The Effects of Reward Type on Employee Goal Setting, Goal Commitment, and Performance
Bibliographic record
Abstract
ABSTRACT: The use of tangible rewards in the form of non-cash incentives with a monetary value has become increasingly common in many organizations (Peltier et al. 2005). Despite their use, the behavioral and performance effects of tangible rewards have received minimal research attention. Relative to cash rewards, we predict tangible rewards will have positive effects on goal commitment and performance but will lead employees to set easier goals, which will negatively affect performance. The overall performance impact of tangible rewards will depend on the relative strength of these competing effects. We conduct a quasi-experiment at five call centers of a financial services company. Employees at two locations earned cash rewards for goal attainment while employees at three locations earned points, with equivalent retail value to cash rewards, redeemable for merchandise. Results show that cash rewards lead to better performance through their effects on the difficulty of the goals employees selected. Implications for theory and practice are discussed. Data Availability: The data used in this study are available upon request.
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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 | 0.004 | 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 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".