Predicting risky driving behaviours using the theory of planned behaviour: A meta-analysis

IF 5.7 1区 工程技术 Q1 ERGONOMICS Accident; analysis and prevention Pub Date : 2024-10-03 DOI:10.1016/j.aap.2024.107797
Klaire Somoray , Katherine M. White , Barry Watson , Ioni Lewis
{"title":"Predicting risky driving behaviours using the theory of planned behaviour: A meta-analysis","authors":"Klaire Somoray ,&nbsp;Katherine M. White ,&nbsp;Barry Watson ,&nbsp;Ioni Lewis","doi":"10.1016/j.aap.2024.107797","DOIUrl":null,"url":null,"abstract":"<div><div>The current <em>meta</em>-analysis explored the efficacy of the theory of planned behaviour (TPB) in predicting high-risk driving behaviours. Specifically, we examined speeding (in relation to exceeding the limit as well as speed compliance), driving under the influence, distracted driving, and seat belt use. We searched four electronic databases (i.e., PubMed, Web of Science, Scopus, and ProQuest) and included original studies that quantitatively measured the relationships between the TPB variables (attitude, subjective norm, perceived behavioural control [PBC], intention, and prospective/objective behaviour). The study identified 80 records with 94 independent samples. Studies were assessed for risk of bias using the JBI checklist for cross-sectional studies and compliance with the TPB guidelines. Together, attitude, subjective norm and PBC explained between 30 % and 51 % of variance found in intention, with attitude showing as the strongest predictor for intention across the different driving behaviours. The findings also showed that the model explained 36 %–48 % variance found in predicting the observed and/or prospective behaviours for distracted driving, speed compliance and speeding. Understanding the varying strengths and thus relative importance of TPB constructs in predicting different risky driving behaviours is crucial for developing targeted road safety interventions.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"208 ","pages":"Article 107797"},"PeriodicalIF":5.7000,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Accident; analysis and prevention","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0001457524003427","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ERGONOMICS","Score":null,"Total":0}
引用次数: 0

Abstract

The current meta-analysis explored the efficacy of the theory of planned behaviour (TPB) in predicting high-risk driving behaviours. Specifically, we examined speeding (in relation to exceeding the limit as well as speed compliance), driving under the influence, distracted driving, and seat belt use. We searched four electronic databases (i.e., PubMed, Web of Science, Scopus, and ProQuest) and included original studies that quantitatively measured the relationships between the TPB variables (attitude, subjective norm, perceived behavioural control [PBC], intention, and prospective/objective behaviour). The study identified 80 records with 94 independent samples. Studies were assessed for risk of bias using the JBI checklist for cross-sectional studies and compliance with the TPB guidelines. Together, attitude, subjective norm and PBC explained between 30 % and 51 % of variance found in intention, with attitude showing as the strongest predictor for intention across the different driving behaviours. The findings also showed that the model explained 36 %–48 % variance found in predicting the observed and/or prospective behaviours for distracted driving, speed compliance and speeding. Understanding the varying strengths and thus relative importance of TPB constructs in predicting different risky driving behaviours is crucial for developing targeted road safety interventions.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
利用计划行为理论预测危险驾驶行为:荟萃分析。
当前的荟萃分析探讨了计划行为理论(TPB)在预测高风险驾驶行为方面的有效性。具体来说,我们研究了超速(与超限和遵守速度有关)、酒后驾驶、分心驾驶和安全带使用。我们搜索了四个电子数据库(即 PubMed、Web of Science、Scopus 和 ProQuest),收录了定量测量 TPB 变量(态度、主观规范、感知行为控制 [PBC]、意图和预期/目标行为)之间关系的原创研究。研究确定了 80 项记录和 94 个独立样本。研究使用横断面研究的 JBI 检查表对研究进行了偏倚风险评估,并对是否符合 TPB 准则进行了评估。态度、主观规范和 PBC 三者共同解释了 30% 到 51% 的意向变异,其中态度对不同驾驶行为的意向预测作用最强。研究结果还显示,在预测分心驾驶、遵守车速规定和超速行驶的观察和/或预期行为时,该模型可解释 36%-48% 的方差。了解 TPB 构建在预测不同危险驾驶行为方面的不同优势和相对重要性,对于制定有针对性的道路安全干预措施至关重要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
CiteScore
11.90
自引率
16.90%
发文量
264
审稿时长
48 days
期刊介绍: Accident Analysis & Prevention provides wide coverage of the general areas relating to accidental injury and damage, including the pre-injury and immediate post-injury phases. Published papers deal with medical, legal, economic, educational, behavioral, theoretical or empirical aspects of transportation accidents, as well as with accidents at other sites. Selected topics within the scope of the Journal may include: studies of human, environmental and vehicular factors influencing the occurrence, type and severity of accidents and injury; the design, implementation and evaluation of countermeasures; biomechanics of impact and human tolerance limits to injury; modelling and statistical analysis of accident data; policy, planning and decision-making in safety.
期刊最新文献
A comprehensive multi-objective framework for the estimation of crash frequency models. Cooperative control of self-learning traffic signal and connected automated vehicles for safety and efficiency optimization at intersections. Do automation and digitalization distract drivers? A systematic review. Influence of road safety policies on the long-term trends in fatal Crashes: A Gaussian Copula-based time series count model with an autoregressive moving average process. Nudges may improve hazard perception in a contextual manner.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1