Mitigating the Demotivating Effects of Frequent Unfavorable Feedback about Goal Progress
Bibliographic record
Abstract
ABSTRACT Performance goals are used pervasively by organizations to motivate individual effort, and feedback about goal progress is often available on a highly frequent basis. While feedback can be beneficial, there is evidence that frequent unfavorable feedback can be demotivating. We use expectancy theory to predict that compared to infrequent feedback, frequent unfavorable feedback about goal progress will reduce effort by negatively impacting individuals’ expectancy of goal attainment. We also predict that these negative effects will be mitigated when accompanied by a goal attainability reminder that bolsters the expectancy of goal attainment. Results from two experiments support both predictions and also show that a goal attainability reminder does not reduce the effort when early frequent feedback is favorable. These findings have practical implications as we demonstrate that a simple and readily implementable reminder about the attainability of assigned goals can mitigate the negative motivational effects of frequent unfavorable performance feedback.
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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 | 0.006 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".