Integrating transformational and participative versus directive leadership theories
Bibliographic record
Abstract
Purpose The purpose of this study is to investigate the extent to which male and female leaders report engaging in participative versus directive intellectually stimulating transformational leadership behaviour across three different contexts (business, government and military). Design/methodology/approach Semi‐structured interviews were conducted with 64 senior leaders (29 female and 35 male) across Canada. Findings Leaders were more likely to describe using a participative versus directive approach to intellectual stimulation. Gender similarities and differences also appeared across contexts: government leaders reported almost twice as many directive examples as business leaders, and men and women in both of these contexts were very similar in their reports about how they enacted intellectual stimulation. In contrast, men and women in the military diverged, with male leaders reporting more participative behaviour than female leaders. Research limitations/implications This study extends the leadership literature through an integration of participative and directive leadership theory with transformational leadership theory. Sample size and self‐report data are possible limitations. Practical implications Findings provide insight into the behaviours leaders engage in to enhance creative thinking and problem solving within organizations across different contexts and suggests that this aspect of transformational leadership is most likely to be enacted in a participative way by both male and female leaders. Originality/value This is one of the first studies to empirically investigate participative versus directive transformational leadership behaviour. Gender differences between contexts are worthy of further study, specifically regarding the implications of these findings for female leaders’ promotion and career progression.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".