Leader emergence: the role of emotional intelligence and motivation to lead
Bibliographic record
Abstract
Purpose As the employment marketplace changes, the meaning of leadership evolves. The question of whether emotional intelligence (EI) is required for leaders has attracted broad interest. This paper seeks to examine the role of EI and motivation to lead (MTL) in predicting leadership. Design/methodology/approach In study 1, students (n=309) first completed surveys and then, one week later (n=264), they engaged in leaderless group discussions where their levels of leader emergence were rated. In study 2, the participants were 115 students who undertook 14‐week class projects. They completed surveys including evaluations of members' leader emergence after they finished the projects. Findings The results suggest that participants who were high in affective‐identity MTL became leaders in leaderless discussions, while high social‐normative MTL individuals assumed leadership roles in long‐term project teams. Both studies found that use of emotions, which is a component of EI, was positively related to affective‐identity and social‐normative MTL and indirectly related to leader emergence. Originality/value This study is one of the first to examine the relationship between EI and MTL, as well as between MTL and leadership emergence.
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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 | 0.007 |
| 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 | 0.001 |
| 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".