Platform Information Provision and Consumer Search: A Field Experiment
Bibliographic record
Abstract
Despite substantial efforts to help consumers search in more intuitive ways, text search remains the predominant tool for product discovery online.In this paper, we explore the effects of visual and textual cues for search refinement on consumer search and purchasing behavior.We collaborate with one of the largest e-commerce platforms in China and study its roll out of a new search tool.When a customer searches for a general term (e.g., "headphones"), the tool suggests refined queries (e.g., "bluetooth headphones" or "noise-canceling headphones") with the help of images and texts.The search tool was rolled out with a long-run experiment, which allows us to measure its short-run and long-run effects.We find that, although there was no immediate effect on orders or total expenditures, the search tool changed customers' search and purchasing behavior in the long-run.Customers with access to the new tool eventually increased orders and expenditures compared to those in the control group, especially for non top-selling products.The purchase increase comes from more effective searches, rather than an increase in activity on the platform.We also find that the effect is not only driven by the direct value of suggested searches, but also by customers indirectly learning to perform more effective searches on their own.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".