Not all stakeholders are equal: Corporate social responsibility variability and corporate financial performance
Bibliographic record
Abstract
Abstract The advocates of “doing well by doing good” have advised firms to invest in corporate social responsibility (CSR), but firms may get lost on how to invest their limited resources in it since CSR is a complex concept involving many activities and different types of stakeholders. In this work, we draw upon the perspective of stakeholder saliency and the stakeholder resource‐based view (SRBV) to propose that stakeholders may have different levels of expectations for CSR and contribute to firm value creation differently. Therefore, firms using different CSR to treat different stakeholders (high CSR variability) will have better financial performance. We further propose that context, in particular media coverage and state ownership, moderates the relationship between CSR variability and firm performance, as stakeholders of highly visible firms and state‐owned enterprises (SOEs) may frown upon a discriminate treatment in CSR. Findings based on a sample of 3313 publicly listed firms and 15,324 firm‐year observations in China's stock markets during the 2010–2018 period provide good support for our predictions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.043 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".