Precipitating abusive supervision: target factors and supervisor blame attributions
Bibliographic record
Abstract
The purpose of the proposed study is to consider why and under which conditions do supervisors engage in abusive behaviours towards their subordinates. To answer my first research question, why do supervisors engage in abusive supervision, I draw on victim precipitation (e.g., Sparks, Glenn, & Dodd, 1977) and conservation of resources (COR; Hobfoll, 1989) theories to argue that certain subordinate performance-related behaviours and characteristics threaten supervisor resources leading to abuse as a stress reaction. To answer my second research question, under which conditions do supervisors engage in abusive supervision, I draw on attribution theory (Heider, 1958; Weiner, 1986). I argue that supervisors abuse subordinates when they attribute responsibility, or blame subordinates for negative performance-related behaviours and characteristics, as a means of protecting or guarding against future resource loss. To answer my research questions, I developed measures for self- and other-perceived general mental ability (GMA) and blame attributions. I obtained data from 211 supervisor-subordinate dyads in Canada and the United States. Respondents were surveyed for information about their work behaviours, characteristics, and relationships. Using Hayes (2013) PROCESS macros, I found partial support for the proposed model and offer refinements to COR and victim precipitation theories. I found relationships between both self- and supervisor-reported subordinate behaviours and characteristics and abusive supervision, largely in the direction hypothesized. I also found supervisor-reported subordinate performance behaviours and perceived GMA to share a stronger relationship with subordinate reports of abusive supervision than subordinate reported behaviours and characteristics in many instances.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".