Perils and Merits of Cross-Channel Returns
Bibliographic record
Abstract
In this article, we study the impact of cross-channel returns on a bricks-and-clicks dual-channel retailer's overall profit, individual channel prices, and individual channel demand under two scenarios: 1) exogenous returns and 2) refund-dependent returns. Our study reveals a number of interesting results. For example, when channel substitutability is high, accepting online purchased returns in the bricks-and-mortar store is likely to drive the in-store price up, despite a drop in the offline demand due to the cannibalization effect. In general, firms should allow cross-channel returns when channel substitutability is high, return handling cost is low, and self-channel returns are not hefty. Unlike the extant literature, we also see that bricks-and-mortar returns impact a multiple-channel retailer's optimal return policy. We are also able to verify that our main findings are fairly consistent under both exogenous and refund-dependent returns scenarios.
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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 | 0.008 | 0.050 |
| 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 | 0.003 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".