The Role of Purchase Quantity in Assortment Choice: The Quantity-Matching Heuristic
Bibliographic record
Abstract
Consumers often purchase multiple items from a product category on a single shopping trip. In doing so, they must frequently choose among items that are grouped in assortments, such as those offered by a particular store or brand. This article examines how the number of to-be-purchased items influences consumer choice among assortments. It is argued that when consumers are uncertain about their preferences, they are more likely to prefer an assortment for which the number of available options matches the desired purchase quantity. This prediction is based on the notion that a match between the size of an assortment and the number of to-be-purchased items enables consumers to simplify the selection process by eliminating the need to trade off the benefits and costs of individual choice alternatives—a strategy referred to as the “quantity-matching heuristic.” The theoretical predictions are supported by data from five empirical studies that offer converging evidence for the role of purchase-quantity goals in assortment choice and identify moderating factors and boundary conditions.
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How this classification was reachedexpand
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 | 0.030 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".