AI-Generated Content Disclosure and Prolonged Short-Video Engagement: A Heuristic-Systematic Risk-Trust Model Among Late-Adolescent and Emerging-Adult TikTok Users.

Yichen Xiao, Juan Du, Yidan Ding, Minyang Zhang, Yumei Jiang, Yilin Yang, Jie Liu

Abstract read

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Article in Behavioral sciences (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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7 authors.

Yichen XiaoSchool of Journalism and Information Communication, Huazhong University of Science and Technology, Wuhan 430074, China.ORCID 0009-0001-6651-2215

Juan DuSchool of Marxism, Huazhong University of Science and Technology, Wuhan 430074, China.

Yidan DingCollege of Literature, Nanjing University, Nanjing 210019, China.ORCID 0009-0004-2428-9248

Minyang ZhangSchool of Journalism and Information Communication, Huazhong University of Science and Technology, Wuhan 430074, China.ORCID 0009-0003-7395-4526

Yumei JiangSchool of Physical Education, Huazhong University of Science and Technology, Wuhan 430074, China.

Yilin YangCollege of Philosophy and Law, Shanghai Normal University, Shanghai 201418, China.ORCID 0009-0001-2786-3477

Jie LiuSchool of Journalism and Information Communication, Huazhong University of Science and Technology, Wuhan 430074, China.

Funding

Chinese Academy of Social Sciences 22BXW056Huazhong University of Science and Technology DJSZ202568Jiangsu Education Department SJCX25_0012

6 · The paper itself

Abstract

Prolonged short-video engagement in the generative-AI era may be shaped by interface cues that encourage or interrupt repeated continuation decisions in algorithmic feeds. This study examines whether AI-generated content disclosure functions as interface-level digital friction for prolonged short-video engagement among late-adolescent and emerging-adult TikTok users. Prolonged watching intention is treated as a cognitive-behavioral proximal tendency relevant to problematic social media use (PSMU), rather than as a clinical diagnosis or an emotional-disturbance outcome. Drawing on the heuristic-systematic model, we tested a dual-pathway risk-trust model in which disclosure directly affects prolonged watching intention, while perceived risk and content trust operate as mediators and AI literacy operates as a person-level boundary condition. An online between-subjects experiment was conducted with 720 valid participants aged 18-24. Disclosure had a positive direct effect on prolonged watching intention, suggesting that AI labels can initially work as salient curiosity and novelty cues. At the same time, disclosure increased perceived risk and reduced content trust, generating negative indirect pathways that constrained prolonged watching intention. AI literacy strengthened both appraisal pathways. The findings reposition AI disclosure from a mere transparency notice to a behavioral cue that can simultaneously attract attention and activate protective appraisal. They contribute to developmental and media-psychological research on prolonged engagement and PSMU-relevant mechanisms without overstating clinical implications.

Indexed as

AI-generated content disclosureAI literacycontent trustdigital frictionemerging adulthoodgenerative AIlate adolescenceperceived riskproblematic social media useprolonged short-video engagement

Identifiers

PMID42510301

DOI10.3390/bs16071179

PMCPMC13405702

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.