Examining Generative AI User Disclosure Intention: A Perceived Affordance Perspective - Tao Zhou; Xiaoying Wu (Journal of Theoretical and Applied Electronic Commerce Research)
Generative AI needs to collect user data to provide more accurate answers. This may raise users’ privacy concern and undermine their disclosure intention. The purpose of this research is to examine generative AI user disclosure intention from the perspective of technological–social affordance. We adopted a mixed method of PLS-SEM and fsQCA to conduct data analysis. The results reveal that perceived affordance of content generation (including information association, content quality, and interactivity), perceived affordance of privacy protection (including anonymity and privacy statement), and perceived affordance of anthropomorphic interaction (including empathy and social presence) affect privacy concern and reciprocity, both of which further affect disclosure intention. The fsQCA identified two paths that trigger user disclosure intention. These results imply that generative AI platforms need to increase users’ perceived affordance in order to promote their disclosure intention and ensure the continuous development of platforms.