Evidence map›Paper›PMID 41436625›Full record

ArticleScientific reports2025

Examining factors affecting the acceptance of AI-powered creativity support tools among the industrial design community in China.

Jinzhi Zou, Khairul Manami Kamarudin, Sazrinee Zainal Abidin, Jiaqi Zhang, Jing Liu

Abstract read
In one paragraph

Article in Scientific reports, 2025. 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

5 authors.

Jinzhi ZouDepartment of Industrial Design, Faculty of Design and Architecture, Universiti Putra Malaysia (UPM), Serdang, Malaysia. gs66238@student.upm.edu.my.
Khairul Manami KamarudinDepartment of Industrial Design, Faculty of Design and Architecture, Universiti Putra Malaysia (UPM), Serdang, Malaysia. manami@upm.edu.my.
Sazrinee Zainal AbidinDepartment of Industrial Design, Faculty of Design and Architecture, Universiti Putra Malaysia (UPM), Serdang, Malaysia.
Jiaqi ZhangDepartment of Industrial Art, Graduate School of System Design, Tokyo Metropolitan University, Tokyo, Japan.
Jing LiuSchool of Mechanical and Electrical Engineering, Guangdong University of Petrochemical Technology, Maoming, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The emergence of artificial intelligence (AI)-powered creativity support tools (CSTs) is recently transforming the creative design industry. Yet within the specific industrial design field, such tools are neither widespread nor well-tailored to unique needs of the community. This gap makes the industrial design community’s acceptance of AI-CSTs uncertain. To address the issue, this study explored key factors influencing the acceptance of AI-CSTs by extending the unified theory of acceptance and use of technology (UTAUT) model. The authors used a structural equation modeling approach to carried out an empirical study. Data were collected through a questionnaire survey with 515 industrial design stakeholders in China. The results indicated that technology optimism and personal innovativeness positively affected performance expectancy. Interactivity and facilitating conditions were positive determinants of effort expectancy. The variable “perceived risk” was constructed by three first-order components, namely ethical risk, privacy risk and output risk. Finally, intention to use was significantly affected by performance expectancy, effort expectancy, price value, and perceived risk. Based on the theoretical findings, we presented general AI-CST promotion strategies and specific AI-CST optimization strategies for the industrial design community. This study contributed to a deeper understanding of designers’ behavioral intentions toward AI-CSTs and provides actionable insights for stakeholders to improve system usability, risk control, and technology fit in creative domains.

Indexed as

Artificial intelligenceCreativity support toolsInteractive designStructural equation modellingTechnology acceptance

Identifiers

PMID41436625
PMCPMC12830684

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.