Ordering and financing strategies in electronic business platform financing with a loss averse retailer
Bibliographic record
Abstract
With the rapid growth of e-commerce, platform-based financing in electronic business (EB) has emerged as an innovative solution for online retailers facing capital constraints. This study develops a Stackelberg game-theoretic framework to analyze strategic financing decisions in a two-tier e-commerce supply chain, where an electronic business platform (EBP) , as the leader, assumes leadership by setting financing interest rates, while a capital-constrained, loss-averse online retailer (LOR), as the follower, optimizes order quantities and financing participation under behavioral risk preferences. A hierarchical game-theoretic framework is established to examine strategic interactions between an EBP and a LOR, and the equilibrium outcomes are given. The model derives optimal decisions for both financing rates and ordering strategies. Results demonstrate that when the retailer's initial capital grows, their necessity for external financing diminishes correspondingly, leading to smaller order quantities due to reduced bankruptcy risk. Moreover, higher levels of loss aversion cause retailers to order less and avoid financing, reflecting risk-sensitive behavior. The study also presents comprehensive numerical analyses to explore additional managerial implications, offering insights into how capital availability and behavioral factors like loss aversion shape decision-making in EB financing environments.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".