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

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. • The study proposes a GEV model to jointly model ordinal-based choice behaviour with latent class membership. • The formulation accounts for choice-makers heterogeneous preferential groups and their ordinal-in-nature choice outcomes. • The latent class & OGEV model is applied to households' e-shopping demands. • The study also applies the OGEV-NB model, capturing consumers' in-store shopping demands and online shopping behaviour. • The RUM principle inherited within the OGEV-NB model allows explicit econometric valuation of in-store shopping activity.