Evidence map›Paper›PMID 42737209›Full record

ArticleFoods (Basel, Switzerland)2026

What Makes an AI Persuasive? The Interaction Effect of AI Shopping Assistants and Algorithmic Recommendation Cues on Consumers' Organic Food Purchase Intention.

Sinan Li, Kai Chen

Abstract read
In one paragraph

Article in Foods (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.

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

2 authors.

Sinan LiSchool of Economics and Management, Beijing Forestry University, Beijing 100083, China.
Kai ChenSchool of Economics and Management, Beijing Forestry University, Beijing 100083, China.ORCID 0000-0002-0396-7765

Funding

Beijing Forestry University 23&ZD096
6 · The paper itself

Abstract

Promoting consumers' organic food purchases is an important way to drive the green transformation of the demand side of food consumption and achieve sustainable development. This study, through two studies, examines the interactive effects and psychological mechanisms of different types of AI shopping assistants (analytic AI vs. empathic AI) and algorithmic recommendation cues (item-referent cues vs. user-referent cues) on consumers' organic food purchase intention. The findings indicate that the two have an interaction effect, with analytic AI combined with item-referent cues being more effective, while empathic AI matched with user-referent cues is more effective. Further analysis revealed that perceived information validity and green trust each play a mediating role, forming a chain mediation path. Specifically, perceived information validity positively influences green trust, and green trust further positively influences purchase intention. This work reveals the matching mechanism between AI source characteristics and algorithmic recommendation cues, expands the application of Cue Utilization Theory and Signaling Theory in AI-driven organic food consumption contexts, and provides practical insights for companies to develop differentiated AI assistant configurations, recommendation information design, and green trust cultivation strategies.

Indexed as

algorithmic recommendation cuesanalytic AIempathic AIgreen trustorganic foodperceived information validitypurchase intention

Identifiers

PMID42737209
PMCPMC13565594

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.