Evidence map›Paper›PMID 42527445›Full record

ArticleScientific reports2026

Effects of promotional and preventive framing strategies in AI generated content on neural responses and trust.

Xiaoyi Wang, Raif Remigio Alves, Qiuyu Hu, Hanlin Chen

Abstract read
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Xiaoyi WangSchool of Management, Zhejiang University, Hangzhou, China.
Raif Remigio AlvesSchool of Management, Zhejiang University, Hangzhou, China. raifalves@zju.edu.cn.
Qiuyu HuSchool of Management, Zhejiang University, Hangzhou, China.
Hanlin ChenSchool of Management, Zhejiang University, Hangzhou, China.

Funding

STI2030 Major Projects 2021ZD0200409
6 · The paper itself

Abstract

Influenced by Regulatory Focus Theory, message framing is crucial in persuasive communication, particularly in marketing. This effect is further enhanced when integrated with artificial intelligence-generated content (AIGC), which offers personalized, scalable, and cost-effective solutions. However, the interaction between message framing, product types, and trust in AIGC remains unexplored. This study addresses this gap by examining how framed messages in AIGC recommendations influence consumer trust across hedonic and utilitarian products using Event-Related Potentials (ERPs). A 2 (message framing: promotion vs. prevention) x 2 (product type: hedonic vs. utilitarian) design was employed. Behaviorally, promotional messages elicited higher trust ratings and faster reaction times than preventive messages across product types. At the neural level, significant interactions between message framing and product type were observed for P3, N400, and LPP. Follow-up analyses showed that, for hedonic products, promotion-framed recommendations elicited larger P3 amplitudes, suggesting greater attentional engagement, and smaller N400 amplitudes, consistent with reduced semantic processing difficulty. The LPP showed broader interaction without reliable simple-framing differences in either product category. Although ERP signals are not direct evidence of trust, they offer insight into the underlying processes shaping AIGC evaluation. This research contributes to the growing literature on consumer neuroscience, trust, and AI-mediated persuasive communication.

Indexed as

Artificial IntelligenceMarketingPersuasive CommunicationTrustAdultEvoked PotentialsFemaleHumansMaleReaction TimeYoung AdultAIGCElectroencephalography (EEG)Event-related potentials (ERP)Message framingRegulatory focus theoryTrust

Identifiers

PMID42527445
PMCPMC13421467

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

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.