Business-to-Consumer Platform Strategy
Bibliographic record
Abstract
We build an economic model to study the problem of offering a new, high-certainty channel on an existing business-to-consumer platform such as Taobao and eBay. On this new channel, the platform owner exerts effort to reduce the uncertainty of service quality. Sellers can either sell through the existing low-certainty channel or go through additional screening to sell on this new channel. We model the problem as a Bertrand competition game where sellers compete on price and exert effort to provide better service to consumers. In this game, we consider a reputation spillover effect that refers to the impact of the high-certainty channel on the perceived service quality in the low-certainty channel. Counter-intuitively, we find that low-certainty channel demand will decrease as the reputation spillover effect increases, in the case of low inter-channel competition. Also, low-certainty channel demand increases as the quality uncertainty increases, in the case of intense inter-channel competition. Furthermore, the platform owner should offer a new high-certainty channel when (i) the perceived quality for this channel is sufficiently high, (ii) sellers in this channel are able to efficiently provide quality service, (iii) consumers in this channel are not so sensitive to the quality uncertainty, or (iv) the reputation spillover effect is high. In the one-channel case, the incentives of the platform owner and sellers are aligned for all model parameters. However, this is not the case for the two-channel solution, and our model reveals where tensions will arise between parties.
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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 | 0.003 |
| 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 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".