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

Kaili Wang, Ya Gao and Khandker Nurul Habib

Journal of choice modelling, 2024, vol. 53, issue C

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.

Keywords: Online shopping demand; Latent class model; Generalized extreme value model; Ordinal discrete choice model (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:eejocm:v:53:y:2024:i:c:s1755534524000538

DOI: 10.1016/j.jocm.2024.100521

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