Exploration of Contributing Factors of Different Distracted Driving Behaviors

Shi, Jing; Peng, Dandan; Xiao, Yao · 2019 · OpenAlex-citations

DOI: 10.7307/ptt.v31i6.2962

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Summary

This study investigates the contributing factors behind three specific distracted driving behaviors: drinking water, answering a phone, and using mobile phone applications (APPs) while driving. Motivated by the significant impact of distraction on traffic safety and the lack of comparative research on the motivations behind different distraction types, the authors aim to identify intrinsic drivers using the Theory of Planned Behaviour (TPB). The research seeks to provide theoretical support for mitigating these behaviors by understanding how attitudes, subjective norms, and perceived behavior control influence driver intentions. Data were collected via an online survey of 424 valid respondents in China, utilizing a questionnaire based on the TPB framework. The survey measured four dimensions—attitude, subjective norm, perceived behavior control, and behavior intention—using a seven-degree Likert scale for each of the three distraction types. The researchers employed Structural Equation Modeling (SEM) to analyze the relationships between these latent variables and actual driving behaviors. Descriptive statistics and Analysis of Variance (ANOVA) were also used to examine the influence of demographic factors such as gender, age, income, and driving experience on the frequency of distracted driving. The results indicate that gender, driving experience, driving frequency, family income, and past crash history significantly affect the frequency of distracted driving, with males and experienced drivers reporting higher frequencies. The SEM analysis revealed that attitude and perceived behavior control are the primary factors influencing distracted driving behaviors across all three categories. Specifically, a positive attitude toward distraction increases behavior intention, while a strong perception of risk reduces it. Subjective norms had a significant impact only on answering phone calls, whereas their influence on drinking water and using APPs was not significant. The integral structural equation model confirmed that behavior intention is the strongest predictor of actual behavior, driven primarily by attitude and perceived control. The study concludes that distracted driving is largely a consequence of behavior intention and perceived behavior control, rather than social pressure, except in the case of phone calls. These findings suggest that interventions should focus on modifying drivers' attitudes and enhancing their perception of risk rather than relying solely on social norms. The research provides a theoretical basis for targeted traffic law education and behavior modification strategies, highlighting the need for distinct approaches for different types of distractions. Limitations include the reliance on self-reported data from an online survey, which may not represent the entire driving population.

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Topics

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• mobile phones
• visual
• distraction detection algorithms
• external distraction
• distraction laws
• manual

Information type

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• Empirical Findings: observational prevalence
• Theoretical Contribution: theory or model, conceptual framework