Is more always better? An experimental examination of the effects of feedback frequency, narcissistic oversensitivity, and growth mindset on performance accuracy
Bibliographic record
Abstract
Abstract The provision of more frequent feedback to employees is increasing, although prior research has found mixed results as to the effect of increased feedback frequency on employee performance. Narcissism research identifies narcissistic oversensitivity as a key narcissistic subdimension that may result in particularly strong responses to performance feedback. We predict and find in an experiment that increased performance feedback frequency has a more negative impact on the performance accuracy of individuals with higher levels of narcissistic oversensitivity and that this negative interactive effect of feedback frequency and narcissistic oversensitivity is mitigated by the priming of a growth mindset. These results should be of practical interest to firms as they design their management control systems to improve employee performance, considering the variation in narcissistic oversensitivity among their employees. These results also contribute to recent accounting research on the effects of feedback frequency and employee mindsets.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".