The Relationship Between Corporate Social Responsibility And Corporate Financial Performance – Evidence From Empirical Studies
Bibliographic record
Abstract
Socially responsible investment (SRI) has experienced strong growth in recent years. In 2012, $1 out of every $9 US assets under professional management was invested in some form of sustainable investment. Global sustainable investment assets have expanded dramatically, rising from $13.3 trillion at the outset of 2012 to reach a total of $21.4 trillion at the start of 2014. Most of the SRI assets are in Europe (63.7 percent), but the relative contribution of the United States has increased from 28.2 percent in 2012 to 30.8 percent in 2014, and over this two-year period, the fastest growing region has been the United States, followed by Canada and Europe. These three regions are also the largest regions in terms of assets, accounting for 99 percent of global SRI.With this growth one most important issues is whether it pays for organizations to concern themselves with social responsibility, and whether there are any tradeoffs to sustainable investing. Much of the present research on this question is based on the views of Friedman and Freeman. But changes in economic development, national and local security, and the growing expectations of stakeholders influence how social performance is defined, and thus corporate performance as well.The aim of this article is to examine correlations between CSR and financial corporate performance, based on empirical studies conducted by other authors in different countries. In total, the analysis comprises 53 studies and the results obtained for 16,119 companies.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".