The age of leadership: Meta‐analytic findings on the relationship between leader age and perceived leadership style and the moderating role of culture and industry type
Bibliographic record
Abstract
Abstract Managers' leadership style has a substantial impact on employee and organizational outcomes. In the present study, we consider the role of leaders’ chronological age in predicting followers’ perceptions of their leadership style. Whereas ample research uncovers relationships between individuals’ age and how these individuals are perceived by others, little is known about how leaders’ chronological age impacts others’ perceptions of their style. Even less is known about how such relationships vary across cultures and industries. We conducted a meta‐analysis (164 unique studies; N = 397,456 observations) to explore these relationships, using the Full‐Range leadership model. We found that leader age was negatively related to perceptions of transformational and transactional leadership, and positively related to perceptions of passive leadership. Further, some of these effects varied on several cultural dimensions: The negative relationship between leader age and transformational leadership was weaker in collectivistic cultures, while the negative relationship with transactional leadership was stronger in high power distance cultures. Industry type also mattered: the relationship between leader age and both transformational and contingent reward leadership styles was amplified in the public sector. Lastly, perceptions of older leaders were more negative when ratings were provided by followers rather than the leaders themselves. Our findings offer both theoretical and practical implications for leading in an increasingly age‐diverse workforce, such as better informing the workforce of present age stereotypes and their imminent effect on organizations.
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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 | 0.017 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.012 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".