The role of employee attributions in burnout of “talented” employees
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine a process through which perceived talent identification affects employee burnout. Design/methodology/approach Data for the study were collected from 242 employees using a cross-sectional survey design. Findings The findings supported the mediating role of work effort in the relationship between perceived talent identification and burnout. Furthermore, the results highlighted the moderating role of employee well-being attributions in the relationship between perceived talent identification and employee work effort. The moderated–mediated relationship for burnout was also supported. Research limitations/implications Using insights from conservation of resources and attribution theories, this study not only examined the direct relationship between perceived talent identification and feelings of burnout but also provided insights into why perceived talent identification leads to different employee outcomes. Practical implications Management should pay attention to the communication processes related to talent identification because employees’ interpretation of the underlying motives of this identification impacts their well-being (i.e. feelings of burnout). Originality/value This study examines employees’ attributions in the context of talent management and demonstrates that these interpretations play an important role in shaping their behaviours.
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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".