The Relationship Between Corporate Social Responsibility and Corporate Financial Performance: A Moderating Effect of Economic Policy Uncertainty
Bibliographic record
Abstract
Within recent decades, researches on corporate social responsibility (CSR) has been receiving more attention over the world. The existing literature on CSR is very diverse, both in evaluating the performance of CSR activities as well as and the relationship between CSR disclosure and firms’ outcome. This paper extends the literature of the latter case, that is, not only it aims to purely examine the relationship between CSR disclosure activities and corporate financial performance (CFP), but also consider this nexus under economic policy uncertainty (EPU) context. Our primary data is collected from more than 500 listed companies in the Vietnamese stock market from 2013 through 2017, while secondary data (CSR and EPU) are self-calculated under serial criteria. Our results support the hypothesis that the more companies intensively disclose CSR, the higher financial performance (both ROA and Tobin’s Q) they could obtain. More interestingly, we find that while EPU seems to weakly moderate the relationship between CSR disclosure and “internal financial performance” (ROA), it will significantly diminish the effect of CSR toward “external financial performance” (Tobin’s Q). The research shed light on an approach to measure CSR disclosure indexes for the emerging market as in Vietnam. Our findings encourage the firm’s managers to pay more attention to CSR disclosure activities due to the positive benefit that their firm could obtain and suggest policymakers to maintain a stable economic background for a sustainable market.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".