Evidence map›Paper›PMID 41862962›Full record

ArticleBMC psychology2026

The influence of information similarity overload on users' withdrawal behaviour from social communities on SNSs.

Thac Dang-Van, Linh T M Doan

Abstract read
In one paragraph

Article in BMC psychology, 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
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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.

Thac Dang-VanFaculty of Economics - Management, Dong Nai Technology University, Biên Hòa, Dong Nai Province, Vietnam.
Linh T M DoanUEH Honours College, University of Economics Ho Chi Minh City, Ho Chi Minh City, Vietnam. linh.doanthimy@westernsydney.edu.vn.

Funding

National Foundation for Science and Technology Development 19/2022/TN
6 · The paper itself

Abstract

Excessive and repetitive information is a common experience in online communities. While prior research has focused on general information overload, the effects of similarity overload, where content is redundant rather than voluminous, remain less clear. Drawing on construal level theory, this study examines how similarity overload contributes to users’ withdrawal from social communities on social networking sites (SNSs). The model tests the mediating roles of boredom and perceived misfit, and the moderating influence of perceived social pressure. A face-to-face survey was conducted with 282 SNS community users in Vietnam. Established scales were adapted to measure similarity overload, boredom, misfit, perceived social pressure, and withdrawal behaviour. Partial least squares structural equation modelling (PLS-SEM) was applied, with bootstrapping to assess mediation and moderation. Reliability, validity and multicollinearity were confirmed. Results show that similarity overload is positively associated with withdrawal, but its effect is primarily indirect. Both boredom and perceived misfit significantly mediated the relationship, revealing affective and cognitive pathways. Perceived social pressure further intensified these effects, with users embedded in strong normative climates more likely to disengage under similarity overload. The findings identify similarity overload as a distinct dimension of information stress that reduces engagement by lowering intrinsic motivation and personal and community alignment. To this end, the study advances construal level theory in digital contexts and offers practical guidance for community managers to mitigate withdrawal by diversifying content and monitoring social norms.

Indexed as

BoredomSocial BehaviorSocial MediaSocial NetworkingAdultFemaleHumansMaleSocial GroupVietnamYoung AdultBoredomInformation similarity overloadPerceived misfitPerceived social pressureWithdrawal behaviour

Identifiers

PMID41862962
PMCPMC13127001

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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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.