亲爱的研友该休息了!由于当前在线用户较少,发布求助请尽量完整地填写文献信息,科研通机器人24小时在线,伴您度过漫漫科研夜!身体可是革命的本钱,早点休息,好梦!

Logistics Performance, Ratings, and Its Impact on Customer Purchasing Behavior and Sales in E-Commerce Platforms

采购 业务 交付性能 客户保留 营销 服务(商务) 电子商务 顾客满意度 服务质量 产品(数学) 计算机科学 过程管理 几何学 数学 万维网
作者
Vinayak Deshpande,Pradeep K. Pendem
出处
期刊:Manufacturing & Service Operations Management [Institute for Operations Research and the Management Sciences]
卷期号:25 (3): 827-845 被引量:102
标识
DOI:10.1287/msom.2021.1045
摘要

Problem definition: We examine the impact of logistics performance metrics such as delivery time and customer’s requested delivery speed on logistics service ratings and third-party sellers’ sales on an e-commerce platform. Academic/practical relevance: Although e-commerce retailers like Amazon have recently invested heavily in their logistics networks to provide faster delivery to customers, there is scant academic literature that tests and quantifies the premise that convenient and fast delivery will drive sales. In this paper, we provide empirical evidence on whether this relationship holds in practice by analyzing a mechanism that connects delivery performance to sales through logistics ratings. Prior academic work on online ratings in e-commerce platforms has mostly analyzed customers’ response to product functional performance and biases that exist within. Our study contributes to this stream of literature by examining customer experience from a service quality perspective by analyzing logistics service performance, logistics ratings, and its impact on customer purchase probability and sales. Methodology: Using an extensive data set of more than 15 million customer orders on the Tmall platform and Cainiao network (logistics arm of Alibaba), we use the Heckman ordered regression model to explain the variation in customers’ rating of logistics performance and the likelihood of customers posting a logistics rating. Next, we develop a generic customer choice model that links the customer’s likelihood of making a purchase to the logistics ratings provided by prior customers. We implement a two-step estimation of the choice model to quantify the impact of logistics ratings on customer purchase probability and third-party seller sales. Results: We surprisingly find that even customers with no promise on delivery speed are likely to post lower logistics ratings for delivery times longer than two days. Although these customers are not promised an explicit delivery deadline, they seem to have a mental threshold of two days and expect deliveries to be made within that time. Similarly, we find that priority customers (those with two-day and one-day promise speed) provide lower logistics ratings for delivery times longer than their anticipated delivery date. We estimate that reducing the delivery time of all three-day delivered orders on this platform (which makeup [Formula: see text] 35% of the total orders) to two days would improve the average daily third-party seller sales by 13.3% on this platform. The impact of delivery time performance on sales is more significant for sellers with a higher percentage of three-day delivered orders and a higher spend per order. Managerial implications: Our study emphasizes that delivery performance and logistics ratings, which measure service quality, are essential drivers of the customer purchase decision on e-commerce platforms. Furthermore, by quantifying the impact of delivery time performance on sales, our study also provides a framework for online retailers to assess if the increase in sales because of improved logistics performance can offset the increase in additional infrastructure costs required for faster deliveries. Our study’s insights are relevant to third-party sellers and e-commerce platform managers who aim to improve long-term online customer traffic and sales. History: This paper has been accepted as part of the 2018 MSOM Data Driven Research Challenge. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2021.1045 .
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
Viiigo完成签到,获得积分10
刚刚
冰薛聪明发布了新的文献求助10
5秒前
11秒前
所所应助曾经的人雄采纳,获得10
11秒前
12秒前
12秒前
自由书文发布了新的文献求助10
15秒前
自由书文发布了新的文献求助10
15秒前
自由书文发布了新的文献求助10
18秒前
自由书文发布了新的文献求助10
18秒前
猫小乐C完成签到,获得积分10
21秒前
爱笑楼房完成签到,获得积分10
22秒前
Chemistry完成签到 ,获得积分10
26秒前
爆米花应助cgc采纳,获得10
28秒前
38秒前
失眠翠芙完成签到,获得积分10
41秒前
45秒前
桐桐应助吴迪采纳,获得10
47秒前
短短急个球完成签到,获得积分10
50秒前
58秒前
1分钟前
吴迪发布了新的文献求助10
1分钟前
曾经的人雄完成签到,获得积分20
1分钟前
orixero应助nanmu采纳,获得10
1分钟前
Dancy发布了新的文献求助30
1分钟前
藏沙完成签到,获得积分10
1分钟前
canter2完成签到 ,获得积分10
1分钟前
1分钟前
1分钟前
颜林林发布了新的文献求助10
1分钟前
cgc发布了新的文献求助10
1分钟前
canter完成签到 ,获得积分10
1分钟前
1分钟前
CTS应助科研通管家采纳,获得10
1分钟前
Rosen发布了新的文献求助10
1分钟前
1分钟前
搜集达人应助科研通管家采纳,获得10
1分钟前
小马甲应助科研通管家采纳,获得10
1分钟前
1分钟前
Hello应助曾经的人雄采纳,获得10
1分钟前
高分求助中
Ideology and Meaning-Making under the Putin Regime 750
Prompt Engineering for Clinicians: Harnessing AI in Everyday Medical Practice 600
Handbook of Luminescence Dating 500
Safety Pharmacology 500
《KNN基无铅压电陶瓷电学性能优化与物理机理研究》 500
Introduction to Industrial/Organizational Psychology 400
Advances in Design and Control Robust Adaptive Control: Deadzone-Adapted Disturbance Suppression 400
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 计算机科学 化学工程 生物化学 物理 内科学 复合材料 催化作用 光电子学 物理化学 电极 细胞生物学 基因 遗传学
热门帖子
关注 科研通微信公众号,转发送积分 6926852
求助须知:如何正确求助?哪些是违规求助? 8615514
关于积分的说明 18276608
捐赠科研通 6347214
什么是DOI,文献DOI怎么找? 3072166
关于科研通互助平台的介绍 2105335
邀请新用户注册赠送积分活动 2049310