Nudging Drivers to Safety: Evidence from a Field Experiment

Abstract

Driving is an integral component of many operational systems, and any small improvement in driving quality can have a significant effect on accidents, traffic, pollution, and the economy in general. However, making improvements is challenging given the complexity and multidimensionality of driving as a task.
We used telematics technology (i.e., real-time sensor data from accelerometer, GPS, and gyroscope in a mobile device) to measure driving performance as well as to deliver nudges to the drivers via notifications. Leveraging a smartphone application launched by our industry partners, we sent three types of performance nudges to drivers, indicating how they performed on the current trip with respect to their personal best, personal average, and latest driving performance.
We found that personal best and personal average nudges improved driving performance compared to the control group, which received no nudges, by, on average, 18.17% and 18.71% standard deviations of the performance scores calculated by the application. This improvement translates to an increase in the inter-accident time by nearly 1.8 years, while also improving driving performance consistency as measured by the standard deviation of the performance score. Using generalized random forests, we showed that high-performing drivers who are not frequent feedback seekers benefit the most from personal best nudges, while low-performing drivers who are also frequent feedback seekers benefit the most from the personal average nudges. Using these findings, we constructed personalized nudges that, according to our counter-factual estimates, should significantly outperform both of these nudges. Finally, we conducted an online experiment in which we replicated our key findings in a non-driving context, and we further identified changes in participants’ efforts in response to different nudges as the mechanism behind the results.

Keywords: Nudges, Empirical Operations Management, Behavioral Operations Management, Field Experiments