智能绿色引导:通过数字足迹和因果机器学习降低产品退货率

IF 5.5 3区 材料科学 Q2 CHEMISTRY, PHYSICAL ACS Applied Energy Materials Pub Date : 2024-08-08 DOI:10.1287/mksc.2022.0393
Moritz von Zahn, Kevin Bauer, Cristina A. Mihale-Wilson, Johanna Jagow, Max Speicher, Oliver Hinz
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引用次数: 0

摘要

本文对基于因果机器学习的个性化绿色引导进行了实证研究,以减少电子商务中的消费者退货。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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Smart Green Nudging: Reducing Product Returns Through Digital Footprints and Causal Machine Learning
This paper empirically investigates the use of personalized green nudging based on causal machine learning to reduce consumer returns in e-commerce.
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来源期刊
ACS Applied Energy Materials
ACS Applied Energy Materials Materials Science-Materials Chemistry
CiteScore
10.30
自引率
6.20%
发文量
1368
期刊介绍: ACS Applied Energy Materials is an interdisciplinary journal publishing original research covering all aspects of materials, engineering, chemistry, physics and biology relevant to energy conversion and storage. The journal is devoted to reports of new and original experimental and theoretical research of an applied nature that integrate knowledge in the areas of materials, engineering, physics, bioscience, and chemistry into important energy applications.
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