How Will In-Person and Online Grocery Shopping and Meal Consumption Activities Evolve After COVID-19?
Bibliographic record
Abstract
COVID-19 has drastically altered the daily lives of many people, forcing them to spend more time at home. This shift significantly increased online grocery shopping and ordering for food while restrictions and social distancing measures were in place. As re-opening begins, little is known about the way virtual and in-person shopping/eating activities will evolve after the pandemic. This study adopts a multivariate ordered probit model to investigate individuals’ preferences toward the following activities after the pandemic: online grocery shopping, in-store grocery shopping, online ordering of food, and eating-out at restaurants. The model retained statistically significant error correlations among the activities, confirming the need for joint modeling. Model results suggested that individuals with lower income and with children are likely to perform grocery shopping and eating-out activities in person. Individuals owning a vehicle and a driver’s license have a higher likelihood of less frequent online shopping and more frequent in-store grocery shopping. Individuals with transit passes prefer to order groceries online and engage in eat-out activities frequently. Individuals residing in mixed land use areas prefer frequent in-store grocery shopping whereas suburban dwellers prefer it less frequently. The model confirms complementarity and substitution effects. For instance, online food ordering revealed a complementary effect on eating-out activities whereas online grocery shopping confirmed a substitution effect on in-store grocery shopping. These findings provide important behavioral insights into travel activity patterns in the post-pandemic era, which will help in understanding the inter-relationships between online and in-person shopping/eating activities, and accommodating such inter-dependencies within the travel demand forecasting models for effective policy-making.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".