Corporate social responsibility as an employee governance tool: Evidence from a quasi‐experiment
Bibliographic record
Abstract
Research summary : This study examines whether companies employ corporate social responsibility ( CSR ) to improve employee engagement and mitigate adverse behavior at the workplace (e.g., shirking, absenteeism). We exploit plausibly exogenous changes in state unemployment insurance ( UI ) benefits from 1991 to 2013. Higher UI benefits reduce the cost of being unemployed and hence increase employees' incentives to engage in adverse behavior. We find that higher UI benefits are associated with higher engagement in employee‐related CSR . This finding suggests that companies use CSR as a strategic management tool—specifically, an employee governance tool—to increase employee engagement and counter the possibility of adverse behavior. We further examine plausible mechanisms underlying this relationship . Managerial summary : This study examines whether companies employ corporate social responsibility ( CSR ) to improve employee engagement and mitigate adverse behavior at the workplace (e.g., shirking, absenteeism). We find that companies react to increased risk of adverse behavior by strategically increasing their investment in employee‐related CSR (e.g., work‐life balance benefits, health and safety policies). Our findings have important managerial implications. In particular, they suggest that CSR may help companies motivate and engage their employees. Hence, companies dealing with employees that are unmotivated, regularly absent, or engage in other forms of adverse behavior, may find it worthwhile to design and implement effective CSR practices. Further, our findings suggest that CSR can be used as employee governance tool. Accordingly, managers could benefit from integrating CSR considerations into their strategic planning . Copyright © 2015 John Wiley & Sons, Ltd.
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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 | 0.050 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".