Evidence map›Paper›PMID 41488962›Full record

ArticleFrontiers in psychology2025

Empowering workforces in AI-driven environments: co-skilling, organizational support, and mitigating job insecurity.

Yujie Zhang, Xiaoxiao Liu, Qiao Yan, Meng Na

Abstract read
In one paragraph

Article in Frontiers in psychology, 2025. 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

4 authors.

Yujie ZhangSchool of Business, Zhuhai College of Science and Technology, Zhuhai, Guangdong, China.
Xiaoxiao LiuSchool of Management, Guangdong University of Science and Technology, Dongguan, Guangdong, China.
Qiao YanSchool of Business, Zhuhai College of Science and Technology, Zhuhai, Guangdong, China.
Meng NaGraduate School of Business, Universiti Kebangsaan Malaysia (UKM), Bangi, Selangor, Malaysia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The rise of artificial intelligence (AI) has transformed workplaces, creating opportunities for innovation but also heightening job insecurity (JIN) among employees. This study examines the impact of Co-Skilling dimensions-participation, engagement, peer collaboration, and learning effectiveness (LE)-on mitigating JIN through perceived organizational support (POS), mental wellbeing (MEW), and skill confidence (SC). Using the Social Learning Theory (SLR) and the JD-R model as theoretical underpinnings, the research highlights the moderating role of employee readiness (ER) for AI in shaping these dynamics. A cross-sectional quantitative design with stratified random sampling was employed, involving 437 responses from employees across manufacturing, healthcare, technology, banking, and retail industries in China. Results demonstrate the significant mediating effects of POS, MEW, and SC, with POS emerging as a critical buffer against insecurity. However, nonsignificant findings in certain SC-related pathways and moderation effects of ER underscore the complexity of addressing JIN in technologically dynamic environments. This study contributes to theory by expanding the applications of SLR and the JD-R model and offers practical implications for organizations to design tailored Co-Skilling initiatives. Future research should explore additional contextual and psychological factors to enhance workforce adaptability in AI-integrated workplaces.

Indexed as

artificial intelligenceco-skillingemployee readiness for AIjob insecuritymental wellbeingperceived organizational supportskill confidence

Identifiers

PMID41488962
PMCPMC12757283

What OpenQuestion holds

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LicenceCC BY
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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.