Corporate Social Responsibility as a managerial learning process
Bibliographic record
Abstract
The purpose of this paper is twofold. 1) We propose for the first time in the literature a theory (managerial learning hypothesis) that may explain why managers engage in corporate social responsibility (CSR). 2) We use an intuitive empirical methodology (Edmans et al. 2017) to test the relevance/irrelevance of our new theory. The idea behind our main contribution is that managers engage in CSR to learn new relevant information from other informed stakeholders. In return, managers will use both the new information and other information they already have to choose the optimal level of firm’s investment (Jayaraman and Wu, 2019). Therefore, we propose to examine whether a strong CSR engagement improves revelatory efficiency (Edmans et al. 2012, 2017). The latter accounts for the extent to which stock prices reveal new information to managers that will help them make value-maximizing choices. Our findings suggest that CSR activities do not allow firm’s managers to extract new information from their stock prices and ultimately improve the efficiency of their investment choices.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".