Modelling household online shopping and home delivery demand using latent class & ordinal generalized extreme value (GEV) models

IF 2.8 3区 经济学 Q1 ECONOMICS
Kaili Wang, Ya Gao, Khandker Nurul Habib
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引用次数: 0

Abstract

The surge in e-commerce during the past decade has led to dramatic changes in consumer shopping behaviour. The study applies two Generalized Extreme Value (GEV) family models to investigate households' e-shopping demands. The study proposes a model structure to jointly model ordinal-based choice behaviour with choice-makers' latent class membership. Introducing latent class structure with the OGEV formulation accounts for the relationship between choice-makers heterogeneous preferential groups and their ordinal choice outcomes. Furthermore, the study also applies the Ordered General Extreme Value (OGEV)-Negative Binomial (NB) model, capturing the interplay between consumers' in-store shopping demands and online shopping behaviour. The RUM principle inherited within the OGEV-NB model allows econometric valuation of in-store shopping activity explicitly considering households' e-shopping demands. Both models are empirically estimated using a dataset collected in the Greater Toronto Area (GTA), Canada. The empirical findings and behavioural implications are also discussed.
利用潜类和序数广义极值 (GEV) 模型为家庭网购和送货上门需求建模
过去十年间,电子商务的迅猛发展导致消费者的购物行为发生了巨大变化。本研究采用两个广义极值(GEV)族模型来研究家庭的电子购物需求。研究提出了一种模型结构,将基于序数的选择行为与选择者的潜在类别成员资格联合建模。在 OGEV 模型中引入潜类结构,解释了选择者的异质偏好群体与其序数选择结果之间的关系。此外,研究还应用了有序一般极值(OGEV)-负二项(NB)模型,以捕捉消费者店内购物需求与网上购物行为之间的相互作用。OGEV-NB 模型所继承的 RUM 原则允许对店内购物活动进行计量经济学估值,明确考虑家庭的电子购物需求。这两个模型都是利用在加拿大大多伦多地区(GTA)收集的数据集进行实证估算的。此外,还讨论了实证研究结果和行为影响。
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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来源期刊
CiteScore
4.10
自引率
12.50%
发文量
31
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