Employee performance and abusive supervision: The role of supervisor over‐attributions
Bibliographic record
Abstract
Summary To understand the relationship between employee performance and abusive reactions from supervisors, we examine the role of supervisors' attributions about employees' performance. Drawing on the fundamental attribution error, we argue that supervisors over‐attribute lower levels of performance to employees' internal factors (i.e., conscientiousness), which then triggers higher levels of abusive supervision. In Study 1, we collected data from 189 supervisor–employee dyads. The results indicated that lower levels of supervisor‐rated employee performance related to supervisor biased attributions to employee conscientiousness, which in turn resulted in employee‐rated abusive supervision. In Study 2, we combined a recall task with a vignette design to replicate and extend our findings. We demonstrated that after adjusting for the baseline level of employee conscientiousness, supervisors over‐attributed poor performance to employee conscientiousness and then engaged in higher levels of abusive behaviors. Further, consistent with premises of fundamental attribution error, we found that in the absence of information about who was at fault for poor performance, supervisors over‐attributed poor performance to internal factors (employee) as compared to external factors (software malfunction). Taken together, our findings demonstrate that biased attributions about employee conscientiousness help explain the relationship between employee performance and abusive supervision.
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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 | 0.000 | 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) | 0.003 | 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".