Evidence map›Paper›PMID 40595864›Full record

ArticleScientific reports2025

An integrated SOR and SCT model approach to exploring chinese public perception of autonomous vehicles.

Jingwen Wu, Sok-Tae Kim

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 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

2 authors.

Jingwen WuInternational Economics & Trade, Guangzhou College of Technology and Business, Guangzhou, China. wujingwen@gzgs.edu.cn.
Sok-Tae KimInternational Trade, Dongguk University, Seoul, South Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The media's influence on public attitudes and decisions exhibits a dual nature, serving as a catalyst for both positive and negative perceptions, especially regarding autonomous vehicles (AVs). Although extensive research has addressed the role of social and mass media in this domain, the differential impacts of mass media and electronic word-of-mouth (eWOM) on the adoption of these technologies remain underexplored. To bridge this gap, this study explores how various media channels shape public perception and adoption intentions toward AVs. A key innovation of this study lies in its integration of the stimulus-organism-response (SOR) model and social cognitive theory (SCT) to examine how external environmental stimuli and internal belief systems jointly shape consumer behavior in autonomous vehicles. Methodologically, the study employed a structured questionnaire to survey Chinese consumers, gathering data on their media-influenced perceptions. This study's theoretical implications were grounded in the TAM framework. Two types of external environmental stimuli: mass media and electronic word-of-mouth, were introduced, enriching the environmental variable system of the technology adoption model. Secondly, by using the integrated perspective of SOR-SCT, it revealed the differentiated influence mechanisms of different information sources on consumers' adoption intentions through three psychological paths: self-efficacy, subjective norms, and trust. Practically, the research offers insights into how service providers can strategically use media channels to enhance public acceptance of new technologies. The results indicate that mass media has a positive impact on the public's willingness to adopt autonomous vehicles. EWOM facilitated the creation of subjective norms by providing authentic, transparent, and diverse information, as well as enabling interaction with other users.

Indexed as

Adoption intentionsAutonomous vehicleElectronic word-of-mouthMass mediaMedia channels

Identifiers

PMID40595864
PMCPMC12219563

What OpenQuestion holds

Textmetadata
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.