Evidence map›Paper›PMID 42168430›Full record

ArticleScientific reports2026

How enterprise social media use affects employee well-being.

Da Eun Song, Seongcheol Kim

Abstract read
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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.

Da Eun SongResearch Institute for Information & Culture, Korea University, Seoul, 02841, South Korea.
Seongcheol KimSchool of Media and Communication, Korea University, Seoul, 02841, South Korea. hiddentrees@korea.ac.kr.

Funding

The IITP of Korea IITP-2026-RS-2020-II201749the Ministry of Education of the Republic of Korea, National Research Foundation of Korea NRF2023S1A5C2A03095169
6 · The paper itself

Abstract

The widespread adoption of enterprise social media (ESM) in hybrid and remote work settings has transformed workplace collaboration by enabling seamless connectivity and real-time interaction. Yet it also requires employees to remain continuously responsive and digitally present. These conditions increase communication overload and blur work-personal boundaries, reflecting the phenomenon of techno-invasion. Such disruptions may heighten psychological strain and diminish well-being. Guided by the Job Demands-Resources (JD-R) model, this study examines how distinct ESM-induced job demands-communication overload and boundary management stress-are associated with employee exhaustion and well-being. We collected survey data from 200 Korean office workers (mean age = 44.38, range = 27-64; 56% male) using validated self-report scales and analyzed the data using partial least squares structural equation modeling (PLS-SEM). The results indicate that intensified ESM use is linked to greater communication overload and boundary management stress, which are also associated with increased exhaustion and reduced well-being. By elucidating how ESM-driven connectivity can undermine employee well-being, this research enriches the technostress literature. Practically, it highlights the need for organizations to implement supportive initiatives that alleviate excessive job demands and foster a sustainable digital work environment that supports employee psychological health.

Indexed as

Social MediaWorkplaceAdultCommunicationDigital MediaEmotional ExhaustionFemaleHumansMaleMedia ExposureMiddle AgedPsychological Well-BeingRepublic of KoreaStress, PsychologicalSurveys and QuestionnairesWorking ConditionsBoundary theoryCommunication overloadEnterprise social mediaExhaustionJob demands–resources modelWell-being

Identifiers

PMID42168430
PMCPMC13402619

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.