Motivating Employees with Goal‐Based Prosocial Rewards*
Bibliographic record
Abstract
ABSTRACT A recent trend in organizations is to motivate employees with goal‐based prosocial rewards, whereby employees must donate their rewards to charities upon goal attainment. We examine the motivational effects of goal‐based prosocial rewards versus cash rewards under different levels of goal difficulty. We develop our hypotheses based on affective valuation theory, which posits that when valuing uncertain outcomes by affect rather than calculation, individuals are largely insensitive to changes in probability of the outcomes, including probability of goal attainment. Experiment results support our hypotheses. Specifically, we find that employees who are rewarded with prosocial (vs. cash) goal‐based rewards are more likely to adopt an affective valuation approach. Consequently, when employees are assigned either an easy goal or a stretch goal, their effort is higher when incentivized with a goal‐based prosocial reward than a cash reward. Furthermore, there is a less curve‐linear relationship between goal difficulty and effort with prosocial (vs. cash) goal‐based rewards. These findings highlight for incentive system designers the motivational advantage of goal‐based prosocial rewards relative to traditional cash rewards. Furthermore, we extend the academic literature by showing how affect‐rich rewards such as prosocial rewards can influence employees' assessment of the probability of goal attainment.
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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 | 0.002 | 0.008 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".