Analyzing the Corporate Social Responsibility Disclosure: Mixed Method Applied on SME and Large Organizations
Bibliographic record
Abstract
Modern organization has to deal with different stakeholders expectations. Indeed, organization activities and practices should be designed and conducted to be sustainable. So, it is required from organization to be socially responsible and operate with integrity regarding the environment. This organizational behavior is called the corporate social responsibility – CSR. In that case, organization should disclose how it is socially responsible. CSR disclosure is recognized as a tool to enhance corporate reputation. This research aims to deals with the content of the CSR disclosure and in that case the possibility to predict the CSR approach throughout specific CSR-related information. In this paper, we investigate about the nature of CSR disclosure content and to what extent specific CSR-related information – CSR approach could be predicted. The sample of this research contains 58 organizations that had been awarded the label of the CSR in Morocco. A content analysis of websites is used for each organization’s CSR communication, found in the corporate websites or annual reports. We use mixed research method for analyzing the content of the CSR disclosure. This method used coding system for analyzing deeply the content related to the CSR and after that the discriminant analysis for testing the ability to predict the CSR approach nature. As results, we raised the CSR disclosure characteristics and hence we explicit how specific CSR-related information highlight different levels of ability to predict CSR approach nature. Our findings, when confronted to the literature, explicit convergences about the nature and the predictability of CSR disclosure content.
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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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".