CSR Motivations in Voluntary Non-Financial Disclosures: The Preparers’ Voice
Bibliographic record
Abstract
CSR reports are communication tools, appropriate for informing stakeholders of the CSR practices conducted by organizations. This article aims to explore the reasons why complex organizations have adopted on purpose the CSR report to meet their needs and to discover why they have chosen to adopt the integrated report as an alternative to the sustainability report. This study is based on an explanatory case study of two healthcare organizations that have exactly implemented Integrated Reporting (IR), instead of Sustainability Reporting. The research method used is the field study. This work points out how organizations create and use CSR reports, even if they are not mandatory. If the IR looks like a “managerial innovation”, there is always a risk that the diffusion of these tools could simply be the latest popular trend, followed by internal or external proponents, rather than a rational decision-making process. The study has implications for the policymakers, the organizations, and their integrated report. The policymakers can understand if this tool can be useful for the organizations, to promote internal CSR. The study contributes to literature about the willingness to publish CSR reports, as an expression of the internal and external factors that influence voluntary reporting choices.
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 | 0.047 | 0.113 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".