Don’t Fake It If You Can’t Make It: Driver Misconduct in Last-Mile Delivery

英里 不当行为 计算机安全 最后一英里(运输) 业务 计算机科学 互联网隐私 心理学 政治学 法学 物理 天文
作者
Srishti Arora,Vivek Choudhary,Pavel Kireyev
出处
期刊:Management Science [Institute for Operations Research and the Management Sciences]
标识
DOI:10.1287/mnsc.2023.01829
摘要

In the last two decades, last-mile delivery (LMD) firms have seen immense growth fueled by the success of e-commerce, leading to faster and cheaper deliveries. Operating on thin margins, LMD firms strive for successful first-time deliveries to avoid the financial and reputational costs of reattempts. Delivery agents (DAs) are integral to LMD efficiency, influencing customer experience, delivery success, and productivity. However, most LMD performance enhancement research focuses on process, technology, and incentives, which presume workers will conform to procedures and monitoring tools will function flawlessly. Nevertheless, in practice, DAs deviate from expected behaviors, that is, indulge in misconduct, negatively affecting delivery efficiency, often resulting in returned parcels. One of the major forms of misconduct is entering fake remarks about deliveries, wherein DAs intentionally do not deliver the parcels and provide fake reasons for it. For instance, even without reaching a delivery address, a DA remarks “customer unavailable” and records a delivery failure. In this study, we collaborated with a leading Indian LMD firm and, using instrumental variable regression, found that such misconduct leads to a spillover productivity loss. This effect reduces the next day’s successful deliveries by 1.60% and first-time-right deliveries by 1.86%. We discuss misconduct’s correlation with factors such as task complexity and offer novel insights into how opportunistic circumstances can influence worker behavior. This paper was accepted by Elena Katok, operations management. Funding: V. Choudhary acknowledges the support received from Ministry of Education, Singapore [Grant RS12/20]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.01829 .
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