Choice Overload in the Grocery Setting: Results from a Laboratory Experiment
Bibliographic record
Abstract
One of the most basic strategic decisions a retailer must take involves determining the product assortment to offer inside the store. Despite the importance of the topic, there are two opposite strands of literature that have come up with completely different points of view. To summarize them, the first one states that the more choices, the better, while the second one states that more choices lead to weaker preferences and lower levels of satisfaction. Furthermore, the majority of studies conducted so far have focused their attention on collecting self-report measures. However, it has been argued thet self-report measures, interviews and questionnaires may have strong biases. Specifically, they are a product of psychological, sociological, linguistic, experiential and contextual variables, which may have little to do with the construct of interest. Thus, the present work intends to enrich the extant literature about the effect of ‘choice overload’ on customer satisfaction and behavior inside the store by analyzing both cognitive and unconscious responses. In order to confirm our hypothesis, an experiment, involving 171 participants, was conducted in a laboratory supermarket in Milan to test the reactions in front of a regular pastry display and a display characterized by fewer options.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".