Bibliographic record
Abstract
Purpose This study associated consumers' food choice motives and socio-demographic characteristics with their attitudes and consumptions towards food shopping with four e-commerce modes: business-to-consumer (B2C), online-to-offline delivery (O2O Delivery), online-to-offline in-store (O2O In-store) and New Retail. It also explored consumer preferences for specific food categories within the four e-commerce modes. Design/methodology/approach An online survey was administered to 954 participants from three Chinese cities: Beijing, Shanghai and Shenzhen. Descriptive analysis and linear regression were used in the data analysis. Findings The following food choice motives (FCMs) and socio-demographic characteristics had a significant effect on food e-commerce attitudes and/or consumption, with some or all of the four e-commerce modes: Taste Appeal, Value for Money, Safety Concerns, Quality Concerns, Processed Convenience, Purchase Convenience, Others' Reviews, City, Gender, Household Size, Age, Income, Occupation and Marital Status. Consumers also have different consumption preferences for food categories in the four e-commerce modes. Originality/value This is the first study to associate consumer FCMs and socio-demographics with their e-commerce attitudes and consumption regarding food in four e-commerce modes: B2C, O2O Delivery, O2O In-store and New Retail.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".