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引用次数: 17

摘要

驾驶是许多操作系统不可或缺的组成部分,驾驶质量的任何微小改进都会对事故、交通、污染和总体经济产生重大影响。然而,考虑到驾驶任务的复杂性和多维性,进行改进是具有挑战性的。在本文中,我们研究了轻推对提高驾驶性能的有效性。特别是,我们利用行业合作伙伴推出的智能手机应用程序,通过通知向驾驶员发送三种类型的提示,显示他们在当前旅程中的个人最佳表现、个人平均表现和最新驾驶表现。我们使用远程信息处理技术(即来自加速度计、全球定位系统(GPS)和移动设备中的陀螺仪的实时传感器数据)来测量由此产生的驾驶性能。与“无轻推”对照组相比,我们发现个人最佳轻推和个人平均轻推提高了驾驶性能,大约提高了应用程序计算的性能分数的18%标准差。此外,根据性能评分的标准偏差,这些助推措施改善了事故间隔时间(近1.8年)和驾驶性能的一致性。注意到驾驶能力和反馈寻求可能因个体而异,我们采用广义随机森林方法,该方法表明,不经常寻求反馈的高性能驾驶员从个人最佳推动中获益最多,而经常寻求反馈的低性能驾驶员从个人平均推动中获益最多。最后,我们通过在非驾驶环境下进行在线实验来研究结果背后的潜在机制。实验表明,表现的提高是由参与者对不同推动的努力的变化直接驱动的,我们的主要发现在替代(非驾驶)设置中是稳健的。我们的分析进一步表明,当参考点的可变性较低时,推动是有效的,这解释了为什么个人最佳和个人平均推动是有效的,而最后一个分数推动则不是。本文被运营管理专业的Vishal Gaur接受。
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Nudging Drivers to Safety: Evidence from a Field Experiment
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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