Mapping the Research Landscape of Reverse Logistics in E-Commerce: A Bibliometric Analysis from 2003 to 2023
Bibliographic record
Abstract
This study aims to conduct a comprehensive bibliometric analysis to map the research landscape of reverse logistics in e-commerce from 2003 to 2023.This comprehensive bibliometric analysis employs VOSViewer and R as essential methodological tools and searches into the evolving landscape of reverse logistics in the e-commerce era.The study uses the Scopus database to gather and thoroughly analyze 1073 documents from 2003 to 2023.With a specific aim to bridge existing gaps in the literature, the research not only identifies prolific authors, productive countries, and top frequent keywords but also utilizes publication and citation trends to highlight periods of growth and stability.Notably, the absence of African contributions prompts critical reflections on global research inclusivity.By providing a unified perspective on reverse logistics in ecommerce, this research enhances academic understanding and offers practical insights for supply chain management.Using VOSViewer and R adds methodological rigor and depth to the study's findings.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.091 | 0.187 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".