ESG in Business Research: A Bibliometric Analysis
Bibliographic record
Abstract
A company’s “value” is increasingly influenced by three criteria: the way it acts to protect the environment, its attitude towards society and the principles of corporate governance it has adopted. That is the Environmental, Social and Governance (ESG) acronym, and it has substantial impact on company value. To further understand the ESG landscape in business research, this article aims to analyze the existing literature and present the current state of knowledge, main trends, and future perspectives. Through the Scopus database, the authors examine a sample of 1034 articles spanning from 2006 to 2022. VOSviewer and Biblioshiny packages are used for performance analysis and visualization of the publication trends, the conceptual structure of the field and the research collaborations. The results suggest that the publication and citation trends of ESG register an upward trend over time. In terms of research institutions, most of the influential ones emanate from the US, while a significant percentage of articles were published in top-tier financial journals. Science mapping via co-authorship analysis bifurcates the sample into six clusters and reveals the major themes and their evolution. Keyword analysis unfolds emerging trends that could be further explored. Given the breadth of the sustainability field and the ever-changing business environment, this paper is of great practical importance in motivating companies to engage in ESG activities. To the authors’ knowledge, no other study has attempted a comprehensive and detailed BA covering multiple aspects and dimensions of ESG in the corporate research field. The theoretical framework of this paper fills this gap and offers an in-depth synthesis of all published papers, providing invaluable insights to scholars, the business community and regulatory authorities, and creating alternative research paths for aspiring researchers.
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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 | 0.015 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.227 | 0.314 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".