Evidence map›Paper›PMID 40723643›Full record

ArticleBehavioral sciences (Basel, Switzerland)2025

How the Human-Artificial Intelligence (AI) Collaboration Affects Cyberloafing: An AI Identity Perspective.

Jin-Qian Xu, Tung-Ju Wu, Wen-Yan Duan, Xuan-Xuan Cui

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 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

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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.

Jin-Qian XuBusiness School, Harbin Institute of Technology, Harbin 150001, China.
Tung-Ju WuSchool of Management, Harbin Institute of Technology, Harbin 150001, China.
Wen-Yan DuanBusiness School, Harbin Institute of Technology, Harbin 150001, China.
Xuan-Xuan CuiSchool of Management, Harbin Institute of Technology, Harbin 150001, China.

Funding

the Fundamental Research Funds for the Central Universities in Harbin Institute of Technology Nonethe National Natural Science Foundation of China 72131005the Natural Science Foundation of Heilongjiang Province YQ2021G004
6 · The paper itself

Abstract

Collaboration with artificial intelligence (AI) not only improves employees' work efficiency but also provides them with opportunities to participate in other behaviors. Among the various behaviors that have garnered the attention of organizations, cyberloafing has historically been a focus. Drawing from social identity theory (SIT), this research examines how human-AI collaboration diminishes cyberloafing by fostering AI identity (dependence, emotional energy, relatedness). A three-wave study (N = 381) revealed that AI collaboration strengthened employees' AI identity, enabling them to recognize their identity as AI collaborators and focus on in-role tasks, thereby reducing cyberloafing. Moreover, the research suggested that openness served as a moderating factor, further amplifying the positive relationship between human-AI collaboration and AI identity. Specifically, employees who exhibit higher levels of openness are more likely to demonstrate heightened AI identity and reduced cyberloafing. Conversely, employees with low openness exhibit less AI identity and more cyberloafing. This research employs SIT in the context of AI collaboration, thereby providing a theoretical foundation and practical guidance for reducing employee cyberloafing in the workplace and promoting organizational development.

Indexed as

cyberloafingdependenceemotional energyhuman–AI collaborationopennessrelatedness

Identifiers

PMID40723643
PMCPMC12292075

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

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 Socratic graph.