Impact of Promised-Delivery-Time on Imported Vaccine Provider’s Agency Marketing Strategy
Bibliographic record
Abstract
To ensure the imported vaccines’ high quality, many countries such as Canada, the UK and China require the imported vaccine provider to cooperate with an exclusive domestic agent for vaccine sales. In this paper, we examine the role of promised-deliver-time (PDT) for the transport of vaccines in the imported vaccine provider’s agent selection: It can either rely on a non-competitive domestic agent (referred to as Pure Agent Marketing Strategy) or a rival domestic agent (referred to as Rival Agent Marketing Strategy) that produces and sells its self-branded vaccines. PDT is made by the logistics service provider (LSP), which helps keep the high quality of the imported vaccines but also significantly constrains the delivery volume. This further alters the imported vaccine provider’s agency marketing strategy. Interestingly, we find that the Rival Agent Marketing Strategy is not necessarily harmful for the imported vaccine provider, especially when the imported vaccine’s brand image advantage is significant and the PDT is long. We further study the impact of the brand substitutability and the imported vaccine provider’s social responsibility, finding that the main results are qualitatively unchanged.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 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".