How Do Disruptions and Last-Mile Delivery Logistics Affect Shopping Behaviour?
Bibliographic record
Abstract
We investigated the effect of pandemic-related disruption on the frequency of non-grocery brick-andmortar shopping.We conducted a quasi-longitudinal survey with structured ques-tions that captured shopping experiences before and during the disruption.We employed machine learning algorithms and statistical tests such as chi-square, random forest model, and permutation test.Based on the permutation test, prior to the disruption, online shop-ping frequency was the sole feature statistically associated with brick-andmortar shopping frequency.During the disruption, perceived safety of online shopping emerged as the only statistically significant feature.Delivery vehicle-induced traffic issues were not statistically associated with brickand-mortar shopping frequency.Although crowdsourced deliveries were not significant, they exhibited a proportional relationship with shopping frequency ac-cording to SHAP values.Regular retrieval of orders from parcel lockers did not result in more frequent visits to brick-and-mortar stores.We investigated the effect of several aspects of online shopping on brick-and-mortar shopping frequency, including the frequency of online shopping, frequency of online-order deliveries, attitudes toward online shopping, and per-ceived issues arising from last-mile delivery logistics.Using a quasi-longitudinal survey and two machine learning-based models, we offer insights into how disruptions alter shopping behaviour and attitudes.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".