Predisposed, Exposed, or Both? How Prosocial Motivation and CSR Education Are Related to Prospective Employees’ Desire for Social Impact in Work
Bibliographic record
Abstract
Researchers have explored important questions about employees’ prosocial motivation to impact others through their work and about employees’ engagement in corporate social responsibility (CSR) initiatives. Studies show that job seekers are attracted to CSR-engaged employers, but little is known about whether and why prospective employees are attracted by job roles that allow them to have positive social impact. We used prosocial motivation theory to develop hypotheses about processes through which a greater desire for social impact in work is associated with being predisposed to it (due to trait-like prosocial motivation), being exposed to the possibility of it (through CSR-related educational choices), and both in partially mediated sequence. Analyses of data from 187 prospective employees provided support for most hypotheses. Our findings inform new directions for research on CSR and recruitment, the CSR education literature, and recruitment practices that leverage prospective employees’ desire for social impact through performing their regular work.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".