Motivating with Rewards: The Good, the Bad, and the Confused
Bibliographic record
Abstract
This symposium addresses important factors that influence the effectiveness of pay-for-performance (PFP) compensation systems, drawing from multiple motivation theories including expectancy theory, self-determination theory and goal setting theory (Deci & Ryan, 1980; Locke & Latham, 1990; Vroom, 1964). The factors considered include employees’ understanding of how they get compensated, how reward salience and a desire for money influence motivation, whether managerial autonomy support and more flexible goal setting can mitigate the controlling effects extrinsic rewards on intrinsic motivation, and how PFP certainty, size and frequency influence deviant behaviors. These studies collectively highlight the complexities of designing effective motivational compensation systems in the workplace. The KISS Principle of Compensation Author: Anja H. Olafsen; University of South-Eastern Norway Author: Marylene Gagne; Curtin University - Perth Author: Claus Wiemann Frolund; University of South-Eastern Norway Reward salience as a key factor undermining motivation Author: Josh Howard; Monash University Author: Duc Tran; Monash University Perceiving Goals as Invariable, Pay-for-Performance, and Desire for Money Author: Bard Kuvaas; BI Norwegian Business School Author: Haien Ding; BI Norwegian Business School The Deviant Effects of Bonuses Author: Marylene Gagne; Curtin University - Perth Author: Jacques Forest; Author: Florence Jauvin; Author: Patrick Coulombe; Université du Québec à Montréal (UQAM) Author: Anja H. Olafsen; University of South-Eastern Norway
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".