Good, Bad, and Ugly Leadership Patterns: Implications for Followers’ Work-Related and Context-Free Outcomes
Bibliographic record
Abstract
This research responds to calls for a more integrative approach to leadership theory by identifying subpopulations of followers who share a common set of perceptions with respect to their leader's behaviors. Six commonly researched styles were investigated: abusive supervision, transformational leadership (TFL), contingent reward (CR), passive and active management-by-exception (MBE-P and MBE-A, respectively), and laissez faire/avoidant (LF/A). Study hypotheses were tested with data from four independent samples of working adults, three from followers ( N = 855) and a validation sample of leaders ( N = 505). Using latent profile analysis, three pattern cohorts emerged across all four samples. One subpopulation of followers exhibited a constructive pattern with higher scores on TFL and CR relative to other styles. Two cohorts exhibited destructive patterns, one where the passive styles of MBE-A, MBE- P and LF/A were high relative to the other styles (passive) and one where the passive styles co-occurred with abusive supervision (passive-abusive). Drawing on conservation of resources theory, we confirmed differential associations with work-related (i.e., burnout, vigor, perceived organizational support and affective organizational commitment) and context-free (i.e., physical health and psychological well-being) outcomes. The passive-abusive pattern was devastating for physical health, yet passiveness without abuse was damaging for psychological well-being. Interestingly, we find a clear demarcation between passiveness as “benign neglect” and passiveness as an intentional and deliberate form of leadership aimed at disrupting or undermining followers—hence, the two faces of passiveness: “bad” and “ugly.” We discuss the novel insights offered by a pattern (person)-oriented analytical strategy and the broader theoretical and practical implications for leadership research.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".