Overcoming Feedback Challenges for Managers with Self-Determination Theory: A Digital Intervention
Bibliographic record
Abstract
As technology becomes more prevalent in the workplace, managers may encounter difficulties in delivering effective feedback to employees, leading to increased stress and a reluctance to provide that feedback. This study assesses the potential advantages of a digital intervention, specifically the nimble bubble online training platform, aimed at assisting managers in overcoming these challenges. Grounded in Self-Determination Theory (SDT), this platform employs brief daily exercises over a six-week period to enhance managers' ability to provide optimal change-oriented feedback. In a randomized controlled trial, 98 managers were assigned to either an experimental group receiving the nimble bubble training or an active control group. Participants completed surveys at three time points: pre-intervention (n=98), immediately post-intervention (n=86), and four weeks follow-up (n=76). Results from multilevel analyses revealed significant positive effects of the intervention on managers' incremental and static competence, as well as reductions in stress levels and feedback avoidance behaviors. Trajectory analyses of the experimental group revealed significant negative quadratic trends for both incremental and static competence, indicating that the intervention's effects on competence initially increased but then stabilized or decreased over time. Additionally, negative linear trends were found for both feedback avoidance and stress, suggesting continuous improvement in these outcomes throughout the study period. Further analysis showed that static competence predicted lower stress levels, and incremental competence predicted decreased feedback avoidance over time. Theoretical and practical implications are discussed.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".