“Buy Online, Pick Up in Store” under Fit Uncertainty: To Offer or Not to Offer
Bibliographic record
Abstract
Retailers offer BOPS (Buy Online, Pick Up in Store) service to improve consumers shopping experience. However, this greatly increases the decision complexity for retailers and consumers. For consumers, whether to purchase online or from a store with the BOPS service is a complex decision. This is especially true when the product has fit uncertainty. That is, consumers are uncertain about product fitness before using it. Also, their store visit cost can be heterogeneous and follows some distribution function. For a retailer, it needs to jointly optimize multiple decisions including the convenience degree of BOPS. To help the retailer develop the jointly optimal decisions, we first build a mathematical model where the retailer sells the product through online and store channel and analyzes the possible effects of BOPS. We find that the retailer should offer BOPS when the channel cost ratio (ratio of shipment fee divided by average store visit cost) is large enough. Through numerical studies, we show that the ratio of profit offering BOPS divided by the benchmark increases with the probability of product fit, shipment fee, and the convenience degree of BOPS. We then consider the case where the convenience degree of BOPS is also a decision itself. We find the optimal convenience degree of BOPS increases along with the average store visit cost and the probability of product fit. When the cost factor of offering the convenience for BOPS is larger than a threshold, the retailer should never offer BOPS.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".