Evidence map›Paper›PMID 42041849›Full record

ArticleEuropean journal of investigation in health, psychology and education2026

A Moderated Mediation Model of AI-Driven Identity Threats and Employee Cyberloafing: The Role of AI-Inclusive Identity.

Alqa Ashraf, Qingfei Min, Aleena Ashraf

Abstract read
In one paragraph

Article in European journal of investigation in health, psychology and education, 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

3 authors.

Alqa AshrafSchool of Economics and Management, Dalian University of Technology, Dalian 116024, China.ORCID 0009-0008-1733-9023
Qingfei MinSchool of Economics and Management, Dalian University of Technology, Dalian 116024, China.
Aleena AshrafSchool of Economics and Management, Dalian University of Technology, Dalian 116024, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study intended to examine how human-AI collaboration-based identity threat appraisals in the form of the loss of autonomy and loss of skill trigger a professional identity that fosters cyberloafing. Based on social identity theory, this study applied a three-wave survey design with 507 employees. The proposed research model was tested using partial least squares structural equation modeling (PLS-SEM) with SmartPLS 4, which enabled the assessment of both measurement and structural models. The perceived loss of skill and loss of autonomy are positively associated with professional identity threat, which mediates their relationships with cyberloafing. AI-inclusive identity weakens these associations for the loss of autonomy, suggesting that employees with strong AI-inclusive identity exhibit weaker professional identity threat. When integrating AI, organizations should mitigate appraisals of the loss of autonomy and loss of skill through participatory design, role redesign, and communication that emphasizes unique human contributions. Supporting healthy AI-human identity integration may reduce counterproductive behaviors such as cyberloafing. By positioning identity threat appraisals as human-AI collaboration-driven antecedents of professional identity threat and cyberloafing, this study extends social identity theory to human-AI contexts. It further demonstrates that over-identification with AI may heighten professional identity threats by diminishing the value of uniquely human contributions.

Indexed as

cyberloafinghuman–AI collaborationloss of autonomyloss of skillprofessional identity threat

Identifiers

PMID42041849
PMCPMC13115243

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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.