How do Followers see Their Leaders and Does it Matter?: Insights From a Person-Centered Analysis
Bibliographic record
Abstract
In response to calls for theory integration and more in-depth analysis of destructive forms of leadership, pattern-centered analyses have emerged that suggest optimal and sub-optimal profiles. We broaden the range of leadership behaviors traditionally included in this type of analysis by including abusive supervision. We use a pattern-oriented approach to validate theoretically meaningful profiles based on the full-range model of leadership and abusive supervision using a sample of full-time employee’s perceptions of their managers. Three theoretically meaningful leadership profiles were established: optimal, passive-dominant and abusive passive dominant (Study 1). Furthermore, we used conservation of resources theory to examine how followers’ personal (i.e., physical health and psychological well-being) and work-related (i.e., burnout and affective organizational commitment) outcomes were associated with each profile on a separate sample (Study 2). As expected, optimal leadership profiles were significantly related to positive personal and work-related outcomes for employees; however, passive-dominant and passive-abusive dominant profiles were both significantly related to negative employee outcomes. Interestingly, the passive-abusive dominant profiles did not have significantly worse outcomes than the passive-dominant profile. Theoretical and practical implications are discussed.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".