How does information resource value of AI-generated content emerge? An exploratory study from the user evaluation perspective

Abstract

AI-generated content (AIGC), a novel information resource, has seen an irreversible growth trend in the information ecosystem. However, most prior AIGC studies focus on technological adoption and static evaluation, while little attention has been paid to the value emergence and value-added processes of AIGC at the information resource level. This study employed in-depth, semi-structured interviews with 22 experienced AIGC users and utilized content analysis to examine the factors influencing users' perceived value of AIGC, identify value-added processes, and explore the underlying mechanisms. Based on these findings, we propose the AIGC-Value-Added Framework, delineating four user-AIGC interaction phases: value exposure, value forming, value anchoring, and value realization, which collectively enhance the resource value of AIGC. This study introduces an integrative framework for understanding how value emerges and is added to AIGC as an information resource, thereby enriching the Library and Information Science literature on information value-adding practices in the AIGC context and offering stakeholders practical insights for optimizing AIGC leverage.