Evidence map›Paper›PMID 42234694›Full record

ArticlePloS one2026

The magic of first impressions: Do facial displays on online platforms affect users' offline conversion rate?

Xue Zhang, Qiutong Li, Chunjia Han, Xinyue Huang

Abstract read
In one paragraph

Article in PloS one, 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.

Xue ZhangSchool of economics and management, Harbin Huade University, Harbin, P.R. China.
Qiutong LiSchool of economics and management, Harbin Huade University, Harbin, P.R. China.ORCID https://orcid.org/0009-0008-1904-4296
Chunjia HanBusiness school, Birkbeck, University of London, Bloomsbury, London, United Kingdom.
Xinyue HuangSchool of management, Heilongjiang University of Science and Technology, Harbin, P.R. China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With the rise of Internet health, online medical platforms play the key role of information bridge and convenient channel in medical service selection. Users tend to rely on doctor images and electronic word-of-mouth evaluations displayed on online medical platforms when selecting medical services. This study aims to explore the impact of facial features displayed on online platforms on users' offline conversion rate. It will help us understand the psychological mechanism behind users' decisions from the perspective of the first impression effect. Python was used to collect pictures and information of 7547 doctors from "haodf.com", a well-known online medical platform in China. Then, we used commercial software to calculate their facial feature values. The results showed that: Appearance attractiveness, smile, and gender of facial displays on online platforms have a significant positive relationship on users' offline conversion rate. Satisfaction evaluations do not significantly regulate the relationship between appearance attractiveness and smile on users' offline conversion rate, but have a significant negative moderating effect on the effect of gender. This study emphasizes the importance of the eWOM platform in image management. It also provides a new perspective for optimizing resource allocation and improving service efficiency. It has important theoretical and practical value for further promoting the innovation of online service systems and realizing the seamless connection between online and offline.

Indexed as

FaceInternetAdultChinaDigital MediaFemaleHumansMaleSmiling

Identifiers

PMID42234694
PMCPMC13232954

What Socratic holds

Textmetadata
LicenceCC BY
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 Socratic graph.