Bibliographic record
Abstract
ABSTRACT Many digital platforms provide a search environment for consumers to evaluate sellers' products. We investigate a strategic platform's preference over two classical search patterns—parallel versus sequential—keeping in check consumers' search behavior (how many products and attributes to evaluate) and sellers' strategies (price and assortment decisions). In the benchmark model with exogenous assortment level, our results show that the platform prefers a parallel (sequential) pattern when the search cost is small (large) or when the assortment level is high (low). However, when the assortment level is a decision by sellers, the platform's preference will be altered qualitatively: The platform prefers a parallel (sequential) pattern when the search cost is large (small), and the analytical predictions are generally consistent with observations in practice. We have identified several novel effects that are built on the fundamental difference between parallel and sequential patterns and use them to explain the platform's search‐pattern preference. Interestingly, our paper shows that the platform can strategically use operational means (assortment prevention effect) and marketing means (pricing prevention effect) to manipulate consumers' search to maximize its profit.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".