Examining generative AI user disclosure intention: an ELM perspective

Generative AI needs to collect massive information including personal information to train the model and improve the accuracy of answers. This may incur privacy risks and reduce user disclosure intention. Based on the elaboration likelihood model (ELM), this research explored generative AI user disclosure intention. The results revealed that disclosure intention receives a dual influence from both central factors (perceived anthropomorphism, perceived accuracy and perceived affordance) and peripheral factors (privacy statement, social influence and privacy risk). Algorithm literacy acts as a significant moderation variable of this dual-path model. The fsQCA identified three configurations leading to disclosure intention. These results imply that generative AI platforms need to improve content quality and reduce privacy risk in order to promote user disclosure intention.