The Influence of Task Environmental Uncertainty on the Balance Between Normative and Strategic Corporate Social Responsibility
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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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".