The Impact of E-commerce on the Economy : A Bibliometric Study
Bibliographic record
Abstract
Abstract In the context of the digital age, this article examines the impact of e-commerce on the global economy. Having become a crucial component of contemporary economy, e-commerce has not only changed the way businesses interact with their customers, but has also caused major upheavals in the global economy. According to STATISTA data, Asia, led by China, dominates the e-commerce market, generating nearly $1.7 billion in 2022. This boom is also seen in the percentage of sales made online, which increased to 19% in 2022 and is expected to reach almost a quarter by 2027. To analyze this impact, the authors undertook a literature review based on a bibliometric analysis of searches carried out from 2000 to 2023 in the SCOPUS database. The study reveals that there is a steady increase in publications on e-commerce over the years. The e-commerce impact areas analyzed in the study include the business productivity, economic growth, financial performance, logistics costs, sustainability, as well as legal implications. These analyses were supplemented by a detailed bibliometric analysis, identifying key trends and the major players in the fields. In short, this study sheds light on the growing and diversified influence of e-commerce on the global economy. Keywords: E-commerce, Global Economy, Bibliometric Analysis, Online Sales Growth, SCOPUS Database, Market Dominance.
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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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.140 | 0.263 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".