Demographic analysis of online grocery shopping during the COVID-19 pandemic: a theoretical perspective with an expanded technology acceptance model
Why this work is in the frame
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Bibliographic record
Abstract
grocery shopping is a necessity.the cOViD-19 pandemic created unprecedented disruptions to all aspects of life including grocery shopping.Many households found it difficult to replace in-store shopping channels as governments enforced closures.the purpose of this study is to uncover how households in canada responded to closures by switching to online shopping.this behavior change was not uneven.We analyze the demographic factors associated with the change in consumer behavior.Using recently published data by statistics canada, our empirical study found that a female consumer (Odds ratio (Or) = 0.69) is less likely to have increased Ogs activities after the start of the pandemic.On the other hand, a consumer that is employed (Or = 1.36), 25-44 years old (Or = 1.68), university-educated (Or = 1.21) consumer, with a higher household income (Or = 1.10) is more likely to have increased Ogs activities.an immigrant consumer (Or = 0.73) is less likely to have increased Ogs activities.Different consumers exhibit different preferences for shopping platforms.this understanding offers a deeper understanding of consumer behavior to marketers, researchers, and policymakers who seek to improve online shopping for certain groups.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it