Evidence map›Paper›PMID 42495224›Full record

ArticleFrontiers in psychology2026

From AI adaptation to innovation: how leader AI crafting shapes employee innovative behavior.

Tianxing Pu, Ren Lei, Abdul Waheed Siyal, Yuanheng Li

Abstract read
In one paragraph

Article in Frontiers in psychology, 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

4 authors.

Tianxing PuSchool of Digital Economics and Management, Wuxi University, Wuxi, China.
Ren LeiSchool of Digital Economics and Management, Wuxi University, Wuxi, China.
Abdul Waheed SiyalSchool of Digital Economics and Management, Wuxi University, Wuxi, China.
Yuanheng LiSchool of Business and Law, University of Wolverhampton, Wolverhampton, United Kingdom.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As artificial intelligence reshapes work, organizations face the challenge of helping employees move beyond routine technological adaptation toward innovation. Although prior research has shown that leader AI crafting facilitates employees' adaptation to AI, whether and how it promotes employees' innovative behavior remains unclear. Drawing on social cognitive theory, this study examines how leader AI crafting relates to employees' innovative behavior through AI self-efficacy and when this relationship becomes stronger. We conducted two studies. Study 1 used a two-wave field survey to examine the moderated mediation model linking leader AI crafting, AI self-efficacy, AI task dependence, and innovative behavior. Across the two studies, the results indicate that leader AI crafting enhances employees' AI self-efficacy, which in turn is associated with higher innovative behavior. The positive effect of leader AI crafting on AI self-efficacy is stronger when AI task dependence is high, and moderated mediation effect is also supported. Study 2 used a vignette-based experiment to test the first-stage moderating role of AI task dependence in the relationship between leader AI crafting and AI self-efficacy. These findings extend the leader AI crafting literature by clarifying how employees move from adapting to AI toward innovating in AI-enabled work contexts, and by identifying AI self-efficacy and AI task dependence as key psychological and contextual mechanisms. Given the technology-industry sample in Study 1 the broader generalizability of these findings remains to be examined in future research.

Indexed as

AI self-efficacyAI task dependenceinnovative behaviorleader AI craftingsocial cognitive theory

Identifiers

PMID42495224
PMCPMC13391400

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.