Continuous Performance Feedback: Investigating the Effects of Feedback Content and Feedback Sources on Performance, Motivation to Improve Performance and Task Engagement
Bibliographic record
Abstract
Organizations are increasingly replacing performance ratings with continuous feedback systems. The current study assesses how people react to continuous performance feedback in terms of its content and sources concerning their performance, motivation to improve, and task engagement. A task-based experiment was conducted with 36 participants who received continuous feedback. The participants were divided into two groups, receiving either quantitative or qualitative feedback content. Feedback was delivered through computer-mediated, person-mediated, or no source. The results highlight that person-mediated feedback, regardless of content, positively influenced performance, motivation, and task engagement. On the other hand, quantitative feedback only showed a positive association with performance. These findings suggest that qualitative feedback is more effective, enhancing motivation and engagement. Managers should prioritize person-mediated feedback to optimize performance, as it yields superior outcomes compared to computer-mediated feedback. However, further research is required to comprehensively understand the effectiveness of continuous performance feedback and its specific characteristics.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".