Slacks and Capabilities: Investigating the Conditions that Enable CSR to Reliably Create Value
Bibliographic record
Abstract
Current literature has shown that an increase in CSR may not reliably lead to an increase in firm value. Yet, theoretical arguments and empirical evidence that investigate the trade-off between an increase in firm value and an increase in uncertainty is scarce. More importantly, research on when CSR can be a reliable strategic asset is scarce. In this paper, we attempt to fill these gaps by: (1) investigating the relationship between CSR and both the mean (measuring the level of impact) and variance (measuring the reliability of impact) of firm value distribution simultaneously, and (2) exploring the conditions under which CSR can be a reliable value enhancer (increase mean but does not increase the variance of value distribution). Using the multiplicative heteroscedasticity estimation model and dynamic panel regression structure, we find that CSR by itself is an unreliable value enhancer (increase value but also increase the variance of value distribution). We also find that the impact of CSR on firm value is more reliable when a firm has more slack in hand or when a firm has higher capabilities in future profitability and risk management. This research provides practical guidance to managers on the conditions under which the efficacy of CSR can be reliably realized.
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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 | 0.008 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".