Evidence map›Paper›PMID 42666202›Full record

ArticleFrontiers in psychology2026

Transforming work or eroding social capital? How reliance on artificial intelligence drives workplace involution.

Ruochen Huang

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

1 author.

Ruochen HuangSchool of Business, Fuzhou Polytechnic University, Fuzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The rapid integration of artificial intelligence (AI) into organizational contexts is reshaping how employees work, interact, and respond to changing workplace dynamics. Although AI is often expected to improve efficiency and reduce workload, emerging evidence points to a potential dark side, as AI adoption may intensify competitive pressure within organizations. Motivated by this paradox, this study examines how AI reliance may erode workplace social capital by fostering involution-an excessive and inefficient form of competition characterized by escalating effort and diminishing returns. This research focuses on digitally enabled and highly competitive workplace settings, where employee performance and outputs are more visible, transparent, and comparable. We propose that, from employees' perspective, AI reliance is associated with higher levels of involution through elevated performance expectations and anxiety. Using survey data from employees in China, the study hypotheses were tested using structural equation modeling. The results support the proposed model and indicate a significant sequential mediation pattern linking AI reliance, performance expectations, employee anxiety, and workplace involution. Notably, the direct relationship between AI reliance and involution is not significant, suggesting that AI reliance is associated with employees' defensive competitive behavior primarily through employees' perception of external evaluative pressures and internal psychological responses. These findings highlight the unintended social consequences of AI adoption in organizations, and underscore the importance of protecting employee wellbeing, communicating realistic performance expectations, and maintaining workplace social capital when implementing AI in digitally enabled organizations.

Indexed as

anxietyartificial intelligence relianceemployee psychologyinvolutionperformance expectationssocial capitalsocial comparisonworkplace behavior

Identifiers

PMID42666202
PMCPMC13521812

What OpenQuestion holds

Textmetadata
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 OpenQuestion graph.