Strategic Blockchain Adoption and Supply Chain Finance: A Game‐Theoretic Approach With Risk Aversion Analysis
Bibliographic record
Abstract
ABSTRACT Blockchain technology holds significant potential for fostering trust in supply chains. The investigation of blockchain technology investments and the evaluation of their efficiency are critical for improving operational performance. This study develops a two‐stage game model involving a supplier and two competitive retailers; the retailers may face financial constraints. They can obtain financing through either bank finance or supply chain finance (SCF). In the benchmark model, SCF emerges as the sole equilibrium, potentially resulting in a prisoner's dilemma. Then we adopt partial and entire blockchain adoption models (model PB and model EB) in which the impact of blockchain technology on demand is considered. Equilibrium conditions are derived for both retailers' selection of SCF. Furthermore, comparative static analyses demonstrate the superiority of model PB in guiding blockchain adoption investment decisions. Numerical analysis also reveals that the bank's risk aversion level significantly influences its blockchain investment strategy. And differentiated compensating balances effectively distinguish retailers and optimize financing decisions. This paper provides a framework for banks to better assess the risks associated with loans to supply chain, establishing more robust risk evaluation mechanisms. It also offers theoretical and practical insights for supply chain managers in determining optimal blockchain adoption strategies under financial constraints.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".