Bibliometric Analysis of ESG Performance from 2019 to 2024 Using VOSviewer
Bibliographic record
Abstract
Environmental, Social, and Governance (ESG) performance has become a central focus in sustainable business practices, influencing investment decisions and corporate strategies globally.This study aims to map and analyze the academic landscape of ESG performance research from 2019 to 2024 using bibliometric techniques, identifying key trends, influential contributors, and research gaps.A total of 974 Scopus-indexed articles were retrieved using Publish or Perish (PoP) software, with the search keyword "ESG Performance" applied to titles, abstracts, and keywords.Data cleaning and filtering were performed using Mendeley, and analysis was conducted using VOSviewer for citation, co-authorship, co-occurrence, and term trend visualizations.The most cited year was 2022, with 8,405 citations.A total of 2,299 authors contributed to ESG performance studies.The most frequently used keyword was "ESG Performance" (117 occurrences).China, India, and Russia were among the top contributing countries.Nine conceptual clusters emerged, with emerging themes including green innovation and digital transformation.ESG research has grown significantly in recent years, yet certain areas such as artificial intelligence, ROE, and CEO narcissism remain underexplored.These gaps offer valuable directions for future research in corporate sustainability.
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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 | 0.007 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.178 | 0.191 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".