Returns operations in omnichannel retailing with buy-online-and-return-to-store
Bibliographic record
Abstract
Many retailers provide customers with product return flexibility by allowing them to buy online and return to store (BORS). We consider a retailer who sells a product through both online and in-store channels to customers who face uncertainty over product fit. We endogenize customers’ purchase and return decisions. Online customers may return misfit products online or to the store, depending on the retailer’s return policy, whereas store customers inspect in store before purchase and will not need to return their products. We examine the impact of BORS on the retailer’s store operations in terms of customer base, inventory decisions, and expected profits. We find that customers respond to BORS only when the return penalty and return rate are both relatively low. BORS can help the retailer attract new customers and also induce channel shifting among existing customers. After offering BORS, the retailer can stock less in-store inventory. We find that not all categories of product suit an in-store return policy. In particular, when the proportion of resalable returns is high, offering BORS will hurt the retailer’s profitability. In addition, we find that introducing BORS does not necessarily increase cross-selling profits. We also analyze the impact of an exchange policy where customers can exchange misfit items for similar items in store. We find that an exchange policy can help the retailer retain more customers and attract more customers to the store, which will further benefit the retailer’s profitability.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".