Evidence map›Paper›PMID 42724803›Full record

ArticleFrontiers in psychology2026

From perceived typicality of in-vehicle stylistic cues to autonomous vehicle acceptance: the roles of perceived behavioral control and trust.

Jing Pan, Yang Qin, Yanfei Zhu, Kexin Chen, Mo Chen

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
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0citing papers in PubMed
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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

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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

5 authors.

Jing PanCollege of Art and Design, Nanjing Tech University, Nanjing, China.
Yang QinCollege of Art and Design, Nanjing Tech University, Nanjing, China.
Yanfei ZhuCollege of Furnishings and Industrial Design, Nanjing Forestry University, Nanjing, China.
Kexin ChenCollege of Art and Design, Nanjing Tech University, Nanjing, China.
Mo ChenCollege of Art and Design, Nanjing Tech University, Nanjing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Although autonomous driving technology has advanced, limited public acceptance remains a major barrier. Moving beyond studies focused on isolated design domains, this study examined whether the perceived typicality of stylistic cues across automated driving behavior, vehicle interior design, and human-machine interface (HMI) presentation is associated with autonomous vehicle acceptance. Methods: The three domain scores were averaged to form the observed In-Vehicle Style Typicality Index (IVSTI). In a stimulus-based online survey, 202 participants evaluated eight stimuli and completed measures of perceived behavioral control (PBC), trust, and behavioral intention. Structural equation modeling was used to examine the proposed associations and indirect effects. Results: The IVSTI was positively associated with PBC and behavioral intention, but not with trust. The indirect association through PBC was statistically significant, whereas the indirect association through trust alone was not. A significant serial indirect association through PBC and trust was observed in the hypothesized model, although alternative analyses did not establish a unique ordering between PBC and trust. Domain-level analyses showed that all three domains were positively associated with PBC when examined separately, whereas only HMI-style typicality retained a significant unique association when modeled simultaneously. Discussion: These findings extend autonomous vehicle acceptance research by integrating three conceptually distinct in-vehicle stylistic domains and identifying PBC as an important statistical pathway linking perceived typicality with behavioral intention.

Indexed as

autonomous vehicle acceptancebehavioral intentionhuman–vehicle interactionin-vehicle experienceperceived behavioral controlperceived typicality of in-vehicle stylistic cuestrust

Identifiers

PMID42724803
PMCPMC13560749

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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.