Evidence map›Paper›PMID 30631219›Full record

ArticleHuman communication research2019

When Similarity Strikes Back: Conditional Persuasive Effects of Character-Audience Similarity in Anti-Smoking Campaign.

Minji Kim

Open access · greenAbstract read
In one paragraph

Article in Human communication research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 1 pooled it
16.8field-weighted citation impact, top 1% of its field
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

8 citing papers in PubMed, 1 synthesis or guideline pooled it, 30 citations in OpenAlex.

  1. Pooled it
  2. Tailoring e-Cigarette Health Messages by Vaping Status: How Source and Message Presentation Type Shape Young Adults' Perceptions.Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco · 2026
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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

1 author at 1 institution in 1 country.

Minji KimCenter for Tobacco Control Research and Education, University of California, San Francisco, CA, USA.
University of California, San Francisco · US

Funding

Constructing Recommender Systems for Effective Health Messages: Smoking CessationR01CA160226 · NCI · UNIVERSITY OF PENNSYLVANIA · PI CAPPELLA, JOSEPH NICHOLAS · 2011 to 2014
$980k
NCI NIH HHS R01 CA160226
6 · The paper itself

Abstract

Featuring an exemplar character similar to the target audience is one of the most widely used tactics of tailored and targeted communication. Having a similar character may increase the perceived relevance of the message and facilitate persuasion. However, the way the similar character is depicted may threaten the audience's social identity and cause a boomerang effect. In an experiment where daily smokers saw and evaluated anti-smoking messages, a significant interaction between character-audience similarity and message theme emerged, mediated by message engagement and character identification. Seeing a similar (vs. dissimilar) smoker character whose health was at risk increased the audience's identification with the character, which in turn was associated with greater perceived effectiveness. However, seeing a similar smoker character endangering innocent others via secondhand smoke undermined perceived effectiveness. The simple main effects of character-audience similarity on message engagement were not significant, although the overall pattern was consistent.

Indexed as

Anti-Smoking CampaignCharacter-Audience SimilaritySocial Identity TheoryTailored and Targeted Communication

Identifiers

PMID30631219
PMCPMC6312093
OpenAlexW2895071310

What OpenQuestion holds

Textmetadata
LicenceTDM
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.