Blockchain-enabled price competition for green product design
Bibliographic record
Abstract
Blockchain technology proffers innovative solutions to address trust-related concerns in enterprise operations. We delve into the operations of two manufacturers engaged in selling a marginal-intensive green product (MIGP) and a development-intensive green product (DIGP). Each manufacturer determines the level of product greenness level, derived from the product’s distinctive environmental attributes, and sets the price accordingly. In this pursuit, we examine four distinct scenarios in total: non-adoption, adoption by one of the manufacturer, and the concurrent adoption by two manufacturers. The results support the subsequent arguments. First, the simultaneous adoption of blockchain by both manufacturers does not consistently foster perpetual improvement in the environmental standards of products, especially concerning the easily discernible DIGP. Second, escalated market competition empowers both MIGP and DIGP manufacturers to attain pricing advantages and market through the unilateral adoption of blockchain. Finally, the decision for both competing manufacturers to adopt blockchain hinges upon adoption costs, the intensity of price competition, and the underlying cost structures of their respective products. Notably, the cost investment coefficient pertaining to MIGP and DIGP emerges as a pivotal determinant that significantly influences the range of applicable equilibrium strategies.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".