Evidence map›Paper›PMID 39913426›Full record

ArticlePloS one2025

Why do people resist AI-based autonomous cars?: Analyzing the impact of the risk perception paradigm and conditional value on public acceptance of autonomous vehicles.

Ie Rei Park, Seoyong Kim, Jungwook Moon

Abstract read
In one paragraph

Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

3 authors.

Ie Rei ParkDepartment of Public Administration, Research Associate, Yonsei University, Seoul, Republic of Korea.
Seoyong KimDepartment of Public Administration, Ajou University, Suwon, Republic of Korea.ORCID 0000-0002-5536-4907
Jungwook MoonKorea Information Society Development Institute (KISDI), Center for AI & Social Policy, Jincheon-gun, Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study examines the factors that lead to the acceptance of AI-based autonomous vehicles. Despite the considerable importance of AI-based autonomous vehicles there has been a lack of analysis based on theoretical models and analysis that considers contextual conditions. The survey was conducted between July 8 and 17, 2019. In order to increase the representativeness of the sample, a quota sampling method was adopted, based on considering gender and region. The sample size of this survey is 2,000 people. According to the response statistics, 26,231 people requested the survey, 3,973 people participated in the survey, and 2,000 people completed the survey. We adopted regression and moderation analysis as main statistical analysis methods. In modeling, we set up the variable from risk perception paradigm as independent variable and the conditional value as moderating variable in explaining the acceptance of AI-based autonomous vehicles. For this work, the analysis was conducted in two stages. Initially, a regression analysis was performed to determine the impact of the risk perception paradigm and conditional value on the acceptance of autonomous vehicles. Secondly, a moderation analysis was conducted to determine whether the perception of self-driving taxis moderates the relationship between the risk perception paradigm and the acceptance of autonomous vehicle. The study revealed that the acceptance of autonomous vehicles is influenced by a number of factors, including knowledge, image, conditional value, and perceived risks. Additionally, the relationship with perceived benefits, image and autonomous vehicle is moderated by conditional value.

Indexed as

Artificial IntelligenceAutomobile DrivingAutomobilesAdolescentAdultAgedFemaleHumansMaleMiddle AgedPerceptionSurveys and QuestionnairesYoung Adult

Identifiers

PMID39913426
PMCPMC11801528

What OpenQuestion holds

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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.