Blockchain-based E-commerce for the COVID-19 economic crisis
Bibliographic record
Abstract
The beginning of 2020 is associated with the emergence and spread of the COVID-19 disease. The characteristics of this virus, such as high transmission power and lack of definitive treatment have caused problems in all aspects of organizational economics. Restrictions that were imposed to deal with the virus affected the global economy. Fear of being exposed to the virus, quarantine and lockdown led to a massive increase in online shopping. However, people’s concern about the health and authenticity of the products offered online and their incompatibility with consumer standards raised concerns about the reliability of the existing e-commerce models. Fraud, counterfeit products, ethical sourcing and product safety are some of the concerns that affected online business acceptance. To address these challenges, we examine the use of a blockchain-based e-commerce approach to guarantee authenticity through blockchains trace and trace capabilities. In this approach, we evaluate the profit gains achieved through addressing consumer concerns on safety and authenticity. To examine these benefits, we use a game theory leader-follower approach. We evaluate the profitability of e-commerce in three scenarios, including when non of seller and e-commerce website use blockchain, only when the seller uses the blockchain and when both the seller and the e-commerce websites use blockchain. The evaluation results show that the situation in which both the seller and the website use blockchain has the highest profitability for the seller due to the maximum reduction of customer concerns.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".