strengths

• Dataset includes 351 survey responses from active users
• Analysis employs PLS-SEM for structural equation modeling
• Study explicitly measures push (fear), pull (temptation), and mooring (digital literacy, self-efficacy) factors

Limitations

• Column-level documentation is absent; field semantics must be inferred after download
• Row count is unknown, which may limit suitability assessment
• Data may reflect geographic bias inherent to a single-country (China) sample

Provenance

Source

Yanqing Xia

Collection Method

Survey data collected from 351 active users of FacePlay and analyzed with PLS-SEM

Freshness

Last updated 2026-04-10 06:02:31; freshness should be verified

Geography

China