How COVID-19 Pandemic Affected Urban Trips? Structural Interpretive Model of Online Shopping and Passengers Trips during the Pandemic
Bibliographic record
Abstract
Changing people’s shopping behavior from face-to-face to online shopping during the COVID-19 pandemic led to reduced shopping trips, and this decrease directly affects traffic congestion and air pollution. Identifying the factors influencing the increase of online shopping behavior during the pandemic can be helpful for policymakers in the post-COVID-19 era. This study aims to discover the effect of factors related to the COVID-19 pandemic and demographic characteristics on shopping attitude and, consequently, on shopping trips. Based on the interviews of ten experts, factors associated with COVID-19 and demographic characteristics are selected as influential factors on shopping attitude and shopping trips. For pairwise comparisons between these factors, a web-based questionnaire was designed and given to thirty experts. The relationship between all factors is examined using interpretive structural modeling (ISM) and Microscopic–Macroscopic (MICMAC) analysis. In addition, to prioritize factors, the IAHP model is employed. Based on the results, five levels of influential factors affect shopping attitude, which affects shopping trips: level 1, age and gender; level 2, income and education; level 3, the household size and the COVID-19 awareness; level 4, COVID-19 attitude and COVID-19 practice; and level 5, norm subject and shopping personal control.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".