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
Both traditional retailers and e-tailers have been implementing omnichannel strategies such as buy online, pick up at store (BOPS). We build a stylized model to investigate the impact of the BOPS initiative on store operations from an inventory perspective. We consider two segments of customers, namely store-only customers who only make purchases offline and omni-customers who strategically choose between offline and online channels. We show that BOPS may either benefit or hurt the retailer depending on two fundamental system primitives: the store visiting cost and the online waiting cost. If the online waiting cost is relatively low and the store visiting cost is even lower, BOPS can induce omni-customers to migrate from online buying to BOPS, leading to demand pooling at the brick-and-mortar (B&M) store. Such demand pooling provides two benefits for the retailer: it reduces the overstocking cost, and after inventory reoptimization, it results in a higher fill rate at the B&M store, which benefits existing customers and potentially attracts more customers to the store. In contrast, if both store visiting and online waiting costs are relatively high with the latter even higher, introducing BOPS can result in demand depooling as a result of the migration of the omni-customers from offline purchasing to BOPS. This leads to a lower fill rate after inventory reoptimization, likely the result of a lower profit margin under BOPS, which turns away store-only customers and hurts the retailer. This paper was accepted by Charles Corbett, operations management.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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