A bibliometric analysis: Corporate social responsibility and firm value
Bibliographic record
Abstract
This literature review was conducted in the form of a bibliometric analysis (Zupic & Čater, 2015) to examine the trends and findings of studies on corporate social responsibility (CSR) and firm value. There were 269 Scopus-indexed publications published between 2007 and 2023 analyzed in this study, then processed with R Biblioshiny to generate and visualize the citation matrix and bibliometric network. VOSviewer and additional analysis were also undertaken. The trend of publications on CSR and business value has significantly increased every year, especially in 2022, where 54 publications were made, hitting an annual publication growth rate of 10.58 percent. The results of the review revealed the USA as the most influential nation, and the Journal of Business Ethics as the journal with the strongest influence. An article with 989 citations was published in the Management Science journal written by Lee S., making it the most influential article. Based on the co-occurrence network, the intensity of research on the relationship between CSR and firm value has increased between 2017 and 2021. Jo H. appeared as the most frequently cited author in this field based on co-citation and the USA and Canada were the top two countries in terms of collaboration among countries. This study provides useful insights for future CSR and business value studies. This research is very important for researchers studying CSR and firm value literature. In particular, the findings allow new researchers to quickly identify the theoretical underpinnings, as the leading researchers and documents identified in this study provide an entry point for new 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 | 0.008 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.159 | 0.202 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".