Based on the conservation of resources (COR) theory, the purpose of this study is to explore the enablers and inhibitors of AI-generated content (AIGC) user intermittent discontinuance.
A mixed method of structural equation modeling and fuzzy-set qualitative comparative analysis was adopted to conduct data analysis.
The results of this study showed that algorithm bias, misinformation, privacy concerns and unexplainability affect usage exhaustion, which promotes user intermittent discontinuance. Switching costs, subjective norm and irreplaceability affect switching exhaustion, which prevents user intermittent discontinuance. The fuzzy-set qualitative comparative analysis identified two paths that lead to user intermittent discontinuance.
The results suggest that AIGC platforms need to lower usage exhaustion and increase switching exhaustion to prevent user intermittent discontinuance and ensure the continuous development.
Previous research has focused on the effect of technological factors such as perceived usefulness on AIGC user behavior. This research provides new insights on AIGC user intermittent discontinuance from a resource conservation perspective. The results also expand the COR theory from traditional organizational behavior to the emerging AIGC context.
