E-commerce and GDP nexus: Evidence across economic and continental groups
Bibliographic record
Abstract
Information and Communication Technology (ICT) is currently developing rapidly along with its increasingly important role for both individuals and organizations. One form of ICT development is E-Commerce which serves as a comprehensive virtual marketplace. Based on macroeconomics context, the role of E-Commerce needs to be explored further, especially the relationship between Business to Consumer (B2C) E-Commerce and Gross Domestic Product (GDP), a topic that remains underexplored. The purpose of this study is to observe the nexus between B2C E-Commerce and GDP using two main classifications, namely: 1) economic status and 2) geographical continent. A panel-data regression analysis was conducted involving 117 countries from 2016 to 2020. The results showed that B2C E-Commerce had a positive and significant effect on GDP. In addition, the increase in e-commerce has been found to have the potential for nation growth in both developing and emerging economies. Notably, Africa and Asia-Oceania continents presented considerable opportunities to harness E-Commerce as a driver of national economic development. These findings provide important managerial and policy implications for governments and stakeholders in defining strategies to promote inclusive digital economic growth.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".