Evidence map›Paper›PMID 41931534›Full record

ArticlePloS one2026

Technostress, psychosocial safety climate, and worker well-being in automated manufacturing: A two-wave study.

Umut Elbir, Kagan Cenk Mizrak

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

2 authors.

Umut ElbirDepartment of Occupational Health and Safety, Istanbul Bilgi University, Istanbul, Turkiye.ORCID https://orcid.org/0000-0003-1416-9731
Kagan Cenk MizrakDepartment of Business Administration, Nisantası University, Istanbul, Turkiye.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The rapid digitalization of manufacturing has introduced new psychosocial risks alongside productivity gains, particularly in the form of technostress. This study investigates how technostress influences worker mental health, burnout, engagement, and safety outcomes in automated manufacturing environments, and whether job resources and psychosocial safety climate (PSC) serve as protective factors. Drawing on the Job Demands-Resources (JD-R) model, a two-wave panel design was employed with data collected from 605 employees at baseline and 450 employees at a four-week follow-up across five automated manufacturing plants. Multilevel and moderation analyses revealed that while direct effects of technostress on outcomes were directionally consistent with theoretical expectations, they were not statistically significant. Instead, job resources-including supervisory ICT support, recovery opportunities, and digital literacy-emerged as significant buffers, mitigating the potential negative effects of technostress. Furthermore, PSC at the crew and organizational level predicted improved engagement, better mental health, and stronger safety behaviors, underscoring its role as a higher-order organizational resource. These findings suggest that technostress in manufacturing is contingent on contextual factors: it becomes harmful when resources are insufficient and protective climates are absent. The study contributes to technostress and occupational health literature by integrating psychosocial and technological stressors in a multilevel framework and offers practical guidance for managers seeking to balance digitalization with worker well-being.

Indexed as

Manufacturing IndustryOccupational HealthAutomationBurnout, ProfessionalHumansMental HealthOrganizational CulturePsychological SafetyPsychological Well-BeingWorking ConditionsWorkplace

Identifiers

PMID41931534
PMCPMC13048442

What OpenQuestion holds

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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 OpenQuestion graph.