Seizing the benefits of age diversity: could empowering leadership be the answer?
Bibliographic record
Abstract
Purpose Acknowledging that only examining the main effects of diversity may be limiting, the authors explore integrating van Knippenberget al.'s (2004) categorization–elaboration model (CEM) of workgroup diversity as a linchpin in the relationship between empowering leadership and performance in age-diverse work groups. While prior research has focused almost exclusively on the impact of transformational leadership in diverse contexts, few studies have found the positive effects of transformational leadership to be diminished in certain age-diverse contexts. Consequently, the authors investigate whether empowering leadership may be a better approach in this context due to its emphasis on accommodating and participative behaviors. Design/methodology/approach Using survey data gathered from work group members across a wide array of industries (N = 214), the authors test for the moderating effects of empowering leadership on the relationship between age diversity and work group performance and its indirect relationship via information elaboration (while controlling for transformational leadership). Findings Empowering leadership positively moderated the direct relationship between age diversity and work group performance and the indirect relationship via information elaboration, whereas transformational leadership had the opposite effect. “Coaching” and “showing concern/interacting with the team” drove the positive effects of empowering leadership, and “personal recognition” and “intellectual stimulation” predicted the negative effects of transformational leadership. Practical implications This research offers insights into how managers can lead age-diverse work groups more effectively (i.e. by utilizing an empowering as opposed to a transformational leadership approach, with a particular emphasis on “coaching” and “showing concern/interacting with the team” behaviors). Originality/value The study identifies an “alternative” moderating contingency to the age diversity–performance relationship (empowering leadership).
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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 | 0.011 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".