The Impact of Linking Three Different Incentive Methods to Specific, Challenging Goals
Bibliographic record
Abstract
Despite a great deal of research investigating incentives and goal setting more broadly, little is known about the linking of goals and goal attainment to different monetary incentive structures, or the manner in which such structural choices impact various job attitudes and job performance. Consequently, a quasi-field experiment, a laboratory experiment, and an on-line survey experiment examined the effects of three monetary incentive systems on task performance (exps. 1, 3), counterproductive behavior (exper. 2), and perceptions of fairness (exps. 1, 3). Additionally, the mediating effect of prolonged effort/persistence (exp. 3) was tested. The results revealed that an all-or-nothing distal goal method of linking a monetary incentive to a goal under-performs the multiple proximal goals and linear piece-rate methods with regard to task performance, counterproductive behavior, and perceptions of fairness. The results of the third experiment revealed that persistence and perceptions of fairness mediate the monetary incentive-task goal performance relationship.
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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 | 0.010 | 0.044 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".