Evidence map›Paper›PMID 40348746›Full record

ArticleScientific reports2025

Artificial intelligence voice gender, gender role congruity, and trust in automated vehicles.

Qiaoning Zhang, X Jessie Yang, Lionel P Robert

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. 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.

Qiaoning ZhangHuman Systems Engineering, Arizona State University, Mesa, AZ, USA. qiaoning@asu.edu.
X Jessie YangDepartment of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI, USA.
Lionel P RobertSchool of Information, University of Michigan, Ann Arbor, MI, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Existing research on human-automated vehicle (AV) interactions has largely focused on auditory explanations, with less attention to how voice characteristics shape user trust. This paper explores the influence of gender similarity between users and AV voices and the role of gender-role congruity rooted in societal stereotypes on cognitive and affective trust in AVs. Findings reveal that gender-role congruity moderates the relationship between gender similarity and trust. When an AV's voice gender aligns with its expected role, gender similarity enhances cognitive and affective trust. However, when gender roles were not congruent, the trust-enhancing effect of gender similarity diminishes. These findings highlight the importance of considering gender in AV voice design for conveying critical driving information and reveal how societal stereotypes shape AV design. The study offers insights for enhancing user trust and acceptance of AV technology, suggesting future research directions for developing AV systems that avoid reinforcing social biases.

Indexed as

Artificial IntelligenceAutomobile DrivingAutomobilesGender IdentityTrustVoiceAdultAutomationFemaleHumansMaleYoung Adult

Identifiers

PMID40348746
PMCPMC12065868

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.