Good, Bad, and Ugly Leadership Patterns: Implications for Followers’ Work-Related and Context-Free Outcomes
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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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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 | 0.000 |
| 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 it