Returns operations in omnichannel retailing with buy-online-and-return-to-store
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it