Survey of 432 students finds protection motivation positively predicts AIGC verification intention, with perceived severity, vulnerability, response efficacy, and self-efficacy as positive influences and maladaptive rewards and response cost as negative; fsQCA identifies three high-intention configu
Self-reported questionnaire data were collected from students who had experience using AIGC in learning contexts
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Examining University Students' Artificial Intelligence-Generated Content (AIGC) Verification Intention from a Protection Motivation Perspective
Survey of 432 students finds protection motivation positively predicts AIGC verification intention, with perceived severity, vulnerability, response efficacy, and self-efficacy as positive influences and maladaptive rewards and response cost as negative; fsQCA identifies three high-intention configu