How AI-Related Task Complexity Shapes Innovative Work Behavior: A Coping Theory Perspective.

Hongyi Cai, Yuhui Ge, Heng Zhao

Abstract read

In one paragraph

Article in Behavioral sciences (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing 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

1 citing paper in PubMed.

How does human-AI collaboration task complexity affect employee work engagement? The roles of humble leadership and AI self-efficacy.Frontiers in psychology · 2026
Article

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.

Hongyi CaiBusiness School, University of Shanghai for Science and Technology, Shanghai 200093, China.

Yuhui GeBusiness School, University of Shanghai for Science and Technology, Shanghai 200093, China.

Heng ZhaoSchool of Economics and Management, Beijing Jiaotong University, Beijing 100044, China.ORCID 0000-0002-7344-4329

Funding

Fundamental Research Funds for the Central Universities 2025YJS124Humanities and Social Sciences Research Project of the Ministry of Education of China 23YJA630027

6 · The paper itself

Abstract

As technological revolutions continue to advance, AI increasingly emerges as a focal driver for enhancing innovation quality. Grounded in coping theory, this study develops a moderated dual-pathway model to examine the mechanisms through which AI-related task complexity influences innovative work behavior. A three-wave field survey was conducted among 353 employees from high-tech enterprises in Beijing and Shanghai. Hypotheses are tested via structural equation modeling. The findings reveal that AI-related task complexity significantly promotes innovative work behavior by fostering problem-focused coping while simultaneously suppressing it by triggering emotion-focused coping. Moreover, AI opportunity perception is found to moderate these relationships, strengthening the positive effect of problem-focused coping and attenuating the negative effect of emotion-focused coping on innovation. This study advances theoretical understanding of employee behavioral responses in AI-integrated work contexts and offers practical insights into how organizations can leverage AI to stimulate innovation among their workforce.

Indexed as

AI opportunity perceptionAI-related task complexityemotion-focused copinginnovative work behaviorproblem-focused coping

Identifiers

PMID41301270

DOI10.3390/bs15111467

PMCPMC12649141

What Socratic holds

Textmetadata

LicenceCC BY

Read underepoch 390

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 Socratic graph.