Mindfulness and leadership flexibility
Bibliographic record
Abstract
Purpose In a context of great complexity, many authors have focused on the beneficial effects of leadership flexibility (Denison et al., 1995), a capacity theoretically associated with mindfulness. The purpose of this paper is to better understand the relationship between mindfulness and behavioral flexibility in leaders. Design/methodology/approach Data were collected from two samples: 100 active leaders from diverse economic sectors and 62 students pursuing an executive MBA degree. Findings The results show that mindfulness is positively associated with the overall score for leader flexibility, and with its two dualities: self-assertive and directive vs collaborative and supportive, and long-term strategy vs short-term execution. Specifically, four of the five dimensions of mindfulness (nonreactivity, nonjudging, acting with awareness and describing) were positively correlated with the overall flexibility score. Practical implications The results suggest that by developing mindfulness, managers might be better able to adapt their leadership style to the demands of different situations. To that end, interventions based on mindfulness are worthwhile options for use within organizations, particularly in the context of leadership development programs. Originality/value While most models of leadership assume a linear relationship between certain leadership behaviors and performance, other voices suggest that effective leaders need to possess great behavioral flexibility so that they can adapt with agility to the multiple needs of the people and situations around them. Few studies have examined the factors that may play a role in leadership flexibility.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".