Product set granularity and consumer response to recommendations
Bibliographic record
Abstract
Many consumer decisions are assisted by product recommendations. When retailers provide such recommendations, there is an inherent tension between (1) presenting a set of products that are close in attractiveness (fine product set granularity) and (2) presenting a wider range of products that are more different in attractiveness (coarse product set granularity). While the former can maximize the attractiveness of the recommended set of products, the latter makes it easier for consumers to determine which of the recommended products is most attractive, thus boosting consumer response. Evidence from a large-scale field study (with naturally occurring variation in the granularity of online recommendation sets) provides strong support for this tension and shows that less fine-grained product recommendation sets promote consumer response. We also find that, in line with our theorizing, coarser set granularity increases the time consumers spend processing detailed information about individual products relative to time they spend comparing products at the set level. These effects are less pronounced when consumer engagement in the decision process is low. The key insights from the field study are replicated in a tightly controlled experiment (using a different product domain). The findings of this research have important implications for how best to integrate large online assortments and product recommendations to stimulate consumer response.
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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 | 0.003 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".