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. In this paper, we investigate the effectiveness of nudging to improve driving performance. In particular, we leverage a smartphone application launched by our industry partners to send three types of nudges through notifications to drivers, indicating how they performed on the current trip with respect to their personal best, personal average, and latest driving performance. We measure the resulting driving performance using telematics technology (i.e., real-time sensor data from an accelerometer, Global Positioning System (GPS), and gyroscope in a mobile device). Compared with the “no-nudge” control group, we find that personal best and personal average nudges improve driving performance by approximately 18% standard deviations of the performance scores calculated by the application. In addition, these nudges improve interaccident times (by nearly 1.8 years) and driving performance consistency, as measured by the standard deviation of the performance score. Noting that driving abilities and feedback seeking may vary across individuals, we adopt a generalized random forest approach, which shows that high-performing drivers who are not frequent feedback seekers benefit the most from personal best nudges, whereas low-performing drivers who are also frequent feedback seekers benefit the most from the personal average nudges. Finally, we investigate the potential mechanism behind the results by conducting an online experiment in a nondriving context. The experiment shows that the performance improvements are directly driven by the changes in participants’ effort in response to different nudges and that our key findings are robust in alternative (nondriving) settings. Our analysis further shows that nudges are effective when the variability in reference points is low, which explains why the personal best and personal average nudges are effective, whereas the last score nudge is not. This paper was accepted by Vishal Gaur, operations management.

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Problem definition: Mobile and internet-of-things (IoT) devices increasingly enable tracking of user behavior, and they often provide real-time or immediate feedback to consumers in an effort to improve their conduct. Growing adoption of such technologies leads to an important question: “Does reviewing immediate feedback improve user behavior?” We study immediate (close to real-time) feedback in the context of automotive telematics, which has been recognized as the most disruptive technology in the automotive insurance industry. Academic/practical relevance: Numerous automotive telematics providers claim unsubstantiated benefits from immediate feedback, while we still barely understand the implications of such feedback on user behavior. Given the high adoption of these technologies in automotive applications, it is important to study the effect of such feedback on behavior. This understanding is important, given that other attempts to make driving safer have led to unintended consequences in the past. Methodology: Using proprietary data on driving behavior, as measured by several parameters, such as harsh braking, speeding, and steep acceleration, we investigate the impact of the driver’s decision to review immediate feedback on driving behavior. We use instrumental variable regression to estimate the effect. Results: Contrary to the claims from multiple telematics providers, we find that, on average, users’ driving performance after they review detailed feedback is nearly 14.9% worse than that of users who do not review their detailed feedback. This impairment in performance translates to 6.9%, or a one-year reduction in interaccident time. Strong negative feedback (e.g., a sharp deterioration in performance) exerts a positive effect on short-term performance, but this only happens for very large drops in performance (3% of cases). Furthermore, we demonstrate that drivers just below the insurance-incentive thresholds exert greater effort following immediate feedback. Managerial implications: Our results provide a key message to firms employing immediate feedback—specifically, that such technology can yield unintended consequences. Furthermore, we show that drivers should receive only strong negative (but not positive) feedback to improve their performance. Finally, our results suggest that insurance incentives should be continuous rather than a step function.

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