Evidence map›Paper›PMID 42792312›Full record

ArticleBehavioral sciences (Basel, Switzerland)2026

When Professional Networking Becomes Exhausting: Passive LinkedIn Use, Communication Overload, and Social Media Fatigue.

Phillip Ozimek, Annemarie Helga Spannenkrebs, Eileen Bohl, Elke Rohmann, Denise Hoffmann, Hans-Werner Bierhoff

Abstract read
In one paragraph

Article in Behavioral sciences (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.

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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

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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

6 authors.

Phillip OzimekDepartment of Humanities, Vinzenz Pallotti University, 56179 Vallendar, Germany.ORCID 0000-0001-7282-4568
Annemarie Helga SpannenkrebsDepartment of Psychology, Ruhr University Bochum, 44801 Bochum, Germany.
Eileen BohlDepartment of Psychology, Ruhr University Bochum, 44801 Bochum, Germany.
Elke RohmannDepartment of Psychology, Ruhr University Bochum, 44801 Bochum, Germany.
Denise HoffmannDepartment of Humanities, Vinzenz Pallotti University, 56179 Vallendar, Germany.
Hans-Werner BierhoffDepartment of Psychology, Ruhr University Bochum, 44801 Bochum, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Professional social networking has received considerably less research attention than private social media, particularly with regard to passive use and social media fatigue. The present study examined the association between passive LinkedIn use and social media fatigue and investigated whether communication overload and information overload may account for this association. In addition, the study provides a preliminary LinkedIn-specific adaptation of an existing measure of professional social media use, the LinkedIn Activity Questionnaire (LAQ). Data were collected from an online sample of 137 LinkedIn users. Correlation analyses and a parallel mediation model were conducted, controlling for weekly LinkedIn use. Passive LinkedIn use was positively associated with communication overload but not with information overload or social media fatigue. Both communication overload and information overload were positively associated with social media fatigue. The parallel mediation analysis revealed a significant specific indirect effect through communication overload, whereas the indirect effect through information overload was not significant. Neither the total nor the direct association between passive LinkedIn use and social media fatigue was significant. These findings suggest that communication overload may represent a particularly relevant pathway linking passive LinkedIn use and social media fatigue in professional social networking contexts. The findings also provide initial support for the usefulness of the LAQ as a LinkedIn-specific operationalization, while further psychometric validation is warranted.

Indexed as

communication overloadinformation overloadLinkedInprofessional social mediasocial media fatigue

Identifiers

PMID42792312
PMCPMC13603597

What OpenQuestion holds

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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.