The Influence of ESG, SRI, Ethical, and Impact Investing Activities on Portfolio and Financial Performance—Bibliometric Analysis/Mapping and Clustering Analysis
Bibliographic record
Abstract
This paper aims to examine the publication metrics of literature related to the influential aspects of ESG (environmental, social, and governance), SRI (socially responsible investing), ethical, and impact investing on the portfolio and financial performance literature. It also seeks to identify major patterns and core themes in this topic and draw lessons from the past literature for future directions. Data from the SCOPUS database were used in this study. The ‘biblioshiny’ R package, also known as ‘bibliometrix 3.0’, was employed to conduct bibliometric analysis, utilising mapping and clustering techniques on 260 articles, in order to distil the comprehensive knowledge and identify emerging trends in ESG, SRI, ethical, and impact investing. The thematic map classified the ESG, SRI, ethical, impact investing and performance relationship themes into four categories of themes: niche themes (SRI, engagement and ESG), motor themes (corporate financial performance, corporate social performance, ESG, ESG factors, sustainability, performance, integrated reporting, gender diversity, and board size), emerging or declining themes (social responsibility, environmental performance, socially responsible investment, ethical investment, and SRI), and basic or transversal themes (financial performance, corporate social performance, ESG performance, environmental, social, and governance). Socially responsible investing, engagement, and ESG imply a position between niche themes and a highly developed topic/emerging or a decreasing theme, while the impact of COVID-19 on sustainability and financial performance implies a position between a highly developed topic/emerging or decreasing theme and a basic theme. The findings contribute to the enhanced understanding of ESG, SRI, ethical, impact investing and performance, which are crucial for an efficient capital market in promoting sustainability and sustainable development. The study offers vital practical implications and future research directions.
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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 | 0.023 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.163 | 0.213 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".