Evidence map›Paper›PMID 42374044›Full record

ArticleScientific reports2026

Analyzing the effect of new technology and knowledge adoption on workers with visual impairment through a serial mediation model.

Jing Bai, Pingping Fu, Wenjie Ye

Abstract read
In one paragraph

Article in Scientific reports, 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

3 authors.

Jing BaiCollege of Pharmaceutical Business, Zhejiang Pharmaceutical University, 315500, Ningbo, China.
Pingping FuNottingham University Business School China, University of Nottingham Ningbo China, 199 Taikang East Road, Ningbo, 315100, China. pingping.fu@nottingham.edu.cn.
Wenjie YeThe Department of Business Administration, Shanghai University of Finance and Economics Zhejiang College, Jinhua, 321000, Zhejiang, China.

Funding

University of Nottingham Ningbo China REST202203001
6 · The paper itself

Abstract

Adopting new technology and knowledge (ANTK) has become a powerful force for enhancing productivity. This study examines the impact of ANTK on work engagement among visually impaired workers, with a focus on the mediating roles of person-job fit and positive identity as a visually impaired individual. Grounded in the Job Demands-Resources (JD-R) model, the study explores how ANTK serves as a critical job resource, mitigating the challenges associated with visual impairments and fostering higher levels of work engagement. Based on data from 204 visually impaired workers, the findings demonstrate that ANTK directly enhances work engagement. Moreover, person-job fit and positive identity sequentially mediate the relationship between ANTK and engagement. The findings provide theoretical contributions to both the JD-R model and the literature on disability and employment by highlighting the psychological mechanisms that link technology adoption to work outcomes. Practically, the study provides valuable insights for organizations seeking to leverage inclusive technologies to enhance accessibility, support identity development, and foster a more equitable and engaging workplace for workers with visual impairments.

Indexed as

EmploymentPersons with Visual DisabilitiesTechnologyVision DisordersAdultFemaleHumansMaleMiddle AgedWork EngagementWorkplaceAdopting new technology and knowledge (ANTK)JD-R modelPerson-job fitPositive identityWork engagementWorkers with visual impairments

Identifiers

PMID42374044
PMCPMC13315273

What OpenQuestion 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 OpenQuestion graph.