The Influence of Task Environmental Uncertainty on the Balance Between Normative and Strategic Corporate Social Responsibility
Why this work is in the frame
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Bibliographic record
Abstract
Corporate social responsibility (CSR) is increasingly ubiquitous, but firms differ in their emphasis on conforming to industry CSR norms versus using CSR strategically to differentiate from competitors. Research explains that managers attempt to balance conformity and differentiation regarding CSR but does not explain what shifts this balance. We draw from optimal distinctiveness research to explain how different types of uncertainty created by industry task environments shift the balance between conforming to industry CSR norms and pursuing differentiated CSR activities. Using variance decomposition on a 9-year panel of 3,184 firms from 357 industries in the United States, we find that managers emphasize normative (strategic) CSR to a greater (lesser) extent in low-munificence and high-complexity task environments, where uncertainty drives managers toward the security of established industry CSR norms, and to a lesser (greater) extent in high-dynamism task environments, where following uncertain CSR norms is less attractive. We also find that the influence of uncertainty created by industry task environments has, on balance, remained constant as business norms shifted from shareholder to stakeholder primacy. Our theoretical framework reveals task environmental uncertainty as an antecedent to how managers attempt to achieve optimal distinctiveness regarding CSR and explains how different sources of uncertainty shape these attempts.
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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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 it