Evidence map›Paper›PMID 42510374›Full record

ArticleBehavioral sciences (Basel, Switzerland)2026

How Is Generative AI Use Associated with Employee Creative Behavior? Uncovering Capability- and Risk-Based Psychological Pathways.

Jingya Yang, Haikun Shan, Ji-Na Lee, Zhaoqi Li

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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0cells of the map it votes in
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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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.

Jingya YangDepartment of Global Management, Seokyeong University, Seoul 02713, Republic of Korea.ORCID 0009-0005-4386-5447
Haikun ShanDepartment of Global Management, Seokyeong University, Seoul 02713, Republic of Korea.ORCID 0009-0007-0998-3831
Ji-Na LeeDepartment of Global Management, Seokyeong University, Seoul 02713, Republic of Korea.
Zhaoqi LiDepartment of Global Management, Seokyeong University, Seoul 02713, Republic of Korea.ORCID 0009-0009-6442-368X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The rapid diffusion of generative AI is profoundly transforming knowledge-intensive work and reshaping how employees innovate. Although prior research has highlighted the positive role of generative AI in improving work efficiency and performance, the underlying mechanisms through which it promotes employee creative behavior remain insufficiently understood. Drawing on Social Cognitive Theory and Psychological Safety Theory, this study develops a moderated dual-path model to examine how generative AI use influences employee creative behavior through creative self-efficacy and psychological safety. Using a three-wave time-lagged survey design, data were collected from 297 employees in highly digitalized industries. The results indicate that generative AI use is positively associated with subsequent self-reported employee creative behavior. Both creative self-efficacy and psychological safety significantly mediate this relationship. Furthermore, digital leadership strengthens the positive effects of generative AI use on creative self-efficacy and psychological safety, thereby further enhancing its indirect effects on employee creative behavior. The findings not only extend the literature on generative AI and employee creative behavior but also deepen the theoretical understanding of AI empowerment mechanisms by identifying two critical pathways: a capability-enhancement pathway and a risk-reduction pathway. This study provides important theoretical insights and practical implications for organizations seeking to effectively leverage generative AI to foster employee creative behavior in the context of digital transformation.

Indexed as

creative self-efficacydigital leadershipemployee creative behaviorgenerative AI usepsychological safety

Identifiers

PMID42510374
PMCPMC13404951

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.