Bibliographic record
Abstract
Along with the popularity of online shopping, the cash-back industry is witnessing dramatic development. Under this backdrop, retailers who sell products with network externalities make different decisions about affiliating with cash-back sites. In this paper, we set up a cash-back model considering network externalities. Our goal is to identify the condition under which it is profitable for retailers whose products exhibit network externalities to affiliate with a cash-back site and to find out the driving force of the profitability. We find that only when there are more low-type consumers than high-type consumers and the degree of network externalities is lower than a certain threshold is it profitable for such a retailer to affiliate with an independent cash-back site, because the cash-back rate is decreasing in the intensity of network externality. It is the price discriminative effect instead of the promotive effect that makes it profitable. We show that the double-marginalization problem between the retailer and an independent cash-back site leads to the cash-back paradox where all consumers pay more for the product in the presence of a cash-back channel. We also show that when the retailer affiliates with two cash-back sites, each site has the incentive to lower the cash-back rate to take advantage of network externalities, which makes the cash-back paradox more likely to happen and makes it less likely for a retailer to benefit from cash-back channels. Furthermore, we suggest the retailer establish his own cash-back channel. Our work provides implications for retailers as well as for cash-back sites and consumers.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.004 | 0.016 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".