Evidence map›Paper›PMID 42642436›Full record

ArticleScientific reports2026

Examining car drivers' intention to pay for traffic safety improvement: an integrated TPB-HBM framework.

Sajid Ali, Xie Yi, Qiu Xiayan

Abstract read
In one paragraph

Article in Scientific reports, 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
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.

Sajid AliSchool of Education, Guangzhou University, Guangzhou, 510006, China.
Xie YiSchool of Education, Guangzhou University, Guangzhou, 510006, China.
Qiu XiayanSchool of Education, Guangzhou University, Guangzhou, 510006, China. qiuqxy@126.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Understanding whether road users are willing to financially support road safety improvement is important for designing feasible and socially acceptable traffic safety policies, especially in resource-constrained urban contexts. This study examines car drivers' intention to pay (ITP) for traffic safety improvement in Peshawar, Pakistan, by integrating the Theory of Planned Behavior (TPB) and the Health Belief Model (HBM). A face-to-face survey of 554 car drivers was conducted, and the proposed framework was tested using covariance-based structural equation modeling. The results show that attitude, perceived behavioral control, perceived susceptibility, perceived severity, perceived benefits, cues to action, and health motivation positively influence ITP, whereas perceived barriers reduce it. Subjective norm is not statistically significant. Multi-group analysis further reveals meaningful heterogeneity across age, family size, income, and accident-experience groups. In addition, the contingent valuation results are converted into fatal and severe-injury risk reduction values, providing evidence that can support road safety appraisal and financing decisions. The findings demonstrate that integrating TPB and HBM offers a broader explanation of drivers' support for traffic safety funding than either framework alone and suggest that risk communication, affordability, and transparent program design are central to increasing public support for safety interventions.

Indexed as

Accidents, TrafficAutomobile DrivingHealth Belief ModelIntentionSafetyAdultFemaleHumansMaleMiddle AgedPakistanSurveys and QuestionnairesTheory of Planned BehaviorYoung AdultHealth belief modelIntention-to-payRisk reduction valuationTheory of planned behaviorTraffic safety

Identifiers

PMID42642436
PMCPMC13507046

What OpenQuestion holds

Textmetadata
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.