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

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  • Wang, Kaili
  • Gao, Ya
  • Nurul Habib, Khandker

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.

Suggested Citation

  • Wang, Kaili & Gao, Ya & Nurul Habib, Khandker, 2024. "Modelling household online shopping and home delivery demand using latent class & ordinal generalized extreme value (GEV) models," Journal of choice modelling, Elsevier, vol. 53(C).
    • Handle: RePEc:eee:eejocm:v:53:y:2024:i:c:s1755534524000538
    DOI: 10.1016/j.jocm.2024.100521
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