Incorporating the Theory of Planned Behaviour into Distracted Driving: Influencing Factors and Intervention Effects

• Main Authors: Fan, Z.B (Author), Kong, Q.W (Author), Peng, J.S (Author), Song, Z. (Author), Yang, X.H (Author), Zhang, L. (Author), Zhou, L. (Author)
• Format: Article
• Language: English
• Published: Hindawi Limited 2023
• Subjects: Analysis of variance (ANOVA); Behavioral research; Chongqing; Dangerous drivings; Demographic variables; Driving behaviour; Mean-variance analysis; Path coefficients; Psychological characteristics; Structural equation models; Theory of Planned Behavior; T-tests
• Online Access: View Fulltext in Publisher

 This study focuses on the psychological characteristics and empirically tests of the factors influencing distracted driving behaviours. This information is used as a reference for an intervention on dangerous driving behaviours. First, a distracted driving scale is constructed based on the theory of planned behaviour (TPB). The questionnaires are distributed in Chongqing, China, and 321 completed questionnaires are obtained. Data are analyzed using mean-variance analysis, one-way ANOVA, T-test, and multivariate test by SPSS 26.0 to determine the significance of distracted behaviours and demographic variables. We use a structural equation model to determine the path coefficients of each latent variable. Finally, we select the drivers with high tendency of distraction from the results of the questionnaires, conduct a four-stage rational emotional behaviour therapy (REBT) experiment, and use a repeated measures ANOVA analysis to test the validity and persistence of the intervention method. Results show that subjective norm is the most influential psychological factor. There are significant differences between the experimental group (2.38, SD = 0.41) and the control group (2.89, SD = 0.40) in the scores of distractions. This indicates that the distracted behaviour intervention achieves adequate validity and consistency. Educational research on distracted driving behaviour can help identify and correct drivers with high distraction tendency. © 2023 L. Zhang et al.