The interplay between charitable donation strategies and sales mode selection in the platform
Bibliographic record
Abstract
• We analyze dynamic strategic interactions in a philanthropic supply chain. • We characterize equilibria with two types of donations and investment in blockchain. • We determine preferences of manufacturer and platform for selling contracts. • Online donations play different roles in agency sales mode and reselling contracts. Motivated by the emergence of offline and online donations, this paper explores the interplay between charitable donations and strategic choice of sales mode in a philanthropic supply chain consisting of a manufacturer and a platform. We consider two donation strategies, offline donations and both offline and online donations that are traceable by blockchain technology, and two business models, i.e., reselling sales mode and agency sales mode. Donations by the manufacturer are used to boost its charitable image, which in turn affects positively the demand. As such image can only be built over time, we adopt a differential game formalism that captures both the strategic interactions between the two players and the dynamic nature of the problem. We characterize and compare the equilibrium strategies and outcomes for different choices of selling mode and donation option. Our findings can be summarized as follows. First, we obtain that only under some conditions that online donations enhance the charitable image, members’ profits, consumer surplus, and social welfare. Second, regardless of the sales mode, the conditions for the platform to adopt online donations are the most stringent, and the conditions for the enhancement of the charitable image are the most lenient. Third, the implementation of online donations does not have much impact on the Pareto regions of the agency mode but has a much greater impact on the Pareto regions of the reselling mode, especially for medium and large online donation amounts. These changes hinge on the trade-offs for members between online and offline donations.
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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 | 0.016 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".