How E-commerce Benefits Consumers from an Economic Perspective
Bibliographic record
Abstract
A rise of e-commerce has a huge impaction on individuals’ consumption behaviors. Many factors may explain its influence, but the relative economic theory is significant to be aware of for a better development in terms of national well-being. We used equilibrium price, Network Effect and the Price Dispersion model to indicate how online stores may benefit consumers through their specific characters such as organized information and comparatively low prices. By comparing models given different variables (variables in each model differed based on its corresponded market) and sketching their relative distribution functions, we observed that when the number of informed consumers increases, more shops would choose to set relatively low prices for their selling goods given there are both online and offline stores in the market. In addition, by applying the price dispersion model, we notice consumers would be better off in general when there is an increased quantity of online stores in the market. We also found online retail market decreases individuals’ valuation of offline store products. Thus, online stores benefit consumers by providing various choices and cheap products, which the offline stores would also follow. In this condition, consumers in general would be benefits from the online store in both online and offline purchase channel, respectively.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".