AI Face-Swapping User Continuance Intentions Survey Data from China

351 survey responses from active users of FacePlay in China were collected to study the technology paradox of AI face-swapping. Yanqing Xia authored this research, which analyzes continuance intention using the Push-Pull-Mooring framework. The data was last updated on figshare in April 2026.

Use Cases

• Modeling user continuance intention based on push (fear) and pull (temptation) effects mentioned in the description
• Analyzing the moderating role of digital literacy and self-efficacy on technology adoption
• Testing structural equation models (PLS-SEM) with survey data on AI tool usage

Strengths

• 351 survey responses provide a substantive sample for analysis
• Data is structured for quantitative analysis using PLS-SEM methodology
• License is CC-BY-4.0, allowing for open reuse

Limitations

• Column-level documentation is absent; field semantics must be inferred after download
• Data may reflect geographic bias inherent to its collection from users in China
• Row count is unknown, which may limit suitability assessment

Provenance

Source

Yanqing Xia

Collection Method

Survey data collected from 351 active users of FacePlay in China

Freshness

Last updated 2026-04-10 06:02:32; freshness should be verified

Geography

China