TY - GEN
T1 - Will AI Replace Human Creators? Exploring the Mechanisms of User Engagement with AI-Generated Content on Social Media
AU - Wang, Yaxian
AU - Xu, Jingjun (David)
N1 - Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s)
PY - 2025/8
Y1 - 2025/8
N2 - Artificial intelligence-generated content (AIGC) is increasingly prevalent on social media, bringing substantial changes to traditional user-generated content (UGC). Previous studies have examined the role of UGC, but there is a lack of comprehensive understanding of user perception between AIGC and UGC, and how AIGC alters user behavior. This study aims to bridge these gaps by exploring the mediating roles of perceived cognitive effort, content quality, authenticity, and novelty in the relationship between AIGC, UGC, and user engagement. Drawing on the Appraisal-Tendency Framework and Uses and Gratifications Theory, this study explains users' ambivalent attitudes toward AIGC, stemming from their conflicting perceptions. By testing competing hypotheses through a mixed-method approach, we identify underlying mechanisms and explain users' preferences. This study aims to enhance the theoretical understanding of AIGC adoption and provide actionable insights for optimizing content strategies on social media.
AB - Artificial intelligence-generated content (AIGC) is increasingly prevalent on social media, bringing substantial changes to traditional user-generated content (UGC). Previous studies have examined the role of UGC, but there is a lack of comprehensive understanding of user perception between AIGC and UGC, and how AIGC alters user behavior. This study aims to bridge these gaps by exploring the mediating roles of perceived cognitive effort, content quality, authenticity, and novelty in the relationship between AIGC, UGC, and user engagement. Drawing on the Appraisal-Tendency Framework and Uses and Gratifications Theory, this study explains users' ambivalent attitudes toward AIGC, stemming from their conflicting perceptions. By testing competing hypotheses through a mixed-method approach, we identify underlying mechanisms and explain users' preferences. This study aims to enhance the theoretical understanding of AIGC adoption and provide actionable insights for optimizing content strategies on social media.
KW - AI-generated content
KW - user-generated content
KW - social media
KW - user engagement
UR - https://www.scopus.com/pages/publications/105025373478
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-105025373478&origin=recordpage
M3 - RGC 32 - Refereed conference paper (with host publication)
T3 - Proceedings of the Americas Conference on Information Systems
BT - AMCIS 2025 Proceedings
PB - Association for Information Systems
T2 - 31st Americas Conference on Information Systems (AMCIS 2025)
Y2 - 14 August 2025 through 16 August 2025
ER -