How Disclosure Features of Corporate Social Responsibility Reports Interact with Investor Numeracy to Influence Investor Judgments
Bibliographic record
Abstract
Abstract Firms’ Corporate Social Responsibility ( CSR ) reports typically frame their strategies in terms of either community or global efforts (i.e., “strategy frame”). Further, the style used to depict CSR performance in reports often highlights either pictures or words (i.e., “presentation style”). These two prominent disclosure features of CSR reports promote a natural fit or misfit in the focus (relatively low‐level or high‐level focus) investors adopt when thinking about the firm and its CSR efforts. Further, these disclosure features likely have different effects on investors depending on their numeracy or, in other words, the way that they naturally process numerical information. In this study, we predict and find that a fit between the strategy frame and the presentation style of a firm's CSR report causes less numerate investors to be more willing to invest than when a fit is not present. Specifically, we find that a fit leads less numerate investors to experience subjective feelings of processing fluency and, in turn, positive affect that serves as a cue that the positive CSR performance information can be relied upon, which positively influences willingness to invest. Our results have implications for both CSR reports as well as other types of firm disclosures that increasingly vary along similar disclosure characteristics. Our results also contribute to both the growing literature on presentation effects in accounting, as well as the broader business literature on CSR reporting.
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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 | 0.012 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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; a candidate call from one teacher head, 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".