Modelling household online shopping and home delivery demand using latent class & ordinal generalized extreme value (GEV) models
Bibliographic record
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. • 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".