Investigating Effects of Out-of-Stock on Consumer Stockkeeping Unit Choice
Bibliographic record
Abstract
Out-of-stock (OOS) is commonly observed in the retail environment with consumer packaged goods, but there have been few empirical studies of the effects of OOS on consumer product choice, because there is a lack of OOS information during households' purchase occasions. The authors study the effects of OOS on consumer stockkeeping unit (SKU) preference and price sensitivity, using a unique data set from multiple consumer packaged goods categories with information on recurring OOS incidents. They obtain several substantive findings: (1) Consumers' price sensitivity tends to be underestimated when OOS is not accounted for in a discrete choice model; (2) for consumers who have shorter interpurchase time, their preference for a SKU is attenuated when it is frequently OOS; and (3) for consumers who purchase from a small number of SKUs, their preference for a SKU is reinforced when facing OOS of other similar SKUs, whereas it is attenuated when facing OOS of other similar and also frequently purchased SKUs. The authors also illustrate that their findings can help retailers evaluate the effect of OOS on category revenue and predict time-varying market shares of SKUs in periods following OOS incidents.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".