Shopping less with shopping lists: Planning individual expenses ahead of time affects purchasing behavior when online grocery shopping
Bibliographic record
Abstract
Abstract How do shopping lists affect purchasing behavior? On the one hand, breaking down a shopping task into its subcomponents might increase predicted budget for the shopping trip and consequently increase the number of purchases made and dollars spent. On the other hand, a shopping list may function as a concrete action plan for the shopping task and decrease the number of purchases made and dollars spent. In two studies, participants were randomly assigned to make a shopping list for their next grocery trip or not make a list and then completed the shopping trip virtually without the shopping list (Study 1) or with the shopping list (Study 2), using a popular online grocery store website. Those who were induced to make a shopping list prior to shopping bought marginally (Study 1) or significantly fewer (Study 2) items in an online grocery trip and spent marginally less money (Study 1). Simply making an overall spending prediction did not have the same effect as writing an itemized shopping list (Study 2), and purchases in this condition did not differ from those in the control group. We also document descriptive information on frequency of use and beliefs about functionality of shopping lists.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".