Evidence map›Paper›PMID 40426426›Full record

ArticleBehavioral sciences (Basel, Switzerland)2025

Will Employee-AI Collaboration Enhance Employees' Proactive Behavior? A Study Based on the Conservation of Resources Theory.

Chenxi Sun, Xinan Zhao, Baorong Guo, Ningning Chen

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 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

14 citing papers in PubMed.

  1. Article
  2. Article
  3. Leadership and Human-AI Collaboration: A Measurement Scale.Behavioral sciences (Basel, Switzerland) · 2026
    Article
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  5. Article
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  13. Will Teacher-AI Collaboration Enhance Teaching Engagement?Behavioral sciences (Basel, Switzerland) · 2025
    Article
  14. 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

4 authors.

Chenxi SunSchool of Business Administration, Northeastern University, Shenyang 110167, China.
Xinan ZhaoSchool of Business Administration, Northeastern University, Shenyang 110167, China.
Baorong GuoBusiness School, Guilin University of Technology, Guilin 541006, China.ORCID 0000-0003-3124-8941
Ningning ChenSchool of Business Administration, Northeastern University, Shenyang 110167, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study explores how employee-AI collaboration can promote employees' proactive behavior by reducing their workload, and examines the mediating role of workload and the moderating effect of AI literacy. Based on a survey of employees across multiple industries, the study finds that employee-AI collaboration significantly reduces employees' workload, which in turn encourages more proactive behavior. In this process, workload serves as a central mediating mechanism, as it helps alleviate task pressure and frees up cognitive resources, enabling employees to take on additional responsibilities and put forward innovative suggestions. Furthermore, with increasing levels of employee-AI collaboration, employees with higher AI literacy tend to experience greater workload relief, while those with lower literacy demonstrate a stronger and more consistent proactive behavioral response. These findings offer theoretical insight into employee-AI interaction and practical implications for enhancing initiative and innovation through effective AI integration.

Indexed as

AI literacyemployee–AI collaborationproactive behaviorworkload

Identifiers

PMID40426426
PMCPMC12109449

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

None linked

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.