What drives technology-based distractions? A structural equation model on social-psychological factors of technology-based driver distraction engagement

• Data collected from 525 drivers: 30+ years old (n = 261) and younger (n = 264).
• Structural equation models built on tech-based distractions and underlying factors.
• Attitudes (most influential), social norms, and personality predict engagement.
• Normative influences are significant for younger drivers, but not 30+ drivers.
• Personality is significant for the 30+ group, and marginal for younger drivers.

BackgroundWith the proliferation of new mobile and in-vehicle technologies, understanding the motivations behind a driver's voluntary engagement with such technologies is crucial from a safety perspective, yet is complex. Previous literature either surveyed a large number of distractions that may be diverse, or too focuses on one particular activity, such as cell phone use. Further, earlier studies about social-psychological factors underlying driver distraction tend to focus on one or two factors in-depth, and those that examine a more comprehensive set of factors are often limited in their analyses methods.ObjectiveThe present work considers a wide array of social-psychological factors within a structural equation model to predict their influence on a focused set of technology-based distractions. A better understanding of these facilitators can enhance the design of distraction mitigation strategies.MethodWe analysed survey responses about three technology-based driver distractions: holding phone conversations, manually interacting with cell phones, and adjusting the settings of in-vehicle technology, as well as responses on five social-psychological factors: attitude, descriptive norm, injunctive norm, technology inclination, and a risk/sensation seeking personality. Using data collected from 525 drivers (ages: 18–80), a structural equation model was built to analyse these social-psychological factors as latent variables influencing self-reported engagement in these three technology-based distractions.ResultsSelf-reported engagement in technology-based distractions was found to be largely influenced by attitudes about the distractions. Personality and social norms also played a significant role, but technology inclination did not. A closer look at two age groups (18–30 and 30+) showed that the effect of social norms, especially of injunctive norm (i.e., perceived approvals), was less prominent in the 30+ age group, while personality remained a significant predictor for the 30+ age group but marginally significant for the younger group.ConclusionFindings from this work provide insights into the social-psychological factors behind intentional engagement in technology-based distractions and in particular suggesting that these factors may be sensitive to demographic differences.