Fear vs. temptation: a push-pull-mooring perspective on users' continuance intention toward AI face-swapping...

Area of Science:

• Technology adoption studies
• Human-computer interaction
• Digital media research

Background:

• AI face-swapping technology presents a "technology paradox," eliciting both user attraction and apprehension.
• Understanding user continuance intention requires examining psychological and individual factors.
• The Push-Pull-Mooring (PPM) framework offers a model to analyze technology adoption dynamics.

Purpose of the Study:

• To investigate the mechanisms behind the technology paradox in AI face-swapping.
• To construct an integrative model of user continuance intention using the PPM framework.
• To analyze the influence of push effects, pull effects, and mooring factors on continuance intention.

Main Methods:

• Data collected from 351 active users of the FacePlay AI face-swapping application in China.
• Partial Least Squares Structural Equation Modeling (PLS-SEM) employed for data analysis.
• Investigated push effects (fear), pull effects (temptation), digital literacy, and self-efficacy.

Main Results:

• Both push and pull effects significantly influence continuance intention, with pull effects (temptation) being more dominant.
• Digital literacy and self-efficacy directly enhance continuance intention.
• Digital literacy and self-efficacy act as dual moderators, buffering negative push effects and amplifying positive pull effects.

Conclusions:

• User attraction to AI face-swapping is primarily driven by temptation, outweighing fear-based apprehension.
• Digital literacy and self-efficacy are crucial for sustained engagement, moderating user responses to technology risks and benefits.
• Findings offer theoretical insights into the technology paradox and practical implications for digital literacy education and risk governance.