Evidence map›Paper›PMID 41668113›Full record

ArticleBMC nursing2026

The impact of innovativeness on Chinese nurses' attitudes toward artificial intelligence: a cross-sectional study based on the technology acceptance model.

Wang Yuanyuan, Xue Mengxin, Min Yuan, Hu Jiaqi, Li Feng, Wang Yiying, Tao Min, Diao Yingying, Du Zhirun, Liu Rui and 2 more

Abstract read
In one paragraph

Article in BMC nursing, 2026. 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.

  1. 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

12 authors.

Wang Yuanyuan *Department of Hematology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, 305 Zhongshan East Road, Xuanwu District, Nanjing, Jiangsu, 210000, China.ORCID http://orcid.org/0009-0002-7570-8487
Xue Mengxin *Department of Hematology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, 305 Zhongshan East Road, Xuanwu District, Nanjing, Jiangsu, 210000, China.ORCID http://orcid.org/0000-0002-9471-4906
Min YuanDepartment of Hematology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, 305 Zhongshan East Road, Xuanwu District, Nanjing, Jiangsu, 210000, China.
Hu JiaqiDepartment of Nursing, The Second Hospital of Tianjin, Tianjin, China.
Li FengDepartment of Hematology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, 305 Zhongshan East Road, Xuanwu District, Nanjing, Jiangsu, 210000, China.
Wang YiyingJiangsu Health Vocational College, Nanjing, Jiangsu, China.
Tao MinDepartment of Hematology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, 305 Zhongshan East Road, Xuanwu District, Nanjing, Jiangsu, 210000, China.
Diao YingyingDepartment of Hematology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, 305 Zhongshan East Road, Xuanwu District, Nanjing, Jiangsu, 210000, China.
Du ZhirunDepartment of Internal Neurology, The Affiliated Jiangning Hospital of Nanjing Medical University, Nanjing, Jiangsu, China.
Liu RuiDepartment of Hematology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, 305 Zhongshan East Road, Xuanwu District, Nanjing, Jiangsu, 210000, China.
Ni Juan *Department of Nursing, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, 305 Zhongshan East Road, Xuanwu District, Nanjing, Jiangsu, 210000, China. nijuanNJ@163.com.
Sun Yanfei *Department of Hematology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, 305 Zhongshan East Road, Xuanwu District, Nanjing, Jiangsu, 210000, China. yanfeivs@163.com.ORCID http://orcid.org/0009-0005-7341-6394

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundArtificial Intelligence (AI) in nursing practice holds transformative potential for healthcare. For these advancements to be successfully incorporated into nursing, nurses need to develop positive attitudes toward adopting AI technologies. This study aims to profile Chinese nurses’ attitudes toward AI and explore the factors. And introduce NI as a critical antecedent, constructing and validating a chain mediation model involving PEOU and PU, thereby advancing the TAM framework.

methodsWe conducted a cross-sectional study with 765 nurses from four Chinese hospitals. Data analysis included non-parametric tests, Spearman’s correlation, multiple linear regression, and chain mediation testing (using SPSS PROCESS Model 6).

results‘The study found that nurses’ attitudes toward AI adoption were significantly influenced by their job hierarchy, knowledge of AI, and perceptions of AI’s importance, usefulness (PU), and ease of use (PEOU), along with frequency of AI usage and nurses’ innovativeness (NI) (p<0.05). PU and PEOU of AI mediated the link between NI and their attitudes toward the use of AI(ATUA).

conclusionNurses generally held positive attitudes toward AI. The study confirmed a chain-mediation pathway: NI→ PEOU→ PU→ ATUA. These findings extend the application of Technology Acceptance Model in nursing and strengthen its predictive power in this context. This study provided a theoretical foundation for fairly promoting and implementing AI in nursing practice.

Indexed as

Artificial intelligenceAttitudesInnovativenessNursesTechnology acceptance model

Identifiers

PMID41668113
PMCPMC12990489

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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 OpenQuestion graph.